LTV / CLV modeling

LTV/CLV modeling is a one-off analytical project (with possible refresh): based on your data (billing, sales, product events, CRM) we compute historical LTV and/or build a predictive value model by segments, channels, cohorts; we link it to acquisition cost (CAC) to assess payback by segment and a reasonable acquisition-price ceiling. Honestly about the nature of the model, this is key: LTV is an ESTIMATE built on assumptions (horizon, retention rate, average check, discounting) and on past behavior; the future may differ, the model is not a guarantee or an exact prediction; different methodologies give different numbers — we fix the assumptions and methodology with you and show a range, not one 'true' figure. Honestly about data: sufficient history and correct data are needed — on short history or small numbers the forecast is unreliable; dirty data = a wrong model (garbage in, garbage out). Honestly about aging: the model reflects current behavior and loses relevance over time — it needs periodic retraining/refresh. Honestly about the essence: this is an estimate and a decision tool, NOT raising LTV and NOT a financial guarantee — the model helps decide how much to pay for acquisition and whom to focus on, but raising LTV (product, retention, upsell) is done by you; the number itself does not increase customer value. Honestly about metrics: relationships in the model are correlations and assumptions, not proven causes. Requirements: access to data and agreed definitions. An important boundary: this is LTV modeling, not predictive churn (separate), not retention work and not a financial audit. If the business is new and there is no history, we honestly say it is too early to model. Picture this: instead of 'we pay for acquisition blindly, unclear if it pays off' you understand how much a segment's customer is worth on average and how much it is reasonable to pay. The base price starts from 25,000 ₽ per project; it depends on data and model depth.

• You do not know how much a customer brings over their lifetime on average.
• You pay for acquisition blindly, without a ceiling by segment.
• You do not understand which segments/channels give the most valuable customers.
• You make acquisition-budget decisions without LTV.

• Collecting and preparing data (billing, sales, events, CRM)
• Agreeing on assumptions and methodology (horizon, retention, check)
• Computing historical LTV and/or a predictive model
• Breakdowns by segments, channels, cohorts
• Linking to CAC and assessing payback by segment
• A report with ranges, assumptions and conclusions
• Reviewing results and limitations with you
• Application recommendations (LTV growth — separately)

• You understand average customer value by segment (as an estimate with a range)
• It is clear how much is reasonable to pay for acquisition
• The most valuable segments and channels are visible
• A base for budget decisions (LTV growth — separately)

• We clarify goals, data, assumptions and segments; collect access
• We prepare data, compute/build the model, link to CAC
• We compile a report with ranges and assumptions, review with you

• Will an LTV model raise my customers' value?

  No. The model estimates value and helps decide how much to pay for acquisition and whom to focus on, but by itself it does not raise LTV. Raising LTV is product, retention, upsell — separate work; the number itself changes nothing.

• Can the LTV forecast be trusted as a fact?

  No. LTV is an estimate on assumptions and past behavior, not a guarantee or an exact prediction; different methodologies give different numbers. We fix the assumptions and show a range, not one 'true' figure; the future may differ.

• I have little history — can you compute it?

  On short history or small numbers the forecast is unreliable, and dirty data will distort the model (garbage in, garbage out). If data is insufficient, we honestly say it is too early to model and suggest what to collect.