B2B data analysis for sales and marketing turns raw CRM and customer data into actionable insights: which customer segments have the highest lifetime value, where win rates are strongest, what drives churn, and where to allocate budget for maximum ROI. Unlike dashboards that show what happened, applied data analysis answers why it happened and what to do next. Common analyses include ICP (Ideal Customer Profile) development, customer segmentation, churn prediction, lead scoring models, and market sizing.
What Data Analysis Actually Looks Like
A Series A B2B platform came to us trying to figure out where to spend their marketing budget. They had 315 closed deals, customers across multiple segments, and a vague sense that some customer types were better than others. They didn't know which ones.
We analyzed their data across five dimensions: company growth velocity, SDR team size, CRM platform, geographic distribution, and RevOps team maturity. Standard stuff. What wasn't standard was what we found.
The key finding
RevOps team size was the strongest predictor of customer success. Not company size. Not industry. Not funding stage. The number of dedicated RevOps professionals.
Companies with exactly two RevOps people had an $8,750 lifetime value, 25% churn, and 62% win rates. Companies with one RevOps person had $2,100 LTV but a 68% win rate and only 22% churn. Companies with three or more RevOps? 55% churn and 50% win rates. Not ready for enterprise yet.
This wasn't intuitive. Nobody on their team would have guessed that "2 RevOps" was the magic number. But the data was clear.
What we also discovered
Dynamics CRM users had a 99% win rate. Ninety-nine percent. The market was severely underserved. Nobody was selling to them effectively.
Pipedrive users had a 7% win rate. Fatal. Every dollar spent acquiring Pipedrive users was essentially wasted.
Dual-CRM setups (HubSpot for marketing, Salesforce for sales) had 58% churn. Integration complexity was killing retention.
We identified $2M per year in preventable churn just by better qualifying which customers to pursue.
The recommendations
40% of marketing budget to the "2 RevOps" segment. 35% to the "1 RevOps" growth flywheel. Those customers were easy to acquire and naturally expanded into the higher-LTV segment. 10% to experiment with Dynamics users. 15% to retention programs.
We didn't just tell them what was happening. We told them what to do about it.
Types of Analysis We Provide
- ICP Analysis. Who are your best customers? Not who you think they are. Who they actually are, based on LTV, churn, win rate, and expansion revenue. We build data-driven profiles you can actually target. Learn more about ICP Analysis →
- Customer Segmentation. How should you group your accounts? By industry? By size? By behavior? We find the segmentation that actually predicts performance, not just the one that looks clean in your CRM.
- Win/Loss Analysis. Why do deals close or stall? What patterns separate wins from losses? We analyze your pipeline to find the signals that matter.
- Churn Analysis. Who's at risk and why? What behaviors predict churn before it happens? We build early warning systems so you can intervene while there's still time.
- Market Analysis. Where are the opportunities? TAM/SAM/SOM sizing. Competitive landscape mapping. Whitespace identification. We help you figure out where to focus.
- Custom Analysis. Whatever question you need answered. If your data can tell us, we'll find it.
Data Analysis by Industry
Every industry has different analysis needs. Healthcare deals with patient acquisition patterns and referral networks. Financial services requires compliance-aware segmentation. SaaS companies need cohort analysis and expansion revenue modeling.
We've analyzed data for companies across these industries:
Technology
Commerce & Logistics
Other Industries
Don't see your industry? Contact us—we've likely analyzed similar data challenges.
What You Get
Not a dashboard. Not a data dump. Actionable output.
- Executive summary. Key findings in plain language. What matters, what doesn't, what to do about it.
- Detailed segment profiles. Each segment defined with specific criteria, performance metrics, and targeting recommendations.
- Strategic recommendations. Budget allocation. Targeting criteria. Segments to pursue. Segments to avoid. Specific, actionable guidance.
- Implementation roadmap. Week-by-week action plan. Not just "what" but "how" and "when."
- Raw data and methodology. Everything documented. You can verify our work, update the analysis, and build on it over time.
How It Works
Step 1: Discovery call. We understand what questions you're trying to answer and what data you have available. Sometimes the question changes once we understand what's possible.
Step 2: Data intake. You share your customer data, deal history, and any relevant metrics. We review and confirm we have what we need.
Step 3: Analysis. We dig in. This typically takes 2-3 weeks for ICP analysis, longer for more complex projects. We'll keep you updated on progress.
Step 4: Findings presentation. We walk through everything. Key findings, supporting data, recommendations. You ask questions, we discuss implications.
Step 5: Deliverable handoff. You get the full report, all supporting data, and methodology documentation. It's yours to use, share, and build on.
"Found segments we'd completely ignored."— Frédéric, VP of Marketing
Pricing
Data analysis projects start at $1,500 for focused questions with clean data. Full ICP analysis runs around $2,500. Complex multi-segment analysis or churn modeling can go higher.
We scope every project clearly upfront. Fixed price, defined deliverables, no surprises.
If your data needs cleaning first, we can bundle that into a single engagement. Better to know upfront than discover it halfway through analysis.
Common Questions
How much data do you need?
Ideally 6+ months of customer data and 100+ closed deals. We can work with less, but the insights will be directional rather than statistically significant. We'll tell you upfront what's possible with your dataset.
What if our data is messy?
Most data is. We often recommend starting with data cleaning before analysis. Garbage in, garbage out applies here. We can do both as part of a single engagement.
How long does analysis take?
ICP analysis typically takes 2-3 weeks from data receipt to final deliverable. More complex analyses (churn modeling, multi-segment strategies) may take 4-6 weeks. We'll scope it clearly upfront.
What's the difference between this and a BI tool?
BI tools show you what happened. We tell you what to do about it. You get strategic recommendations, not just dashboards. Budget allocation. Targeting criteria. Specific segments to pursue or avoid. Actionable insights, not pretty charts.
Ready to Find What's Hiding in Your Data?
Two options:
Not sure yet? Tell us what questions you're trying to answer. We'll give you an honest assessment of what's possible with your data.
Ready to dig in? Let's talk scope. We'll define the project clearly before we start.
Related: ICP Analysis | Data Cleaning | Data Enrichment | Pricing