Attribution modeling

Attribution modeling is an analytical project: we break down the customer path by touchpoints (channels, campaigns, devices), set up and compare attribution models (last-click, first-click, linear, position-based, time-decay, and data-driven with enough data), show how channel contribution changes across models, and help choose a methodology for your decisions. Honestly about the nature of attribution, this is key: any model is a simplifying assumption about how touchpoints influence the decision to buy; none is 'truth' — last-click overvalues the lower funnel, first-click the upper, data-driven needs a lot of data and is also built on correlations; we show a range and the logic, not one 'correct' figure. Honestly about data limits: cookie loss, cross-device, privacy (iOS/ATT, blockers) and offline conversions make the full path fundamentally incomplete — part of attribution is modeled/estimated, not observed. Honestly about the essence: this is modeling and analytics for decisions, NOT raising conversions and NOT running ads — attribution helps distribute budget more honestly, but growth is done by you or your specialist; the model itself does not increase sales. Honestly about metrics: attribution shows contribution and correlations, not proven causation — for causation experiments are needed (incrementality, holdout — separate services). Requirements: access to touchpoint and conversion data, sufficient volume. An important boundary: this is attribution modeling, not full end-to-end analytics, not incrementality experiments and not running ads. If there are few channels and a simple path, complex models are overkill — basic attribution is enough. Picture this: instead of 'by last-click all the success goes to brand queries and the top of the funnel looks useless' you see channel contribution in several models and decide deliberately. The base price starts from 25,000 ₽ per project; it depends on data volume and the number of channels.

• By last-click you undervalue top-of-funnel channels.
• You do not understand the real contribution of channels to conversions.
• You distribute budget by one simplified model.
• Different systems show different contribution and it is unclear what to trust.

• Breaking down the customer path by touchpoints
• Setting up and comparing attribution models
• A data-driven model with enough data
• Showing how channel contribution changes across models
• A methodology recommendation for your decisions
• A report with ranges and assumptions
• Flagging data limits (cookie/cross-device/offline)
• Reviewing results with you

• More honestly distributed channel contribution
• You see how the picture changes across models
• An agreed attribution methodology for decisions
• A base for budget (growth — separately)

• We clarify channels, data, decision goals; collect access
• We set up and compare models, analyze contribution
• We compile a report with ranges, recommend a methodology

• Which attribution model is the correct one?

  There is no single correct one. Each model is a simplifying assumption with its own biases (last-click overvalues the lower funnel, first-click the upper, data-driven needs a lot of data). We show several models and a range so you choose a methodology for your decisions deliberately.

• Will attribution raise conversion?

  No. It helps distribute budget more honestly and understand channel contribution, but by itself it does not raise sales. Growth is ad and product work done by you or your specialist.

• Will attribution prove which channel 'worked'?

  No. Attribution shows contribution and correlations, not proven causation. For causation experiments are needed — incrementality tests and holdout (separate services). Plus cookie loss and offline make the path incomplete, part is modeled.