{"id":8111,"date":"2025-10-17T08:43:47","date_gmt":"2025-10-17T08:43:47","guid":{"rendered":"https:\/\/phantombuster.com\/blog\/?p=8111"},"modified":"2025-10-20T06:52:52","modified_gmt":"2025-10-20T06:52:52","slug":"how-to-use-chatgpt-in-google-sheets","status":"publish","type":"post","link":"https:\/\/blogv2.phantombuster.com\/blog\/sales-prospecting\/how-to-use-chatgpt-in-google-sheets\/","title":{"rendered":"How to Use ChatGPT in Google Sheets\u00a0for Sales: A Step-by-Step Playbook"},"content":{"rendered":"

Want to use AI to generate more leads\u00a0faster without leaving your spreadsheet?<\/p>\n

In this guide, you’ll learn how to use GPT in Google Sheets to reduce manual steps in\u00a0lead\u00a0scoring, segmentation, and outreach<\/a>. From qualifying leads to writing personalized messages and summarizing sales calls, you’ll discover copyable formulas you can apply across your CRM columns.<\/p>\n

No code required to start. Advanced users can add Apps Script later.<\/p>\n

Setting up ChatGPT in Google Sheets<\/h2>\n

Install the GPT for Sheets and Docs add-on, then enable its functions in Extensions. No coding required for the examples below.<\/p>\n

Installation steps<\/h3>\n
    \n
  1. Go to the Google Workspace Marketplace<\/a> and search for “GPT for Sheets and Docs”<\/li>\n
  2. Click Install<\/strong> and grant the necessary permissions when prompted\"Installing<\/li>\n
  3. Open your Google Sheet and navigate to Extensions > GPT for Sheets and Docs > Enable GPT functions<\/strong>\"Enable<\/li>\n
  4. You’ll see a sidebar appear with options to get started and access templates<\/li>\n<\/ol>\n

    Good to know: You can paste in your own OpenAI API key via Extensions \u2192 GPT for Sheets and Docs \u2192 API Key. Using your own API key can be more cost effective for high-volume users than relying solely on the default add-on pricing.<\/p><\/blockquote>\n

    Note on pricing:<\/strong>\u00a0Pricing and limits<\/a> change frequently. Check the add-on’s pricing page and OpenAI model pricing for current rates. Higher-end models typically cost more per token. Some advanced features like GPT_WEB (web search) and GPT_VISION (image analysis) may\u00a0require additional billing or an OpenAI API key. Feature availability and billing vary by plan, so check the add-on documentation.<\/p>\n

    Troubleshooting tip:<\/strong> If you don’t see the GPT for Sheets add-on after installation, try refreshing your browser or reopening the Google Sheet.<\/p>\n

    Essential ChatGPT functions for sales Google Sheets<\/h2>\n

    Once installed, the add-on exposes custom functions you can call from cells.<\/p>\n

