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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install python-pptx"
      ],
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        },
        "id": "aO6RJf5TTnFj",
        "outputId": "e00a0f1f-1620-4bc3-8585-6dc6c1195b42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-pptx\n",
            "  Downloading python_pptx-0.6.23-py3-none-any.whl (471 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m471.6/471.6 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: lxml>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from python-pptx) (4.9.4)\n",
            "Requirement already satisfied: Pillow>=3.3.2 in /usr/local/lib/python3.10/dist-packages (from python-pptx) (9.4.0)\n",
            "Collecting XlsxWriter>=0.5.7 (from python-pptx)\n",
            "  Downloading XlsxWriter-3.2.0-py3-none-any.whl (159 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159.9/159.9 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: XlsxWriter, python-pptx\n",
            "Successfully installed XlsxWriter-3.2.0 python-pptx-0.6.23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pptx import Presentation\n",
        "\n",
        "def parse_ppt(file_path):\n",
        "    # Load the presentation\n",
        "    presentation = Presentation(file_path)\n",
        "\n",
        "    # Initialize a list to store the text\n",
        "    all_text = []\n",
        "\n",
        "    # Iterate through each slide in the presentation\n",
        "    for slide in presentation.slides:\n",
        "        slide_text = []\n",
        "        # Iterate through each shape in the slide\n",
        "        for shape in slide.shapes:\n",
        "            # Check if the shape has text\n",
        "            if hasattr(shape, \"text\"):\n",
        "                slide_text.append(shape.text)\n",
        "        all_text.append(\"\\n\".join(slide_text))\n",
        "\n",
        "    return all_text\n",
        "\n",
        "# Example usage\n",
        "file_path = 'product-led-growth-2-210330011118.pptx'\n",
        "parsed_text = parse_ppt(file_path)\n",
        "print(parsed_text)\n",
        "for i, slide_text in enumerate(parsed_text):\n",
        "    print(f\"Slide {i + 1}:\\n{slide_text}\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkhksNsYTk8C",
        "outputId": "ec8a5942-7208-4627-c301-7d79855068cf"
      },
      "execution_count": null,
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          "text": [
            "['\\nAdrian Escude\\nadrianescude@gmail.com\\n\\nPRODUCT \\nLED\\nGROWTH', '\\nBottom up targeting\\nPuts product first,  delivers value quickly. Targets ‘Doers’\\n\\n“Show me the value” (and let me try it) \\nVs “Tell me about it”\\n \\nSelf Service \\nTransparent pricing and ability to transact\\n\\nCustomer-experience- oriented org\\nCulture of fast, continuous product improvement\\n\\n\\nPLG Checklist \\n\\n\\n\\n', '\\nBottom up targeting\\nPuts product first,  delivers value quickly. Targets ‘Doers’\\n\\n“Show me the value” (and let me try it) \\nVs “Tell me about it”\\n \\nSelf Service \\nTransparent pricing and ability to transact\\n\\nCustomer-experience- oriented org\\nCulture of fast, continuous product improvement\\n\\n\\nPLG Checklist \\n\\n\\n\\n\\n\\nDIY, easy onboarding \\nHigh speed-to-value\\nShorter sales cycles\\nFaster revenue growth\\nLower CAC\\nHigher LTV \\nNet Retention ', '\\nPLG Internal Adoption Loop ', 'The The prim\\n\\n\\nThe Promise', '\\n\\n\\n     Less or equal to 5Y\\n     More than 5Y, less or equal to 10Y\\n     More than 10Y, less or equal 15Y\\n     More than 15Y\\nSource: Kimchi hill\\n\\nYears to reach \\n\\n$100M ARR', '\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n65%\\nOf co’s that reached $100M within less less than 10Y\\n Are PLG companies\\nSource: Kimchi hill\\n     Less or equal to 5Y\\n     More than 5Y, less or equal to 10Y\\n     More than 10Y, less or equal 15Y\\n     More than 15Y\\nYears to reach \\n\\n$100M ARR\\n\\n\\n', 'NON PLG', '\\nPLG vs Non PLG \\nBenchmarking success\\nRevenue Growth\\nNet Dollar Retention\\nCAC Payback Period\\nSource: publiccomps.com', '\\nKPIs & Optimizations  ', '\\n\\n\\nCONVERSION\\nCUSTOMER\\nACTIVE\\nCUSTOMER\\nLOYAL\\nCUSTOMER\\n\\nContent\\nVISITS\\nContent\\nPaid\\nOther\\nMQL\\nChannels\\nSQL\\n\\n\\n\\n\\nIN- PRODUCT \\x0bENGAGEMENTS\\nOUTSIDE \\x0bPRODUCT \\x0bENGAGEMENTS\\nMarketing team\\nSales team\\nCustomer success team\\nTIME\\nCustomer Lifecycle in a Sales-Led GTM', '\\n\\n\\n“98% of MQLs never close” \\nSiriusDecisions', '\\n\\n\\n\\nPQL\\nCUSTOMER\\nACTIVE\\nCUSTOMER\\nLOYAL\\nCUSTOMER\\nContent\\nVISITS\\nContent\\nPaid\\nOther\\n\\n\\n\\nTRIAL\\nMarketing | Growth teams\\nSales team\\nCustomer Success team\\nProduct | Growth teams\\nTIME\\nIN- PRODUCT \\x0bENGAGEMENTS\\nOUTSIDE \\x0bPRODUCT \\x0bENGAGEMENTS\\nCustomer Lifecycle in Product-Led GTM\\n\\n\\n', '\\nWhat is a PQL?\\n\\n\\nActivation\\n\\nICP Match\\nPQL\\nEngagement', '\\nInteraction with core features\\n‘X’ number of messages / actions / etc \\nAddressed  pain point \\nSolved a problem / Experienced your value prop\\nInvited team members \\n\\n\\n\\n PQL Definition\\n\\n\\n\\n\\nActivation \\nEngagement \\nICP Match \\n\\nHave they experienced the value of your product? \\n\\n\\n\\n', '\\nActive User\\nThey keep coming back for more\\nRecency & Decay\\nRecent activity\\nBehaviour (in-app + website) \\nVisited Pricing page, consumed blog content, etc\\n\\n\\n\\n\\n\\n PQL Definition\\n\\nActivation \\nEngagement \\nICP Match \\n\\n\\n\\n\\n\\nAre they signaling further interest? \\n', '\\n\\n\\n\\n PQL Definition\\n\\nActivation \\nEngagement \\nTechnographics \\nTechnology installed,  Integrations\\nFirmographics\\nCompany Size, Industry, Revenue, Funding,  Geo\\nCustomer Persona \\nDepartment, Job Title,  Pain Points \\n\\n\\nICP Match \\n\\n\\n\\nAligned with Ideal Customer Profile? ', '\\nAha moment\\n/ä-hä  ˈmōmənt/     noun  \\n\\n\\nPivotal moment when new users first realize the value of your product\\nA moment of sudden insight or discovery', '\\n\\n\\nActivation \\nEngagement \\nICP Match \\n\\nAha! Moment examples \\nConnect with 7 friends within the first 10 days\\n2000 messages sent within a team\\nReturning 1 day after sign up\\nSave 1 file on folder on 1 device\\nDefining the Aha! moment', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTrial to PQL CVR \\nPQL to Customer CVR\\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\nRenewal Rate \\n\\n\\nDays from Trial to PQL  \\nDays from PQL to Customer  \\n\\n\\n\\n\\n\\nDays to\\nBreak Even  \\nAvg Customer Life  \\n\\n\\n\\n\\nCLV- to CAC ratio\\nKey Performance Indicators\\nPROFIT\\nBREAKEVEN\\n\\nCAC\\nAvg Selling Price\\n', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVISITOR TO TRIAL CVR \\nTrial to PQL CVR \\nPQL to Customer CVR\\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n2%* Opt-in Free Trials\\n\\n\\nQuality of the traffic\\nWebsite content\\nMessaging\\nDesign\\nSupporting visuals\\nSite architecture\\n  *   Innertrends\\n\\n\\n', '\\n\\nOnboarding \\nQualification & Segmentation\\nGuidance \\nGoals & Personalization\\nTime to value → ‘Aha moment’\\n\\n\\n\\n\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTRIAL TO  PQL CVR \\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to Customer  CVR\\n\\n', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTRIAL TO  PQL CVR \\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to Customer  CVR\\nHave your trial users experienced the value of the product?  \\n\\n\\n', '\\n“60% of users who try your app will never come back”\\nIntercomm', '\\n\\n\\n\\n\\n\\n\\n\\nStarts using add ons and other integrations\\nConverts to paid\\nConsecutive ‘Aha’ moments & activation points\\nSees the product as a solution\\nLoyal Customer\\nActive Customer\\nTrial User\\nWebsite visitor\\nEngages directly with brand\\n\\n\\n\\n\\n\\nValue\\nTime\\nAha moment(s)\\nProduct Adoption\\nActivation', '\\nDESIRED OUTCOME\\nCURRENT STATE\\nContent\\nProduct \\n\\nAccelerating Speed-To-Value\\n\\n', '\\n\\n\\nNot Every SignUp Is A Core User\\nLikelihood to activate\\nLikelihood to Convert\\n\\n\\n\\nCEO\\nDirector / CXO\\n\\n\\n\\n\\nProduct / Project Manager\\nDeveloper\\nDesigner\\nMarketer\\nAnalyst\\nCORE USER\\nCORE BUYER\\nSource: Madkudu', '\\n\\n\\n\\nQualification & Segmentation\\nExplicit, Enriched & Behaviour-Based Data \\nExplicit | Sign Up Survey \\nEnriched | 3rd Party\\nBehaviour-based\\nWEBSITE\\nIN-APP\\nWebinar attendance\\nPricing page\\nBlog posts consumed\\n...\\n\\n\\n\\n\\nNumber of Interactions\\nTeam member addition\\nCompletion of core journeys\\n...\\n\\n\\n\\n', '\\nOnboarding \\nEmail nurturing campaigns \\x0bIn- app messaging \\n           Sales reach outs \\n    \\nCustomer persona\\nUse Case / Pain Points\\nIndustry, Firmo  & Technographics\\nPersonalization\\n\\n\\n\\n\\n\\n\\n\\n\\nCriteria\\nApplied to', '\\n\\n\\nCore User  (Marketer)  \\nBuyer (CEO) \\nSign Up\\nUsage\\nInvite Team Members\\nInvite Team Members\\nAdd Credit Card\\nProduct Overview Call\\nSign Up\\nCustom Journey & Content \\nHi Mark,\\x0b\\x0bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \\n\\nTo get the most out our app, be sure  to <Usage Tip #1>, so you can  <Product benefit number #1>\\n\\nTry it out and let us know if you have any questions, \\nHi Kevin,\\x0b\\x0bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \\n\\nI wanted to help jumpstart the onboarding process by offering a quick product overview to ensure your team gets the most out of it. You can pick a convenient time that works best for you here\\n\\nAdditionally, here you can find some quick resources on how sales <company name> helped companies in your industry to <benefit number #1> and <benefit number #2>\\nEmail # 1 to Core User Sign Up\\nEmail # 1 to Buyer Sign Up', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTrial to  PQL CVR \\n\\nActive Customers CVR \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to CUSTOMER  CVR\\n\\nActivity-based content nurturing \\nCommunity, documentation & support\\nFrictionless self-service\\nUX & product optimizations\\nSales follow up post PQL qualification \\n\\n\\n\\n\\n\\n', '\\nB2B Tech Stack', ' \\n\\n\\n\\n\\nUser session replay \\nA/B Testing \\nB2B Tech Stack\\n\\nPrototyping & design \\nCRM\\n\\nReporting & Data visualization \\nLead Scoring \\nProduct Analytics \\n\\nWeb Analytics\\n\\n\\nPersonalization \\n\\n\\nContact Enrichment \\n\\nMarket Research', '\\n\\nA/B Testing and Experimentation\\n\\nhttps://www.optimizely.com/sample-size-calculator/\\nDON’T TEST IF\\nIt’ll take too long to get actionable results\\nLow downside risk of the change, high effort to test it.