Real-Time Personalization: The Gift That Helps Gift Giving

The Christmas shopping season means big sales for retailers. And there have been no shortage of articles about the role personalization plays when it comes to selling to holiday season shoppers in order to maximize engagement and conversion. But frequently left out of this conversation are the limits of personalization during this period of gift giving when people are shopping for others instead of themselves.

Most of the time, when I login to an eCommerce retailer such as Amazon, I’m shopping for myself and my needs. This means the product recommendations and deal notices I’m shown will probably correspond to my tastes and buying habits. This is the kind of consumer activity that personalization platforms are built for.

When Personalization Doesn’t Work

But what happens when you login, not to buy something for yourself, but to purchase gifts for other people in your life? What does personalization have to offer a consumer in that situation? If a retailer can only leverage historical data on past user behavior, they’re ill-suited to meet the challenge of personalizing product recommendations for a consumer buying for other people in their lives.

In order to fully engage users who are not shopping for themselves, you need technology that allows you to leverage data as it’s being collected. What you need is real-time, in-session personalization.

For instance, a woman who visits a clothing retailer she’s purchased from before will be shown products related to the items she’s viewed and bought in the past. Also, the landing page she’ll be shown will be geared towards women’s clothing. But what if she’s there looking to buy something for her husband as a Christmas present? Then recommendations geared towards her personal fashion tastes will not be applicable in this gift-buying situation.

Real-Time Personalization to the Rescue

If the retailer uses real-time personalization technology that utilizes machine learning, they can better serve this gift-giving cohort and get them to convert. For example, you can use collaborative filtering recommendation algorithms to leverage other users’ engagement toward specific products. This strategy allows businesses to make recommendations based on other users’ past engagement and purchase behavior, instead of just relying on each specific user’s profile.

The New Testament tells us that “It is more blessed to give than to receive.” And with machine learning and real-time personalization, it has never been easier for retailers to suggest the right products for customers to give. This is a very blessed time, indeed.

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