Marketplace analytics

Marketplace analytics is the systematic collection, processing and interpretation of data about traffic, product pages, orders and inventory to make managerial decisions and grow sales on the platform.

How marketplace analytics works

Analytics is built on three data layers: traffic source and user behavior, product-page metrics, and order fulfilment metrics. Data comes from platform tracking (views, CTR, add-to-cart), the seller's accounting systems (stock levels, purchases, cost), and logistics systems (shipment statuses, returns). All this is combined into end-to-end reports and dashboards to see which actions affect sales and margin.

• Tracking platform: product page views, CTR, time on page, traffic sources (organic, Kaspi internal ads, external traffic).
• Product page and commerce: price, stock, seller rating, reviews, presence of promos and participation in campaigns.
• Order fulfilment: processing time, delivery, return rate, percentage of cancellations and failed deliveries.

Key metrics and what they show

Below are the metrics a seller on Kaspi.kz should monitor continuously. For each metric there is a brief explanation and a practical target based on the experience of Kazakhstani sellers.

• Product page views (Impressions/Views) — show the level of interest and the impact of SEO/banners. Goal: increase views while keeping relevant traffic.
• Product page CTR (click-through rate) — share of users who clicked into the product page. For decent pages CTR is often in the 2–6% range; below that, change the image and title.
• Page conversion (CR) — share of page visitors who become buyers. Typical marketplace values are 1–5%; in Kazakhstan a 2–4% conversion for popular categories is considered workable.
• Average order value (AOV) — average order amount. A 10–20% increase is achievable with cross-selling accessories and bundles.
• GMV and revenue — gross merchandise value and net revenue. Track by days, weeks and promotional events.
• Stock and days of inventory (DSI) — how many days current stock will last. Optimal range is 15–45 days depending on turnover.
• Return and cancellation rates — important for ranking and costs. For appliances returns can be 5–8%, for clothing 10–20% — it depends on category.
• Order processing time — affects rating and customer experience. Goal: 0–24 hours for shipping from the seller's warehouse or via partner delivery services (learn more).

Why analytics matters for a seller on Kaspi.kz

Kaspi.kz is a high-volume marketplace with strong internal algorithms that prioritize listings based on relevance, availability and fulfilment quality. Analytics helps sellers:

• Understand which products generate traffic but don't convert, so you can prioritize improvements.
• Manage stock to avoid lost sales from stockouts and reduce holding costs from excess inventory.
• Optimize pricing and promotions to increase Buy Box share and margin.
• Detect fulfilment and logistics issues early to protect seller rating and reduce costs from returns or cancellations.

Practical examples from Kaspi.kz sellers

Examples of common seller scenarios and what analytics revealed:

• A product had high impressions but low CTR. After updating the main image and simplifying the title, CTR rose from 1.2% to 3.8%, and conversion improved accordingly.
• Conversion dropped after a price increase. Segmenting orders by traffic source showed paid campaigns were less price-sensitive — shifting ad budget restored sales while protecting margin.
• Frequent returns on a clothing SKU were traced to sizing inconsistencies. Updating size charts and adding clearer photos cut returns by half.
• High cancellation rate during peak promo days was linked to insufficient prep time for the warehouse. Adjusting forecasting and increasing buffer stock reduced cancellations and improved seller rating.

Tools and automation for data collection

Start simple and scale: use platform reports and CSV exports first, then move to automated extracts and visualization.

• Built-in marketplace reports — quick access to impressions, clicks and orders.
• CSV or Google Sheets connectors — suitable for daily control and small teams.
• ETL scripts (Python, SQL) or ready-made connectors — consolidate platform tracking, ERP/1C and logistics data by SKU and date.
• BI and visualization tools — Looker Studio, Power BI, Metabase for dashboards and deeper analysis.

Key integration point: a single SKU identifier and timestamps across systems. Automate data pulls and implement data quality checks to avoid gaps that distort margin and stock reports.

Practical tips: what to do with the data

• Prioritize improvements by potential impact: listings with high views but low conversion are usually the fastest wins.
• Set alert thresholds for anomalies: sudden drops in CTR, spikes in cancellations or inventory going below the safety level.
• Run A/B tests for titles, images and prices, and measure results over 3–7 days to account for traffic variability.
• Segment metrics by traffic source, region and device to spot micro-opportunities and risks.
• Translate insights into workflows: who updates listings, who handles supplier quality issues, and who adjusts logistics.

How to start: minimum analytics set for the first week

In the first week, set up daily monitoring of a compact set of metrics to detect immediate problems and opportunities:

• Daily product page views and CTR by SKU.
• Daily orders and conversion rate per SKU.
• Stock levels and days of inventory (DSI) per SKU.
• Number of orders and percentage of returns/cancellations.

Define baseline targets for each metric and track deviations. Produce short daily summaries and one weekly report to adjust priorities quickly.

Conclusion and a practical tip

Marketplace analytics is not just about collecting numbers but turning them into prioritized actions that improve conversion, reduce costs and protect seller rating on Kaspi.kz. Start with the essential metrics, automate data flows, and focus first on listings that show the biggest mismatch between traffic and conversion — those are usually the fastest wins.