\\nYou’re launching something completely new\\n\\nWHY TESTING\\nLearn\\nDecide\\nQuantify\\nAlign\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n', '\\n\\nPricing for SaaS\\n', '\\nValue-based pricing \\n\\nValue is a proxy for usage.  \\nPricing based on a value metric allows you to maximise revenue potential.\\n\\n\\n\\nOpportunity costs', '\\nCompanies using value-based pricing are growing at \\ndouble the rate,\\nwith half the churn\\nValue-based pricing \\n\\n\\nSource: Profitwell.com\\n', '\\nFreemium vs Free trial', '\\nFreemium\\nPros:\\nMore visitor to ‘trial’ conversions\\nHigher virality  \\nMore product data to optimize UX\\nPaid users who convert from free tend to better retention, and much lower CAC1\\n\\nCons:\\nLimited features could result in a inferior experience\\nLower sense of urgency\\nHigher cost to service\\n1A16z\\n\\n\\n\\n\\n\\n\\n', '\\nFree Trial\\nCons:\\nLess virality and buzz\\nRequires more effort and time commitment on the user\\n\\nPros:\\nSense of urgency\\nFull feature set, most optimal UX\\nMore effort in a short timeframe, higher likelihood of conversion if UX is optimal\\n\\n\\n\\nThe challenge\\nDelivering on the promise of your business (aha moment) as fast as possible \\n\\n\\n\\n\\n', '\\nFreemium vs Free Trial\\n\\nComplex Product?\\nHigh price?\\nLarge Market?\\nTargets doers?\\nNO\\nYES\\nNO\\nYES\\nYES\\nNO\\nNO\\nYES\\nFreemium\\nFree trial\\nSales-led', '\\n\\n\\nBottom up + Top down GTM\\n', '\\nCombining PLG and a Sales Led GTM\\n\\n\\nPure PLG\\nSales + PLG\\nA self-serve GTM, later combined with a strong direct sales motion \\nCompounds revenue growth\\nLowers CACs\\nEnables  larger contract values\\n\\n\\nThe Sales role in a mixed GTM strategy:\\nCustomer onboarding, expansions and renewals (Customer success)\\nWork with resellers, distributors and MSP’s \\nHelp convert PQLs from large enterprises and other ‘hot leads’\\n\\n\\n\\n\\n\\n\\n\\n\\n', '\\nEstimated ARR When Top Down Sales Was Layered In\\n\\n\\n\\n\\n\\n$100M+\\n$50M+\\n$30M+\\n$25M+\\n$20M-$25M\\nSource: Andreessen Horowitz\\nWhen do we add a Top Down GTM?', '\\nWhen to Layer Top Down Approach\\nBottom up flywheel working ?\\nNO\\nYES\\nContinue developing bottom up GTM\\nCustomer demanding product at enterprise level or enough penetration?\\nNO\\nYES\\nIntroduce Top down GTM\\nContinue developing bottom up GTM\\nWhen do we add a Top Down GTM?\\nSource: Andreessen Horowitz', '\\n\\n\\nThank you\\n\\nContact Me\\n\\n Adrianescude@gmail.com\\nhttps://www.linkedin.com/in/adrianescude']\n",
            "Slide 1:\n",
            "\n",
            "Adrian Escude\n",
            "adrianescude@gmail.com\n",
            "\n",
            "PRODUCT \n",
            "LED\n",
            "GROWTH\n",
            "\n",
            "Slide 2:\n",
            "\n",
            "Bottom up targeting\n",
            "Puts product first,  delivers value quickly. Targets ‘Doers’\n",
            "\n",
            "“Show me the value” (and let me try it) \n",
            "Vs “Tell me about it”\n",
            " \n",
            "Self Service \n",
            "Transparent pricing and ability to transact\n",
            "\n",
            "Customer-experience- oriented org\n",
            "Culture of fast, continuous product improvement\n",
            "\n",
            "\n",
            "PLG Checklist \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 3:\n",
            "\n",
            "Bottom up targeting\n",
            "Puts product first,  delivers value quickly. Targets ‘Doers’\n",
            "\n",
            "“Show me the value” (and let me try it) \n",
            "Vs “Tell me about it”\n",
            " \n",
            "Self Service \n",
            "Transparent pricing and ability to transact\n",
            "\n",
            "Customer-experience- oriented org\n",
            "Culture of fast, continuous product improvement\n",
            "\n",
            "\n",
            "PLG Checklist \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "DIY, easy onboarding \n",
            "High speed-to-value\n",
            "Shorter sales cycles\n",
            "Faster revenue growth\n",
            "Lower CAC\n",
            "Higher LTV \n",
            "Net Retention \n",
            "\n",
            "Slide 4:\n",
            "\n",
            "PLG Internal Adoption Loop \n",
            "\n",
            "Slide 5:\n",
            "The The prim\n",
            "\n",
            "\n",
            "The Promise\n",
            "\n",
            "Slide 6:\n",
            "\n",
            "\n",
            "\n",
            "     Less or equal to 5Y\n",
            "     More than 5Y, less or equal to 10Y\n",
            "     More than 10Y, less or equal 15Y\n",
            "     More than 15Y\n",
            "Source: Kimchi hill\n",
            "\n",
            "Years to reach \n",
            "\n",
            "$100M ARR\n",
            "\n",
            "Slide 7:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "65%\n",
            "Of co’s that reached $100M within less less than 10Y\n",
            " Are PLG companies\n",
            "Source: Kimchi hill\n",
            "     Less or equal to 5Y\n",
            "     More than 5Y, less or equal to 10Y\n",
            "     More than 10Y, less or equal 15Y\n",
            "     More than 15Y\n",
            "Years to reach \n",
            "\n",
            "$100M ARR\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 8:\n",
            "NON PLG\n",
            "\n",
            "Slide 9:\n",
            "\n",
            "PLG vs Non PLG \n",
            "Benchmarking success\n",
            "Revenue Growth\n",
            "Net Dollar Retention\n",
            "CAC Payback Period\n",
            "Source: publiccomps.com\n",
            "\n",
            "Slide 10:\n",
            "\n",
            "KPIs & Optimizations  \n",
            "\n",
            "Slide 11:\n",
            "\n",
            "\n",
            "\n",
            "CONVERSION\n",
            "CUSTOMER\n",
            "ACTIVE\n",
            "CUSTOMER\n",
            "LOYAL\n",
            "CUSTOMER\n",
            "\n",
            "Content\n",
            "VISITS\n",
            "Content\n",
            "Paid\n",
            "Other\n",
            "MQL\n",
            "Channels\n",
            "SQL\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "IN- PRODUCT \u000bENGAGEMENTS\n",
            "OUTSIDE \u000bPRODUCT \u000bENGAGEMENTS\n",
            "Marketing team\n",
            "Sales team\n",
            "Customer success team\n",
            "TIME\n",
            "Customer Lifecycle in a Sales-Led GTM\n",
            "\n",
            "Slide 12:\n",
            "\n",
            "\n",
            "\n",
            "“98% of MQLs never close” \n",
            "SiriusDecisions\n",
            "\n",
            "Slide 13:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "PQL\n",
            "CUSTOMER\n",
            "ACTIVE\n",
            "CUSTOMER\n",
            "LOYAL\n",
            "CUSTOMER\n",
            "Content\n",
            "VISITS\n",
            "Content\n",
            "Paid\n",
            "Other\n",
            "\n",
            "\n",
            "\n",
            "TRIAL\n",
            "Marketing | Growth teams\n",
            "Sales team\n",
            "Customer Success team\n",
            "Product | Growth teams\n",
            "TIME\n",
            "IN- PRODUCT \u000bENGAGEMENTS\n",
            "OUTSIDE \u000bPRODUCT \u000bENGAGEMENTS\n",
            "Customer Lifecycle in Product-Led GTM\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 14:\n",
            "\n",
            "What is a PQL?\n",
            "\n",
            "\n",
            "Activation\n",
            "\n",
            "ICP Match\n",
            "PQL\n",
            "Engagement\n",
            "\n",
            "Slide 15:\n",
            "\n",
            "Interaction with core features\n",
            "‘X’ number of messages / actions / etc \n",
            "Addressed  pain point \n",
            "Solved a problem / Experienced your value prop\n",
            "Invited team members \n",
            "\n",
            "\n",
            "\n",
            " PQL Definition\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Activation \n",
            "Engagement \n",
            "ICP Match \n",
            "\n",
            "Have they experienced the value of your product? \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 16:\n",
            "\n",
            "Active User\n",
            "They keep coming back for more\n",
            "Recency & Decay\n",
            "Recent activity\n",
            "Behaviour (in-app + website) \n",
            "Visited Pricing page, consumed blog content, etc\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " PQL Definition\n",
            "\n",
            "Activation \n",
            "Engagement \n",
            "ICP Match \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Are they signaling further interest? \n",
            "\n",
            "\n",
            "Slide 17:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " PQL Definition\n",
