Practical guide to sales and product analytics on Kaspi.kz

Introduction: why a seller on Kaspi needs a sales analyst

If you run a Kaspi store in 2026, sales figures are no longer collected by hand — they need to be interpreted. Sales analytics, product analytics and ABC analysis turn a set of numbers into manageable business logic: which SKUs to keep in stock, which to promote, and which to remove from the platform. In this article I provide a practical step‑by‑step guide for sales department analysts and sales analysts to quickly increase profit and reduce costs.

1. Basics of sales analytics: terms and first KPIs

Start by defining roles: a sales analyst is a specialist who builds reports on revenue, margin and channel efficiency. Often the same person performs the functions of a product analyst on marketplaces, especially in small stores.

1.1 Basic KPIs to launch immediately

1. Revenue by SKU, category and brand — daily/weekly/monthly breakdown.
2. Margin by SKU — account for Kaspi commission, delivery costs and returns.
3. Inventory turnover (days of stock) — stock in units / average daily sales.
4. Product page conversion — views → purchases. See the guide to improving product pages product page on the marketplace.
5. Return and claims rate — a high rate lowers the seller's rating.

Example target values for a mass‑market store on Kaspi in 2026: product page conversion 1.8–4%, average margin 18–30%, returns 4–8% depending on category (electronics higher, home goods lower).

1.2 How to collect data

• Export via the Kaspi seller dashboard or the marketplace API — marketplace API.
• End‑to‑end tables of sales, stock, purchases, advertising spend and BuyBox bids.

2. Sales analysis methods: what to use and how often

Product analytics methods on marketplaces depend on the goals. I separate them into tactical (daily) and strategic (monthly/quarterly).

2.1 Daily tasks for the sales analyst

• Monitor sales by SKU and Buy Box — who is taking the Buy Box, how CPA changes.
• Check stock levels and trigger emergency purchases for A‑class items (see ABC analysis).
• Control advertising bids: pause campaigns with ROI below the breakeven point.

2.2 Weekly and monthly reports

1. ABC analysis of products and assortment review.
2. Cohort analysis by first purchase date and repeat purchases — LTV vs CAC.
3. ABC groups combined with inventory forecasting — reduce warehouse dead stock.

2.3 Analysis methods: quantitative and qualitative

• Quantitative: ABC analysis, ABC‑XYZ, cohort analysis, ABC analysis at the SKU/item level.
• Qualitative: product card evaluation, review analysis (see the article about reviews Reviews on Kaspi), Mystery Shopping.

3. ABC analysis of products: a practical step‑by‑step guide

ABC analysis of products is an essential tool for a seller. It shows which products generate the bulk of revenue and where to focus attention. Below is a practical algorithm for doing an ABC analysis for Kaspi in one day and applying the results.

3.1 Data preparation

1. Collect for a 90‑day period: units sold, revenue, cost of goods sold, number of returns and stock by SKU. Export from the Kaspi seller cabinet or via the Marketplace API.
2. Add static attributes: category, brand, item code. If you don’t have an item code — create one following the instructions Item code.
3. Calculate revenue by SKU and its share of total revenue.

3.2 Building the ABC

1. Sort SKUs by revenue in descending order.
2. Calculate cumulative revenue share and set thresholds: A = first 70–80% of revenue, B = next 15–20%, C = remaining 5–10%. In Kazakhstan and on Kaspi, for most sellers we recommend A = 75%, B = 20%, C = 5% as a starting point.
3. Check the distribution by number of SKUs: typically 10–20% of SKUs fall into group A, 20–30% — B, the rest — C.

3.3 Next steps by group

• Group A: keep 30–45 days of stock, regular replenishment, focus on Buy Box and ad campaigns. For A‑SKUs lower discounts are acceptable since they drive turnover.
• Group B: moderate stock levels, monitor promotions and seasonality. Run A/B tests on cards and prices.
• Group C: minimal stock or removal from assortment. Use discount bundles, bundling or clearance sales. Details on bundle strategy — product bundle strategy.

Practical case: a home appliances store on Kaspi, 2026. After ABC analysis 15% of SKUs produced 78% of revenue (A); the manager reduced the C assortment by 40% and reallocated ad budget to A, which increased overall margin by 3.2%.

3.4 ABC + XYZ: account for demand stability

ABC shows revenue contribution, XYZ — demand predictability. The A+X combination (high revenue and stable demand) is a prime candidate for FBO and premium placement. For A+Z — keep buffer stock and plan for fast delivery.

4. Tools for analytics: from Excel to BI and automation

Below is a set of tools used by a sales analyst in a real Kaspi store in 2026.

4.1 Basic set: Excel and SQL

• Excel/Google Sheets with Power Query for initial processing of exports and building ABC analysis.
• SQL (Postgres/MySQL) to store transactions and make quick queries: sales by SKU, returns, advertising costs.

4.2 BI tools

• Power BI or Google Data Studio for dashboards: revenue, margin, stock, BuyBox dynamics.
• Custom dashboards for product and sales analytics — with filters by period and category.

