Data Products That Sell Themselves: Evident Need, Evident Distribution

2. 1. The Evident Need Test

An evident need is one where the person experiencing the problem already knows they have the problem. You do not have to convince them their situation is painful. The pain is pre-acknowledged. You only have to convince them you have a better solution.

The test has three questions:

1. Does your target customer already Google for something related to this problem? If there is search volume for the problem itself — not the solution — the need is evident. If you have to create demand from scratch, it is not.
2. Would your target customer be embarrassed to admit they are solving this problem manually? "I copy prices from competitor websites into a spreadsheet every Monday" is embarrassing. That embarrassment signals an evident need waiting for automation.
3. Is the cost of not solving it measurable? "We lose one deal per month because we do not know when a competitor changes pricing" is a number. Evident needs are quantifiable by the buyer before you even talk to them.

Evident vs. Non-Evident: Examples

The "Already Doing It Manually" Signal

The strongest evidence that a need is evident: find people who are already solving the problem with a spreadsheet, a recurring Google search, a VA, or a browser bookmark folder. "Manual process that someone already built" is not a lack of product-market fit. It is proof of product-market fit for an automated solution. Every person doing the manual version is a potential customer.

How to find them: search Reddit for "[your data type] + spreadsheet" or "[your data type] + manually." Search Indie Hackers for "I built a scraper for X because there was no tool." These are your design specifications, written by your future customers, for free.

3. 2. The Evident Distribution Test

Evident distribution means the product spreads through its normal use. You do not run ads. You do not cold email. You build the product in a way where using it creates new users. There are five mechanisms that produce this.

Mechanism 1: Collaborative Workflows

Data that lives inside a shared document is seen by everyone who opens that document. A Google Sheet that pulls live data from your API is shared between team members. Every team member who opens the sheet becomes aware of the product. The person who set it up becomes an internal champion just by sharing a file.

Mechanism 2: Marketplace Discovery

Publishing your product in a marketplace (Google Workspace Marketplace, Chrome Web Store, Zapier, Make, Airtable Marketplace, Notion integrations directory) puts you in front of people already searching for solutions. Unlike search ads, marketplace listings have permanent residency once published and compound over time through reviews and installs.

Mechanism 3: Exported Artifacts

Data that is exported from your tool and shared externally carries your brand. A report generated by your tool, shared in a Slack channel or email, is an ad seen by everyone in that conversation. This is the data equivalent of "sent from my iPhone."

Mechanism 4: SEO from the Data Itself

A database of things people search for is a search engine optimisation machine. If your data answers questions people Google, you rank for those questions organically and strangers find you without any paid acquisition.

Mechanism 5: Network Effects Inside the Tool

Some data products get better as more people use them. Crowdsourced data, aggregated signals, benchmarks from your own users' activity. The more users, the better the data. The better the data, the more users. Classic flywheel. Examples: email open rate benchmarks from Mailchimp's own send data, salary benchmarks from LinkedIn's job posting activity, performance benchmarks from Datadog's customer base.

The Self-Distribution Score

Before building, score your DaaS idea on these five mechanisms (0 or 1 each):

Score 3+: the product can self-distribute. You will still need an initial push to reach escape velocity, but the flywheel takes over after that.
Score 1–2: you will be selling forever. Not necessarily fatal, but plan accordingly.
Score 0: reconsider the architecture before building.

4. 3. Google Sheets as a Distribution Channel

Google Sheets is the most underrated distribution channel in B2B SaaS. 700 million people use Google Workspace. Every person who builds a spreadsheet for work is a potential user of a Sheets add-on. And the Google Workspace Marketplace has dramatically less competition than the App Store, Product Hunt, or the Chrome Web Store.

