eCommerce Personalization vs. Individualization

November 10, 2016

Individualization is what industry analysts are calling eCommerce personalization 2.0. All too often, people associate personalization with product recommendations because that is what vendors have been selling and implementing over the past 15 years. But individualization is so much more than a basic product recommender. Individualization is the ability to deliver relevant, personal experiences for your customers across sessions, devices and touch points through personalized search and sort, promotions, and product and content recommendations deployed in real time throughout your commerce channels.

This evolution of the personalization industry is based on advances in big data and real-time technologies that allow for massive storage and computation. These systems also allow for real-time retrieval of individual customer data with new machine learning models that use and respond to this data. And the results are impressive: real-time individualization is driving dramatic increases in customer engagement, conversion and brand loyalty.

So, what are the key differences you should look for when selecting a solution to know if it is old-school personalization or new individualization?

Key Difference No. 1: Online Customer Data Hub
Does the solution you are evaluating collect data about individuals and use that data to drive algorithms? Old school product recommenders did not need a 1:1 profile because they were only using collective intelligence data to power collaborative filtering algorithms. Ask your vendor if they collect and store data about individual customers. More importantly, can they show you that data at the individual customer level and is that data accessible to you and your systems in real time via an API so you can use it in other marketing and sales systems?

Key Difference No. 2: Individual Data Mining
Does the solution extract information from the individual profile such as individual customer preference or behavioral segment membership (For example, developing preferences by category, sub-category, brand, style, etc.)? With access to individual data, the ability to better understand and categorize each customer based on behavior becomes a reality. Ask your vendor if they use their customer data to better understand and categorize each customer based on their behavior. Also ask if that data available to you and your systems in real time via an API so you can use it.

Key Difference No. 3: Individualization Algorithms
While collaborative filtering based on data from all your shoppers remains a powerful part of the modeling arsenal, the 2.0 systems will have new algorithms designed to take advantage of each customer’s data and individual behavior. These types of 1:1 algorithms were not possible to compute much less access in real time without the latest technology and real-time infrastructure. Ask your vendor if they have models that specifically use each customer’s data and ask them to explain how those work to drive a 1:1 experience.

Understanding the key differences between first generation personalization and next generation individualization could be game changing for your business. Simply implementing a product recommendation engine and calling it personalization is not going to meet the needs of your demanding customer or the competitive needs of your business, especially as your competitors are driving one-to-one individualized experiences on their websites and across devices.

To learn more about Kibo’s next generation individualization platform, check out this Real-Time Individualization overview.