    Here are the most useful ChatGPT applications for sales professionals:<\/p>\n

    Basic function: =GPT()<\/h3>\n

    Use =GPT() for simple prompts that return a single text output.<\/p>\n

    =GPT(\"your prompt here\", cell reference)<\/code><\/pre>\n
    This function works when you want to generate insight or content from a single prompt.<\/p>\n
    Example:<\/strong><\/p>\n
    =GPT(\"Summarize this company description in 50 words:\", A2)<\/code><\/pre>\n
    List function: =GPT_LIST()<\/h3>\n
    Use when you want multiple items returned into separate cells.<\/p>\n
    =GPT_LIST(\"Generate a list of 5 follow-up questions based on this prospect response:\", A2)<\/code><\/pre>\n
    This will output each question in a separate row below the formula.<\/p>\n
    Classification function: =GPT_CLASSIFY()<\/h3>\n
    Use for categorizing prospect data:<\/p>\n
    =GPT_CLASSIFY(A2, \"hot lead, warm lead, cold lead\")<\/code><\/pre>\n
    This analyzes the content in A2 and classifies it into one of the provided categories.<\/p>\n
    \"Example<\/p>\n
    Here’s a breakdown of the top four GPT functions to start with:<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
    Function<\/strong><\/td>\nDescription<\/strong><\/td>\nExample with Sales Data<\/strong><\/td>\nWhat You’ll Get<\/strong><\/td>\n<\/tr>\n
    =GPT()<\/td>\nGenerating content from scratch<\/td>\n=GPT(“Create a personalized outreach message…”, A2:E2)<\/td>\nEmail drafts, summaries, call notes<\/td>\n<\/tr>\n
    =GPT_CLASSIFY()<\/td>\nClassifies into categories<\/td>\n=GPT_CLASSIFY(C2, “Hot Lead, Warm Lead, Cold Lead”)<\/td>\nOne selected category<\/td>\n<\/tr>\n
    =GPT_EXTRACT()<\/td>\nExtracts info from text<\/td>\n=GPT_EXTRACT(D2, “Extract pain points”)<\/td>\nList of key elements or insights<\/td>\n<\/tr>\n
    =GPT_SUMMARIZE()<\/td>\nSummarizes long text<\/td>\n=GPT_SUMMARIZE(F2, “Summarize this call in 3 bullets”)<\/td>\nConcise bullet-point summary<\/td>\n<\/tr>\n
    =GPT_TAG()<\/td>\nApplies tags based on content<\/td>\n=GPT_TAG(B2, “Decision Maker, Influencer, etc.”)<\/td>\nOne or more relevant tags<\/td>\n<\/tr>\n
    =GPT_FORMAT()<\/td>\nCleans\/standardizes formatting<\/td>\n=GPT_FORMAT(C2, “Format this company name”)<\/td>\nConsistent, cleaned-up text<\/td>\n<\/tr>\n
    =GPT_TRANSLATE()<\/td>\nTranslates from multiple languages to another.<\/td>\n=GPT_TRANSLATE(G2, “en”, “es”)<\/td>\nTranslated version of the input<\/td>\n<\/tr>\n
    =GPT_LIST()<\/td>\nGenerates multi-item lists<\/td>\n=GPT_LIST(D2, “List 5 objections this prospect might have”)<\/td>\nSeveral items returned in separate cells<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Additional functions like =GPT_EXTRACT(), =GPT_TAG(), =GPT_FORMAT(), and =GPT_TRANSLATE() offer more specialized data handling for extracting information, applying tags, cleaning formatting, and translating text from one language to another.<\/p>\n
    Tips for using GPT functions in sales Google Sheets<\/h3>\n
      \n
    1. Structure your Google Sheets efficiently:<\/strong> Keep source data in one column or two columns depending on how your prompts are structured, with adjacent columns for AI analysis.<\/li>\n
    2. Apply to ranges:<\/strong> You can copy these formulas down to apply to entire columns of prospect data.<\/li>\n
    3. Be specific in\u00a0prompts:<\/strong> The more specific your instructions, the better the output. Include details about tone, length, and purpose, and how you’d like the AI to format or edit the result.<\/li>\n
    4. Combine with regular formulas:<\/strong> You can nest GPT functions within CONCATENATE or IF statements for more dynamic applications.<\/li>\n<\/ol>\n
      Practical applications for sales professionals<\/h2>\n
      Here are specific ways you can use ChatGPT in your sales Google Sheets, with examples and templates you can apply today. You can copy code from any of the examples below and paste it directly into your Google Sheet.<\/p>\n
      Top of funnel (TOFU): Prospecting and initial outreach<\/h3>\n
      Lead research summaries<\/h4>\n