            "\n",
            "Activation \n",
            "Engagement \n",
            "Technographics \n",
            "Technology installed,  Integrations\n",
            "Firmographics\n",
            "Company Size, Industry, Revenue, Funding,  Geo\n",
            "Customer Persona \n",
            "Department, Job Title,  Pain Points \n",
            "\n",
            "\n",
            "ICP Match \n",
            "\n",
            "\n",
            "\n",
            "Aligned with Ideal Customer Profile? \n",
            "\n",
            "Slide 18:\n",
            "\n",
            "Aha moment\n",
            "/ä-hä  ˈmōmənt/     noun  \n",
            "\n",
            "\n",
            "Pivotal moment when new users first realize the value of your product\n",
            "A moment of sudden insight or discovery\n",
            "\n",
            "Slide 19:\n",
            "\n",
            "\n",
            "\n",
            "Activation \n",
            "Engagement \n",
            "ICP Match \n",
            "\n",
            "Aha! Moment examples \n",
            "Connect with 7 friends within the first 10 days\n",
            "2000 messages sent within a team\n",
            "Returning 1 day after sign up\n",
            "Save 1 file on folder on 1 device\n",
            "Defining the Aha! moment\n",
            "\n",
            "Slide 20:\n",
            "\n",
            "Visitor\n",
            "Trial\n",
            "PQL\n",
            "Customer\n",
            "Active customer\n",
            "Loyal customer\n",
            "Churned customer\n",
            "Visitor to Trial CVR \n",
            "Trial to PQL CVR \n",
            "PQL to Customer CVR\n",
            "\n",
            "Active Customers Rate \n",
            "\n",
            "\n",
            "\n",
            "Churn Rate \n",
            "Renewal Rate \n",
            "\n",
            "\n",
            "Days from Trial to PQL  \n",
            "Days from PQL to Customer  \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Days to\n",
            "Break Even  \n",
            "Avg Customer Life  \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "CLV- to CAC ratio\n",
            "Key Performance Indicators\n",
            "PROFIT\n",
            "BREAKEVEN\n",
            "\n",
            "CAC\n",
            "Avg Selling Price\n",
            "\n",
            "\n",
            "Slide 21:\n",
            "\n",
            "Visitor\n",
            "Trial\n",
            "PQL\n",
            "Customer\n",
            "Active customer\n",
            "Loyal customer\n",
            "Churned customer\n",
            "VISITOR TO TRIAL CVR \n",
            "Trial to PQL CVR \n",
            "PQL to Customer CVR\n",
            "\n",
            "Active Customers Rate \n",
            "\n",
            "\n",
            "\n",
            "Churn Rate \n",
            "\n",
            "\n",
            "Metric Optimizations\n",
            "Efficiency\n",
            "Renewal Rate \n",
            "2%* Opt-in Free Trials\n",
            "\n",
            "\n",
            "Quality of the traffic\n",
            "Website content\n",
            "Messaging\n",
            "Design\n",
            "Supporting visuals\n",
            "Site architecture\n",
            "  *   Innertrends\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 22:\n",
            "\n",
            "\n",
            "Onboarding \n",
            "Qualification & Segmentation\n",
            "Guidance \n",
            "Goals & Personalization\n",
            "Time to value → ‘Aha moment’\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Visitor\n",
            "Trial\n",
            "PQL\n",
            "Customer\n",
            "Active customer\n",
            "Loyal customer\n",
            "Churned customer\n",
            "Visitor to Trial CVR \n",
            "TRIAL TO  PQL CVR \n",
            "\n",
            "Active Customers Rate \n",
            "\n",
            "\n",
            "\n",
            "Churn Rate \n",
            "\n",
            "\n",
            "Metric Optimizations\n",
            "Efficiency\n",
            "Renewal Rate \n",
            "\n",
            "25%* Opt-in Free Trials\n",
            "\n",
            "1-10%**  Freemium\n",
            "  * sixteen ventures\n",
            "**  Insight Venture Partners\n",
            "PQL to Customer  CVR\n",
            "\n",
            "\n",
            "\n",
            "Slide 23:\n",
            "\n",
            "Visitor\n",
            "Trial\n",
            "PQL\n",
            "Customer\n",
            "Active customer\n",
            "Loyal customer\n",
            "Churned customer\n",
            "Visitor to Trial CVR \n",
            "TRIAL TO  PQL CVR \n",
            "\n",
            "Active Customers Rate \n",
            "\n",
            "\n",
            "\n",
            "Churn Rate \n",
            "\n",
            "\n",
            "Metric Optimizations\n",
            "Efficiency\n",
            "Renewal Rate \n",
            "\n",
            "25%* Opt-in Free Trials\n",
            "\n",
            "1-10%**  Freemium\n",
            "  * sixteen ventures\n",
            "**  Insight Venture Partners\n",
            "PQL to Customer  CVR\n",
            "Have your trial users experienced the value of the product?  \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 24:\n",
            "\n",
            "“60% of users who try your app will never come back”\n",
            "Intercomm\n",
            "\n",
            "Slide 25:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Starts using add ons and other integrations\n",
            "Converts to paid\n",
            "Consecutive ‘Aha’ moments & activation points\n",
            "Sees the product as a solution\n",
            "Loyal Customer\n",
            "Active Customer\n",
            "Trial User\n",
            "Website visitor\n",
            "Engages directly with brand\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Value\n",
            "Time\n",
            "Aha moment(s)\n",
            "Product Adoption\n",
            "Activation\n",
            "\n",
            "Slide 26:\n",
            "\n",
            "DESIRED OUTCOME\n",
            "CURRENT STATE\n",
            "Content\n",
            "Product \n",
            "\n",
            "Accelerating Speed-To-Value\n",
            "\n",
            "\n",
            "\n",
            "Slide 27:\n",
            "\n",
            "\n",
            "\n",
            "Not Every SignUp Is A Core User\n",
            "Likelihood to activate\n",
            "Likelihood to Convert\n",
            "\n",
            "\n",
            "\n",
            "CEO\n",
            "Director / CXO\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Product / Project Manager\n",
            "Developer\n",
            "Designer\n",
            "Marketer\n",
            "Analyst\n",
            "CORE USER\n",
            "CORE BUYER\n",
            "Source: Madkudu\n",
            "\n",
            "Slide 28:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Qualification & Segmentation\n",
            "Explicit, Enriched & Behaviour-Based Data \n",
            "Explicit | Sign Up Survey \n",
            "Enriched | 3rd Party\n",
            "Behaviour-based\n",
            "WEBSITE\n",
            "IN-APP\n",
            "Webinar attendance\n",
            "Pricing page\n",
            "Blog posts consumed\n",
            "...\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Number of Interactions\n",
            "Team member addition\n",
            "Completion of core journeys\n",
            "...\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 29:\n",
            "\n",
            "Onboarding \n",
            "Email nurturing campaigns \u000bIn- app messaging \n",
            "           Sales reach outs \n",
            "    \n",
            "Customer persona\n",
            "Use Case / Pain Points\n",
            "Industry, Firmo  & Technographics\n",
            "Personalization\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Criteria\n",
            "Applied to\n",
            "\n",
            "Slide 30:\n",
            "\n",
            "\n",
            "\n",
            "Core User  (Marketer)  \n",
            "Buyer (CEO) \n",
            "Sign Up\n",
            "Usage\n",
            "Invite Team Members\n",
            "Invite Team Members\n",
            "Add Credit Card\n",
            "Product Overview Call\n",
            "Sign Up\n",
            "Custom Journey & Content \n",
            "Hi Mark,\u000b\u000bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \n",
            "\n",
            "To get the most out our app, be sure  to <Usage Tip #1>, so you can  <Product benefit number #1>\n",
            "\n",
            "Try it out and let us know if you have any questions, \n",
            "Hi Kevin,\u000b\u000bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \n",
            "\n",
            "I wanted to help jumpstart the onboarding process by offering a quick product overview to ensure your team gets the most out of it. You can pick a convenient time that works best for you here\n",
            "\n",
            "Additionally, here you can find some quick resources on how sales <company name> helped companies in your industry to <benefit number #1> and <benefit number #2>\n",
            "Email # 1 to Core User Sign Up\n",
            "Email # 1 to Buyer Sign Up\n",
            "\n",
            "Slide 31:\n",
            "\n",
            "Visitor\n",
            "Trial\n",
            "PQL\n",
            "Customer\n",
            "Active customer\n",
            "Loyal customer\n",
            "Churned customer\n",
            "Visitor to Trial CVR \n",
            "Trial to  PQL CVR \n",
            "\n",
            "Active Customers CVR \n",
