Personalization layer implementation

Personalization layer implementation is a system that adapts the site content to a specific visitor: personal “you may like” recommendations, blocks by interests, offers by segment (a newcomer sees bestsellers and a first-order discount, a regular — products of their category and special terms). The basis is behavior and history: what they viewed, what they bought, which channel they came from. Let us clarify the border: an ordinary recommendation system offers “similar products” in one block; “recently viewed” (see the “Recently viewed implementation” service) simply shows the history. A personalization layer is broader — it changes the banners, offers, categories and selections across the whole site for the person, based on the segment and a set of signals, not just the browsing history. Importantly and honestly: personalization works only on data and traffic — on a small site with a couple of visits it has nothing to learn from, and the effect will be zero; you need a noticeable flow and accumulated history. It is not the “AI magic” from ads, but the setup of rules and models for your data, which is then constantly tuned. About money, honestly: from 60,000 ₽ is the launch; ongoing support, model tuning and reporting usually go as a separate subscription, the size of which we discuss on the project. About privacy: we use user behavior, so we work by cookie consent (see the “Cookie banner with CMP” service); and even with proper consent, personalization that is too “smart” can be off-putting — we keep the balance and do not show that we know too much about a person. It pays off on large catalogs and services with repeat visits; for a simple site it is redundant and expensive, and we will say so directly. Picture this: a regular client comes in — and sees their category, relevant new items and a personal condition, instead of a faceless storefront. The base price starts from 60,000 ₽; it depends on the depth (rules or models), the number of scenarios and integrations.

• All visitors see the same thing — the storefront does not account for interests and history.
• Relevant products get lost — a person does not find what would suit them.
• A newcomer and a regular client are shown the same — both the first sale and retention are missed.
• There is behavioral data, but it does not work for sales — it lies as dead weight.

• Collection and preparation of behavioral data (views, purchases, channels)
• Audience segmentation (newcomer/regular, by interests, by channel)
• Personal blocks across the whole site: recommendations, banners, offers, selections by segment
• Recommendation rules or models for your data
• Linking with cookie consent and privacy (without intrusive tracking)
• A/B testing of personalization against the impersonal version — measuring the real effect
• Responsive, tidy personal blocks on a phone
• Constant tuning and reporting (as a separate subscription)

• The visitor sees relevant things — a higher chance to find and buy
• A newcomer and a regular get different things — it works for both the first sale and retention
• Behavioral data starts working for sales — a chance for a gain appears (the final result depends on traffic, the catalog and the audience)
• The real effect is visible — we compare personalization with the impersonal version via A/B

• You leave a request, we assess your traffic and data — whether it is enough for personalization
• We design segments, scenarios, rules or models, tie them to privacy
• We implement it, test against the impersonal version via A/B, and tune it

• How is personalization different from “recently viewed” and a recommendation system?

  “Recently viewed” (see the “Recently viewed implementation” service) simply shows your history. A recommendation system offers “similar products” in a separate block. A personalization layer is broader than both: by segment and behavior it changes the banners, offers, categories and selections for the person across the whole site, not one block. It is a layer that manages relevance everywhere.

• Does it really work or is it marketing “AI magic”?

  It works, but only on data and traffic — and it is not magic but the setup of rules and models for your data with constant tuning. On a small site without flow and history, personalization has nothing to learn from, and there will be no effect. So first we honestly assess whether you have the data, and if not — we will talk you out of it. We measure the effect via A/B, not promises.

• How much does support cost after launch?

  The launch is from 60,000 ₽. After that personalization needs maintenance: tuning models, adding scenarios, reviewing reports — this is usually a separate subscription, and we honestly discuss its size at the start rather than hide it. Without maintenance the system gradually goes stale.