AI-POWERED RECOMMENDATION ENGINE FROM BAONE
for retail and e-commerce
Product recommendations have long been a key tool for boosting retail sales. However, with the evolution of technology and the advent of next-generation neural networks, purchase probability forecasting has reached an unprecedented level of accuracy and efficiency.

BaOne recommendation models are rooted in our extensive experience of delivering consulting projects for clients across diverse industries. This unique expertise is a cornerstone of our competitive edge, enabling us to successfully meet business objectives. We tailor our knowledge and hands-on experience to suit the needs of each client, which translates into greater effectiveness and efficiency of our solutions.

At BaOne, we have developed an AI/ML-driven recommendation engine specifically intended for large retail businesses. It combines high-accuracy purchase probability predictions with the flexibility

POTENTIAL VALUE

The AI-driven recommendations from BaOne have delivered a remarkable boost in both sales and profit margin compared to a control group that used an automated recommendations system from another vendor.
  • ROUGHLY 20% INCREASE
    in the number of purchases
  • 10%
    GROWTH
    across active product matrix
  • UP TO 2 p.p. INCREASE
    in profit margin

HOW DOES IT WORK?

UNPACKING THE MAGIC BEHIND BAONE’S AI-POWERED PRODUCT RECOMMENDATIONS

The BaOne recommender draws on the various inputs from client IT systems (apps, website, retail purchases) to train its AI model for better predictive performance.

We see AI as a tool that must be carefully fine-tuned to the unique needs of each business for it to perform at its best. Our recommendation service is available as an out-of-the-box, ready-to-use transformer-based solution that predicts the timing of a purchase and items in a customer’s future shopping cart. However, we are flexible to adapt our algorithms to the specific needs of each client for seamless integration with their sales systems and enhanced user satisfaction.

We take an iterative approach to navigating our success journey with the client. Starting from simple ML solutions, we move gradually to implementing more sophisticated and powerful GPU-based recommender systems capable of handling vast amounts of data to achieve substantially improved business metrics.
Through this phased process, we help clients unlock new margin opportunities, leveraging the increased ROI to move to more powerful and complex computational models as early as the MVP pilot stage.

Apart from recommending related or complementary products, we also focus on promoting familiar brand products with higher margins and optimum end-user prices, to maximize the value we deliver. This approach drives both retail sales and profit margins to the advantage of the business and customers alike.

We also place great emphasis on analyzing customer behavior and forecasting demand to create personalized solutions that foster customer loyalty and engagement. Beyond improving current performance, our recommender models contribute to longer-term business growth by providing insight into market trends and the target audience's preferences.

The BaOne bespoke recommendation model is designed to identify products that are most likely to be purchased by a particular customer, rather than a segment or cluster or customers. Our recommendations are configured to meet personal preferences across various touchpoints throughout the customer journey. Examples include personalized next-purchase recommendations, frequently-bought-together feature or optimized listings.

BAONE APPROACH

  • Collect and analyze data, identify key business metrics, RFM analysis, cohort analysis
  • Optimize the algorithm and form hypotheses for the specific business case
  • Customize, refine and optimize the recommendation model for specific placements (cart, product page, listing, etc.)
  • Launch models and collect feedback (retraining, modifications)
  • Data is processed by machine learning algorithms using business-specific filters, limitations and rules. The resulting AI model-based recommendations come in three types:
    • Product-to-Product – based on user activities
    • Customer-to-Product – based on a combination of user activities (viewing photos, searching)
    • Customer-to-Customer – based on activities of another user with a similar shopping history
  • Build both on-prem and SaaS-based recommender systems
  • Support pilot implementation and A/B model testing

CASE STUDY: BUILDING A PRODUCT RECOMMENDATION MODEL FOR A MAJOR FOOD RETAILER

Customer

A major food retailer with over 900 stores, 13 million active customers and 100 million purchases

Why use an AI solution

With its huge customer base, the retailer faced challenges in:
  • Providing personalized product recommendations
  • Measuring the impact of personal discounts
  • Managing the product matrix though targeted offers to customers interested in specific products

Project objectives

To boost:
  • Margin, by 1-2 p.p.
  • Average number of purchases, by 20-30%
  • Active product matrix, by 10%

Solution

We developed a hybrid recommendation model based on neural network embeddings and collaborative filtering to:
  • Leverage personal purchase histories and customer preferences, even with very large customer bases
  • Ensure continuous retraining on various user signals, such as purchases, searches, comparisons, cart activity, etc.
  • Predict future purchases based on purchase frequency

Value delivered

  • +1.83% increase in net product margin
  • 2.5x growth in conversion rate
  • +10% increase in activity across product matrix categories
  • Enabled management of personalized offers through customized channels, such as email, mobile apps, push notifications, SMS, etc.
  • Enabled segmentation management and user activation through offers based on RFM analysis

FAQ

  • Vitaly Baum
    A technology expert with over fifteen years of experience serving international companies.
    Specializes in digital transformation programs across various industries, with a special focus on implementing IT products for retail and e-commerce (both B2B and B2C), oil producers, OFS firms, insurance companies and pensions funds.
    innovations@baone.ae
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