Whitespace is a join
Matching your CRM against a market list turns "who should we call?" into a query instead of a guess.
Dashboard · Sales & marketing
Who to call next.
A rebuild of the AIPD dashboard from my time at Accruent: customer data matched against a full market list and enriched with third-party data, so every account executive opens a view of their own territory with the next product to pitch and the next company to call. Every company, contact, and number here is fictional example data.
The story · Accruent
Finding the whitespace.
At Accruent I managed the end-to-end development of the AIPD dashboard, a sales and marketing tool for finding new customers.
Ours versus everyone. It compared our internal customer data in Salesforce with all of Dun & Bradstreet's data. Anything in the market list that wasn't already a customer was an opportunity to reach a potential new one.
Enough to make a recommendation. Adding third-party data like ZoomInfo gave us enough on each prospect to recommend which Accruent product was the ideal fit and who to contact, based on how those products had historically performed in that industry.
How it was used
Account executives were spread across the country, mostly in or near big cities. They'd open the dashboard to see their existing customers, upsell recommendations, and, most importantly, new companies and contacts to reach out to. The view was tailored to whoever logged in: a New York AE didn't see Los Angeles data.
- SalesforceOur customers and what they own
- Dun & BradstreetEvery company in the market, minus our customers
- Third-party enrichmentFirmographics, signals, and contacts (e.g. ZoomInfo)
- Fit scoreBest product and who to call, per AE territory
The dashboard below is not the Accruent dashboard and uses no Salesforce, Dun & Bradstreet, or ZoomInfo records. The AEs, companies, contacts, signals, and dollar values are invented. Product areas are generic categories, and pins sit near real metro-area towns for flavor only.
Interactive · Example data
One territory, yours.
Switch who's signed in and the whole dashboard follows. Filter the prospects, then tap one to see why its product fits and who to contact.
Example data
How the illustrative fit score works: for each product area, add an industry win-history score (0 to 35, example values), points for facilities (3 to 25) and employees (3 to 15), and 15 for each third-party signal that points to that product. The top score is the recommendation; it's capped at 100. Upsell uses the same score for products a customer doesn't own yet, plus 10 when the product pairs naturally with one they already have. Estimated pipeline adds each upsell's example value to each shown prospect's example value weighted by its fit score. Every company, contact, signal, and dollar figure is fictional.
Beyond sales tools
Why this matters for organizations.
Most companies already own the data to find their next customers. It's just split across systems that don't talk.
Recommend, don't just list
A prospect list is a start. A recommended product and a named contact is something a seller can act on today.
Show the why
A transparent score earns trust. Sellers use recommendations they can explain to a customer.
Tailor the view
Filtering to the signed-in seller keeps the dashboard focused and keeps territory data where it belongs.
Grow the base too
Upsell recommendations put existing customers next to new prospects, so growth isn't only about new logos.
Data quality pays
Enrichment only works when records match. Clean company and contact data is what makes the whole thing usable.
Example data only: the AEs, companies, contacts, signals, and values are fictional. No Salesforce, Dun & Bradstreet, or ZoomInfo records are used. Map tiles © OpenStreetMap contributors. Built by me with AI. Updated Oct 2026.
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