Predictive scoring
We implement predictive scoring: the model estimates probabilities for customers — who is likely to leave (churn), who will bring more (LTV), which lead is 'hotter'. This helps prioritize effort and budget. Honestly upfront: scoring gives PROBABILITIES, not exact predictions of the future — the model errs, requires data and 'ages' over time (drift), needing retraining; there is a bias risk in data; and it is decision support, not autopilot — the final actions are the human's.
Predictive scoring — overview
Predictive scoring is machine-learning models that, from historical data, estimate the probability of future events for customers/leads: churn probability, customer lifetime value (LTV) forecast, lead conversion probability, propensity to buy. The result is a score/rating that helps prioritize: whom to retain, on whom to spend budget, which leads to work first. Honestly about the probabilistic nature, this is key: scoring predicts PROBABILITIES, not facts. 'Churn probability 80%' does not mean 'will definitely leave' — it is a statistical estimate that sometimes will not come true. The model errs on individual people but is useful at scale (prioritizing groups). Promising 'we know exactly who will leave and how much they will bring' is dishonest. Honestly about data and drift: quality depends on your historical data; and over time customer behavior and the market change — the model 'ages' (drift), accuracy drops, so monitoring and periodic retraining are needed. 'Set and forget' does not work. Honestly about bias: if data has skews, the model inherits them and may unfairly score some groups — we honestly check for this rather than ignore it. Honestly about 'support, not autopilot': scoring helps make decisions but should not blindly automatically punish/reward people without human control, especially in sensitive cases. Honestly about the effect: with good data it helps spend effort more effectively but does not guarantee revenue/retention growth by itself — actions on the results and their verification are needed. Honestly about access: historical data and event labels are needed. An important boundary: this is customer/lead scoring; anti-fraud — 881; recommendations — 872; analytics — 854. Picture this: instead of 'spending equally on everyone blindly' — priorities by probabilities, with an understanding of their limits. The base price starts from 55,000 ₽ (depends on data and tasks).
Problems we solve
- Effort and budget are spent equally on everyone, without priorities.
- It is unclear which customers are about to leave.
- Leads are processed in order, not by promise.
- No customer-value forecast for decisions.
What's included in the Predictive scoring service
- Scoring models (churn/LTV/lead conversion etc.)
- Score ratings for prioritizing effort
- Reliance on your historical data and labels
- Bias checking in the data
- Drift monitoring and retraining
- Honest boundaries (probabilities, not facts; decision support)
- A link with analytics (854) and recommendations (872)
- Handover and review with you
What you get
- Prioritization of customers/leads by probabilities
- More effective distribution of effort and budget
- Honest boundaries (errs on individuals; useful at scale)
- A monitoring and retraining process (not 'set and forget')
How the work goes: steps
- We collect historical data and event labels; clarify tasks
- We train models, check for bias and accuracy
- We deploy as decision support, set up drift monitoring with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will scoring say exactly who will leave and how much they will bring?
No, and that is honest: scoring gives probabilities, not facts. 'Churn probability 80%' does not mean 'will definitely leave' — it is a statistical estimate that for an individual may not come true. The model errs in particulars but is useful at scale for prioritizing groups. Promising exact knowledge of the future would be deception — we work with probabilities honestly.
Will the model work stably after setup?
Over time accuracy drops — that is drift, and we honestly build it in. Customer behavior and the market change, and the model 'ages'. So monitoring and periodic retraining are needed, not a one-off setup. 'Set and forget' does not work in scoring; we maintain the model so it stays useful.
Can we act automatically on scoring without people?
Not blindly, and that is honest. Scoring is decision support, not autopilot. Automatically punishing or rewarding people by a score without human control is risky: the model errs and may be biased. In sensitive cases a human must confirm decisions. We check the model for bias and embed it as help, not as an unconditional verdict.
About the provider
The «Predictive scoring» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.