The retail industry still remains under pressure – the choice Data science between growth and demand, rising costs, retention of sales channels.
Main trends:
- Personal offers instead of package.
- Active focus on open standards and APIs.
- Optimizing supply chains.
- Expanding monolithic applications, introducing new applications – quickly solving new problems.
In the field of Big Data Management and Data Science for the retail industry, the following tasks can be distinguished:
- Infrastructure.
- Data warehouse optimization solutions.
- Organization of fast output of Data Science models to production.
- Tuning existing Hadoop solutions.
- Reducing the TCO of information storage for any database.
- Sales.
- Sales forecasting.
- Conversion increase.
- Social media, heat analysis, competitor analysis, social media trends. Networks.
- Analysis of the grocery basket.
- Segmentation of receipts.
- Optimization of goods on the shelves.
- Demand forecasting (optimization of goods in warehouses).
- Cross-selling.
- Client analytics.
- Customer segmentation.
- Behavioral models and templates.
- Personal suggestions.
- Customer churn.
- Enrichment of customer profiles.
- Identification of customers who respond positively to promotions.
- Customer Lifetime Value Analysis.
- Fraud detection.
- Write-off of goods.
- Loyalty programs.
Data Science UA offers services to solve the listed tasks. In the following, most demanded areas; we have accumulated experience and a significant amount of developments.
1. Customer churn
Each organization has its own definition of customer churn. Analysts tend to focus on voluntary churn because this is usually due to factors in the company-customer relationship that are controlled by companies.
Churn is a Key Performance Indicator (KPI) for a product or service, and retention rate provides a better understanding of the health of a business or product. The machine learning model allows you to identify the most vulnerable to churn customers in a subset of your customer base and initiate marketing activities to keep them.
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2. Analysis of the grocery basket
Grocery basket analysis is an approach that allows you to understand what products a customer buys together and what product is a driver for selling another. The analysis results are used to complement the offer to the customer – “other users also bought this”.
The application of this model allows you to increase the total sales volume and optimize the placement of goods on store shelves, as well as supply chains (warehouse stocks and supplies in the context of individual groups of goods).
3. Sales forecast
Sales forecasting is the most requested aspect in machine learning. The analytical model is based on various factors, such as:
- Cashier’s checks.
- Data from corporate systems (CRM) and loyalty cards.
- Seasonality information.
- Historical information based on time series.
- Social media trends.
- Weather and its forecast.
- Corporate data.
Sales forecasting models allow you to get a reasonable estimate of the expected sales volumes of goods in various aspects – a general forecast, a forecast for a product, a forecast for a group of goods, and others.
The result of high-quality forecasting makes it possible to achieve an increase in the efficiency of the trade organization and significantly reduce costs due to the action of negative factors:
- shortage of demanded goods;
- sale of warehouse balances;
- logistics costs;
- other.
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