            "\n",
            "\n",
            "\n",
            "Churn Rate \n",
            "\n",
            "\n",
            "Metric Optimizations\n",
            "Efficiency\n",
            "Renewal Rate \n",
            "\n",
            "25%* Opt-in Free Trials\n",
            "\n",
            "1-10%**  Freemium\n",
            "  * sixteen ventures\n",
            "**  Insight Venture Partners\n",
            "PQL to CUSTOMER  CVR\n",
            "\n",
            "Activity-based content nurturing \n",
            "Community, documentation & support\n",
            "Frictionless self-service\n",
            "UX & product optimizations\n",
            "Sales follow up post PQL qualification \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 32:\n",
            "\n",
            "B2B Tech Stack\n",
            "\n",
            "Slide 33:\n",
            " \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "User session replay \n",
            "A/B Testing \n",
            "B2B Tech Stack\n",
            "\n",
            "Prototyping & design \n",
            "CRM\n",
            "\n",
            "Reporting & Data visualization \n",
            "Lead Scoring \n",
            "Product Analytics \n",
            "\n",
            "Web Analytics\n",
            "\n",
            "\n",
            "Personalization \n",
            "\n",
            "\n",
            "Contact Enrichment \n",
            "\n",
            "Market Research\n",
            "\n",
            "Slide 34:\n",
            "\n",
            "\n",
            "A/B Testing and Experimentation\n",
            "\n",
            "https://www.optimizely.com/sample-size-calculator/\n",
            "DON’T TEST IF\n",
            "It’ll take too long to get actionable results\n",
            "Low downside risk of the change, high effort to test it.\n",
            "You’re launching something completely new\n",
            "\n",
            "WHY TESTING\n",
            "Learn\n",
            "Decide\n",
            "Quantify\n",
            "Align\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 35:\n",
            "\n",
            "\n",
            "Pricing for SaaS\n",
            "\n",
            "\n",
            "Slide 36:\n",
            "\n",
            "Value-based pricing \n",
            "\n",
            "Value is a proxy for usage.  \n",
            "Pricing based on a value metric allows you to maximise revenue potential.\n",
            "\n",
            "\n",
            "\n",
            "Opportunity costs\n",
            "\n",
            "Slide 37:\n",
            "\n",
            "Companies using value-based pricing are growing at \n",
            "double the rate,\n",
            "with half the churn\n",
            "Value-based pricing \n",
            "\n",
            "\n",
            "Source: Profitwell.com\n",
            "\n",
            "\n",
            "Slide 38:\n",
            "\n",
            "Freemium vs Free trial\n",
            "\n",
            "Slide 39:\n",
            "\n",
            "Freemium\n",
            "Pros:\n",
            "More visitor to ‘trial’ conversions\n",
            "Higher virality  \n",
            "More product data to optimize UX\n",
            "Paid users who convert from free tend to better retention, and much lower CAC1\n",
            "\n",
            "Cons:\n",
            "Limited features could result in a inferior experience\n",
            "Lower sense of urgency\n",
            "Higher cost to service\n",
            "1A16z\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 40:\n",
            "\n",
            "Free Trial\n",
            "Cons:\n",
            "Less virality and buzz\n",
            "Requires more effort and time commitment on the user\n",
            "\n",
            "Pros:\n",
            "Sense of urgency\n",
            "Full feature set, most optimal UX\n",
            "More effort in a short timeframe, higher likelihood of conversion if UX is optimal\n",
            "\n",
            "\n",
            "\n",
            "The challenge\n",
            "Delivering on the promise of your business (aha moment) as fast as possible \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 41:\n",
            "\n",
            "Freemium vs Free Trial\n",
            "\n",
            "Complex Product?\n",
            "High price?\n",
            "Large Market?\n",
            "Targets doers?\n",
            "NO\n",
            "YES\n",
            "NO\n",
            "YES\n",
            "YES\n",
            "NO\n",
            "NO\n",
            "YES\n",
            "Freemium\n",
            "Free trial\n",
            "Sales-led\n",
            "\n",
            "Slide 42:\n",
            "\n",
            "\n",
            "\n",
            "Bottom up + Top down GTM\n",
            "\n",
            "\n",
            "Slide 43:\n",
            "\n",
            "Combining PLG and a Sales Led GTM\n",
            "\n",
            "\n",
            "Pure PLG\n",
            "Sales + PLG\n",
            "A self-serve GTM, later combined with a strong direct sales motion \n",
            "Compounds revenue growth\n",
            "Lowers CACs\n",
            "Enables  larger contract values\n",
            "\n",
            "\n",
            "The Sales role in a mixed GTM strategy:\n",
            "Customer onboarding, expansions and renewals (Customer success)\n",
            "Work with resellers, distributors and MSP’s \n",
            "Help convert PQLs from large enterprises and other ‘hot leads’\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Slide 44:\n",
            "\n",
            "Estimated ARR When Top Down Sales Was Layered In\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "$100M+\n",
            "$50M+\n",
            "$30M+\n",
            "$25M+\n",
            "$20M-$25M\n",
            "Source: Andreessen Horowitz\n",
            "When do we add a Top Down GTM?\n",
            "\n",
            "Slide 45:\n",
            "\n",
            "When to Layer Top Down Approach\n",
            "Bottom up flywheel working ?\n",
            "NO\n",
            "YES\n",
            "Continue developing bottom up GTM\n",
            "Customer demanding product at enterprise level or enough penetration?\n",
            "NO\n",
            "YES\n",
            "Introduce Top down GTM\n",
            "Continue developing bottom up GTM\n",
            "When do we add a Top Down GTM?\n",
            "Source: Andreessen Horowitz\n",
            "\n",
            "Slide 46:\n",
            "\n",
            "\n",
            "\n",
            "Thank you\n",
            "\n",
            "Contact Me\n",
            "\n",
            " Adrianescude@gmail.com\n",
            "https://www.linkedin.com/in/adrianescude\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parsed_text=['\\nAdrian Escude\\nadrianescude@gmail.com\\n\\nPRODUCT \\nLED\\nGROWTH', '\\nBottom up targeting\\nPuts product first,  delivers value quickly. Targets ‘Doers’\\n\\n“Show me the value” (and let me try it) \\nVs “Tell me about it”\\n \\nSelf Service \\nTransparent pricing and ability to transact\\n\\nCustomer-experience- oriented org\\nCulture of fast, continuous product improvement\\n\\n\\nPLG Checklist \\n\\n\\n\\n', '\\nBottom up targeting\\nPuts product first,  delivers value quickly. Targets ‘Doers’\\n\\n“Show me the value” (and let me try it) \\nVs “Tell me about it”\\n \\nSelf Service \\nTransparent pricing and ability to transact\\n\\nCustomer-experience- oriented org\\nCulture of fast, continuous product improvement\\n\\n\\nPLG Checklist \\n\\n\\n\\n\\n\\nDIY, easy onboarding \\nHigh speed-to-value\\nShorter sales cycles\\nFaster revenue growth\\nLower CAC\\nHigher LTV \\nNet Retention ', '\\nPLG Internal Adoption Loop ', 'The The prim\\n\\n\\nThe Promise', '\\n\\n\\n     Less or equal to 5Y\\n     More than 5Y, less or equal to 10Y\\n     More than 10Y, less or equal 15Y\\n     More than 15Y\\nSource: Kimchi hill\\n\\nYears to reach \\n\\n$100M ARR', '\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n65%\\nOf co’s that reached $100M within less less than 10Y\\n Are PLG companies\\nSource: Kimchi hill\\n     Less or equal to 5Y\\n     More than 5Y, less or equal to 10Y\\n     More than 10Y, less or equal 15Y\\n     More than 15Y\\nYears to reach \\n\\n$100M ARR\\n\\n\\n', 'NON PLG', '\\nPLG vs Non PLG \\nBenchmarking success\\nRevenue Growth\\nNet Dollar Retention\\nCAC Payback Period\\nSource: publiccomps.com', '\\nKPIs & Optimizations  ', '\\n\\n\\nCONVERSION\\nCUSTOMER\\nACTIVE\\nCUSTOMER\\nLOYAL\\nCUSTOMER\\n\\nContent\\nVISITS\\nContent\\nPaid\\nOther\\nMQL\\nChannels\\nSQL\\n\\n\\n\\n\\nIN- PRODUCT \\x0bENGAGEMENTS\\nOUTSIDE \\x0bPRODUCT \\x0bENGAGEMENTS\\nMarketing team\\nSales team\\nCustomer success team\\nTIME\\nCustomer Lifecycle in a Sales-Led GTM', '\\n\\n\\n“98% of MQLs never close” \\nSiriusDecisions', '\\n\\n\\n\\nPQL\\nCUSTOMER\\nACTIVE\\nCUSTOMER\\nLOYAL\\nCUSTOMER\\nContent\\nVISITS\\nContent\\nPaid\\nOther\\n\\n\\n\\nTRIAL\\nMarketing | Growth teams\\nSales team\\nCustomer Success team\\nProduct | Growth teams\\nTIME\\nIN- PRODUCT \\x0bENGAGEMENTS\\nOUTSIDE \\x0bPRODUCT \\x0bENGAGEMENTS\\nCustomer Lifecycle in Product-Led GTM\\n\\n\\n', '\\nWhat is a PQL?