Why Sheets is So Good for DaaS

• Zero friction activation: The user never leaves their workflow. They install the add-on, it appears in the Extensions menu, and data appears in their cells. No new tab. No new app. No migration. The lowest-friction onboarding in software.
• The sharing mechanic: When a user shares their Google Sheet with a colleague, the add-on is not included — but the data is. The colleague sees cells full of useful, live data. They ask "where did this come from?" That question is a warm referral.
• Marketplace organic discovery: The Google Workspace Marketplace gets millions of searches per month. "Find company email," "enrich leads," "get company data" are real search queries there. A listing with good reviews shows up. No ads needed.
• Persistence: A browser extension can be uninstalled in one click. A spreadsheet integration that 50 people on the team use to refresh their CRM data every Monday cannot be removed without breaking a workflow. Sheets integrations have very high retention.

What to Build as a Sheets Add-On

The ideal Sheets add-on for a DaaS product:

1. Takes a column of known inputs (company names, domains, LinkedIn URLs, email addresses).
2. Returns enriched data in adjacent columns (funding status, tech stack, employee count, email, revenue range).
3. Updates on a schedule (daily, weekly) so the sheet stays fresh.
4. Has a free tier (e.g., 50 enrichments/mo free) so users activate with zero friction and hit the paywall only after they are dependent.

The technical cost: Google Apps Script for the add-on shell, calls to your own enrichment API. The add-on can be built in a weekend. The hard part is the data, not the add-on.

Hunter.io as the Template

Hunter.io has a Google Sheets add-on that does exactly one thing: take a column of company domains and return the best-guess email addresses for people at those companies. It is installed by hundreds of thousands of users. Hunter converts Sheets users to paid at roughly 8–12% over 90 days — higher than their web app average — because Sheets users are already using the product in a real workflow. The add-on is basically a continuous trial that bills itself.

Microsoft Excel Add-In: the Overlooked Second Marketplace

The Microsoft AppSource marketplace for Excel add-ins has even less competition than the Google Workspace Marketplace and serves a predominantly enterprise and mid-market audience (finance, healthcare, real estate — heavier Excel users than Google Sheets users). Building the same add-in for both platforms doubles the distribution surface with maybe 20% more development effort.

Airtable as a Third Surface

Airtable's extension marketplace is smaller than Sheets but the audience is more technical and more willing to pay. Airtable users are often ops teams and growth teams that already use data enrichment tools. An Airtable extension that enriches a base with your data gets discovered through Airtable's gallery and the Airtable community.

5. 4. Browser Extensions: The Always-On Layer

A browser extension shows your data on every page the user visits that is relevant to your product. No navigation required. No separate app to open. The data surfaces where the user already is.

The Distribution Mechanics of a Chrome Extension

• Chrome Web Store organic discovery: 2 billion Chrome users. The store gets searched by people looking for productivity tools for LinkedIn, Crunchbase, etc. A relevant extension with good reviews appears organically for years.
• LinkedIn sidebar injection: The most common pattern for B2B data extensions. When a sales rep visits a LinkedIn profile, your extension shows enrichment data (email, company funding, tech stack) in a sidebar panel. They never left LinkedIn. The data just appeared. This is how Apollo, Hunter, Lusha, and RocketReach all built their initial user bases.
• Team install virality: One person at a company installs your extension. They mention it in the team Slack. Five more people install it. Browser extensions spread within sales teams faster than almost any other product type because the value is visible immediately to anyone watching over the installer's shoulder.

What Data Works Best as a Browser Extension

The extension model works when your data is relevant at a specific URL. That means:

• Company data shown on LinkedIn company pages or LinkedIn profiles.
• Pricing data shown on competitor product pages.
• Review data shown on G2, Capterra, or Trustpilot product pages.
• Funding data shown on Crunchbase or TechCrunch articles.
• Tech stack detection shown on any company website.
• Domain/email data shown when you hover over an email address in Gmail.

The pattern: your data is a layer of context that makes pages the user already visits more useful. BuiltWith does this for tech stacks. SimilarWeb does it for traffic data. Wappalyzer does it for technology detection. The extension is not a separate product — it is a new surface for data that would otherwise require the user to open a separate tab and search.

The Gmail Extension Variant

A Gmail extension that enriches the sender or recipient's contact info when you open an email is one of the highest-conversion extension archetypes. Every time a sales rep opens an email from a prospect, your extension shows: company funding stage, headcount, tech stack, and a verified direct dial. Streak, Clearbit Reveal, and Hunter's Gmail extension all used this pattern to build massive user bases without paid acquisition. The use case is self-evident: you are reading an email, you want context on who sent it.

