What is AI Driven Personalization?
What comes to your mind when you think of AI-driven personalization in retail and commerce?
Do salespeople recognize you the minute you walk into the store and give recommendations based on your purchase history?
Perhaps the dressing rooms are equipped with smart displays which show you complementary products to the outfits you’ve already chosen to try on?
Maybe a conversational bot alerts you even before you decided to go shopping that your favorite brand of wine is in stock again and available on aisle 12?
We might achieve these futuristic-sounding goals in 5 years or so, but you also need to understand what’s actually possible today with AI-personalization of your customer experience.
What are the challenges that retail stores and e-commerce companies need to overcome to truly personalize their customers’ experience? Why is it that you’re not getting the hypertargeted and relevant shopping scenarios we highlighted above?
If you’re an intermediate or advanced practitioner interested in the latest AI research applications for personalization, we’ve created premium education to help you and your team understand the most recent and relevant breakthroughs. Click here to purchase our research summaries of how AI is being used in personalization.
Challenges to Customer Experience Personalization
We’ve teamed up with PayPal to highlight both the possibilities and the key technical challenges to implementing superior customer experience personalization, with a focus on retail and commerce applications.
You’re welcome to watch the webinar in full below, or simply read through this summary article of the key takeaways from our insight series.
In the real world, there are a number of challenges to providing a truly personalized experience to all your customers:
- Issues with identifying the user and location – the user needs to have the app installed with Location Services turned on OR a mobile version of the site needs to have access to the user’s location and user’s identity based on a cookie/IP/device. Data shows that most e-commerce business can only recognize fewer than 10% of their consumers.
- Issues with having access to the up-to-date local store inventory, which might be difficult in certain industries.
- Personalization challenge – it is not obvious which items should be recommended to a particular user at a particular time even when you have access to user’s purchase and view history.
When a customer enters the store, we don’t know the exact user intent and what she is likely to buy this time. Moreover, usually, we have no clue what the user’s favorite product is. Is it something she purchased last time in the physical store? Or is this that bottle of wine that she added to the shopping cart yesterday evening but hasn’t purchased yet? Or maybe this is yet another product that the customer often views on the site but never adds to the shopping cart? It’s unclear what action is most representative of user intent at any specific point in time.
If this in-depth educational content on using AI in marketing is useful for you, you can subscribe to our Enterprise AI mailing list to be alerted when we release new material.
Solving the Technical Barriers To Personalizing Customer Experience
There can be different approaches to solving the above-mentioned challenges, and in this article, we will present the approach suggested by Jetlore, the AI-driven predictive marketing company that is now part of the PayPal family.
Founded by Stanford PhDs, Jetlore focuses on solving the personalization challenge by learning from the full spectrum of user history and from all the attributes of user behavior across all the channels. With this approach, a company aggregates customer data from in-store purchases, online purchases, web & mobile views as well as search queries.
Moreover, the seller also considers the attributes of the products that a user has interacted with, including the price range, country of origin, type of product, etc. This aggregated data enables discovering of the important patterns, not visible otherwise, and thus providing true personalization by giving useful recommendations.
Such a personalization approach has several important features that further improve the quality of recommendations:
- User profiles are based on semantic attributes rather than specific products.
- Content can be scored and ranked for each user in real time.
- It is possible to learn promptly from users’ reactions and adapt the content accordingly.
Such an approach can be a very effective solution to the personalization challenge that provides all the customers with a highly personalized consistent experience.
Maximizing Your Customer Lifetime Value
It is important to remember that personalization has a number of far-reaching benefits for companies beyond just better customer experiences.
Top performing e-commerce businesses win in a highly competitive landscape by having a five times higher customer lifetime value than their competitors in the second quartile. Their high ROIs come from the loyalty of their customers, who appreciate the company’s ability to tailor to each individual user preferences.
Modern AI-driven personalization approaches need to move away from transactional commerce, where companies see a customer in the context of a single transaction. Nowadays, successful e-commerce businesses take a holistic view of their customers. They are ready to jeopardize some short-term conversion to build strong relationships with customers and thus, maximizing customer lifetime value.
Enjoy this article? Sign up for more updates on using AI in marketing.
We’ll let you know when we release more technical education.