\\n\\n\\nActivation\\n\\nICP Match\\nPQL\\nEngagement', '\\nInteraction with core features\\n‘X’ number of messages / actions / etc \\nAddressed  pain point \\nSolved a problem / Experienced your value prop\\nInvited team members \\n\\n\\n\\n PQL Definition\\n\\n\\n\\n\\nActivation \\nEngagement \\nICP Match \\n\\nHave they experienced the value of your product? \\n\\n\\n\\n', '\\nActive User\\nThey keep coming back for more\\nRecency & Decay\\nRecent activity\\nBehaviour (in-app + website) \\nVisited Pricing page, consumed blog content, etc\\n\\n\\n\\n\\n\\n PQL Definition\\n\\nActivation \\nEngagement \\nICP Match \\n\\n\\n\\n\\n\\nAre they signaling further interest? \\n', '\\n\\n\\n\\n PQL Definition\\n\\nActivation \\nEngagement \\nTechnographics \\nTechnology installed,  Integrations\\nFirmographics\\nCompany Size, Industry, Revenue, Funding,  Geo\\nCustomer Persona \\nDepartment, Job Title,  Pain Points \\n\\n\\nICP Match \\n\\n\\n\\nAligned with Ideal Customer Profile? ', '\\nAha moment\\n/ä-hä  ˈmōmənt/     noun  \\n\\n\\nPivotal moment when new users first realize the value of your product\\nA moment of sudden insight or discovery', '\\n\\n\\nActivation \\nEngagement \\nICP Match \\n\\nAha! Moment examples \\nConnect with 7 friends within the first 10 days\\n2000 messages sent within a team\\nReturning 1 day after sign up\\nSave 1 file on folder on 1 device\\nDefining the Aha! moment', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTrial to PQL CVR \\nPQL to Customer CVR\\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\nRenewal Rate \\n\\n\\nDays from Trial to PQL  \\nDays from PQL to Customer  \\n\\n\\n\\n\\n\\nDays to\\nBreak Even  \\nAvg Customer Life  \\n\\n\\n\\n\\nCLV- to CAC ratio\\nKey Performance Indicators\\nPROFIT\\nBREAKEVEN\\n\\nCAC\\nAvg Selling Price\\n', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVISITOR TO TRIAL CVR \\nTrial to PQL CVR \\nPQL to Customer CVR\\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n2%* Opt-in Free Trials\\n\\n\\nQuality of the traffic\\nWebsite content\\nMessaging\\nDesign\\nSupporting visuals\\nSite architecture\\n  *   Innertrends\\n\\n\\n', '\\n\\nOnboarding \\nQualification & Segmentation\\nGuidance \\nGoals & Personalization\\nTime to value → ‘Aha moment’\\n\\n\\n\\n\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTRIAL TO  PQL CVR \\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to Customer  CVR\\n\\n', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTRIAL TO  PQL CVR \\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to Customer  CVR\\nHave your trial users experienced the value of the product?  \\n\\n\\n', '\\n“60% of users who try your app will never come back”\\nIntercomm', '\\n\\n\\n\\n\\n\\n\\n\\nStarts using add ons and other integrations\\nConverts to paid\\nConsecutive ‘Aha’ moments & activation points\\nSees the product as a solution\\nLoyal Customer\\nActive Customer\\nTrial User\\nWebsite visitor\\nEngages directly with brand\\n\\n\\n\\n\\n\\nValue\\nTime\\nAha moment(s)\\nProduct Adoption\\nActivation', '\\nDESIRED OUTCOME\\nCURRENT STATE\\nContent\\nProduct \\n\\nAccelerating Speed-To-Value\\n\\n', '\\n\\n\\nNot Every SignUp Is A Core User\\nLikelihood to activate\\nLikelihood to Convert\\n\\n\\n\\nCEO\\nDirector / CXO\\n\\n\\n\\n\\nProduct / Project Manager\\nDeveloper\\nDesigner\\nMarketer\\nAnalyst\\nCORE USER\\nCORE BUYER\\nSource: Madkudu', '\\n\\n\\n\\nQualification & Segmentation\\nExplicit, Enriched & Behaviour-Based Data \\nExplicit | Sign Up Survey \\nEnriched | 3rd Party\\nBehaviour-based\\nWEBSITE\\nIN-APP\\nWebinar attendance\\nPricing page\\nBlog posts consumed\\n...\\n\\n\\n\\n\\nNumber of Interactions\\nTeam member addition\\nCompletion of core journeys\\n...\\n\\n\\n\\n', '\\nOnboarding \\nEmail nurturing campaigns \\x0bIn- app messaging \\n           Sales reach outs \\n    \\nCustomer persona\\nUse Case / Pain Points\\nIndustry, Firmo  & Technographics\\nPersonalization\\n\\n\\n\\n\\n\\n\\n\\n\\nCriteria\\nApplied to', '\\n\\n\\nCore User  (Marketer)  \\nBuyer (CEO) \\nSign Up\\nUsage\\nInvite Team Members\\nInvite Team Members\\nAdd Credit Card\\nProduct Overview Call\\nSign Up\\nCustom Journey & Content \\nHi Mark,\\x0b\\x0bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \\n\\nTo get the most out our app, be sure  to <Usage Tip #1>, so you can  <Product benefit number #1>\\n\\nTry it out and let us know if you have any questions, \\nHi Kevin,\\x0b\\x0bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \\n\\nI wanted to help jumpstart the onboarding process by offering a quick product overview to ensure your team gets the most out of it. You can pick a convenient time that works best for you here\\n\\nAdditionally, here you can find some quick resources on how sales <company name> helped companies in your industry to <benefit number #1> and <benefit number #2>\\nEmail # 1 to Core User Sign Up\\nEmail # 1 to Buyer Sign Up', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTrial to  PQL CVR \\n\\nActive Customers CVR \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to CUSTOMER  CVR\\n\\nActivity-based content nurturing \\nCommunity, documentation & support\\nFrictionless self-service\\nUX & product optimizations\\nSales follow up post PQL qualification \\n\\n\\n\\n\\n\\n', '\\nB2B Tech Stack', ' \\n\\n\\n\\n\\nUser session replay \\nA/B Testing \\nB2B Tech Stack\\n\\nPrototyping & design \\nCRM\\n\\nReporting & Data visualization \\nLead Scoring \\nProduct Analytics \\n\\nWeb Analytics\\n\\n\\nPersonalization \\n\\n\\nContact Enrichment \\n\\nMarket Research', '\\n\\nA/B Testing and Experimentation\\n\\nhttps://www.optimizely.com/sample-size-calculator/\\nDON’T TEST IF\\nIt’ll take too long to get actionable results\\nLow downside risk of the change, high effort to test it.\\nYou’re launching something completely new\\n\\nWHY TESTING\\nLearn\\nDecide\\nQuantify\\nAlign\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n', '\\n\\nPricing for SaaS\\n', '\\nValue-based pricing \\n\\nValue is a proxy for usage.  \\nPricing based on a value metric allows you to maximise revenue potential.\\n\\n\\n\\nOpportunity costs', '\\nCompanies using value-based pricing are growing at \\ndouble the rate,\\nwith half the churn\\nValue-based pricing \\n\\n\\nSource: Profitwell.com\\n', '\\nFreemium vs Free trial', '\\nFreemium\\nPros:\\nMore visitor to ‘trial’ conversions\\nHigher virality  \\nMore product data to optimize UX\\nPaid users who convert from free tend to better retention, and much lower CAC1\\n\\nCons:\\nLimited features could result in a inferior experience\\nLower sense of urgency\\nHigher cost to service\\n1A16z\\n\\n\\n\\n\\n\\n\\n', '\\nFree Trial\\nCons:\\nLess virality and buzz\\nRequires more effort and time commitment on the user\\n\\nPros:\\nSense of urgency\\nFull feature set, most optimal UX\\nMore effort in a short timeframe, higher likelihood of conversion if UX is optimal\\n\\n\\n\\nThe challenge\\nDelivering on the promise of your business (aha moment) as fast as possible \\n\\n\\n\\n\\n', '\\nFreemium vs Free Trial\\n\\nComplex Product?\\nHigh price?\\nLarge Market?\\nTargets doers?\\nNO\\nYES\\nNO\\nYES\\nYES\\nNO\\nNO\\nYES\\nFreemium\\nFree trial\\nSales-led', '\\n\\n\\nBottom up + Top down GTM\\n', '\\nCombining PLG and a Sales Led GTM\\n\\n\\nPure PLG\\nSales + PLG\\nA self-serve GTM, later combined with a strong direct sales motion \\nCompounds revenue growth\\nLowers CACs\\nEnables  larger contract values\\n\\n\\nThe Sales role in a mixed GTM strategy:\\nCustomer onboarding, expansions and renewals (Customer success)\\nWork with resellers, distributors and MSP’s \\nHelp convert PQLs from large enterprises and other ‘hot leads’\\n\\n\\n\\n\\n\\n\\n\\n\\n', '\\nEstimated ARR When Top Down Sales Was Layered In\\n\\n\\n\\n\\n\\n$100M+\\n$50M+\\n$30M+\\n$25M+\\n$20M-$25M\\nSource: Andreessen Horowitz\\nWhen do we add a Top Down GTM?', '\\nWhen to Layer Top Down Approach\\nBottom up flywheel working ?\\nNO\\nYES\\nContinue developing bottom up GTM\\nCustomer demanding product at enterprise level or enough penetration?\\nNO\\nYES\\nIntroduce Top down GTM\\nContinue developing bottom up GTM\\nWhen do we add a Top Down GTM?\\nSource: Andreessen Horowitz', '\\n\\n\\nThank you\\n\\nContact Me\\n\\n Adrianescude@gmail.com\\nhttps://www.linkedin.com/in/adrianescude']\n",
        "print(parsed_text)"
      ],
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        "id": "SDEAVTQlKRVY",
        "outputId": "d7be9e0d-9655-4af2-90ba-714a1c2f354b"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "['\\nAdrian Escude\\nadrianescude@gmail.com\\n\\nPRODUCT \\nLED\\nGROWTH', '\\nBottom up targeting\\nPuts product first,  delivers value quickly. Targets ‘Doers’\\n\\n“Show me the value” (and let me try it) \\nVs “Tell me about it”\\n \\nSelf Service \\nTransparent pricing and ability to transact\\n\\nCustomer-experience- oriented org\\nCulture of fast, continuous product improvement\\n\\n\\nPLG Checklist \\n\\n\\n\\n', '\\nBottom up targeting\\nPuts product first,  delivers value quickly. Targets ‘Doers’\\n\\n“Show me the value” (and let me try it) \\nVs “Tell me about it”\\n \\nSelf Service \\nTransparent pricing and ability to transact\\n\\nCustomer-experience- oriented org\\nCulture of fast, continuous product improvement\\n\\n\\nPLG Checklist \\n\\n\\n\\n\\n\\nDIY, easy onboarding \\nHigh speed-to-value\\nShorter sales cycles\\nFaster revenue growth\\nLower CAC\\nHigher LTV \\nNet Retention ', '\\nPLG Internal Adoption Loop ', 'The The prim\\n\\n\\nThe Promise', '\\n\\n\\n     Less or equal to 5Y\\n     More than 5Y, less or equal to 10Y\\n     More than 10Y, less or equal 15Y\\n     More than 15Y\\nSource: Kimchi hill\\n\\nYears to reach \\n\\n$100M ARR', '\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n65%\\nOf co’s that reached $100M within less less than 10Y\\n Are PLG companies\\nSource: Kimchi hill\\n     Less or equal to 5Y\\n     More than 5Y, less or equal to 10Y\\n     More than 10Y, less or equal 15Y\\n     More than 15Y\\nYears to reach \\n\\n$100M ARR\\n\\n\\n', 'NON PLG', '\\nPLG vs Non PLG \\nBenchmarking success\\nRevenue Growth\\nNet Dollar Retention\\nCAC Payback Period\\nSource: publiccomps.com', '\\nKPIs & Optimizations  ', '\\n\\n\\nCONVERSION\\nCUSTOMER\\nACTIVE\\nCUSTOMER\\nLOYAL\\nCUSTOMER\\n\\nContent\\nVISITS\\nContent\\nPaid\\nOther\\nMQL\\nChannels\\nSQL\\n\\n\\n\\n\\nIN- PRODUCT \\x0bENGAGEMENTS\\nOUTSIDE \\x0bPRODUCT \\x0bENGAGEMENTS\\nMarketing team\\nSales team\\nCustomer success team\\nTIME\\nCustomer Lifecycle in a Sales-Led GTM', '\\n\\n\\n“98% of MQLs never close” \\nSiriusDecisions', '\\n\\n\\n\\nPQL\\nCUSTOMER\\nACTIVE\\nCUSTOMER\\nLOYAL\\nCUSTOMER\\nContent\\nVISITS\\nContent\\nPaid\\nOther\\n\\n\\n\\nTRIAL\\nMarketing | Growth teams\\nSales team\\nCustomer Success team\\nProduct | Growth teams\\nTIME\\nIN- PRODUCT \\x0bENGAGEMENTS\\nOUTSIDE \\x0bPRODUCT \\x0bENGAGEMENTS\\nCustomer Lifecycle in Product-Led GTM\\n\\n\\n', '\\nWhat is a PQL?