METHOD 4 : MACHINE LEARNING & AI
LEVERAGING AI TO INCREASE UPSELLS AND CROSS-SELLS There’s much a retailer can take away from this example, and it IS possible to implement this work in a single quarter. You can make quick wins with data science if you have a clean data set and enough data to develop machine learning models. In terms of the current economic climate, operationalizing an entire machine learning program is a proactive approach to recession-proofing your business for the long-term. The blog post “Analytics & Data Science During a Recession: Maximizing Revenue in Hard Times,” by Scott Sanders, Ph.D., a leading data scientist, makes the point that some retailers are wasting unnecessary time and resources fine-tuning predictive forecasts when they should focus on leveraging machine learning models to optimize marketing investments.
Prioritize these machine learning models to improve purchase propensity: 1. Market Basket Analysis 2. Upsell Propensity 3. Cross-Sell Propensity 4. Feature Recommendation 5. Conversion Propensity
As demonstrated in this example, AI and machine learning will determine if it’s more profitable to recommend an upsell or cross-sell to a customer, and if your strategy should aim for basket size or basket frequency. You’ll also gain insights about which products to recommend during the checkout process and what to recommend during follow-up visits. The feature recommendation model is a dynamic option for its many valuable applications. If you sell a variety of products with varying features, this model can determine which features are most important to your customers. In addition, these insights lead to more personalized experiences by highlighting those features or showcasing similar products.
Data to Dollars
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