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Theme
Procurement management

Predictive analytics
for sellers
Kaspi

Forecast demand, avoid stockouts, and optimize purchasing with AI

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87%
Accuracy
−31%
Overstock
−84%
Stockouts

90-day forecast

Why sellers lose money on procurement

Problem

Purchasing by guesswork

Sellers buy by guesswork and lose money. Overstock ties up capital, while stockouts mean missed sales and lost search ranking.

Capital is tied up in excess inventory
Stockouts drop your search rankings
Competitors capture your customers
AWW Solution

Accurate forecasts instead of guesswork

AWW predictive analytics analyzes sales history, seasonality, and trends — forecasting demand for 30–90 days. You buy exactly what you'll sell.

90-day forecast for every SKU
Alerts before stock runs out
Detects trends 2–3 weeks earlier
Features

Everything for accurate purchasing

30–90 day demand forecasting

LSTM and Prophet algorithms analyze history and external factors to predict sales volume for each SKU.

Stockout alerts

The system warns in advance which items will run out in a week. You can reorder before losing sales.

Trend detection

The model spots category growth 2–3 weeks before the peak. You buy earlier than competitors and capture the gains.

Inventory optimization

Accurate reorder recommendations for each item. Minimum tied-up capital, maximum turnover.

How it works

Three steps to precise purchasing

1

Connect your data

Integrate your Kaspi store. The system will automatically import sales history and current inventory.

2

Get forecasts

The algorithm will analyze the data and provide demand forecasts for 30, 60, and 90 days for each product.

3

Make precise purchases

Follow purchasing recommendations. Receive alerts about shortages and trends in real time.

Results

Numbers, not promises

87%

demand forecast accuracy

−31%

reduction in overstock

−84%

reduction in product shortages

Real results

Data, not guesswork

Sellers on Kaspi.kz make decisions based on AWW predictive analytics

FAQ

Frequently Asked Questions

For a basic forecast, 30 days of sales history is sufficient. The more data, the more accurate the forecast — ideally at least 3 months. The system starts working immediately and improves accuracy as more data accumulates.
The model analyzes sales history, current inventory levels, prices, seasonal patterns, holidays (Nauryz, March 8, Black Friday) and category trends on Kaspi.kz.
The algorithm automatically detects seasonal patterns in your data and adjusts forecasts accordingly. Kazakhstan's holidays and peak periods are already incorporated into the model.
For new products without sales history, the system uses data from similar items in the category and overall market trends. Accuracy increases as your own data accumulates.
Forecasts are recalculated daily with new sales data. Alerts about shortages and anomalies are delivered in real time.
The system provides precise purchase quantity recommendations for each SKU. You can see how many units to order and by what date to avoid stockouts.