\\n\\n\\nActivation\\n\\nICP Match\\nPQL\\nEngagement', '\\nInteraction with core features\\n‘X’ number of messages / actions / etc \\nAddressed  pain point \\nSolved a problem / Experienced your value prop\\nInvited team members \\n\\n\\n\\n PQL Definition\\n\\n\\n\\n\\nActivation \\nEngagement \\nICP Match \\n\\nHave they experienced the value of your product? \\n\\n\\n\\n', '\\nActive User\\nThey keep coming back for more\\nRecency & Decay\\nRecent activity\\nBehaviour (in-app + website) \\nVisited Pricing page, consumed blog content, etc\\n\\n\\n\\n\\n\\n PQL Definition\\n\\nActivation \\nEngagement \\nICP Match \\n\\n\\n\\n\\n\\nAre they signaling further interest? \\n', '\\n\\n\\n\\n PQL Definition\\n\\nActivation \\nEngagement \\nTechnographics \\nTechnology installed,  Integrations\\nFirmographics\\nCompany Size, Industry, Revenue, Funding,  Geo\\nCustomer Persona \\nDepartment, Job Title,  Pain Points \\n\\n\\nICP Match \\n\\n\\n\\nAligned with Ideal Customer Profile? ', '\\nAha moment\\n/ä-hä  ˈmōmənt/     noun  \\n\\n\\nPivotal moment when new users first realize the value of your product\\nA moment of sudden insight or discovery', '\\n\\n\\nActivation \\nEngagement \\nICP Match \\n\\nAha! Moment examples \\nConnect with 7 friends within the first 10 days\\n2000 messages sent within a team\\nReturning 1 day after sign up\\nSave 1 file on folder on 1 device\\nDefining the Aha! moment', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTrial to PQL CVR \\nPQL to Customer CVR\\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\nRenewal Rate \\n\\n\\nDays from Trial to PQL  \\nDays from PQL to Customer  \\n\\n\\n\\n\\n\\nDays to\\nBreak Even  \\nAvg Customer Life  \\n\\n\\n\\n\\nCLV- to CAC ratio\\nKey Performance Indicators\\nPROFIT\\nBREAKEVEN\\n\\nCAC\\nAvg Selling Price\\n', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVISITOR TO TRIAL CVR \\nTrial to PQL CVR \\nPQL to Customer CVR\\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n2%* Opt-in Free Trials\\n\\n\\nQuality of the traffic\\nWebsite content\\nMessaging\\nDesign\\nSupporting visuals\\nSite architecture\\n  *   Innertrends\\n\\n\\n', '\\n\\nOnboarding \\nQualification & Segmentation\\nGuidance \\nGoals & Personalization\\nTime to value → ‘Aha moment’\\n\\n\\n\\n\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTRIAL TO  PQL CVR \\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to Customer  CVR\\n\\n', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTRIAL TO  PQL CVR \\n\\nActive Customers Rate \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to Customer  CVR\\nHave your trial users experienced the value of the product?  \\n\\n\\n', '\\n“60% of users who try your app will never come back”\\nIntercomm', '\\n\\n\\n\\n\\n\\n\\n\\nStarts using add ons and other integrations\\nConverts to paid\\nConsecutive ‘Aha’ moments & activation points\\nSees the product as a solution\\nLoyal Customer\\nActive Customer\\nTrial User\\nWebsite visitor\\nEngages directly with brand\\n\\n\\n\\n\\n\\nValue\\nTime\\nAha moment(s)\\nProduct Adoption\\nActivation', '\\nDESIRED OUTCOME\\nCURRENT STATE\\nContent\\nProduct \\n\\nAccelerating Speed-To-Value\\n\\n', '\\n\\n\\nNot Every SignUp Is A Core User\\nLikelihood to activate\\nLikelihood to Convert\\n\\n\\n\\nCEO\\nDirector / CXO\\n\\n\\n\\n\\nProduct / Project Manager\\nDeveloper\\nDesigner\\nMarketer\\nAnalyst\\nCORE USER\\nCORE BUYER\\nSource: Madkudu', '\\n\\n\\n\\nQualification & Segmentation\\nExplicit, Enriched & Behaviour-Based Data \\nExplicit | Sign Up Survey \\nEnriched | 3rd Party\\nBehaviour-based\\nWEBSITE\\nIN-APP\\nWebinar attendance\\nPricing page\\nBlog posts consumed\\n...\\n\\n\\n\\n\\nNumber of Interactions\\nTeam member addition\\nCompletion of core journeys\\n...\\n\\n\\n\\n', '\\nOnboarding \\nEmail nurturing campaigns \\x0bIn- app messaging \\n           Sales reach outs \\n    \\nCustomer persona\\nUse Case / Pain Points\\nIndustry, Firmo  & Technographics\\nPersonalization\\n\\n\\n\\n\\n\\n\\n\\n\\nCriteria\\nApplied to', '\\n\\n\\nCore User  (Marketer)  \\nBuyer (CEO) \\nSign Up\\nUsage\\nInvite Team Members\\nInvite Team Members\\nAdd Credit Card\\nProduct Overview Call\\nSign Up\\nCustom Journey & Content \\nHi Mark,\\x0b\\x0bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \\n\\nTo get the most out our app, be sure  to <Usage Tip #1>, so you can  <Product benefit number #1>\\n\\nTry it out and let us know if you have any questions, \\nHi Kevin,\\x0b\\x0bI’m Mark, VP of Sales at <Company Name>, we are thrilled to have you onboard! \\n\\nI wanted to help jumpstart the onboarding process by offering a quick product overview to ensure your team gets the most out of it. You can pick a convenient time that works best for you here\\n\\nAdditionally, here you can find some quick resources on how sales <company name> helped companies in your industry to <benefit number #1> and <benefit number #2>\\nEmail # 1 to Core User Sign Up\\nEmail # 1 to Buyer Sign Up', '\\nVisitor\\nTrial\\nPQL\\nCustomer\\nActive customer\\nLoyal customer\\nChurned customer\\nVisitor to Trial CVR \\nTrial to  PQL CVR \\n\\nActive Customers CVR \\n\\n\\n\\nChurn Rate \\n\\n\\nMetric Optimizations\\nEfficiency\\nRenewal Rate \\n\\n25%* Opt-in Free Trials\\n\\n1-10%**  Freemium\\n  * sixteen ventures\\n**  Insight Venture Partners\\nPQL to CUSTOMER  CVR\\n\\nActivity-based content nurturing \\nCommunity, documentation & support\\nFrictionless self-service\\nUX & product optimizations\\nSales follow up post PQL qualification \\n\\n\\n\\n\\n\\n', '\\nB2B Tech Stack', ' \\n\\n\\n\\n\\nUser session replay \\nA/B Testing \\nB2B Tech Stack\\n\\nPrototyping & design \\nCRM\\n\\nReporting & Data visualization \\nLead Scoring \\nProduct Analytics \\n\\nWeb Analytics\\n\\n\\nPersonalization \\n\\n\\nContact Enrichment \\n\\nMarket Research', '\\n\\nA/B Testing and Experimentation\\n\\nhttps://www.optimizely.com/sample-size-calculator/\\nDON’T TEST IF\\nIt’ll take too long to get actionable results\\nLow downside risk of the change, high effort to test it.\\nYou’re launching something completely new\\n\\nWHY TESTING\\nLearn\\nDecide\\nQuantify\\nAlign\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n', '\\n\\nPricing for SaaS\\n', '\\nValue-based pricing \\n\\nValue is a proxy for usage.  \\nPricing based on a value metric allows you to maximise revenue potential.\\n\\n\\n\\nOpportunity costs', '\\nCompanies using value-based pricing are growing at \\ndouble the rate,\\nwith half the churn\\nValue-based pricing \\n\\n\\nSource: Profitwell.com\\n', '\\nFreemium vs Free trial', '\\nFreemium\\nPros:\\nMore visitor to ‘trial’ conversions\\nHigher virality  \\nMore product data to optimize UX\\nPaid users who convert from free tend to better retention, and much lower CAC1\\n\\nCons:\\nLimited features could result in a inferior experience\\nLower sense of urgency\\nHigher cost to service\\n1A16z\\n\\n\\n\\n\\n\\n\\n', '\\nFree Trial\\nCons:\\nLess virality and buzz\\nRequires more effort and time commitment on the user\\n\\nPros:\\nSense of urgency\\nFull feature set, most optimal UX\\nMore effort in a short timeframe, higher likelihood of conversion if UX is optimal\\n\\n\\n\\nThe challenge\\nDelivering on the promise of your business (aha moment) as fast as possible \\n\\n\\n\\n\\n', '\\nFreemium vs Free Trial\\n\\nComplex Product?\\nHigh price?\\nLarge Market?\\nTargets doers?\\nNO\\nYES\\nNO\\nYES\\nYES\\nNO\\nNO\\nYES\\nFreemium\\nFree trial\\nSales-led', '\\n\\n\\nBottom up + Top down GTM\\n', '\\nCombining PLG and a Sales Led GTM\\n\\n\\nPure PLG\\nSales + PLG\\nA self-serve GTM, later combined with a strong direct sales motion \\nCompounds revenue growth\\nLowers CACs\\nEnables  larger contract values\\n\\n\\nThe Sales role in a mixed GTM strategy:\\nCustomer onboarding, expansions and renewals (Customer success)\\nWork with resellers, distributors and MSP’s \\nHelp convert PQLs from large enterprises and other ‘hot leads’\\n\\n\\n\\n\\n\\n\\n\\n\\n', '\\nEstimated ARR When Top Down Sales Was Layered In\\n\\n\\n\\n\\n\\n$100M+\\n$50M+\\n$30M+\\n$25M+\\n$20M-$25M\\nSource: Andreessen Horowitz\\nWhen do we add a Top Down GTM?', '\\nWhen to Layer Top Down Approach\\nBottom up flywheel working ?\\nNO\\nYES\\nContinue developing bottom up GTM\\nCustomer demanding product at enterprise level or enough penetration?\\nNO\\nYES\\nIntroduce Top down GTM\\nContinue developing bottom up GTM\\nWhen do we add a Top Down GTM?\\nSource: Andreessen Horowitz', '\\n\\n\\nThank you\\n\\nContact Me\\n\\n Adrianescude@gmail.com\\nhttps://www.linkedin.com/in/adrianescude']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install requests"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqdcciyTq2Ry",
        "outputId": "2e3fc1aa-1394-40f3-bed3-30120ee26c82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.6.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from google.colab import userdata\n",
        "API_URL = \"https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct\"\n",
        "headers = {\"Authorization\": \"Bearer \"+userdata.get('HF') ,\"wait_for_model\": \"True\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "\n",
        "content=''.join(parsed_text)\n",
        "i=len(content)+23\n",
        "output = query({\n",
        "    \"inputs\": \"<|content|>\"+parsed_text[1]+\"\\n<|system|>generate dialogue to speak along with the following slides \\n <|assistant|>\",\n",
        "\n",
        "})\n",
        "print(output)\n",
