We live in a time of information. The web presents us with new data every day to choose from, to make decisions, or to discover new opportunities we didn’t know of yet. Overseeing this big amount of information becomes more and more difficult. Recommender Systems based on purchase data are a very important step in the personalized marketing of tomorrow. Recommender Systems help us to manage big amounts of customer data and to extract preferences on the individual customers‘ level.

What is a Recommender System?

A recommender system, or recommendation engine, is a piece of software that filters available information from customers‘ past purchase history using different algorithms to suggest products or services the individual customer will most likely be interested in.

In the case of a new customer without any past history, the engine could display the best-selling items or products that are most profitable to the business.

One of the most popular kinds of recommendation engines is (Item-to-Item) Collaborative Filtering which was used by Amazon already in the early 2000’s.

You can also find more information about product recommendations or AI-based solutions for ecommerce in our posts The Value of AI-based Purchase Predictions and AI Is Changing Ecommerce.

Benefits of Recommender Systems in Marketing and Sales

In marketing and sales, recommender systems become more and more important for delivering a better service to the customers and increasing the revenue. The most essential benefits of recommendation engines in sales are:

  • Reducing Expenses: With AI-based recommendation engines, personalizing the user experience and customer journey becomes fast and simple, so you can reduce the workload of your staff and save money.
  • Higher Traffic: Using personalized email messages, a good recommendation engine brings more visitors to your shop or website.
  • Real-Time Recommendations: By considering the customer’s browsing history as well as current site usage, the AI-based recommendation engine can provide real-time product recommendations noticing even a slight change in shopping habits.
  • Engaging Shoppers: By personalized product recommendations, the customers can discover the product line more deeply and elaborately finding even such items they didn’t know they would like.
  • Appreciating customers: Individualized recommendations show your customers how valuable they are to you as a person, eventually increasing their loyalty.
  • Expanding Order Value: A personalized recommendation engine enhances the average order value per customer and boosts so the business revenue.
  • Managing Marketing and Inventory: A good recommendation engine can manage your marketing and inventory goals to advertise promotional or overstocked products.
  • Product Bundling: Last but not least, by building sets of multiple products, so called product bundles, your marketing can become more efficient, offering the customer greater value for a better price.

Product Bundling Strategies Based on Recommendation Systems

For creating successful product sets, a good product bundling strategy is required. The most important steps in such a development process are:

  1. Analyzing your current sales
  2. Choosing the most popular product combinations
  3. Bundling those products to sets
  4. Mixing them with related items
  5. Offering a competitional discount

Clearly, many of the tasks in the list above benefit from a data-driven or machine learning approaches. There exist various algorithms tailored towards market basket data analysis. One particular example is Frequent Item Set Mining by running the Apriori algorithm. You can also learn more about product bundling strategies, e.g., how Microsoft used them in the early 90s, in the following article: The Joy of Bundling

Combining Customer-level Purchase Prediction and Recommender Systems

At goedle.io, we have developed a platform that can precisely predict which customer is going to purchase another item in the future. Based on historic buying behavior, we train machine learning models and make predictions for new customers in real-time. Most recently, we have also started to combine these predictions with category or product predictions. I.e., we cannot only predict which customer is going to make an order in the future but also predict from which category this purchase is going to come from.

goedle.io’s dashboard allows you to see category-level purchase predictions for each customer. The screenshot in the above shows four different predictions for this particular customer on October 4, 2019. The predictions contain a general purchase likelihood (here medium) and three predictions for different product categories (Purchase Category 1 – 3).

Conclusion

AI-based Recommender Systems help your customers to find the products and services they need and to enjoy an individualized customer experience. On top of that, they are a great tool supporting you in optimizing your product bundling strategy to make your marketing more efficient.

Our team is happy to help you choose and establish an AI-based strategy for your Recommender Systems and an optimized product bundling strategy. For further information on purchase predictions and related topics, please send an email to info@goedle.io.

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