      \ud83d\udfe0\ud83d\udfe0 \u2014 Intermediate<\/p>\n
      Pull company and profile data with PhantomBuster automations, then summarize in Sheets with =GPT(). Consolidate\u00a0prospect information you’ve collected<\/a> into concise research briefs for sales calls.<\/p>\n
      Important:<\/strong> GPT functions cannot visit URLs directly. First, extract profile and website text into cells using PhantomBuster’s LinkedIn automations<\/a>, then reference those cells in your formulas.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n
      A: Profile Text<\/th>\nB: Company Info<\/th>\nC: Research Summary<\/th>\n<\/tr>\n
      [Extracted LinkedIn profile<\/a> text]<\/td>\n[Extracted company description]<\/td>\n=GPT(“Create a 3-bullet research summary for a sales call based on this profile: ” & A2 & ” and company: ” & B2 & “. Focus on role responsibilities, potential pain points, and recent activity.”)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Result:<\/strong><\/p>\n
      Sales Call Research Brief:\r\n\u2022 As Director of Supply Chain Operations, prospect likely manages logistics optimization, supplier relationships, and inventory management across multiple locations\r\n\u2022 Potential pain points include supply chain visibility, forecast accuracy, and order fulfillment speed based on manufacturing industry trends\r\n\u2022 Company recently announced expansion into European markets (press release from last month), suggesting potential need for scalable systems<\/code><\/pre>\n
      \"Research<\/p>\n
      Call prep enhancement:<\/strong> Add a column for generated questions based on the research summary:<\/p>\n
      =GPT(\"Based on this research summary, generate 3 thoughtful discovery questions for my call: \" & C2)\r\n<\/code><\/pre>\n
       <\/p>\n
      Always verify facts from AI outputs before using them in outreach.<\/p>\n
      Contact data cleaning and standardization<\/h4>\n
      \ud83d\udfe2 \u2014 Beginner<\/p>\n
      Clean up messy CRM fields so the AI understands what it’s reading.\u00a0Inconsistent data makes analysis difficult\u00a0and reduces the quality of AI outputs.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n\n\n
      A: Raw Job Title<\/th>\nB: Standardized Title<\/th>\nC: Seniority Level<\/th>\n<\/tr>\n
      Chief Marketing Officer<\/td>\n=GPT(“Standardize this job title:”, A2)<\/td>\n=GPT(“Classify this job title as: C-Suite, VP-level, Director, Manager, Individual Contributor:”, A2)<\/td>\n<\/tr>\n
      Marketing Director, Global Demand Gen<\/td>\n=GPT(“Standardize this job title:”, A3)<\/td>\n=GPT(“Classify this job title as: C-Suite, VP-level, Director, Manager, Individual Contributor:”, A3)<\/td>\n<\/tr>\n
      Head of Growth & Digital Marketing<\/td>\n=GPT(“Standardize this job title:”, A4)<\/td>\n=GPT(“Classify this job title as: C-Suite, VP-level, Director, Manager, Individual Contributor:”, A4)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Results:<\/strong><\/p>\n
      Row 2: \"Chief Marketing Officer\" | \"C-Suite\"\r\nRow 3: \"Director of Marketing, Demand Generation\" | \"Director\"\r\nRow 4: \"Head of Growth Marketing\" | \"Director\"\r\n<\/code><\/pre>\n
       <\/p>\n
      Bulk processing tip:<\/strong> Create a template Google Sheet for standardizing imported CRM data, then use the cleaned data for segmentation, personalization, and analysis.<\/p>\n
      Personalizing outreach at scale<\/h4>\n
      \ud83d\udfe2 \u2014 Beginner<\/p>\n
      Once your leads are scored, it’s time to reach out. Generic templates rarely get responses.<\/p>\n
      Use ChatGPT to draft messages from enriched data you pull with PhantomBuster’s LinkedIn<\/a> Search Export and AI Enricher automations. Start by populating your columns with prospect details using PhantomBuster’s data collection capabilities.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n
      A: Name<\/th>\nB: Title<\/th>\nC: Company<\/th>\nD: Pain Point<\/th>\nE: Personalized Outreach<\/th>\n<\/tr>\n
