Whitespace Intelligence
(Identify category/SKU gaps and grow wallet share)
Sell your customers what they're already buying — from someone else.
Your customers are buying. Mostly somewhere else.
Buyers research silently. You're invisible for the rest.
Reps pitch what they know. Not what the account needs.
Same-store growth is the new boardroom metric.
Closing even 15% of the ~70% your customers spend with competitors reshapes the same-store growth line.
Distributor revenue | Conservative annual upside (~6%) | Aggressive annual upside (~12%) |
|---|---|---|
$50M | $3.0M | $6.0M |
$100M | $6.0M | $12.0M |
$200M | $12.0M | $24.0M |
$250M | $15.0M | $30.0M |
$500M | $30.0M | $60.0M |
$1B | $60.0M | $120.0M |
The customer can't ask for what they don't know exists.
Tracks what's sold. Blind to what isn't.
Shows a category gap. Can't rank what to pitch first.
Sell what they know. Not what would grow the account.
Whitespace Intelligence is the layer your stack is missing — The 30% Problem solved — sitting on top of the systems you already run.
Within 12 months of launch. $9.1M attributed.
$68M
7,000+
14
$9.1M
Send us a sample of your transaction data. Get your own number for your own book.
Concentration in the mid-tail
What "won" actually means
Five stages, one continuous loop. Sharper every month.
Detect
SKU-level machine learning predicts purchase likelihood per product, rolls it up to category, then to account-level Estimated Annual Opportunity. The model learns each customer's vertical, size, and ordering pattern — and surfaces the categories and products with the highest fit and dollar potential.
Rank
Every account–category pair scored 0–100 and tiered Hot / Warm / Cool by likelihood × value. Each pair classified by play type — Expansion, Introduce, or Win Back — to drive the messaging angle. Re-ranked every morning on new signals.
Pitch
The ranked queue lands where reps already work — daily digest, order entry, customer-service inbox, CRM tasks, e-commerce basket. One queue, every morning. No new app to open.
Win
Five-stage state machine: To Review → Pitching (CRM) → Snoozed → Won / Dismissed. Send to CRM creates the opportunity record with full context — account, category, SKUs, playbook, projected ARR. Rep acts in one click. The deal closes — or the dismissal trains the model.
Prove
The Won Dashboard measures revenue won — specific products, specific customers, specific dollars — attributed back to the queue. Per-rep, per-SKU, per-action audit trail. Not estimates. The number you take to your CFO and your board.
Behind the loop — Detect and Win
The Hardest Problem in Whitespace: Fit
Not every gap is an opportunity. A restaurant doesn't need surgical PPE. A hospital doesn't need bar glassware. Distributors haven't solved this internally because flat category-penetration reports treat every gap as an opportunity — and the queue is too noisy to work. SETVI scores fit by referencing peer accounts in the same vertical, customer size, and order history. The model surfaces the evidence to the rep alongside the recommendation: "82% of similar customers buy this." Reps walk into the pitch with hard data, not a hunch — and trust the queue enough to work it daily.
Rep-Trained Refinement
Dismissals teach which fits to trust. Won deals confirm which plays close. Snooze patterns reveal optimal contact windows. Six months in, predictions sharpen materially — based on how your reps actually work your book. The system gets harder to compete with over time, not easier.
An email arrives from Beacon Hospitality with an attached PDF. The PDF has 19 line items, free-form descriptions, and a part-number convention specific to Beacon's procurement system. Within seconds: 18 of 19 lines auto-matched against your catalog with 95%+ confidence. Pricing validated against Beacon's contract. Ship-to pulled from Beacon's defaults. One line — a discontinued SKU — flagged with a substitution suggestion. Order written to your ERP. Auto-acknowledgment back to the buyer. Total elapsed time: under two minutes. Total rep time: ten seconds to approve the substitution.
Every part of the pipeline. One order workflow.
Seven capabilities, one queue, one CRM push, one closed loop. Built for distributors and the systems they already run.
Next Best Action Queue
Cross-account ranked list of the highest-value opportunities. Reps see exactly which account, which category, which play to pitch next. Sorted by Estimated Annual Opportunity, re-ranked each morning. Every account carries a play mix — Expansion / Introduce / Win Back chips at a glance.
Workflow States: Review · Pitching · Snoozed · Won · Dismissed
Five workflow states per account and per SKU. Defer to a future date with one click. Bulk-classify SKUs as To CRM, Snoozed, Won, or Dismissed. Won revenue surfaces back to the rep automatically. Every dismissal trains the model.
Priority Scoring & Play Types
Every account–category pair scored 0–100 and tiered Hot / Warm / Cool by likelihood × value. Each recommendation classified by play — Expand Introduce Win Back — to drive the messaging angle. ML-backed, peer-compared, defendable when leadership asks how you know.
Similar-Account Evidence
Every recommendation backed by peer adoption data. Each SKU shows the percentage of peer accounts in the same vertical and size class that buy it. Reps walk into the pitch with hard data — "82% of similar customers buy this" — instead of a hunch. Removes the guesswork, and removes the rep's hesitation.
