AI based hyper personalized product recommendations for every e-commerce retailer

product recommendations for retailer

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Hyper-personalization is the e-commerce holy grail

“In the online world, businesses have the opportunity to develop very deep relationships with customers. Both through accepting preferences of customers and then observing their purchase behaviour over time. In  that way you can get that individualised knowledge of the customer, and use that to accelerate their discovery process. If we can do that, then the customers are going to feel a deep loyalty to us, because we know them so well.”

Jeff Bezos - e-commerce pioneer and Amazon founder

Personalization is one of the most important topics in e-commerce marketing. The interest in personalization is growing. It significantly increases conversion, produces fuller shopping carts and it delivers a higher return on ad spend. But it increases customer satisfaction and customer lifetime value as well resulting in more retention.

In 2018, Amazon reported that 45% of their overall revenue came from cross and upsell through recommendations. Amazon’s secret lies in the AI-powered real-time product recommendations engine.

Hyper-personalization is the holy grail. Examples of AI based hyper personalized e-commerce applications include personalized recommendations, personalized search, product review insights and customer segmentation. Artificial Intelligence (AI), Machine learning (ML), Natural Language Processing (NLP) and Neural Networks (NN) are the techniques required to deliver this.

AI-based product recommendations are the best first step

Let’s zoom in on one of these examples; hyper-personalized product recommendations. This is the best starting point for the AI based e-commerce personalization journey.

There are many forms of product recommendations. In most cases these recommendations are implemented as widgets or pop-ups and can be placed anywhere on the e-commerce retailer’s website.
  • On a product detail page, products can be recommended that offer an alternative or a logical add-on to the product currently being considered.
  • On a homepage or a category homepage, trending products or products based on the personal browsing history of the visitor can be recommended.
  • On the check-out page the software can recommend items that have dropped in price, or products that are cheap and fast-moving. Or accessory products such as batteries can be suggested, all personalized to the identified taste and behaviour of the individual visitor.
Doing it properly requires sophisticated technology
The first step is to collect and effectively manage large quantities of complex data. For this, a sophisticated big data infrastructure is required.

Cutting-edge AI & ML is based on specialized techniques, such as supervised and unsupervised learning, word & image embeddings and attention within neural networks, bidirectional transformers, and ensembling and hyper-parameter tuning. Cutting-edge platforms stand out from the crowd, in that they can combine these technologies effectively, and in a scalable fashion.

Applying these techniques on large amounts of complex data, results in real-time insights in individual buying preferences. But also in customer segments, latent product features and how all these characteristics relate to each other. This is then the basis for formulating product recommendations with a high probability of conversion. These recommendations are ranked in order of expected probability of success. This order is subsequently rearranged in real-time based on personal preferences, and on the success of the recommendation on the actual e-commerce site. Recommendations that do not perform are therefore continuously filtered out. This is called real-time optimisation.

Widgets embedded within the e-commerce retailer’s website are populated with these optimised recommendations. Deploying these widgets is extremely simple; a small piece of JavaScript is embedded within the e-commerce platform.

In some cases, the e-commerce retailer wants to push certain specific products. Some platforms, such as CartUp AI allow e-commerce retailers, on top of their cutting-edge AI technology, to hard-wire some specific product associations. For example, always propose the Zelda game as one of the recommendations with a PlayStation console. This is specifically relevant when the e-tailer makes a marketing deal with one of their product suppliers. It is then possible to overrule the AI based recommendations, in favour of specific products.

Fortunately, this technology is now available for medium sized retailers

It took Amazon and other leading retailers decades to build and mature their tools. In 2021, the limits of long lead-times and high development costs have evaporated. These cutting-edge technologies are now within reach for many e-commerce retailers. All based on algorithms that are readily available, trained and validated.

The new generation of personalization software solutions, such as CartUp AI, are affordable, can be implemented in a matter of a few weeks, and have minimal IT impact. All nearly risk-free.

Only very few suppliers offer the real thing

There are several suppliers in the market who provide one or more services like personalized recommendations. Few of them apply the sophisticated techniques mentioned above. Most are rules-based, instead of AI-first, or focus on only a single application, such as search or segmentation. Rules-based systems differ greatly from AI-first platforms. The effectiveness of rules-based techniques is limited to the actual knowledge of professionals involved, and it requires manpower to constantly maintain. But also it is almost immediately out-of-date, as assortments and customer preferences change over time.

Often completer and more sophisticated AI-first platforms are only accessible within the context of a full platform solution, such as Salesforce, and are not platform agnostic. So, you may not be able to use it, unless your shop runs on Salesforce.
A supplier that stands out in this market is CartUp AI. CartUp AI offers all the technologies mentioned above, on a fully scalable. Their platform agnostic solution is easy to implement, can be procured with a risk-free guarantee, and has proven its power with customers, such as Intertoys in the Netherlands. For this launching customer CartUp AI is delivering 5 to 10% revenue increase within a couple of weeks with only an initial set of 3 recommendation widgets.
Act now and stand out in 2021!

AI-based hyper-personalization is increasingly relevant for e-commerce retailers. The use of necessary cutting-edge techniques is no longer limited to the big giants, such as Amazon, whose success is based on this technology. In 2021, how can a retailer really stand out and compete with peers and giants, and face the future with confidence, without embracing these technologies?

We believe the answer is to embrace these advanced personalization technologies. So, start now to significantly increase the success of your web shop, with the use of an AI e-commerce personalization platform.

By selecting the right solution, this can be affordable, offer a rapid payback, and it is easy to test in a small pilot. Start with product recommendations and expand your success with other AI-based features. Maximize the opportunity to boost your revenue and profit in 2021!

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