Your data holds the answer.Not knowing it is already costing you.
Fixed-scope engagements that deliver measurable business outcomes using your existing data. No multi-month roadmaps. Intelligence your team can act on immediately.
Your data already knows
which accounts are leaving,
which are ready to grow.
Most analytics initiatives stall because they start too big and take too long. Our AI Accelerators are the opposite — fixed-scope engagements that use your existing data to deliver actionable intelligence in weeks, not quarters.
Every engagement ends with a deployed solution, quantified business impact, and a clear path to scale — not a report that sits in a drawer.
Outcomes are based on findings across B2B manufacturing and distribution engagements. Impact ranges reflect conservative recovery assumptions of 10–20%.
Revenue Intelligence
Most seasoned sales teams with a large number of accounts can tell you what happened last quarter. Very few can tell you which accounts are quietly shifting spend to a competitor right now — and even fewer can give their reps a ranked list of who to call about it. Slow revenue decline, wallet share loss, and cross-sell gaps do not announce themselves. The patterns that reveal all of this have been sitting in your order history the entire time. Revenue Intelligence reads them — automatically, every day, without anyone doing anything.
Where your revenue is growing, where it's at risk, and what your team should do about it.
Signal-driven account intelligence for B2B manufacturers and distributors. Every growth signal and risk signal below runs automatically across your full account base, every day, from order data you already own. No new data to collect. No CRM required. No data science team.
| Signal | What it tells your team | Included | Value at stake |
|---|---|---|---|
| Wallet share growth | Is this period's revenue genuinely new, or are we just holding what we had? Revenue decomposed per account into a waterfall: retained base, volume expansion, new SKU revenue, and lapsed revenue — at the account level, rolled up by rep and portfolio.Answers the question your team cannot answer today: is growth coming from new market share, or are we defending existing accounts? New customer contribution isolated separately. | ✓ | $200K–$1.5M identified |
| Cross-sell whitespace | What could this customer buy from us that they don't? Every account mapped against your full product catalog. The gap between what they currently buy and what they could buy — quantified in dollars, ranked by customer lifetime value.The median B2B manufacturing customer touches 0.14% of available catalog. That gap is your nearest-term growth lever — requiring no new customers, no new products, no new marketing spend. | ✓ | $150K–$1.2M identified |
| Expansion signals | Which accounts are showing buying behavior that signals readiness to grow? Increasing order frequency, broadening product mix, compressing reorder cycles — identified before competitors notice the same pattern.Expansion inside an existing account costs a fraction of acquiring a new one. Revenue Intelligence makes the signal available to every rep, not just the experienced ones. | ✓ | $75K–$375K identified |
| Blind spots | What should this customer be buying, based on what similar customers actually buy? Every account compared against peer accounts in the same segment. Spend going elsewhere — quantified in dollars, before the customer tells you about it.Peer groups derived from your customer segment and region data, reviewed and confirmed during setup. Minimum 8–10 accounts per segment for statistically reliable comparison. | ✓ | $100K–$800K identified |
| Signal | What it tells your team | Included | Value at stake |
|---|---|---|---|
| Revenue at risk | Every account scored automatically against its own 3–5 year order history — across your full account base, every day. A $500K account running at 60% of its own baseline is flagged with dollar exposure named, not a segment average.Revenue Intelligence runs this for every account, every day, without anyone doing anything. The signal fires while the account is still recoverable — not after the variance shows up in a quarterly review. | ✓ | $500K–$2.5M identified |
| Accounts in slow decline | Four signals running in parallel — sudden pullback, slow persistent drift, missed order cycles, and SKU abandonment. An account escalates to High Risk only when two signals agree. No false alarms. No black box.An account dropping 2% per month looks like noise in any monthly report. Across order cycles, Revenue Intelligence accumulates the evidence and flags it — typically 6–10 weeks earlier than periodic review discovery. | ✓ | 6–10 wks earlier detection |
| Ranked call list | A CLV-weighted priority list delivered to every rep on a schedule your team sets. Every rep contacts the accounts most likely to need attention first — not the ones that happened to call last, or the ones the rep feels comfortable with.Manual prioritization systematically under-serves high-value accounts in early decline. A ranked list is a risk management tool: the accounts most recoverable get contacted while they still are. Same quality of analysis for every rep. | ✓ | At-risk accounts reached while recoverable |
| Revenue health report (NRR) | Net Revenue Retention calculated automatically from your order data every month — retained, expanded, new customer, contraction, and churned revenue in board-ready format, delivered to designated stakeholders automatically.Leadership has a current NRR figure on a consistent schedule without anyone doing anything to produce it. No analyst request, no finance export, no manual preparation. | ✓ | Always current. Zero prep. |
Demand Intelligence
Demand is forecast using spreadsheets, historical averages, and tribal knowledge. Accuracy typically sits at 60–70%, but the dollar cost of that gap has never been measured. Excess inventory accumulates in some SKUs while others stock out. Production schedules react to demand rather than anticipate it. The organization knows forecasting is a problem — it does not know what fixing it is worth.