SKU-Level Drill-Down
Click any category to see the specific products. Recommended SKUs with priority score, est. spend, est. annual opportunity, and GP margin. Filter by Play Type, Priority, Category, Customer, or Ship-To. Reps pitch specific products, not just categories. Managers identify category-wide opportunities across the customer base.
By Customer Portfolio
Your book of business, lensed by opportunity — not flat by name. Toggle to see opportunities aggregated per account, ranked by total annual upside. Priority scores, play-mix chips, and category counts per account. Click any row to drill into that customer's full category list.
Closed-Loop CRM Tracking
Push to Salesforce, HubSpot, Dynamics 365, NetSuite, and others — and keep tracking. One-click Send to CRM creates the opportunity record with full context: account, category, SKUs, playbook, projected ARR. Pitching (CRM) tab shows items in flight, days in CRM, and opportunity value. No copy-paste. No lost data.
Manager & Sales Ops Console
Review any territory. Group, territory, or rep-level rollup of Estimated Annual Opportunity. Drill from team view to account to SKU in one flow. Assign to a rep's daily queue, push to CRM, or both. Reallocate coverage based on opportunity, not org-chart inertia.
Won Dashboard
Prove the dollars won. Estimated Annual Opportunity identified vs. won, real-time. Win rate and trend by rep, territory, or company-wide. Per-account, per-SKU attribution — which action won which dollar. ROI to defend in front of the CFO or the board, with hard numbers.
Inside every surface your reps already work.
Email. CRM. Order entry. Customer-service inbox. E-commerce basket. Each surface tuned to the moment of decision it lives in.
Daily Digest
Top opportunity accounts every morning, ranked by Estimated Annual Opportunity. Delivered as an email — where reps already work.
Inline Insights
Whitespace categories surfaced as reps build orders. Cross-sell prompts at the moment the order is being typed.
Outlook Plugin
Account opportunity and play type on every support email. The CSR knows what the account could be buying before they reply.
Tasks & Workflows
Auto-routed to the right rep — Salesforce, HubSpot, Dynamics 365, NetSuite. Opportunities tracked with full context, days in flight, projected ARR.
E-commerce
Cross-sell prompts and category suggestions inside any e-commerce platform. Customers self-serving still see whitespace categories at the moment they're shopping.
Four ways teams use Whitespace Intelligence every week.
The three plays your reps run from the queue every week — plus the territory rollup view your sales ops and CFO use to forecast same-store growth.
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Closed-loop with any major CRM. Powered by your transaction data.
Whitespace Intelligence runs on your invoice and order history — the data you already have. The closed loop pushes recommended actions to the CRM your reps already use, tracks items in flight, and attributes won revenue back. Below is what your team actually does, week by week, and who owns it.
A read-only export of 18–24 months of invoice and order history from your ERP — flat files via SFTP or direct connector. Your IT lead pairs with a SETVI data engineer for a single integration call. SETVI handles modeling, peer-cohort matching, and category taxonomy. Owner: SETVI Data Engineering · 1 IT contact on your side.
CRM connector configured (Salesforce, HubSpot, Dynamics 365, NetSuite). Daily digest distribution list set up. Agents deployed to order entry, customer service inbox, and e-commerce. Owner: SETVI Customer Success + your CRM admin.
Live training with your reps and CSRs, modeled on top-adopter practices from existing deployments. SETVI Customer Success embeds with your sales leadership for the first 60 days post-launch — direct line, no ticket queue. Owner: SETVI Customer Success + your VP Sales.
Per-account, per-SKU, per-rep won-revenue attribution. Defensible to your CFO. Predictions sharpen materially over the first 6 months as reps work the queue and the rep-trained refinement loop kicks in.
Enterprise-grade. Always.
Common questions buyers have.
What's the pricing shape — per-rep, per-account, percentage of revenue?
Two models, picked together with you: (1) annual platform license sized to revenue tier and number of seats — predictable, billed annually, no surprises; (2) success-aligned model with a lower base plus a percentage of attributed won revenue from the Won Dashboard. Most distributors pick the platform license for budgeting predictability. Distributors who want to put SETVI's skin in the game pick the success-aligned model. Either way, no IT-side infrastructure cost — we run the model, you run the customer relationships.
How does Whitespace Intelligence relate to Retention Intelligence?
Same engine, opposite direction. Retention Intelligence catches dollars walking out the door — predicting what an account will stop buying so a rep can save it. Whitespace Intelligence surfaces dollars sitting on the table — predicting what an account could buy so a rep can grow it. Most distributors deploy them together: retention plugs the leak, whitespace fills the bucket. Both share the same five surfaces, the same CRM closed loop, and the same Won/Recovery attribution methodology.
How is this different from a category-penetration report we could build in BI?
Different shape of system. A penetration report is flat — every gap shows up, weighted by nothing. Whitespace Intelligence ranks each gap by likelihood × value, scored 0–100, classified by play type, backed by peer-account fit evidence. The report tells you "here are 3,000 gaps." The queue tells you "these 12 are worth your morning." The closed-loop attribution then proves which ones won.