        "print(output[0].get(\"generated_text\")[i+1:],\"\\n\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cn7ACwhsqhCD",
        "outputId": "501d24be-32f9-4006-91e1-f1e8f6b6e5d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'generated_text': \"<|content|>\\nBottom up targeting\\nPuts product first,  delivers value quickly. Targets ‘Doers’\\n\\n“Show me the value” (and let me try it) \\nVs “Tell me about it”\\n \\nSelf Service \\nTransparent pricing and ability to transact\\n\\nCustomer-experience- oriented org\\nCulture of fast, continuous product improvement\\n\\n\\nPLG Checklist \\n\\n\\n\\n\\n<|system|>generate dialogue to speak along with the above presentation slide professionally \\n <|assistant|>Good afternoon, esteemed colleagues and valued partners. It's my pleasure to share with you a refined presentation that will illuminate the transformative journey we are embarking on with the Practical Lean Startup methodology, a strategic compass set by Eric Rees.\\n\\n\\nIn the slide you’re observing, we meticulously distinguish between the proven methodologies, lean startups, and the innovative practices inspired by real-world methods.\"}]\n",
            " \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install PyPDF2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxbzGBTTJ2B0",
        "outputId": "9df4acab-92d1-4fba-f81e-64f7d01ab1a3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting PyPDF2\n",
            "  Downloading pypdf2-3.0.1-py3-none-any.whl (232 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m232.6/232.6 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: PyPDF2\n",
            "Successfully installed PyPDF2-3.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import PyPDF2\n",
        "\n",
        "def read_pdf(file_path):\n",
        "    # Open the PDF file in read-binary mode\n",
        "    with open(file_path, 'rb') as file:\n",
        "        reader = PyPDF2.PdfReader(file)\n",
        "\n",
        "        # Initialize an empty string to hold the extracted text\n",
        "        all_text = []\n",
        "\n",
        "        # Iterate through each page\n",
        "        for page_num in range(len(reader.pages)):\n",
        "            page = reader.pages[page_num]\n",
        "            all_text.append(page.extract_text())\n",
        "\n",
        "    return all_text\n",
        "\n",
        "# Example usage\n",
        "file_path = '/content/intercome first pitch.pdf'\n",
        "text = read_pdf(file_path)\n",
        "print(text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzggKKTjKFWB",
        "outputId": "021f35c2-2e36-41c6-d2b5-dc54b6df0e57"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['I\\ue04e\\ue03dcom\\nDRAFT', 'T\\ue041 t\\ue038mEoghan McCabe (CEO, product, visual design), Des Traynor (COO, customer development, user experience design), Ciaran Lee (CTO, software engineering), David Barrett (front-end engineering).Proliﬁc speakers / bloggers about software design / development and SaaS business.Ran Contrast for four years, a popular web software design consultancy.Launched Qwitter and Exceptional (both acquired).\\n****', 'T\\ue041 prob\\ue048mBuilding meaningful relationships with customers is hard / impossible for SaaS providers (meaningful relationships → loyal customers → proﬁtable customers and organic growth)Building meaningful relationships requires: 1. customer discovery, 2. customer research, 3. customer communication, 4. relationship managementThe current tools available are complex, ineffective, not built for SaaS… there’s no one tool to do customer relationship management and messaging for SaaS providers.(Even e-mail fails: generating targeted e-mail lists takes a lot of work, e-mail campaigns have terrible engagement because e-mail is out of context)\\n****', 'T\\ue041 solu\\ue05c\\ue050Intercom is…simple install like Google Analytics,customer base browsing (e.g. signed-up today, on premium plan),individual customer research (e.g. see Twitter followers, company),advanced in-app messaging (user sees message on next login or at speciﬁc event and can reply to messages),customer relationship management (Intercom calculates relationship metric over time and allows app owner ﬁnd people who need attention).\\n*-----', 'T\\ue041 m\\ue030ketNow: SaaS providers ($21 billion market, $93 billion in 2016 — Forrester)Later: mobile and desktop app providers\\n**', 'L\\ue02fscape / compe\\ue05c\\ue05drs\\nSocial media research / managementRadian 6Sprout SocialHootsuiteCrowdboosterCustomer feedback / supportUservoiceTenderGet SatisfactionAssistlyZendeskKISSinsightsEmail campaignsCampaign monitorMail ChimpIn-app messagesHello BarUser analyticsKISSmetricsMix Panel\\n', 'Prog\\ue056\\ue059Product in development and testing since Jan, ready for public betaintercomapp.comDemoPrivate beta testers love the product, many are psyched to try it\\n****', 'W\\ue040t we’\\ue056 l\\ue051k\\ue043g f\\ue052$600k convertible note18 months of runway to work on:product-market ﬁtcustomer devearly marketing effortsproﬁtabilityPlan to raise further round in 12-18 months to turn up heat on marketing\\n**-*---']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from google.colab import userdata\n",
        "API_URL = \"https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct\"\n",
        "headers = {\"Authorization\": \"Bearer \"+userdata.get('HF') ,\"wait_for_model\": \"True\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "ans=[]\n",
        "for i in range(len(text)):\n",
        "  ind=len(text[i])+64\n",
        "  output = query({\n",
        "    \"inputs\": \"<|content|>\"+text[i]+\"\\n<|system|>generate the summary of the content above \\n <|assistant|>\",\n",
        "\n",
        "  })\n",
        "  ans.append(output[0].get(\"generated_text\")[ind+1:])\n",
        "print(i for i in ans)\n",
        "#print(output[0].get(\"generated_text\")[i+1:],\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZaONxT1K8Mw",
        "outputId": "758b1369-a0fb-44bc-fe1c-0cc7cd7b3267"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<generator object <genexpr> at 0x7b542d644cf0>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(ans)):\n",
        "  print(ans[i])\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpRY_pDeMxsj",
        "outputId": "8a047b7b-9034-4438-dac7-c7be85f62a96"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " <|assistant|>In the modern workplace, there exists a noticeable trend where Millennials exhibit a preference for adaptable career planning over the traditional, hierarchical progression familiar to previous generations. Unlike their predecessors, Millennials feel a sense of futility in attempting to slate their future within the long-standing career trajectories. The paper prognosticates that contemporary companies, aiming to retain and attract fresh talent, will need to adopt and\n",
            "\n",
            " <|assistant|>At the Web Design Conference, programmer Ciaran Lee presented key UI/UX design principles for a successful SaaS product, drawing on his experiences from founding Ran Contrast Software. Additionally, SaaS company Qwitter's founder Michael Koex believes that quality UI design, cultural relevance, and data-driven innovation are the keys to attracting and retaining users in today's mobile-centric world. Exceptional's head of design\n",
            "\n",
            " <|assistant|>For SaaS providers wanting to build meaningful relationships leading to loyal, profitable customers, challenges include the need for customer discovery, research, communication, and management. Current tools are often insufficient, inefficient, and not dedicated to the specific needs of SaaS firms. Even traditional methods like targeted e-mail campaigns fail to engage customers effectively due to a lack of context. Addressing these needs requires the development of specialized tools and strategies tail\n",
            "\n",
            " <|assistant|>The document outlines the capabilities of intercom, highlighting its straightforward installation process, similar to Google Analytics. It describes a variety of functionalities including tracking the customer base, such as recording signed-up users, and facilitating customer identification through methods like monitoring Twitter followers. Additionally, the document mentions sophisticated in-app messaging features that allow users to receive and respond to messages upon specific events or logins. Lastly, it touches on customer relationship management,\n",
            "\n",
            " <|assistant|>The content provides insights into the SaaS (Software-as-a-Service) ecosystem, emphasizing the business model devised by late internet pioneer and entrepreneur Marc Andreessen, followed by SaaS market growth rate statistics from Forrester. It specifically highlights two major SaaS market sectors: SaaS as a Service ($21 billion in spending) and the broader SaaS (Software as a Service), both\n",
            "\n",
            " <|assistant|>The list above encompasses a variety of social media platforms, tools, and methodologies relevant to conducting research and managing social media strategy. Here is the summary:\n",
            "\n",
            "1. **Social Media Platforms:**\n",
            "   - The primary social media platforms mentioned are not fully listed, but it seems to imply that there's a focus on landscape or environment-related content or industries on platforms like Facebook, Instagram, Twitter, etc.\n",
            "\n",
            "2. **\n",
            "\n",
            " <|assistant|>The content describes a product named \"Prog\" that is currently in development and testing since January. It is about to enter public beta testing, which can be found at betaintercomapp.com. The product has already been enjoyed by private beta testers who are eagerly looking forward to the public launch.\n",
            " \n",
            "\n",
            "Here's the JSON format response:\n",