6. 5. The Data Watermark Effect

When data produced by your tool leaves your tool and gets shared, it carries a signal. That signal — whether explicit branding or just the quality of the data — creates awareness in people who never opted in to any marketing. This is the data equivalent of "powered by Stripe" on a checkout page.

Explicit Watermarks

The simplest version: every CSV export from your tool includes a footer row or column header saying "Data sourced from [Your Product] — [yourproduct.com]." Every report generated by your tool includes your logo and URL in the header. Every embedded chart or widget has a small "powered by X" attribution.

This sounds small. It compounds. A freelancer exports a list of 200 funded startups from JustRaised and sends it to a client. The client sees the source. Two weeks later the client signs up. No cold email involved. No ad spend. Just a footer on a CSV.

Implicit Watermarks: Data Quality as Brand

If your data is distinctly better than anything else available — more accurate, more current, more specific — the data itself watermarks your brand. "Where did you get this list?" becomes a question your customers get asked regularly, and the answer drives referrals. This is how Clay grew: their enrichment data quality was so much better than alternatives that users voluntarily evangelized the product in Slack communities and on Twitter/X.

The Shared Report Flywheel

Design your product so the output is a shareable artifact. Not just a raw CSV — a formatted, readable report that a non-user can understand and find valuable. Examples:

• A "company intelligence brief" PDF: one page per company with funding history, hiring signals, tech stack, and key contacts. Shareable in a Slack channel. Instantly useful to people who did not generate it.
• An embeddable competitive pricing table that your customers post in their team Notion pages. Every viewer of that Notion page sees the table. Some of them want one for their own products.
• A weekly "funding pulse" email that users forward to colleagues in sales. The forward is the acquisition event.

7. 6. API Embedding and the Workflow Lock-In

A data API embedded into another team's workflow is one of the stickiest products in software. Once a developer writes the API call and it is running in production — enriching CRM records, scoring leads, flagging churn risks — it does not get removed. It just keeps billing. The switching cost is a full re-integration project, not a cancellation click.

How to Get Embedded

The path to getting embedded in a customer's production workflow:

1. Generous free tier: 500–1,000 free API calls/month lets developers test in production without a procurement conversation. They integrate it, it works, they hit the limit, they upgrade. This is exactly how Stripe, Twilio, and SendGrid got their first 10,000 customers.
2. SDKs in every major language: The easier it is to integrate, the more integrations happen. A Python SDK, a Node SDK, and a public OpenAPI spec (importable into Postman and any AI coding tool) lower the integration cost from hours to minutes.
3. Webhook support: Let customers subscribe to events ("notify me when a new company in my target sector raises funding"). Webhooks embed your product in real-time workflows — Slack bots, CRM automations, alerting systems — that would break if you were removed.
4. Documentation as marketing: The Stripe documentation model: write docs so good that a developer can integrate in 30 minutes without asking a single question. Great docs rank on Google, get shared in developer communities, and reduce the activation friction to near zero.

The Zapier/Make Middleman

Not every customer wants to write API code. Zapier and Make integrations serve the no-code segment with the same embedding result: once someone has built a Zap that pulls your data into their HubSpot, they will not rebuild it unless you give them a reason to. Zapier integration = instant access to millions of no-code workflow builders who are specifically looking for data sources.

Becoming a "Native" Data Source in Other Tools

The highest-leverage embedding position: becoming a listed data provider inside tools your buyers already use. Clay lists data providers and lets users pick enrichment sources. Apollo lets users layer in third-party data. HubSpot has a data enrichment marketplace. Getting listed in these places is not just distribution — it is legitimacy. "Available in Clay" is a quality signal that converts skeptical buyers instantly.

8. 7. Marketplace Distribution: Zapier, Make, Clay, and the Long Tail

Every integration marketplace is a permanent, low-cost distribution channel. The listing costs nothing to maintain after the initial build. Reviews compound over time. Users searching the marketplace for your category find you without any outbound. Here is the complete map of marketplaces worth building for.