How do you handle the "fit" problem? A restaurant doesn't need surgical PPE.
Fit is the single hardest problem in whitespace, and it's the reason flat penetration reports are too noisy to work. The model scores fit by referencing peer accounts in the same vertical, customer size, and order history — then surfaces the evidence right next to the recommendation: "82% of similar customers buy this." A category that 5% of peers buy isn't whitespace, it's noise — and the model treats it as such. A category that 80% of peers buy and this account doesn't, with a peer-median spend of $14K, is exactly the recommendation that earns the rep's time.
How quickly does the model become useful?
Six-week ramp: data integration in weeks 1–2, agents deployed in weeks 3–4, enablement in weeks 5–6. Won Dashboard live by week 7 with measurable ROI from that point forward. Predictions sharpen materially over the first six months as reps work the queue and the rep-trained refinement loop kicks in.
Where do reps actually work the queue?
Most reps work it from a daily email digest — top opportunity accounts, recommended categories, recommended SKUs, ~10 minutes to clear. From there, push to your CRM and own it. Customer-service teams see opportunity inline in Outlook. Order-entry staff see whitespace categories as they build orders. E-commerce surfaces them in the basket. No new daily app to open.
How do you handle "we would have sold that anyway"?
It's the most common objection to any growth attribution — and the data answers it directly. Won-revenue attribution is structured to be conservative: the Won Dashboard separates Expansion plays (where the account was already buying related categories and may have organically grown) from Introduce and Win Back plays (where the category was either never bought or had been lost — much harder to argue would have happened anyway). For customers who want hard proof, SETVI offers a 60-day holdout test: turn the queue off for 25% of reps or accounts and compare cohort growth.
How is our data protected?
Fully isolated, encrypted at rest and in transit. Role-based access controls and complete audit trail on every prediction and action. SOC 2 Type II audit currently in progress.
What is "The 30% Problem" — is it really 30/70?
"30/70" is shorthand for the working benchmark across mid-market distribution — your average customer buys roughly 25–40% of fitting categories from you, and the other 60–75% from someone else. The exact ratio varies by vertical and customer size: tight specialist verticals run closer to 40/60; broadline mid-market distributors often run closer to 25/75. Whitespace Intelligence runs the analysis against your own transaction data and tells you your actual ratio — by customer, by category, by territory. Most distributors are surprised by how big their 70% really is.
Most of our whitespace exists because the customer doesn't even know we sell those categories. Does this help with that?
That's exactly the problem the Introduce play is built for, and it's the larger half of The 30% Problem. A 10-year customer buying gloves from you has no reason to ask whether you also do janitorial chemicals — they assume you don't, and they Google someone else. Whitespace Intelligence surfaces those categories to your rep with peer-fit evidence: "82% of similar customers buy this category from a distributor." The rep's first sentence to the customer becomes "did you know we also sell this, and your peers are already buying it" — which is a fundamentally different conversation than waiting for the customer to ask. Customer-side blindness is the single biggest source of whitespace in most distribution books, and the Introduce play closes it.
How is "Estimated Annual Opportunity" calculated?
SKU-level prediction rolls up to category, then to account-level Estimated Annual Opportunity. The model combines purchase-likelihood probability (learned from peer accounts in the same vertical and size class), projected order frequency, and median peer spend per category. Every recommendation also carries a 0–100 priority score and a Hot / Warm / Cool tier — defendable to leadership, not a black box.
What are the three play types — Expansion, Introduce, Win Back — actually for?
The play type determines the messaging angle. Expansion means selling deeper into a category the account already buys — the conversation is "you're already buying us for X, here's adjacent volume we should be capturing too." Introduce is a category the account has never bought — the conversation leads with peer fit evidence: "82% of similar customers buy this from a distributor; let's talk about what you'd want." Win Back recovers a category they used to buy but stopped — the conversation is recovery, not first sale. Same engine, three different scripts.
What if rep adoption is uneven across the team?
It will be — that's the point of the data, and it's a feature for forecasting. The Won Dashboard shows attributed revenue by rep, so it's transparent who's working the queue and who's not. SETVI's customer success team works directly with sales leadership during weeks 5–6 (and ongoing) to drive adoption modeled on top-adopter practices: treating Next Best Action as the default first action when opening any account, using the queue proactively on outbound calls, trusting peer-fit evidence without re-validating against gut. The headroom is the story — if the bottom of your rep cohort performed at half the rate of the top adopter, that's typically several hundred thousand of additional grown revenue without a single new account.
How do you prove won revenue?
The closed loop. Every won dollar is attributed back to a specific customer, a specific SKU, and the rep action that won it — the original opportunity context follows the deal from queue to CRM to invoice. Account- and SKU-level revenue is tracked against pre-deployment baseline. Not estimates — the audit trail is per-account, per-SKU, per-action.
Do we need to change our CRM?
No. Whitespace Intelligence pushes to Salesforce, HubSpot, Dynamics 365, NetSuite, and others — and keeps tracking from there. One-click Send to CRM creates the opportunity record with full context. No double entry. No data silos. Reps stay in the CRM they already use; managers see what reps are working with opportunity weight attached.