      Jennifer<\/td>\nVP Marketing<\/td>\nTechGrowth<\/td>\nLead generation<\/td>\n=GPT(“Write a personalized cold email to ” & A2 & “, ” & B2 & ” at ” & C2 & “. Their main challenge is ” & D2 & “. Reference [your product] and how it addresses automated lead generation<\/a>. Keep it under 100 words, conversational tone, with a soft call to action for a meeting.”)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Result:<\/strong><\/p>\n
      Subject: Quick thought on TechGrowth's lead generation\r\n\r\nHi Jennifer,\r\n\r\nI noticed TechGrowth has been expanding its market presence recently - impressive work! Given your focus on lead generation challenges, I thought you might be interested in how we helped similar VP Marketing leaders automate their lead acquisition processes.\r\n\r\nWe recently helped a company in your space increase qualified leads by 37% while cutting manual prospecting time in half.\r\n\r\nWould a 15-minute call next Tuesday or Wednesday make sense to share these specific approaches?\r\n\r\nBest,\r\n[Your Name]<\/code><\/pre>\n
      \"Personalized<\/p>\n
      Template variations:<\/strong> Create multiple columns with different approaches (formal, casual, question-based, case study focused) to test what works best with different prospect segments.\u00a0Send selectively and personalize\u2014avoid mass, unsolicited outreach.<\/p>\n
      Sales objection handling<\/h4>\n
      \ud83d\udfe0\ud83d\udfe0 \u2014 Intermediate<\/p>\n
      Use ChatGPT to draft tailored responses to common objections without canned scripts.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n
      A: Objection<\/th>\nB: Prospect Industry<\/th>\nC: Response<\/th>\n<\/tr>\n
      “Your solution looks good, but it’s outside our budget for this year.”<\/td>\nHealthcare<\/td>\n=GPT(“Write a response to this sales objection: ‘” & A2 & “‘ from a prospect in the ” & B2 & ” industry. Focus on value, ROI, and possibly alternative options like quarterly billing. Keep it conversational and non-pushy.”)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Result:<\/strong><\/p>\n
      \"I completely understand budget considerations, especially in healthcare where resources are carefully allocated. Many of our healthcare clients initially had similar concerns before discovering our solution typically pays for itself within 4-5 months through staff efficiency gains and reduced error rates.\r\n\r\nWould it be helpful to see a healthcare-specific ROI calculation based on your organization's size? Alternatively, we do offer quarterly billing options that some departments find easier to manage within existing budget cycles.\r\n\r\nEither way, I'm happy to work with your timeline to find the right approach when it makes sense for your team.\"\r\n<\/code><\/pre>\n
       <\/p>\n
      Objection library:<\/strong> Build a comprehensive objection\/response library categorized by industry and objection type for quick reference during live calls.<\/p>\n
      Middle of funnel (MOFU): Lead qualification and engagement<\/h3>\n
      Lead qualification and prioritization<\/h4>\n
      \ud83d\udfe2 \u2014 Beginner<\/p>\n
      Manually scoring leads based on multiple criteria is time-consuming and often inconsistent. ChatGPT can analyze prospect data and return a priority score in your Google Sheets.<\/p>\n
      Example setup:<\/strong><\/p>\n
       <\/p>\n
       <\/p>\n\n\n\n\n
      A: Company<\/th>\nB: Title<\/th>\nC: Company Size<\/th>\nD: Recent Activity<\/th>\nE: Lead Score<\/th>\n<\/tr>\n
      Acme Corp<\/td>\nCMO<\/td>\n500+<\/td>\nViewed pricing page 3x this week<\/td>\n=GPT(“Score this lead from 1-10 based on the following criteria: Title: ” & B2 & “, Company size: ” & C2 & “, Recent activity: ” & D2 & “. Explain your reasoning briefly.”)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Result:<\/strong><\/p>\n
      Score: 8\/10. Reasoning: CMO indicates decision-maker status, 500+ employees suggests budget capacity, and multiple pricing page visits show strong buying intent. This lead should be prioritized for immediate outreach.\r\n<\/code><\/pre>\n
       <\/p>\n
      \"Lead<\/p>\n