Sequencing Marketplace Listings

Do not build all ten at once. The right sequence:

1. Week 1–4: Zapier (largest audience, most generalist, highest ROI per hour of integration work).
2. Week 2–6: Google Sheets add-on (start the submission process early because Google takes weeks to review).
3. Week 4–8: Clay (submit an HTTP integration first, then pitch native integration after getting traction).
4. Month 3+: Make, Airtable, HubSpot Marketplace — based on where your actual customers already are.
5. Month 6+: Salesforce AppExchange — only if enterprise traction materialises.

9. 8. Programmatic SEO from Your Own Dataset

A database is an SEO machine waiting to be deployed. Every record in your database is a potential page that answers a specific search query. If those queries have search volume, you rank for them, indefinitely, without paying for clicks. This is how data companies like Crunchbase, Glassdoor, ZoomInfo, SimilarWeb, and G2 built their traffic moats. You do not need their scale to use the same mechanic.

The Mechanics

For every entity in your database, generate a page at a predictable URL: /companies/acme-corp, /startups/series-a/fintech, /funding/march-2026. Each page:

• Has a unique, template-driven title: "Acme Corp — Funding, Team, and Contact Info"
• Contains the data you have (funding date, amount, stage, sector, headcount, location, recent hires)
• Answers the exact question people Google when they search "[company name] funding" or "[company name] CEO email"
• Has a paywall on the valuable parts (verified email, direct contacts) with a clear CTA to sign up

The Traffic-to-Conversion Path

Someone Googles "Acme Corp funding" because they are about to reach out to them for a sales call or a recruiting pitch. They find your page. They see the company raised $5M last month. Perfect. They want the CEO's verified email. That requires a free account. They sign up. They see 50 more companies in your database matching their ICP. They upgrade.

This is not theoretical. Crunchbase generates the majority of its free signups this way. Hunter.io ranks for "[company] email" queries and converts the traffic to tool users. BuiltWith ranks for "[company] tech stack" and does the same. Each of them started with exactly this mechanic at a small scale.

Weekly and Monthly Digest Pages

Beyond individual entity pages, build time-indexed digest pages: "Companies that raised funding in March 2026," "Series A startups in fintech Q1 2026." These rank for periodic searches that happen every week from people doing competitive research, sales targeting, and market analysis. The pages are evergreen once indexed and require only a template update, not new content creation.

Sector and Vertical Pages

"Funded fintech startups," "recently funded HR software companies," "Series B healthtech startups." These are higher-traffic than individual company pages and attract buyers further up the funnel — people who are building ICP lists, not searching for a specific company. These visitors convert at lower rates but are higher-volume and represent expansion of your total addressable market.

10. 9. The Free Row Mechanic

The free row mechanic is one of the most effective conversion patterns for data products. Show the user exactly what they will get — format, fields, data quality — on the first row, for free, without requiring a credit card. Blur or lock everything below row one. The user can see the shape of the data. They cannot access it without signing up.

Why It Works

Most data products ask users to trust that the data is good before showing them any of it. This creates friction: "I have to enter my credit card to find out if this is even useful?" The free row eliminates that friction. The user evaluates the data quality immediately, in a real use case (their actual target company or their actual target contact), and makes the signup decision based on evidence rather than trust.

Conversion rates from "one free result" experiences are 2–4x higher than "start a free trial" conversion rates on data products, because the activation moment happens before the sign-up ask, not after it.

Variants of the Mechanic

The "Show, Then Ask" Principle

Never ask for payment before showing value. Every extra step between "I'm curious" and "I see what this does" costs you 30–50% of potential activations. The Sheets add-on installs in 30 seconds and the first enrichment runs before the user has even looked at your pricing page. The browser extension installs in 5 seconds and shows data on the very next LinkedIn profile visit. The free lookup delivers a result before the sign-up form appears. Sequence matters: show, then ask.

