Bandit algorithms

Bandit algorithms is a project: we set up adaptive traffic allocation (epsilon-greedy, Thompson sampling, etc.) that during the test shifts impressions toward variants with better interim results, plus goals, metric collection and control. Honestly about the nature of the method, this is key: the bandit optimizes ALLOCATION during the test (less traffic to weak variants), but pays for it with statistical cleanliness — it is worse for obtaining a precise unbiased effect estimate than classic A/B, and its results are harder to interpret causally; it is a trade-off of 'use the leader faster' versus 'measure more precisely'. Honestly about applicability: bandits are good for short campaigns, many variants or when it is important to minimize losses quickly (e.g. rotating banners/headlines); for a reliable 'how much exactly better' conclusion A/B is more honest — we advise what is appropriate without pushing. Honestly about the result: the method does NOT guarantee a conversion increase — if there is no difference between variants, the bandit will not create one; enough traffic and correct metrics are needed (garbage in, garbage out). Honestly about risk: under non-stationarity (behavior changes over time) and peeking the bandit may 'lock in' on a false leader — correct setup and control are needed. Honestly about the essence: this is setup and running, NOT implementation and NOT guaranteed growth; the platform (if paid) is separate. An important boundary: this is the bandit approach, not classic A/B (for a precise estimate — separate) and not designing variants. If you specifically need a precise effect estimate or traffic is low, the bandit is not the best choice — we say so honestly. Picture this: instead of 'half the test sends traffic to a clearly weak variant' the system itself shifts impressions to the better one — when justified. The base price starts from 30,000 ₽ per project; it depends on the number of variants and the tool.

• In a classic test traffic goes to weak variants for a long time.
• Many variants or a short campaign — a regular A/B is inconvenient.
• You need to quickly minimize losses on bad variants.
• You do not know whether a bandit or a classic A/B suits you.

• Setting up adaptive allocation (epsilon-greedy/Thompson etc.)
• Defining goals, metrics and control
• Launching and watching the traffic shift
• Risk control (non-stationarity, false leader)
• Applicability assessment: bandit vs classic A/B
• An honest result analysis (including 'no difference')
• A report and recommendation
• Reviewing results with you

• Fewer losses on weak variants during the test
• Suitable for many variants and short campaigns
• An honest understanding of the method's limits
• A base for decisions (precise estimate — via A/B; growth — separately)

• We assess the task: bandit or A/B; collect access
• We set up adaptive allocation, goals, control; launch
• We analyze the result and risks, give an honest recommendation

• Is a bandit always better than A/B?

  No. A bandit reduces losses on weak variants during the test but sacrifices statistical cleanliness and is worse at a precise unbiased effect estimate. For a reliable 'how much exactly better' A/B is more honest. It is a speed-vs-precision trade-off — we choose for the task.

• Does a bandit guarantee a conversion increase?

  No. If there is no real difference between variants, the bandit will not create one. The method only allocates traffic more efficiently when a difference exists; growth depends on the quality of the variants and hypotheses themselves.

• What is the risk of a bandit?

  Under behavior changing over time (non-stationarity) or wrong setup the bandit may 'lock in' on a false leader. Correct setup, enough traffic and control are needed — we ensure that and honestly warn about the limits.