Definition
Data-driven decision making is a systematic approach where choices about price, assortment, promotions and inventory are based on measurable metrics and analytical conclusions rather than intuition or isolated experience.
How it works: from data to action
The sequence for making data-driven decisions follows a simple cycle: data collection → cleaning and validation → analysis → hypotheses → testing → implementation and monitoring. Each step requires concrete metrics and documented procedures.
- Data collection. Sources: Kaspi Seller Center reports, order and inventory exports, return reports, competitor price lists (parsing), CRM and warehouse accounting data. Frequency: at minimum daily for inventory and prices, weekly for conversions and returns.
- Cleaning and validation. Remove duplicates, check for impossible values (for example, negative inventory), and match SKUs across systems. Without clean data you cannot get reliable analysis.
- Analysis and visualization. Key calculations: CTR = clicks / impressions, CR = orders / clicks, AOV = revenue / orders, return rate = returns / shipments, turnover = sales for the period / average inventory. It’s important to view metrics by segment: SKU, brand, category, city.
- Hypotheses and tests. Form testable assumptions: "If we lower the price by 5%, conversion will grow by 15%." Test on a controlled group of SKUs or during a time-limited promotion.
- Implementation and automation. After confirmation, scale the solution and automate price, inventory and product card updates via API or automation tools.
Why it matters for a Kaspi.kz seller
On Kaspi.kz sales efficiency is directly linked to managing visibility, pricing and stock. Data-driven decisions give concrete advantages:
- Reduce losses from out-of-stock. If a seller records stockouts on more than 5% of working days per month, that commonly results in losing 7–12% of potential revenue. Forecast analytics can cut dropouts to 1–2%.
- Price optimization. In practice, lowering the price on a slow-moving SKU by 3–7% often increases sales by 20–60% depending on elasticity. For popular items a 5% price increase without conversion loss raises margin directly at checkout.
- Lower returns. Analysing return reasons by category and product page can reduce returns by 30–50% — for example, by improving photos, detailed descriptions and specifying dimensions.
- Improve Buy Box and search visibility. Consistent pricing, fast restocking and high CR help keep or win placement in search results and the Buy Box, which directly increases impressions and orders.
Concrete examples and cases on Kaspi.kz
Here are typical scenarios where data-driven changes brought measurable results:
- Repricing a slow SKU group. A seller segmented slow-moving SKUs and ran a two-week price drop test on a subset. Results: CR rose by 28%, AOV slightly decreased but overall revenue and sell-through improved, allowing faster stock turnover.
- Fixing high-return SKUs. By analysing return reasons, one merchant updated descriptions and added size charts for a clothing line. Returns dropped by 40% and conversion increased due to fewer customer doubts.
- Inventory forecasting for peak season. Using historical sales, lead times and return rates, a seller automated reorder points. During the peak period they reduced stockouts by 70% and avoided overstock in low-velocity SKUs.
Practical tips: what to measure and how to act
- Prioritise a small set of KPIs. Start with impressions, clicks, CTR, CR, AOV, revenue, margin, return rate and turnover. Track them by SKU, category and city to spot bottlenecks.
- Segment before you act. Do not apply one-size-fits-all changes. Segment SKUs by demand velocity, margin tier and seasonality, then apply different pricing or promo logic per segment.
- Define success metrics before testing. For any price or promo test, fix KPIs (CR, AOV, margin, returns) and the minimum period for statistical significance (usually 2–4 weeks depending on traffic).
- Automate routine reactions. For inventory below a threshold, set automatic replenishment requests; for price undercutting by competitors, trigger repricing rules with guardrails on minimum margin.
- Keep an action log. Document what changes were made, when and why, so you can attribute effects correctly and avoid repeating mistakes.
Mistakes and risks when making data-based decisions
Common pitfalls:
- Poor-quality data. Duplicates, wrong SKU mapping, negative or impossible values and inconsistent timestamps distort metrics and lead to bad recommendations.
- Confusing correlation with causation. A change in sales after a price adjustment might be caused by seasonality, a marketplace-wide promo, or supply issues — always check external factors.
- Too short tests. Acting on short-term fluctuations can lock you into suboptimal settings. Ensure tests run long enough for reliable results.
- Ignoring side effects. Focusing only on CR or revenue without tracking margin, returns or search position can produce decisions that harm long-term profitability.
Tools and automation for the seller
Useful tools and integration points:
- Kaspi Seller Center API and exports. Use them to sync prices, inventory and orders programmatically.
- BI and dashboards. Metabase, Power BI or similar tools for visualization and monitoring; set daily summaries for critical KPIs.
- ETL and data quality processes. Regular pipelines to clean, deduplicate and validate data before analysis.
- Repricers and replenishment systems. Repricer services for tactical price adjustments and automated reorder systems that consider lead time and return rate.
Brief summary and practical tip
Start small: pick a few KPIs, set up daily exports from Kaspi Seller Center, and run simple tests on a controlled SKU group. Document results, automate repeatable actions, and scale what works. Even modest, consistent improvements in pricing, stock management and product pages accumulate into substantial revenue growth on Kaspi.kz.
Часто задаваемые вопросы
- Which key metrics should a Kaspi.kz seller track first?
- Prioritise impressions, clicks and CTR, conversion (CR), average order value (AOV), revenue and margin, as well as return rate and inventory turnover. View these metrics by SKU, category and city to identify bottlenecks and segments with growth or decline in demand.
- How often should prices and inventory be updated on the marketplace?
- Update inventory and prices at least once a day, and multiple times a day when demand or promotions are highly dynamic. Analyse conversions, returns and sales metrics weekly, and prepare strategic reports monthly for procurement planning.
- How to properly plan and run an A/B test when lowering prices for a group of SKUs?
- Allocate control and test SKU groups with similar characteristics and historical metrics, set a testing period long enough for statistical significance (usually 2–4 weeks), and fix KPIs: CR, AOV, margin and total revenue. Include secondary metrics — returns and changes in search position — to assess long-term impact.
- What typical data-cleaning mistakes lead to wrong decisions?
- Common errors include unresolved duplicates and incorrect SKU matching across systems, ignoring negative or implausible values (e.g., negative inventory), and inconsistent currencies or timestamps. Such defects skew CR, turnover and demand forecasts, causing bad recommendations for procurement and pricing.
- What tools and automation help scale solutions for a seller in Kazakhstan?
- Use Kaspi Seller Center exports and API to sync prices and inventory, BI tools (Metabase, Power BI) for visualization and ETL processes for regular data cleaning. Repricer services and automatic replenishment systems that consider lead time and return rate are useful for operational price and stock management.
- How to account for returns and cancellations when forecasting inventory and orders?
- Include net sales — sales minus expected returns — in forecasts by calculating the return rate from historical data and segments. Add a safety buffer based on the volatility of returns and supplier lead time, and update forecasts regularly when trends change.
