Personalization

Personalization — tailoring content, prices and offers to a specific buyer based on data about their behavior, purchases and attributes. The goal is to increase the relevance of interactions and the conversion to an order.

How personalization works

Personalization is built on three layers of data and logic:

• Historical data: orders, returns, average order value, purchase frequency, date of last purchase. For Kaspi.kz sellers this is data from the seller card and CRM, exported via Merchant API or data dumps.
• Behavioral information: product page views, search clicks, adds to cart, traffic source (Kaspi mobile app or web). This data comes from the platform clickstream and analytics.
• Context: delivery region, device, time of day, loyalty status (for example, Kaspi Plus member), ad campaign and current discounts.

Technically personalization is implemented by three approaches or their combinations:

1. Rules-based: if a buyer is in Almaty and was browsing refrigerators — show local delivery options and items with FBS in the region. A simple, fast option to start with.
2. Collaborative filtering: "customers who bought X also bought Y." Suitable for recommendations on product pages and in the cart.
3. Content filtering and hybrid: considers product attributes (brand, specs) and buyer behavior; useful for new products and cold traffic.

Implementation involves data integration (API/exports), profile storage, generating recommendations (real‑time or batch), and frontend insertions into product pages, search, mailings and banners.

Why sellers on Kaspi.kz need personalization

Personalization impacts concrete commercial metrics that matter to a seller:

• Conversion (CR): personalized recommendations on product pages typically yield a CTR uplift of 10–30% and conversion increases of 5–40% depending on the category and data quality.
• Average order value (AOV): targeted cross‑sells and upsells raise AOV. In practice AOV can grow 5%–25% when bundling is done properly.
• Repeat purchases and LTV: personalized trigger emails or push messages offering accessories or consumables increase repurchase rates by 10–50% in high‑maintenance categories (electronics, beauty).
• Advertising efficiency: targeted impressions within Kaspi campaigns deliver higher ROI compared to broad reach campaigns.

For sellers on Kaspi.kz personalization helps:

• reduce the share of ineffective ad impressions by showing offers only to relevant segments;
• increase relevance and CTR across product cards, search results and category pages;
• boost average check through targeted bundles and recommended accessories;
• decrease cancellations and returns by recommending products that match user intent and regional availability;
• improve retention by re‑engaging previous buyers with tailored offers and replenishment reminders.

Examples from Kaspi.kz sellers' practice

Below are concise examples that illustrate common personalization use cases on the marketplace:

• Electronics retailer — accessory bundles: based on the model purchased, show compatible cases, chargers and extended warranties on the product page. Result: higher AOV and fewer follow‑up support requests.
• Home appliances seller — regional availability: for buyers in remote cities prioritize items available via local FBS warehouses to offer faster delivery and lower cancellations.
• Beauty brand — replenishment triggers: detect purchase cadence for consumables and send personalized push/email reminders with a discount just before the expected repurchase date.
• Sporting goods seller — seasonal recommendations: increase visibility of seasonally relevant items (for example, skis in winter) for users in regions where demand is high.

Practical tips for implementing personalization

• Start simple: implement rules‑based personalization first (region, category interests, loyalty status). It’s fast, transparent and requires minimal engineering.
• Prioritize data quality: ensure order history, stock status and returns are up to date. Bad data leads to irrelevant recommendations and lower ROI.
• Use Merchant API exports: automate periodic dumps of historical and catalog data rather than relying on manual CSVs when possible.
• Segment by intent: treat viewers, cart abandoners and previous buyers differently — each segment needs its own offer logic.
• Measure and iterate: run small A/B tests, monitor business KPIs and expand the winning experience gradually.
• Protect UX: avoid overpersonalization — show diverse alternatives to prevent narrowing choices and ensure relevance for first‑time visitors.

Metrics, testing and compliance

Key metrics to monitor:

• conversion to order,
• average order value (AOV),
• CTR of recommendations,
• retention / repeat purchases,
• for advertising — CPA and ROAS.

Testing approach:

• use randomized A/B tests with equal segment sizes, track statistical significance and run tests long enough to cover purchase cycles;
• measure primary business metrics (not only CTR), and check for side effects like increases in returns or customer complaints;
• segment results by region, device and traffic source to detect heterogenous effects.

Compliance and privacy:

• collect and store only the necessary data; keep personal identifiers encrypted or anonymized;
• obtain user consent where required and follow Kaspi platform rules and Kazakhstan personal data laws;
• document access procedures and deletion flows to satisfy internal audits and external requests.

Short checklist for launching personalization on Kaspi.kz

1. identify priority use cases (product page recommendations, cart cross‑sell, re‑engagement);
2. gather required data via Merchant API or scheduled exports (orders, stock, returns, clickstream);
3. implement simple rules to validate impact quickly;
4. set up A/B testing and KPI tracking dashboards;
5. iterate to hybrid models (collaborative + content) as data volume grows;
6. ensure compliance with data protection rules and platform policies.