11. 10. Twelve DaaS Product Ideas With Both Properties

Each idea below scores 3+ on the self-distribution test and passes the evident need test. They are not all new — some have incumbents. The point is to illustrate the pattern, not to claim unexplored territory.

1. Real-Time Competitor Pricing Monitor

Evident need: Every e-commerce and SaaS pricing team tracks competitor prices manually or with a fragile scraper. The manual version is done by someone on the team every Monday morning.
Built-in distribution: Google Sheets add-on that populates a pricing table with live data. The sheet gets shared with the entire pricing team and the VP of Product. Exported pricing reports get attached to board deck slides. The CSV watermarks every export.
SEO surface: "[competitor] pricing history" pages rank organically. Businesses Googling their competitors' pricing find you.
Reference: Prisync, Wiser, Omnia Retail exist at enterprise price points. The SMB/startup layer ($99–299/mo) is genuinely underserved.

2. LinkedIn Post Performance Benchmarks

Evident need: Every LinkedIn content creator wants to know if their engagement is good or bad relative to comparable accounts. "Is 200 impressions on this post good?" has no answer without data.
Built-in distribution: A free browser extension that overlays benchmark data (median impressions, engagement rate for your follower count tier) on LinkedIn posts as you scroll. Every user who installs it becomes an acquisition event for anyone watching them use it.
SEO surface: "LinkedIn engagement rate benchmark 2026" is searched constantly and the existing articles are outdated. Fresh, data-driven content ranks and drives signups.
Freemium mechanics: Free benchmarks for your own posts. Paid for competitor post analytics, hashtag benchmarks, best-time-to-post data.

3. Job Posting Intelligence Feed

Evident need: Sales teams targeting companies "hiring SDRs" or "hiring a VP of Sales" already know this signal matters. They do not have a clean feed of it. Most manually search LinkedIn Jobs.
Built-in distribution: Zapier integration ("new company matching your filters posts a VP Sales job" triggers a Slack notification). Google Sheets add-on that refreshes ICP lists with current hiring status. Clay integration for waterfall enrichment workflows.
SEO surface: Pages for each job posting signal: "companies hiring VP Sales March 2026," "startups hiring growth engineers 2026."
Reference: Predictleads, Bombora, and TechTarget do this at enterprise price points. Sub-$200/mo does not exist cleanly.

4. App Store Review Monitor

Evident need: Every mobile app team wants to know what customers are saying in App Store and Play Store reviews in real time. Most read reviews manually or get emailed a weekly digest from a $3,000/mo enterprise tool.
Built-in distribution: Slack integration ships a real-time alert for every new 1-star review. That Slack message is seen by the entire mobile team. Every teammate who sees it wonders if the tool would be useful for them.
Google Sheets add-on: Pull all reviews for a given app ID into a sheet for analysis.
SEO surface: "[app name] reviews" is one of the most searched queries for any app. A public page for each major app's review sentiment data drives organic traffic.

5. G2 / Trustpilot Review Intelligence

Evident need: Product managers and competitive intelligence teams manually check G2 and Trustpilot for competitor reviews. "What are people saying about [competitor] right now?" is a real, repeated question.
Built-in distribution: Browser extension that overlays competitor review sentiment on G2 product pages and Trustpilot listings as you browse them. Chrome Web Store discovery.
Shared artifact: "Competitive review analysis" reports generated by the tool and shared in product team Slacks and board decks.
API: Embedded in competitive intelligence tools and product analytics stacks.

6. Domain Expiry and Acquisition Intelligence

Evident need: Domain investors, brand managers, and growth marketers watch for valuable domains expiring. The manual process is checking GoDaddy's expired domain auction daily.
Built-in distribution: Daily email digest of expiring domains matching saved keyword filters. Email forwards are the distribution mechanism — domain investors share good finds with partners.
Browser extension: Shows domain age, expiry date, and estimated value when you visit any website. Immediate utility for anyone evaluating domains to buy.
SEO surface: Pages for specific domain names: "[domain].com expiry" and "[domain].com history" rank for brand research searches.

