What We Build
Real examples, not abstractions
Churn prediction model
Scores every active customer weekly using transaction and behavior history, flagging accounts at risk weeks before they leave.
Customer lifetime value model
Ranks your customer base by true long-term worth, so sales and marketing know exactly which accounts deserve the most attention.
Next-best-offer recommendation engine
Learns from purchase patterns to surface the right cross-sell or upsell for each customer, ranked by conversion probability.
Lookalike prospecting model
Profiles what your best customers have in common, then scores new prospects against that pattern to focus outreach where it converts.
Sentiment & feedback scoring pipeline
Links survey and support text to churn risk and product gaps automatically, so feedback drives action instead of sitting in a report.
Behavioral segmentation model
Rebuilds your customer segments from actual behavior instead of years-old assumptions, revealing groups that respond differently to offers.
What We Solve
Common challenges — and exactly how we address them
Churn is invisible until it's too late
Customers leave without warning signs, and you only find out after they've already gone.
Predictive models built on your actual customer transaction and behavioral history flag at-risk accounts weeks before they churn.
You don't know which customers are actually valuable
Revenue looks healthy on paper, but a handful of accounts drive most of it — and you're treating everyone the same.
Customer Lifetime Value models segment your base by true long-term worth, so sales and marketing can prioritize the accounts that actually move the needle.
Cross-sell and upsell feel like guesswork
Your team pitches the wrong products to the wrong customers at the wrong time, and conversion rates show it.
Recommendation engines trained on purchase patterns surface the next best offer for each customer — personalized, timely, and ranked by conversion probability.
You're fishing in the wrong pond
New client acquisition relies on broad targeting and sales intuition, wasting budget on prospects who were never going to convert.
Lookalike modeling identifies the traits your best customers share, then finds prospects in the market who match that profile — so every outreach effort is aimed at someone worth chasing.
NPS scores sit in a spreadsheet and do nothing
You collect customer satisfaction data religiously but can't connect it to actual business outcomes or individual customer risk.
Text analytics and sentiment models link survey responses and support interactions to churn probability, product gaps, and revenue impact — turning feedback into action.
Segmentation is stuck in the past
Your customer segments were defined years ago by someone's intuition and haven't changed — even though your customer base has.
Unsupervised clustering models rediscover your customer base from scratch using actual behavioral data, revealing segments that respond differently to pricing, messaging, and product.
How It Works
From first conversation to working system
- 1
Data Assessment
We review your customer data — transactions, events, demographics — and identify what's available and useful.
- 2
Model Design
We design the right models for your goal: churn prediction, LTV scoring, segmentation, or next-best-action.
- 3
Training & Validation
We train on your historical data and validate against real past outcomes before deploying.
- 4
Integration
We connect model outputs to your CRM, marketing platform, or ops tools so teams can act on them.
- 5
Tracking
We monitor model performance over time and recalibrate as customer behavior evolves.
Ready to explore this for your business?
Tell us about your situation. We'll show you what's possible — in plain language.
Schedule a free meeting