G2 Intent Data: Score, Segment and Personalize Leads in Google Sheets

G2 intent data is one of the most underused signals in B2B prospecting. Every rating, review, and "switched from" note on a software profile is a prospect telling you, in their own words, how happy they are with the tools they pay for. Read at scale, that signal lets you score accounts, segment them by buying readiness, and personalize outreach with real pain points instead of generic pitches. This guide shows you how to turn G2 insights into a working revenue engine, directly inside Google Sheets, with Derrick.

What is G2 intent data?

G2 intent data is the set of buying signals you can extract from a vendor's G2 profile: average star rating, review volume, recent review excerpts, and the "switched from" and "switched to" notes reviewers leave behind. Unlike classic intent providers that infer interest from anonymous web behavior, G2 data is declared: a named reviewer states what they use, what frustrates them, and what made them move. For an outbound team, that turns a flat list of accounts into a ranked list of accounts you can attack in the right order, with the right angle.

Most teams only look at G2 for competitive intel, then stop. They miss three things hiding in plain sight:

• Intent signal: a prospect reviewing a tool adjacent to yours is in an active evaluation mindset.
• Concrete pain points: negative reviews list, line by line, exactly what frustrates your target persona.
• Switch triggers: "switched from X" notes flag churn moments you can time your outreach around.

The hard part has always been volume. G2 reviews are scattered across hundreds of thousands of profiles, and reading them by hand does not scale. That is the gap Derrick closes by pulling the data into a spreadsheet column you can sort and act on, and it sits alongside the rest of your B2B intent data workflow.

Use Case 1: Qualify a prospect's product stack instantly

Goal: understand which tools your prospect runs, how well rated those tools are, and where the gaps or frustrations sit, before you ever write the first line of an email.

Step by step

1. Start from a list of target companies in Google Sheets.
2. Run Website Technologies to detect their current stack: CRM, sales engagement, support, analytics, and so on.
3. Feed the detected vendors into G2 Insight to retrieve average ratings, review volume, and selected review excerpts for each one.

Concrete example

Your prospect runs Outreach.io. G2 Insight returns an average score of 3.2 and several reviews mentioning complexity and poor usability. That account is likely struggling, which makes it a strong fit for a simpler or cheaper alternative. You now have an opener grounded in their reality, not your feature list.

Why this is valuable

• You write messages based on real customer experience, not assumptions.
• You detect accounts with high switch potential before a competitor does.
• You use actual review language as a personalized conversation starter.

Qualifying stacks this way also feeds your wider B2B intent data marketing strategy, where stack maturity becomes a segmentation axis of its own.

Use Case 2: Target frustrated users of your competitors

Goal: surface users of your direct competitors who are already unhappy, so your message lands as a relief instead of an interruption.

Step by step

1. Detect the tools in use with Website Technologies.
2. Pull G2 scores and recent reviews with G2 Insight.
3. Analyze the latest three reviews with Ask Claude or Ask OpenAI right in the sheet. Sample prompt: "Here are three reviews of [tool] used by this prospect. Are they positive, neutral, or negative? Summarize the main pain points in one sentence."

Concrete example

Your prospect uses Salesloft. The reviews flag recurring issues with integrations and slowness. If your product handles both better, that is your attack angle, written in the prospect's own words. This is the practical layer under any serious read of product review signals: the review is not the goal, the angle it unlocks is.

Why this is valuable

• You instantly sort fans from frustrated users across a whole list.
• You send laser-targeted icebreakers based on declared friction.
• You talk about their reality, not your roadmap.

Use Case 3: Score leads with G2 plus stack detection plus AI

Goal: automatically rank the leads most open to switching, so reps spend their time on the warmest accounts first.

Step by step

1. Start from your list of accounts.
2. Enrich with Website Technologies to reveal installed tools, then G2 Insight to gather ratings, reviews, and recent feedback.
3. Send everything to Ask Claude or Ask OpenAI. Sample prompt: "Here are the tools used by this company and the associated G2 reviews. Does this company seem satisfied with its current stack? Give a switch score from 1 to 5 with a short explanation."

Result

You add a "Switch Score" column to your sheet, sort descending, and attack the warmest, most frustrated leads first. The rest of the list does not disappear: it becomes a nurture queue you revisit when a new review or funding event changes the picture.

Why this is valuable

• No more guesswork about who to call first.
• You prioritize on real buying signals, not gut feeling.
• You go from cold to context-aware in about a minute per account.

A switch score also pairs naturally with how you read overall product rating data: the rating tells you the baseline sentiment, the score tells you what to do about it.

