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Automatic Big Data Machine Learning Marketing Solutions for Retail

AuDaScience BRAINs™ applications bring personalized promotions and campaigns to the next level, increasing retail customers’ loyalty and basket.

Chain stores are faced with the daily challenge of retaining customers and expanding their baskets and expenditure.

One of the primary methods of attracting customers back to the store is by sending promotions of various products through different contact channels such as SMS, print-on the-slip, kiosk, direct mail, emails and mobile apps.

Chains that send personalized promotions to customers, based on their consumption profile, have discovered that they have managed to increase the promotion conversion rate, strengthen the loyalty of these customers and expand their shopping cart.
To optimize this channel, more and more chains are trying to deploy analytical processes for identifying the best products for each customer as personalized marketing recommendations/coupons. This marketing process is based on internal statistical predictive analytics tools available on the market or through consulting companies and service providers.

The primary methods of recommending a promotion/coupon to a customer, taking into account a large variety of products and huge amounts of data, are:

  • Customer segmentation models - provide insight about matching customers to products based on the segment they belong to. These models do not provide matching of a single product to a customer and therefore lead to low suitability and conversion rates.
  • Statistical basket analysis or collaborative filtering models - take into consideration the customer's consumption profile. Due to statistical restrictions these models ignore the available personal information (socio-demographic, card information, coupon and promotion response history, etc.) and therefore lead to low suitability and utilization rates.

Sales BRAINs enables chains to realize the real vision of 1-to-1 marketing and bring about a tangible increase in the average customer basket, expressed in growth in the yearly net profit per customer compared to the recommendations based on the classic market methods.

Sales BRAINs empowers retail chain store marketing staff to identify the best products for each loyalty program member to generate up-sell or cross-sell by:

  • Automatic development and execution of thousands of statistical probability models estimating each customer's susceptibility to offers for every product sold in the chain. These models take into consideration each customer's consumption behavior and other profile characteristics to deliver substantially higher suitability and conversion rates than any other method.
  • Prioritization of the recommendations also on the basis of optimal allocation which takes into account budgetary, inventory and product mix constraints, minimum/maximum per recommendation, etc.

Retention BRAINs enables the chain to predict which customers will reduce activity in different categories or formats using complex automatic churn prediction modeling.
Value BRAINs provides the evaluation of the impact of different marketing actions on customers’ lifetime value (i.e., how different cashbacks impact customers' LTV and recommendations on the optimal cashback which will optimize each customer's LTV and increasing the chain's profitability).

AuDaScience BRAINs applications enable marketing staff without any statistical or SQL know-how to control the entire development and deployment process of thousands of complex machine learning and optimal allocation models through a user-friendly interface in less than a day and without writing a single line of code.

BRAINs applications can be deployed in a company within a few days, and allowing the chain to improve its targeted promotions and coupons allocation process and show quick ROI within 1-2 months.