Customer Segmentation

Definition

Customer segmentation is the division of a buyer base into homogeneous groups by behavior, value and characteristics to enable personalized commercial actions. The goal is to increase conversion, average order value and the share of repeat purchases through targeted offers and optimized marketing spend.

How customer segmentation works

Segmentation relies on data about the buyer's interactions with products and sales channels. In ecommerce this is usually combined across three levels:

• Behavioral data — product page views, adds-to-cart, abandoned carts, returns.
• Transactional data — purchase frequency, average order value, order dates. The RFM method (Recency, Frequency, Monetary) is commonly used.
• Demographic and channel data — city, payment method (Kaspi Pay, cash on delivery), delivery type (postamat/courier), device (mobile/desktop).

The typical workflow: data collection → preprocessing (deduplication, normalization) → segment identification (rule-based or clustering) → validate segments against KPIs → automate triggers and campaigns.

Why segmentation matters for a Kaspi.kz seller

For a seller on Kaspi, segmentation delivers concrete advantages that affect sales and margin:

• Higher conversion: targeted promos by segment can lift conversion by 10–30% depending on category and offer quality. This is confirmed by seller tests in electronics and home appliances categories.
• Ad budget efficiency: instead of blanket discounts you spend marketing on groups with higher purchase likelihood, lowering CPA.
• Higher LTV: focusing on customers with a high average order value and likelihood to repurchase can increase LTV by 15–50% with proper retention strategies.
• Fewer returns and disputes: segments based on return frequency allow extra checks and warnings on the product page or adjustments to delivery policy.
• Targeted logistics: for example, groups preferring postamat pickup in Almaty and Astana can receive local bonuses, reducing delivery cost.

Examples of segments and concrete actions on Kaspi

Below are typical segments with concrete commercial tactics that sellers in Kazakhstan commonly use on Kaspi.

1. New customers

• Criterion: made their first purchase within the last 30 days.
• Actions: offer a coupon for the next purchase, provide usage instructions for the product, include cross-sell suggestions in the order page and welcome messages in the buyer's Kaspi account.
• Expected effect: increase in repeat purchases and improved retention in the first 60–90 days.

2. Cart abandoners

• Criterion: added items to cart but did not complete checkout within 24–72 hours.
• Actions: trigger reminders (push/SMS or message in the Kaspi personal account) within 1–24 hours, offer a small incentive (discount, free delivery, extra loyalty points), show relevant cross-sells on the product card.
• Expected effect: recover a portion of lost orders, improving conversion for this segment by double-digit percentages depending on stimulus.

3. High-value customers

• Criterion: top X% by Monetary and Frequency (e.g., top 20% by spend or repeat purchases in the last 12 months).
• Actions: VIP offers, exclusive bundles, priority support, personalized outreach and early access to new products.
• Expected effect: increased retention and cross-sell, higher average order value and LTV.

4. Frequent buyers (regulars)

• Criterion: high purchase frequency but moderate average check (e.g., purchases every 30–60 days).
• Actions: subscription or bundle offers, replenishment reminders for consumables, loyalty promotions that reward frequency.
• Expected effect: reduce churn and increase basket size through bundling and subscriptions.

5. Return-prone customers

• Criterion: elevated return rate (returns as share of orders above a threshold).
• Actions: add pre-purchase checks (size guides, detailed specs), offer assisted selling via chat, modify return windows or require additional confirmation for high-return SKUs.
• Expected effect: lower return rate and fewer disputes, improving net margin.

6. Local pickup / postamat preference

• Criterion: customers who repeatedly choose postamat or specific pickup points in cities like Almaty and Astana.
• Actions: offer localized promos, pickup-only bundles, or free local delivery vouchers; highlight stock availability in nearby postamats on the product page.
• Expected effect: lower delivery costs and faster fulfillment, improved conversion in those locations.

7. Inactive customers (churn risk)

• Criterion: no purchases in the last 6–12 months but had at least one prior order.
• Actions: reactivation campaigns with personalised incentives, new-product announcements, or tailored content reminding them of previous purchases.
• Expected effect: win-back of a share of lapsed customers and uplift in repeat purchase rate.

Practical tips on implementation and metrics

• Start with RFM: compute Recency (days since last purchase), Frequency (orders count) and Monetary (total spend) and segment by percentiles or clusters as a baseline.
• Choose periods by category: fast-moving consumables need shorter windows (30–90 days), durable goods need longer windows (180–365 days).
• Validate segments: measure conversion, repeat rate and average check per segment. Use holdout groups to estimate incremental impact of campaigns.
• Prioritise data quality: deduplicate customer IDs, reconcile multi-device behavior and ensure accurate mapping of orders to customers.
• A/B test offers: test incentive size and type (discount vs free delivery vs loyalty points) and track time-to-conversion for each variant.
• Monitor KPIs: segment conversion, CPA/ROAS, average order value, repeat purchase rate and LTV. Adjust segmentation rules when performance drifts.

Integration and automation (tools and AWW)

Automation is key to scaling segmentation. Typical automation components:

• Data pipeline: ETL jobs that pull order history, behavioral events and refunds from Kaspi seller reports and platform APIs into a unified analytics store.
• Segmentation engine: scheduled jobs or real-time rules that assign customers to segments; can be implemented in SQL, Python or a marketing automation platform.
• Campaign execution: integrate with messaging channels — Kaspi personal messages, push, SMS, email and paid ads audiences for retargeting.
• Tools: BI for analysis (e.g., Metabase/Looker), CDP/CRM for orchestration, and advertising platforms for audience activation. For sellers using AWW, connect segment exports to AWW workflows to automate triggers, promos and reporting.
• Measurement: store experiment and campaign identifiers, use holdout groups and incremental lift tests to confirm ROI.

Implementation steps:

1. Collect and centralize data (orders, behavior, returns, payments, delivery type).
2. Define segmentation rules or run clustering for data-driven groups.
3. Run small pilots and A/B tests, measure uplift vs holdout.
4. Automate triggers and schedule recurring segment refreshes (daily/weekly depending on use case).
5. Scale successful scenarios and keep monitoring for drift.

Conclusion

Well-designed customer segmentation helps Kaspi sellers target offers more efficiently, increase conversion and LTV, and reduce wasted marketing spend. Start simple with RFM, validate with tests, and automate the best-performing scenarios using available seller tools and AWW workflows to scale impact.