The Benefits of Predictive Analytics in Trading

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Applications of Predictive Analytics

You can also predict what the demand for a particular product will be and plan an advertising campaign:

Forecast of daily demand at the outlet/product level for 28 days.

Forecast of demand during the promotion chiropractor email address period.

Accounting for product substitution during the promotion period.

Taking into account price changes and seasonal fluctuations in demand.

Taking into account climatic factors, locations of retail outlets, and their sizes.

Forecast for the launch of new products, opening of new stores.

Competitor analysis.

Predictive analysis in trade is useful in the process of developing products that are important for buyers:

Identifying a group of products that have a significant impact on purchasing behavior.

By identifying these products, the seller can establish a new pricing strategy and thereby influence purchasing behavior.

Aggressive pricing policy allows sellers to regulate traffic, influence the overall market situation and their profits.

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Alexander Kuleshov
Alexander Kuleshov
General Director of Sales Generator LLC
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Predictive analytics is used in trade to optimize the constant and promotional cost of goods. The following methods are used for this:

Recommend the most suitable price.

Price recommendation to increase sales.

Calculation of restrictions on turnover, markup, supplies, and display.

Calculation of price elasticity.

Individual offers for buyers.

Multi-channel sales.

The Benefits of Predictive Analytics in Trading

Predictive analytics is useful for segmenting customers:

Segmentation by behavioral characteristics, marketing segmentation.

Targeted marketing campaigns.

Customer basket analytics.

Recommendation services.

Cross-selling, increasing sales.

Optimal Alternative Tactics.

Calculating buyer risk to stop customer churn.

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You can also segment customers based on behavioral characteristics using multidimensional information analysis:

To conduct promotions, segments of buyers are formed (this division increases conversion).

Different groups of buyers are offered different discounts, which also has a positive effect on the number of purchases.

At the earliest stage, customers who are most likely to become non-converters are identified and offered appropriate promotions.


Using Predictive Analytics in Manufacturing
Using Predictive Analytics in Manufacturing

Tasks of predictive analytics in industry:

Analyze and predict factors affecting product performance.

Predict equipment failure, i.e. service according to condition, not according to regulations.

Make predictions about the production of goods and the consumption of resources.

Provide advance notice of potential emergencies.

Predictive analytics is especially important in companies where there is a lot of data and its analysis can influence decision-making and minimize risks.

Very often, even if organizations have process data, they do not use it properly, although this information can significantly improve all operational processes and increase productivity. Such optimization is possible if the enterprise is well automated and has a well-established process for collecting and storing data.

Various intelligent systems that study the entire production process, predict its future, calculate optimal indicators, change the specified parameters and issue recommendations to the dispatcher cope with this task perfectly. Machine learning technology allows creating a mathematical model of the production process. The resulting model is integrated with the automated process control system, MES and ERP systems of the organization.