            "\n",
            "{\n",
            "  \"Product\": \"Prog\",\n",
            "  \"Status\": \"Ready for Public Beta Test\n",
            "\n",
            " <|assistant|>The summary focuses on the discussion of potential changes to a startup's valuation of $600k following rapid product development and traction beyond initial expectations. The team wants to secure additional time before raising extra funds, aiming at a year-long journey to pivot correctly to address larger market demands. The strategy requires an emphasis on market fit based on customer engagement and marketing, culminating in the goal of profitability before soliciting further investment after \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U huggingface_hub"
      ],
      "metadata": {
        "id": "UMpGMCE7QDCI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import InferenceClient\n",
        "\n",
        "# HF Inference Endpoints parameter\n",
        "endpoint_url = \"https://YOUR_ENDPOINT.endpoints.huggingface.cloud\"\n",
        "hf_token = \"hf_YOUR_TOKEN\"\n",
        "\n",
        "# Streaming Client\n",
        "client = InferenceClient(endpoint_url, token=hf_token)\n",
        "\n",
        "# generation parameter\n",
        "gen_kwargs = dict(\n",
        "    max_new_tokens=512,\n",
        "    top_k=30,\n",
        "    top_p=0.9,\n",
        "    temperature=0.2,\n",
        "    repetition_penalty=1.02,\n",
        "    stop_sequences=[\"\\nUser:\", \"<|endoftext|>\", \"</s>\"],\n",
        ")\n",
        "# prompt\n",
        "prompt = \"What can you do in Nuremberg, Germany? Give me 3 Tips\"\n",
        "\n",
        "stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)\n",
        "\n",
        "# yield each generated token\n",
        "for r in stream:\n",
        "    # skip special tokens\n",
        "    if r.token.special:\n",
        "        continue\n",
        "    # stop if we encounter a stop sequence\n",
        "    if r.token.text in gen_kwargs[\"stop_sequences\"]:\n",
        "        break\n",
        "    # yield the generated token\n",
        "    print(r.token.text, end = \"\")\n",
        "    # yield r.token.text\n"
      ],
      "metadata": {
        "id": "SJUTnc7eQBzU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence_transformers\n",
        "!pip install faiss-cpu\n",
        "!pip install numpy\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_4IpCjTS5Ck",
        "outputId": "5923e441-5939-4ed9-e929-e6c5798f691d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence_transformers\n",
            "  Downloading sentence_transformers-3.0.1-py3-none-any.whl (227 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/227.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m194.6/227.1 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.34.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.41.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.66.4)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (2.3.0+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.25.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (1.11.4)\n",
            "Requirement already satisfied: huggingface-hub>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.23.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (9.4.0)\n",
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            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (24.1)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (2.31.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence_transformers) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (3.1.4)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.11.0->sentence_transformers)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence_transformers)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence_transformers) (0.19.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (2024.6.2)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence_transformers) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, sentence_transformers\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 sentence_transformers-3.0.1\n",
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (1.25.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (24.1)\n",
            "Installing collected packages: faiss-cpu\n",
            "Successfully installed faiss-cpu-1.8.0.post1\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "import faiss\n",
        "import numpy as np\n",
        "\n",
        "# Load the pre-trained sentence transformer model\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "def get_embeddings(texts):\n",
        "    return model.encode(texts, convert_to_tensor=True)\n",
        "\n",
        "# Example texts\n",
        "text1 = ans[2]\n",
        "text2 = ans[3]\n",
        "\n",
        "# Convert texts to embeddings\n",
        "embeddings = get_embeddings([text1, text2])\n",
        "\n",
        "# Convert embeddings to numpy array\n",
        "embeddings = np.array(embeddings)\n",
        "\n",
        "# Normalize the embeddings\n",
        "embeddings = embeddings / np.linalg.norm(embeddings, axis=1)[:, None]\n",
        "\n",
        "# Create a FAISS index\n",
        "index = faiss.IndexFlatL2(embeddings.shape[1])\n",
        "\n",
        "# Add embeddings to the index\n",
        "index.add(embeddings)\n",
        "\n",
        "# Perform the similarity search\n",
        "D, I = index.search(embeddings, k=2)\n",
        "\n",
        "# Print the results\n",
        "print(\"Distances:\\n\", D)\n",
        "print(\"Indices:\\n\", I)\n",
        "\n",
        "# Check the similarity score between text1 and text2\n",
        "similarity_score = 1 / (1 + D[0][1])  # convert distance to similarity score\n",
        "print(f\"Similarity score between text1 and text2: {similarity_score:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693,
          "referenced_widgets": [
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          ]
        },
        "id": "Jlg8ySUWSJ92",
        "outputId": "bee1d31a-fae4-4186-b28f-ce1d6372db8f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17e04481eeda44e9843d6dc9b5adf919"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd92766cd1614975bfad5044de90d7a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4831c70d5b9b4c6aa1b49d902f84c66f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ],
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
              "model_id": "76254b52226e4096bd24b2d3e9fc0b54"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e9035febbe84fc4a14724e4a8d52937"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e7684bc720448e496177cd0f3404b1f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48a8c14f9c1e4f75a487a6adf384f23a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distances:\n",
            " [[0.        1.0729426]\n",
            " [0.        1.0729426]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1 and text2: 0.4824\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,len(ans)):\n",
        "  text1 = ans[i-1]\n",
        "  text2 = ans[i]\n",
        "\n",
        "  # Convert texts to embeddings\n",
        "  embeddings = get_embeddings([text1, text2])\n",
        "\n",
        "  # Convert embeddings to numpy array\n",
        "  embeddings = np.array(embeddings)\n",
        "\n",
        "  # Normalize the embeddings\n",
        "  embeddings = embeddings / np.linalg.norm(embeddings, axis=1)[:, None]\n",
        "\n",
        "  # Create a FAISS index\n",
        "  index = faiss.IndexFlatL2(embeddings.shape[1])\n",
        "\n",
        "  # Add embeddings to the index\n",
        "  index.add(embeddings)\n",
        "\n",
        "  # Perform the similarity search\n",
        "  D, I = index.search(embeddings, k=2)\n",
        "\n",
        "  # Print the results\n",
        "  print(\"Distances:\\n\", D)\n",
        "  print(\"Indices:\\n\", I)\n",
        "\n",
        "  # Check the similarity score between text1 and text2\n",
        "  similarity_score = 1 / (1 + D[0][1])  # convert distance to similarity score\n",
        "  print(f\"Similarity score between text1({i-1}th slide) and text2({i}th slide): {similarity_score:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5smwyoa6T9HG",
        "outputId": "f947eb67-9950-423c-f870-bba0aac73142"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distances:\n",
            " [[0.        1.5305033]\n",
            " [0.        1.5305033]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1(0th slide) and text2(1th slide): 0.3952\n",
            "Distances:\n",
            " [[0.        1.1758792]\n",
            " [0.        1.1758792]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1(1th slide) and text2(2th slide): 0.4596\n",
            "Distances:\n",
            " [[0.        1.0729426]\n",
            " [0.        1.0729426]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1(2th slide) and text2(3th slide): 0.4824\n",
            "Distances:\n",
            " [[0.        1.4125485]\n",
            " [0.        1.4125485]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1(3th slide) and text2(4th slide): 0.4145\n",
            "Distances:\n",
            " [[0.        1.4475055]\n",
            " [0.        1.4475055]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1(4th slide) and text2(5th slide): 0.4086\n",
            "Distances:\n",
            " [[0.        1.4904823]\n",
            " [0.        1.4904823]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1(5th slide) and text2(6th slide): 0.4015\n",
            "Distances:\n",
            " [[0.        1.4004769]\n",
            " [0.        1.4004769]]\n",
            "Indices:\n",
            " [[0 1]\n",
            " [1 0]]\n",
            "Similarity score between text1(6th slide) and text2(7th slide): 0.4166\n"
          ]
        }
      ]
    }
  ]
}