4.3 Specialized platforms and automation

• Repricers and price monitoring tools — see repricing and price monitoring. On Kaspi it’s important to keep a competitive price for the Buy Box.
• Automation platforms: AWW as an automation tool provides a repricer, stock analytics and scenario-based messaging via WhatsApp — see WhatsApp marketing.
• Inventory forecasting tools and machine learning for demand prediction — as assortment grows, it’s profitable to introduce ML models.

4.4 Integrations with Kaspi: what to set up right away

• Order and delivery status exports (Kaspi Доставка / Kaspi Постамат).
• Reconciliation of payment receipts via Kaspi Pay and Kaspi Рассрочка.
• Advertising spend and KPIs from Kaspi Marketing — recommendations in the article Kaspi Marketing: a complete guide.

5. Applying analytics in e‑commerce: concrete scenarios

Below are five practical scenarios a sales analyst can implement next week.

Scenario 1. Quick turnover increase by reallocating budget

1. Run ABC analysis for 90 days.
2. Redirect 60% of the ad budget from C group to the top 20% of A‑SKUs.
3. After 30 days measure changes in ROI and conversion. Expected revenue growth 8–15% in high‑margin categories.

Scenario 2. Reducing warehousing costs

1. For A+X keep 30–45 days of stock. For B — 15–25 days. For C — minimal, with frequent push sales.
2. Implement automatic ordering rules: if sales over 7 days exceed average by 20%, create a supplier order.

Scenario 3. Reducing returns via card analytics

1. Group returns by reason — damage, mismatch with description, missing components.
2. Fix product cards with frequent returns: photos, sizing, video instructions. See the article 7 mistakes in product cards on Kaspi.

Scenario 4. Increasing LTV and reducing CAC

1. Do cohort analysis by first purchase date and track repeat sales at 30/60/90 days.
2. Launch triggered messages via WhatsApp and email (AWW supports WhatsApp campaigns) for the A customer segment.

Scenario 5. Solving a price war

1. Monitor competitor prices and Buy Box at the item level. Use repricing and minimum retail price rules.
2. Include parameters: minimum margin, logistics cost, Kaspi commission. Price automation must account for the breakeven point.

6. The role of the analyst: tasks, metrics and soft skills

The sales analyst is not only about SQL and dashboards. In small teams they combine the roles of marketplace product analyst and assortment manager.

6.1 Responsibilities of the sales analyst

• Maintain up‑to‑date dashboards and reports on ABC product analysis and sales analytics.
• Run A/B testing of product cards and promotions — see A/B testing of product cards.
• Work with purchasing and marketing teams: turn analytics into operational decisions.

6.2 Metrics to evaluate the analyst’s effectiveness

• Reduction of C‑group stock (%), increase in A‑group turnover (%), reduction in return rate (%).
• Lower customer acquisition cost (CAC) and higher LTV/CAC.

6.3 Skills

Technical: SQL, Excel, Power BI, basic ML models. Business: ability to turn hypotheses into A/B tests, knowledge of Kaspi operations (FBO, FBS, Kaspi Доставка). Soft: communication with purchasing and logistics, fast decision‑making in price wars.

7. From report to action: weekly checklist for the analyst

1. Update sales and stock exports.
2. Start daily monitoring of BuyBox and competitor prices.
3. Recalculate ABC analysis once a month and allocate purchases.
4. Check A‑SKU product cards: rating, reviews, CTR and conversion.
5. Optimize ad budget: stop campaigns with ROI below the breakeven point.

8. Cases and practical examples from Kazakhstan (2026)

A few brief cases based on typical operations of Kaspi sellers in 2026.

Case 1. Category “Home goods”

The store applied ABC analysis and found that 12% of SKUs produced 82% of revenue. After reallocating budget and increasing stock for A‑SKUs, sales grew by 11% within a month. Profitability improved due to reduced frozen stock in the C group.

Case 2. Electronics and returns

Analytics showed returns concentrated in 8 SKUs out of 400. After updating cards and adding video instructions, returns for these SKUs fell from 9% to 3.5%.

9. Analyst mistakes and how to avoid them

• Trusting revenue alone — not accounting for margin and commission. Always calculate marginal profit by SKU.
• Not accounting for seasonality and Kaspi promotions (example: Kaspi Жума). Plan stock with marketplace sales in mind.
• Ignoring reviews and reputation — they affect CTR and conversion. See the article Reviews on Kaspi.

10. Where to learn and what materials to read

I recommend starting with practical guides for Kaspi: how to open a store and set up exports — How to open a Kaspi store, then deepening into product selection and uploading price lists — How to find profitable products and Price list for Kaspi.

For terms and niche tools the AWW sections are useful: ABC analysis of products, Sales analytics, Marketplace analytics and Marketplace API. AWW helps automate repricing and trigger messaging if you want to save the analyst’s time.

Conclusions

Product and sales analytics is a set of tools and procedures that allows a Kaspi seller to reduce warehousing costs, increase margin and retain the Buy Box. Start with ABC product analysis and basic KPIs, then move to automation via BI and repricers. The role of the sales analyst is not only reporting but translating data into concrete commercial actions.

Useful links

• ABC analysis of products
• Sales analytics
• Marketplace analytics
• Product card on the marketplace
• Marketplace API