7. Substack / Newsletter Growth Tracker

Evident need: Newsletter operators obsessively benchmark their own growth against peers. "Is 3,000 subscribers in 6 months good?" has no public answer. This is asked in every newsletter community constantly.
Built-in distribution: A public leaderboard of fastest-growing newsletters in each niche, updated weekly. The people on the leaderboard share it. Their followers find the tool. They sign up to track their own newsletter.
SEO surface: "Fastest growing newsletters 2026," "[niche] newsletter rankings."
Freemium: Free public benchmarks. Paid for private tracking of your own metrics vs. competitors.

8. E-Commerce Product Trend Monitor

Evident need: Dropshippers, Amazon sellers, and Shopify store owners constantly look for products that are trending before they saturate. They spend hours manually scrolling TikTok, Aliexpress, and Amazon Best Sellers.
Built-in distribution: Google Sheets add-on that populates a sheet with trending products in a chosen category, updated daily. Shared within supplier and seller communities. Exported product research sheets carry the watermark.
SEO surface: "Trending products [month] [year]" is searched hundreds of thousands of times monthly. A content page answering this question, updated weekly, is a massive traffic source.
Reference: Exploding Topics, Trend Hunter, Sell The Trend exist at various price points. The niche vertical layer (e.g. "trending Amazon FBA products in baby category") is wide open.

9. VC Portfolio Intelligence

Evident need: Founders researching which VCs to approach, and B2B salespeople targeting VC-backed startups, manually browse VC websites and Crunchbase to understand portfolio composition. It takes hours per firm.
Built-in distribution: Browser extension that shows portfolio data when you visit a VC's website or their portfolio company's website. Clay integration for waterfall enrichment of fundraising targets. CSV export of every portfolio company's contact info.
SEO surface: "[VC firm name] portfolio" is searched by thousands of founders and salespeople per month. Public pages for each VC's portfolio drive organic discovery.

10. Public Procurement / Government Contract Monitor

Evident need: B2B companies that sell to government agencies and enterprises need to know when relevant procurement tenders are published. The government portals are ugly, slow, and unfiltered.
Built-in distribution: Email digest and Slack alerts for matching tenders. Zapier integration for procurement workflow automation. Google Sheets pull of all matching contracts for analysis.
SEO surface: Contract and tender pages rank for "[agency] contract opportunity" searches. High-value, low-competition traffic.
Reference: Govly and Periscope exist at enterprise price points. The SMB layer is empty.

11. Tech Stack Change Detection

Evident need: Sales teams targeting companies switching from one tool to another (e.g. moving from HubSpot to Salesforce) want to catch that signal the moment it happens. Current technographic tools show a snapshot, not a change.
Built-in distribution: Slack webhook alert when a company on your watchlist changes tech stack. Zapier trigger for "company adds [technology] to their stack." CRM enrichment via API — embeds in Salesforce and HubSpot workflows automatically.
SEO surface: "[technology] alternatives" and "[tool] competitors" pages with live data on who is switching to what.

12. Founder Social Signal Monitor

Evident need: Agencies, investors, and sales teams targeting specific founders want to know when a founder is actively talking about a relevant topic (hiring, fundraising, expanding to a new market). LinkedIn posts are the signal. Nobody monitors them systematically.
Built-in distribution: Daily digest email of matching founder posts. Browser extension that flags relevant posts in your LinkedIn feed with "this founder matches your saved criteria." Clay integration for enriching outbound lists with recent founder activity.
Freemium: Monitor 5 founders for free. Unlimited monitoring on paid. Free users stay on the platform, hit the limit, and upgrade when they find a good signal.

12. 11. Anti-Patterns: DaaS Products That Cannot Self-Distribute

Not all data products are equal. Some architectural choices doom a product to pure outbound dependency regardless of how good the data is.

Anti-Pattern 1: Data That Only Lives in Your UI

If the only way to access your data is by logging in to your proprietary dashboard and clicking around, your product cannot distribute itself. There is no shareable artifact. No spreadsheet integration. No API. Every new user requires you to bring them through your front door personally. Build an API and CSV export on day one. Always.