      Pro tip:<\/strong> Enrich your sheet<\/a> with PhantomBuster’s LinkedIn Profile Enricher automation<\/a> to add recent role changes, activity, and skills. Always follow LinkedIn’s terms and use data responsibly.<\/p>\n
      A lead that might score 6\/10 based on basic information could reveal itself as an 8\/10 when enriched with data showing they’ve recently transitioned to a decision-making role or engaged with content related to your solution.\u00a0This improves scoring consistency.<\/p>\n
      Consistency framework:<\/strong> Create a cell with your scoring criteria to reference in all scoring prompts. Avoid sensitive PII in criteria and keep a shared scoring rubric so results stay consistent across reps.<\/p>\n
      $Z$1: \"Scoring criteria: 1-3 (Low priority): Non-decision makers or small companies with minimal activity. 4-7 (Medium priority): Manager-level at mid-size companies with some engagement. 8-10 (High priority): Decision-makers at target companies with recent high-intent activity.\"\r\n<\/code><\/pre>\n
       <\/p>\n
      Then include in your formula: =GPT(“Score this lead from 1-10 based on the following information…” & $Z$1)<\/p>\n
      Competitor analysis<\/h4>\n
      \ud83d\udd34 \ud83d\udd34 \ud83d\udd34 \u2014 Advanced<\/p>\n
      Extract and analyze competitor mentions from prospect communications.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n
      A: Prospect Communication<\/th>\nB: Competitor Analysis<\/a><\/th>\nC: Differentiation Points<\/th>\n<\/tr>\n
      “We’re currently evaluating your product along with CompetitorX and CompetitorY. CompetitorX seems to have a stronger mobile app, but we like your reporting features.”<\/td>\n=GPT(“Identify competitors mentioned in this text and summarize what the prospect said about each:”, A2)<\/td>\n=GPT(“Based on the competitor comparison in this text, suggest 3 specific differentiation points to emphasize with this prospect:”, A2)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      \"Competitor<\/p>\n
      Result:<\/strong><\/p>\n
      Competitors mentioned:\r\n1. CompetitorX - Prospect believes they have a stronger mobile app\r\n2. CompetitorY - No specific feedback provided\r\nPositive about your solution: Reporting features\r\n\r\nDifferentiation points to emphasize:\r\n1. Highlight our upcoming mobile app enhancements (Q3 roadmap) while emphasizing our superior reporting capabilities\r\n2. Showcase integrations between our reporting features and mobile notifications\/alerts that CompetitorX doesn't offer\r\n3. Provide customer testimonials specifically from users who switched from CompetitorX due to limitations they discovered beyond the mobile interface\r\n<\/code><\/pre>\n
      Sales call summaries<\/h4>\n
      \ud83d\udfe0\ud83d\udfe0 \u2014 Intermediate<\/p>\n
      Condense lengthy call notes into actionable summaries.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n
      A: Call Notes<\/th>\nB: Call Summary<\/th>\nC: Action Items<\/th>\n<\/tr>\n
      [Lengthy paragraph of free-form notes from a sales call]<\/td>\n=GPT(“Summarize these sales call notes into key points (max 3 bullets):”, A2)<\/td>\n=GPT(“Extract clear action items from these call notes, formatted as a numbered list with owners and timeline if mentioned:”, A2)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Result:<\/strong><\/p>\n
      Call Summary:\r\n\u2022 Prospect experiencing 30% growth and current system can't scale; needs solution implemented by Q3\r\n\u2022 Primary decision criteria: ease of integration with SAP, training requirements, and cost\r\n\u2022 VP Operations will be final decision-maker; we need to prepare ROI case and implementation timeline\r\n\r\nAction Items:\r\n1. Send integration case study for SAP environments (Sales rep, by Friday)\r\n2. Schedule technical demo with their IT team (Sales rep, next week)\r\n3. Prepare customized ROI calculator (Solutions engineer, before follow-up call)\r\n4. Create implementation timeline showing completion before Q3 (Project manager, by next meeting)\r\n<\/code><\/pre>\n
