We react instead of predicting
We find out about the problem after it has cost us something.
What is actually going on
Prediction fails more often on unreliable inputs than on weak algorithms. If usage, support and billing disagree about the same account, the forecast inherits that disagreement. The account view comes first, the model second, and that order is what separates a model people use from one they quietly ignore.
Who usually owns this
CIO, CRO, Supply chain director
How people describe it
- "identify customers likely to cancel"
- "improve demand forecast accuracy"
- "detect suspicious transactions automatically"
What resolves it
- Forecasting and Optimization Consulting. Demand forecasting, inventory optimization and predictive maintenance built on reconciled data, with accuracy measured against outcomes rather than asserted.
- Machine Learning Consulting Services. Machine learning consulting that starts with the data the model reads: entity resolution, labelled targets, a production pipeline, and monitoring that keeps it accurate.
- Pipeline and Revenue Analytics Consulting. Pipeline analytics, account level scoring, and RevOps reporting for B2B SaaS. Built on the CRM, marketing automation, and warehouse you already use.
- Healthcare Analytics Consulting Firm. Healthcare analytics for providers, payers, and life sciences. HIPAA and HITRUST ready data foundation, MPI, HEDIS reporting, and value based care metrics.
Read up on it first
- Why Expansion Opportunities Get Missed in Accounts. Start here. Median net revenue retention is 103% against gross retention of 91%, so the typical company's expansion is almost entirely spent offsetting its own churn. Four reasons the opportunity gets missed, and not one of them is sales effort. One client's renewal reminder was arriving 45 days after the contract expired.
- Rules-Based Prioritization vs Predictive Scoring. Choosing an approach. This is a question about the shape of your signal, not your analytical maturity. A rule produced $2.8M of cross-sell in year one at one client. In three of the four engagements where we did ship a model, the thing that moved the result was the pipeline, the instrumentation or the motion attached to the score.
- How to Scope an Account-Growth Analytics Pilot. Scoping the work. A pilot exists to settle one question with a number. Eight success criteria, four of them about whether sellers actually used it, because an unused signal scores zero however accurate it was. Plus the decision rule for what happens at the end, written while nobody is invested in the answer.
Proof
$2.1M annual upsell opportunity identified
By week 16, we had real-time analytics running on all 8 vessels. The revenue team used this data to pinpoint $2.1M in annual upsell potential by mapping underperforming revenue categories to specific routes. We cut inventory waste by $380K a year by adjusting provisioning to match demand patterns unique to each voyage.
- $2.1M, Annual upsell opportunity identified.
- $380K, Annual inventory waste reduced.
- 8, Vessels with real-time analytics.
- 2 hrs, Data transmission cycle.
Getting it approved
Other problems
What is actually going on
Who usually owns this
How people describe it
What resolves it
Read up on it first
Getting it approved
Three pieces, in the order the question usually arrives. Each one is evidence-led and names its sources.
Whoever signs this off is asking a different question from the one you are asking.
is the worksheet for that conversation. Yours whether or not you hire us.