Build the scoring model that fits your funnel

The three use cases above share one engine: detect the stack, read the G2 sentiment, let AI compress it into a number you can sort on. Once that pipeline runs in a sheet, the scoring model is yours to shape. A simple, transparent weighting works well to start:

• Rating gap: a low average rating on a tool you replace scores high. A frustrated user is a ready user.
• Review recency: a negative review from last month beats one from two years ago. Recency is intent.
• Switch language: an explicit "switched from a competitor" or "looking to move" note is the strongest single signal in the dataset.
• Stack maturity: ratings across five or more tools signal a mature buyer who evaluates carefully; a single rated tool signals an emerging stack with room to land and expand.

Because the analysis lives in the same sheet as your CRM export, the score is never a black box. A rep can click any row, read the underlying reviews G2 Insight pulled, and see exactly why the model flagged the account. That auditability is what gets a scoring system actually adopted instead of ignored.

Common mistakes to avoid

• Treating G2 data as a one-off pull. Sentiment shifts. Re-run G2 Insight on your active list monthly so a fresh wave of negative reviews surfaces accounts that just became reachable.
• Personalizing on the rating alone. A 3.2 average says little on its own. The specific pain point inside the reviews is what makes an email feel written for one person.
• Scoring without reading. Spot-check the AI's switch scores against the raw reviews for your first batch, so you trust the model before you scale it.
• Ignoring the happy accounts. High ratings are not dead ends. They tell you which incumbents are entrenched, so you can adjust your angle from "replace" to "complement".

How Derrick runs this inside Google Sheets

Derrick is a Google Sheets sidebar, not a set of brittle formulas or a separate platform to learn. Each step above is a feature you point at an input column; it fills the output columns for every row. No scripts, no APIs to wire up, no engineer required.

• Website Technologies detects a prospect's tech stack from their domain. Cost: 2 credits per website.
• G2 Insight retrieves ratings, review volume, and review excerpts to enrich each account. Cost: 2 credits per line.
• Ask Claude and Ask OpenAI summarize sentiment and generate the switch score directly in the sheet. Cost: 2 credits per line for Ask Claude, 1 credit per line for Ask OpenAI.

You can start on the free plan with 100 credits per month to test the workflow on a small list, then scale the same setup to thousands of accounts on a paid plan. Derrick works at every volume, from a single rep's weekly list to a full revops pipeline. If you would rather run the enrichment from your AI assistant, Derrick MCP exposes the same data to ChatGPT, Claude Desktop, and any MCP-compatible tool.

Ready to turn G2 reviews into a ranked pipeline? Try Derrick free in Google Sheets and score your first list today.

G2 intent data versus classic intent providers

Classic intent platforms sell you anonymized surges in topic consumption: a spike in companies researching "sales engagement" this week. That is useful for top-of-funnel timing, but it is anonymous and probabilistic. G2 intent data is the opposite: named, declared, and specific. The two are complementary, and knowing which to reach for matters.

For most outbound teams the highest-leverage move is to let a classic provider tell you when a market is warming, then use G2 insights to decide who to write to first and what to say. The second half of that sentence is where reply rates are won or lost.

From switch score to a repeatable cadence

A score is only worth the action it triggers. The teams that get the most out of G2 intent data wire it into a weekly rhythm rather than a one-time list build. A simple cadence looks like this: every Monday, re-run G2 Insight and the AI scoring on the active account list; any account that crossed into the hot band that week gets routed to a rep with the supporting reviews attached; warm accounts stay in nurture; cold accounts get a recheck date set 90 days out. Because every step runs in the same sheet, the cadence costs minutes, not a sprint, and it compounds: each pass surfaces accounts that were not reachable the week before. That is the difference between treating reviews as trivia and treating them as a live pipeline of buying signals.

What to do when a tool is not on G2

Not every vendor your prospects use has a deep G2 footprint, especially newer or niche tools. When G2 Insight returns thin coverage for an account, the workflow still holds, you just lean harder on the other signals already in your sheet. The stack detected by Website Technologies tells you what the company runs even when reviews are sparse, which is itself a qualification signal: a company standing up a brand-new category of tool is mid-evaluation by definition. You can also widen the lens by asking your AI step to reason over adjacent, well-reviewed tools in the same category, since sentiment about a category leader often predicts how a prospect feels about the smaller tool they actually chose. The point is that no single signal carries the whole decision. G2 ratings, review text, switch language, and stack maturity each add a layer, and the switch score is simply the place where those layers get combined into one number a rep can act on. Treating the model as a blend rather than a single magic field is what keeps it accurate as your list grows and as the data behind any one account thins out or goes stale.

Keep the model honest by reviewing the bottom of your ranked list as well as the top. The accounts the score buried tell you whether your weighting is too aggressive on any one factor, and a quick monthly calibration keeps the whole pipeline trustworthy.