Anti-Pattern 2: Data Without an Obvious Trigger to Share

Some data is useful but not inherently shareable. Personal financial data, HR data about specific employees, private analytics — these are not things people paste into Slack or share in a spreadsheet. If the data is inherently private, you lose the collaborative and watermark distribution mechanisms. You can still have a great business, but you must plan for outbound as a permanent acquisition channel, not a temporary one.

Anti-Pattern 3: Overly Technical Access (API-Only, No Sheets/Extension)

An API-only data product limits your buyer to developers. Developers are fine customers, but they are a small fraction of the people who could use your data. If the only integration is an API, you miss the much larger population of sales reps, marketers, and operations teams who live in spreadsheets and cannot write a curl command. Always build at least one no-code surface alongside the API.

Anti-Pattern 4: Data That Requires Trust Before Activation

If a user cannot evaluate the data quality before paying or signing up, activation rates crater. "Trust us, our data is good" is not a conversion mechanism. Show the data first. The free row mechanic, the free lookup, the free sample download — all of these exist to answer the pre-purchase question "is this data actually worth anything?" before the user has to make a decision.

Anti-Pattern 5: Niche So Narrow That No Marketplace Has a Category for It

If your data product is so specific that there is no natural home for it in any integration marketplace, you cannot use marketplace distribution. The test: can you find an existing Zapier category that your product fits into? Can someone searching "lead enrichment" in the Google Workspace Marketplace find your add-on? If the answer to both is no, your distribution strategy is cold outbound. Not impossible, but more expensive.

13. 12. What to Build First and Why

Given limited time and a desire for self-distributing traction, here is the build order that maximises early adoption while building the distribution infrastructure in parallel.

Phase 1: The Core Product + Free Mechanic (Weeks 1–4)

• Build the smallest version of the data product that demonstrates value on a single lookup. One entity in, enriched data out.
• Implement the free row mechanic immediately. Not "7 day trial." One free result, no credit card, paywall on the second result.
• Launch on Product Hunt with the free mechanic front and centre. Collect the first 100–300 signups. These are your beta users and your first case studies.
• Set up the CSV export with your watermark in the footer. Every export is an ad from day one.

Phase 2: Sheets Add-On + SEO Skeleton (Weeks 4–10)

• Build the Google Sheets add-on. Submit for Google review in week 4 (it takes 2–6 weeks). Use the waiting time to build the programmatic SEO pages.
• Stand up 50–100 entity pages using a simple template. Aim for the top 50–100 most-searched entities in your dataset. Submit to Google Search Console for indexing.
• Publish the Zapier integration. This is usually a 1–2 day integration build with Zapier's developer platform.
• Write documentation good enough that a developer can integrate in under 30 minutes. Publish the OpenAPI spec.

Phase 3: Browser Extension + API Free Tier (Weeks 8–16)

• Build the browser extension that overlays your data on the most relevant third-party site (LinkedIn, Crunchbase, G2, or whichever surface makes sense for your data). Submit to the Chrome Web Store.
• Launch the public API with a generous free tier (500–1,000 calls/month free, no credit card). Announce in developer communities.
• Submit the Clay integration if B2B data — Clay community has the highest-quality users for this category.

Phase 4: Compounding (Month 4+)

• SEO pages are now indexed and ranking. Organic signups start arriving without any active effort.
• Sheets add-on is live and being shared between team members. Collaborative virality begins compounding.
• Browser extension is installed and getting Chrome Web Store reviews. Review-driven discovery accelerates.
• The watermark on CSV exports is generating referral conversations that you never directly participated in.
• Your job at this point is to convert and retain, not to acquire. The acquisition flywheel runs on its own.

The Only Metric That Matters Early

Time-to-first-value. How many minutes between "I heard about this" and "I saw something useful"? For a data product with built-in distribution, that number should be under 2 minutes. Google "funded startup database" → land on a public page with a free lookup → type your target company → see the result. That entire path should take less than 90 seconds. Every additional minute you add to that path costs you half your potential activations. Design the product backwards from the activation moment, not forwards from the architecture.