Know what poor forecasting is costing you — and walk away with a model that does better.
We segment your SKU catalog by demand type, identify which SKUs are forecastable vs. which require buffer policy, quantify the dollar cost of your accuracy gap across inventory, stockouts, and production, and deliver a working forecast model running on your data. You leave with intelligence, not a recommendation.
| Value driver | Business impact | Included | Typical finding |
|---|---|---|---|
| SKU demand segmentation — forecastable vs. buffer-managed vs. make-to-order | Every SKU classified by demand pattern using coefficient of variation and intermittency ratio. Forecastable SKUs get a statistical model. Erratic SKUs get a buffer stock policy. Intermittent SKUs get a make-to-order trigger. Each managed by the method that fits it.Most organizations apply the same forecasting method to all SKUs regardless of demand pattern. This is why forecast error is concentrated in a predictable subset of products. | ✓ | SKU catalog fully classified |
| Excess inventory cost — quantified in dollars | Carrying cost of over-stocked SKUs calculated from your actual inventory data: capital tied up, warehouse space consumed, obsolescence risk by product line.Typical finding in B2B manufacturing: 5–12% of inventory value is excess driven by forecast error. Requires inventory on-hand quantity and unit cost in the data extract. | ✓ | 5–12% of inventory value |
| Stockout cost — made visible for the first time | Revenue impact from out-of-stock events estimated from order history — late reorders, reduced quantities, and accounts that did not reorder following a stockout pattern. The cost is real but has never appeared on a budget line.Stockout cost is systematically invisible in standard P&L reporting. This makes it visible and attributable to specific SKUs and customer segments. Ranges vary too widely by product type to generalize — your data will produce the number. | ✓ | Quantified from your data |
| Customer-driven demand volatility — who is destabilizing your production schedule | Order frequency variance and volume irregularity calculated per customer. Identifies which accounts generate stable, plannable demand and which drive reactive production scheduling — so you can address the behavior or price in the cost.Derived from order history alone. No additional data required. | ✓ | Top volatility drivers named |
| Leading demand signals — forward indicators within your order history | Order frequency shifts, order size compression, and reorder cycle changes that precede volume changes by 4–8 weeks — identified from your transaction history and built into the forecast model as leading indicators.Qualified: signals are derived from within your order history. External signals (distributor inventory, quote volume) require additional data feeds not included in this engagement. | ✓ | 4–8 wk forward visibility |
| Working forecast model — production-ready | An actual forecast model built on your SKU and customer data, validated against holdout history, and delivered in a format your team can run. Not a recommendation to buy forecasting software.Accuracy improvement of 10–20 percentage points over spreadsheet methods is typical for the forecastable SKU segment. We quantify the improvement against your baseline before handing it over. | ✓ | 10–20pp accuracy improvement |
Pricing Intelligence
No one has a systematic view of actual transaction prices versus list prices. The same product sells for materially different prices to similar customers — with no documented justification. Discounting is ad hoc and unmonitored. Every exception made individually seems reasonable. Added up across all accounts and transactions, the total given away is a number no one has seen and no one expects.
Know exactly where price is leaking — and which accounts to correct first.