       <\/p>\n
      \"Sales<\/p>\n
      Follow-up automation:<\/strong>\u00a0Trigger follow-up drafts from your summary, then send via PhantomBuster’s outreach automations (e.g., LinkedIn message sequences<\/a>) while honoring platform limits.<\/p>\n
      =GPT(\"Write a follow-up email based on this call summary: \" & B2 & \" and these action items: \" & C2)\r\n<\/code><\/pre>\n
       <\/p>\n
      Bottom of funnel (BOFU): Closing and forecasting<\/h3>\n
      Win\/loss analysis<\/h4>\n
      \ud83d\udd34 \ud83d\udd34 \ud83d\udd34 \u2014 Advanced<\/p>\n
      Analyze the factors behind closed deals (won or lost) to improve your approach.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n
      A: Deal Outcome<\/th>\nB: Deal Details<\/th>\nC: Deal Analysis<\/th>\nD: Strategy Recommendations<\/th>\n<\/tr>\n
      Lost<\/td>\n$75K deal with FinTech startup, 3-month sales cycle, lost to Competitor Z<\/td>\n=GPT(“Analyze this lost deal and identify likely contributing factors: ” & B2)<\/td>\n=GPT(“Based on this lost deal analysis, provide 3 specific recommendations to improve future similar opportunities: ” & C2)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Result:<\/strong><\/p>\n
      Loss Analysis:\r\nPrimary factors likely include price sensitivity typical of startups, insufficient ROI demonstration during the short sales cycle, and Competitor Z's stronger reputation in the FinTech vertical specifically. The abbreviated timeline may have prevented fully developing champion relationships or addressing all stakeholder concerns.\r\n\r\nStrategy Recommendations:\r\n1. Develop a FinTech-specific ROI calculator that demonstrates payback period within first year - critical for startup budget justification\r\n2. Create a streamlined 30\/60\/90-day sales process specifically for startups to accommodate faster decision timelines\r\n3. Prepare competitive differentiation document focused specifically on Competitor Z's limitations in the FinTech space, with testimonials from similar clients\r\n<\/code><\/pre>\n
       <\/p>\n
      \"Win\/loss<\/p>\n
      Pipeline movement prediction<\/h4>\n
      \ud83d\udd34 \ud83d\udd34 \ud83d\udd34 \u2014 Advanced<\/p>\n
      Use GPT outputs to flag deals likely to advance or stall based on recent activity.<\/p>\n
      Example setup:<\/strong><\/p>\n\n\n\n\n
      A: Deal Stage<\/th>\nB: Last Activity<\/th>\nC: Days in Stage<\/th>\nD: Prospect Engagement<\/th>\nE: Movement Prediction<\/th>\n<\/tr>\n
      Proposal<\/td>\nSent proposal 10 days ago, no response to 2 follow-ups<\/td>\n15<\/td>\nInitially enthusiastic, now unresponsive<\/td>\n=GPT(“Based on this information, predict whether this deal is likely to advance, stall, or decline. Current stage: ” & A2 & “. Activity: ” & B2 & “. Time in stage: ” & C2 & ” days. Engagement level: ” & D2 & “. Provide reasoning and recommended action.”)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      Result:<\/strong><\/p>\n
      Prediction: STALL (70% probability)\r\n\r\nReasoning: The combination of extended time in proposal stage (15 days), lack of response to multiple follow-ups, and the shift from enthusiasm to unresponsiveness indicates a high probability of deal stagnation. The prospect may have either found another solution, encountered internal budget\/priority shifts, or the primary champion could be facing internal resistance.\r\n\r\nRecommended Action: Implement a \"proposal rescue\" approach - send a value-focused message to widen stakeholder involvement. Consider offering something of immediate value (relevant industry report, competitive analysis) unrelated to the proposal to re-establish engagement without directly addressing the silence.\r\n<\/code><\/pre>\n
       <\/p>\n
      Early warning system:<\/strong> Use this approach to create a weekly pipeline health check that identifies at-risk opportunities before they fully stall.\u00a0Combine with PhantomBuster exports of recent prospect activity to refresh inputs before each weekly check.<\/p>\n
      Best practices for data privacy when using AI in sales<\/h2>\n