We build a complete price waterfall from list price to pocket price, run anomaly detection across every transaction, and deliver a ranked correction list with specific account-level targets — ready to implement within 30 days of engagement close.
| Value driver | Business impact | Included | Typical finding |
|---|---|---|---|
| Price realization gap — list vs. actual collected | The difference between your standard or list price and the price you actually collect — per customer, per product, per transaction. Mapped across your entire account base, most likely for the first time.Typical finding in B2B manufacturing: 2–5% of revenue is unrealized at the transaction level before discounts and rebates are applied. | ✓ | 2–5% of revenue |
| Discount leakage — accumulated exceptions quantified | Every discount, rebate, special price, and concession summed across all transactions — the total given away through exceptions that accumulate invisibly over time.Separate from the realization gap. Typical finding: an additional 1–3% of revenue in discount leakage on top of the base realization gap. Rebates held in a separate system may require an additional data pull. | ✓ | 1–3% of revenue additional |
| Pricing anomaly detection — transactions outside the defensible range | Statistical detection across all transactions to identify accounts or line items that fall outside the price distribution for their product and customer segment — flagged with dollar impact and ranked by recovery potential.No price change history required. Fully derived from transaction data. Produces specific, named anomalies: "Account X paid $X for Product Y — 28% below peer segment median. Dollar gap: $42K annualized." | ✓ | Top anomalies named in dollars |
| Price corridor per product — defensible range with correction targets | For each product, the statistical price distribution across comparable accounts — the floor, median, and ceiling where similar buyers actually transact. Accounts outside the floor are your highest-priority corrections."Product Y transacts between $14 and $28 for accounts in this segment. Account X is at $11 — normalize to $19 as a defensible first step." Specific and implementable. | ✓ | Per-product correction targets |
Margin Intelligence
Revenue looks healthy. Margin is a different story. After all discounts, rebates, returns, freight, and service exceptions are applied, a meaningful share of your customer base is margin-negative — and the organization does not know it because the P&L does not show profitability at account level. The CFO has no number to commit to because no one has built the model.
Know which customers make you money — and what improving that is worth.
We build a customer profitability matrix from your actual transaction and cost data, identify which customer behaviors drive margin destruction, and model the improvement opportunity across specific, sequenced actions. Your CFO walks away with a quantified 12-month target they can commit to — grounded in your data.
| Value driver | Business impact | Included | Typical finding |
|---|---|---|---|
| Customer profitability map — true margin per account | Every customer ranked by true margin after all discounts, rebates, returns, and transaction-visible costs are applied. The first time leadership has seen profitability at account level — not just revenue rank.Leadership is consistently surprised by both ends: accounts generating more margin than their revenue rank suggests, and accounts generating far less. The ranking changes materially once all costs are included. | ✓ | Full account base ranked |
| Margin-negative accounts — identified by name, in dollars | Accounts where total revenue collected is less than total cost to serve after every discount, return, and service exception — identified by name, with the dollar margin gap quantified per account.Typical finding across B2B manufacturing engagements: 10–20% of accounts are margin-negative at true pocket margin. The sales team is unaware because account-level profitability is not visible in standard reporting. | ✓ | 10–20% of accounts |
| Cost-to-serve by customer behavior — what drives margin destruction | Which customer behaviors consume disproportionate cost: small frequent orders, high return rates, non-standard requests, freight exceptions. Identified and quantified so you can address the behavior, reprice the exception, or restructure the relationship.Qualified: cost-to-serve analysis covers transaction-visible costs — freight, returns, order handling charges. Rep time and customer service labor require additional data not typically in an ERP export and are not included in this engagement. | ✓ | Behaviors ranked by cost impact |
| Margin improvement simulation — sequenced actions with modeled impact | A forward model built on your profitability data that quantifies the margin improvement from specific actions: repricing named accounts, addressing high-cost behaviors, shifting product mix toward higher-margin categories. Actions ranked by impact and sequenced by feasibility.This gives your CFO a specific, defensible improvement target for the next 12 months — derived from your data, not from industry benchmarks or consultant assumptions. | ✓ | 12-month target quantified |
Your data already holds the answer.
Let's find it together.
Every engagement starts with a focused conversation about your situation — your data, your accounts, your gaps. No pitch decks. No generic demos.
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