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ML-Based Personalization in the Age of Rising Consumer Expectations

Posted on: February 12, 2020 | Posted by: Sona

Think about the last time you went to your local grocery store. You’ve probably been there hundreds of times. You likely know where to find almost anything in almost any aisle. You go there because the pricing is affordable and the quality and variety of products are good. Plus, the store is conveniently located. It’s like a trifecta of all the things needed for great customer experience. 

If asked, you’d probably say you’re pretty loyal to your local grocery store and not tempted to switch.

But what if your local grocer offered everything else  — except for shopping carts? You’d probably be pretty tempted to switch grocers then! After all, a shopping cart is an integral expectation of the grocery-buying experience. There are enough choices for grocery stores that you will be willing to seek out another if your local grocer is not meeting your expectations.  

And although that’s an unusual example, this same story plays out every day at thousands of online retailers across the web. In fact, according to Brendan Witcher, VP, Principal Analyst at Forrester, 61% of users would not return to a site that didn’t offer a satisfactory customer experience. 

Brendan,  an expert on consumer behavior and technology trends in the commerce engagement space, was a guest speaker at a recent ZineOne webinar on ML-Based Personalization in the Age of Rising Consumer Expectations. During the webinar, he addressed topics such as:

  • The impact of personalization on customer experience
  • Real-life examples of personalization 
  • Recommendations for and a roadmap to 1:1 personalization

Below is a summary of Brendan’s key points and some nuggets from Forrester’s research that he shared during the webinar.

Personalization is a Priority

The impact that personalization has on customer behavior cannot be ignored. Fully 77% of consumers have chosen, recommended or paid more for a brand that provides a personalized service or experience, Brendan said. But what parts of the user experience are brands personalizing? 

According to a Forrester survey, three-fourths of brands are personalizing their website content, while over half of them personalize product offers and other promotions. Nearly half personalize product recommendations. Seventy-two percent of users surveyed rank “personalizing the customer experience” at the top of their list of opportunities to improve customer experiences.

Smart brands know that consumers are willing to pay a premium for a personalized experience, so they want to go out of their way to deliver one, and do so easily and affordably. However, there are issues in doing so. While 89% of retailers are investing in personalizing their customer experience, only 40% of consumers say that the information they get from retailers is relevant to their tastes and interests.

In other words, there is a personalization gap. It’s not enough to just slap a user’s name on an email and call it personalization. In the past, the way that personalization was achieved was by segmenting users. This is the wrong approach. Just because you know a single thing about them doesn’t mean that you can fill in the gaps and presume to know everything about them. 

According to Brendan, personalization based on segmentation provides the “wrong” experience for most of your customers. And if your personalization efforts aren’t delivering a valuable experience to the user, they’re just a waste of time — everyone’s time. 

Where Desire Meets Data

Now, this isn’t to say that companies and organizations simply don’t want to personalize the customer experience. But desire alone isn’t going to get you from where you are to where you want to be. 

You need to be able to intelligently leverage the data that’s coming in. It’s fine to be customer-obsessed, but you must also be data-led

Now, keep in mind that data-led does not mean data-driven: 

Data-driven is when you make a strategic decision and then use data to back it up and supplement it. 

Data-led is when you use data to make the strategic decision itself instead of being locked into preconceived notions of how this or that should be. 

The biggest customer-experience-focused brands: Amazon, Sephora, StitchFix — they’re all using data-led decision making, Brendan said.

Data Allows Us to See a 360-Degree View of the Customer

Data fills in the blanks – not just in terms of demographics like age, gender, and income, but also affinities, attitudes, and behaviors. It helps us better understand sentiments, opinions, environments, emotions, and beliefs. It paints an entire picture of the person, not just a segment. 

Not surprisingly, this information is deep, rich, and personalized to the user. It also requires a holistic, omnichannel view of the user. In order to fully function, it requires many different screens, channels, and technologies to communicate and share information with each other. The user, too, must be willing to share that information. Top-tier brands know this and are willing to provide things of value to the user in exchange for those details.

For example, Stitch Fix promises personalized, customized clothing matched to a user’s particular tastes and style in exchange for some relevant information about them. This is truly an individualized level of personalization. It’s not just zeroing in on a basic segment or two like age and gender. And it’s not making assumptions based on wide swaths of information like browsing patterns or pages visited. 

Instead, it’s looking at every touchpoint, measuring, listening, curating and carefully assessing this data to keep learning, keep improving and keep refining. ML (Machine Learning) is turning consumer preferences into full-on, one-on-one, relevant customer experiences. 

AI + Advanced Analytics Powered by ML = Optimized Decisions

Today, some savvy businesses and brands have begun looking to AI to help close the gap between personalization and relevancy. Now, with that being said, it’s not enough just to invest in the tools that can make this happen. It requires a complete alignment and strategy overhaul that combines culture, organization, technology, and metrics to become not just customer-obsessed, but data-led.

Fortunately, Brendan has several recommendations to help you do just that:

  1. Identify gaps in your company’s ability to deliver on customer expectations and operational excellence
  2. Review each digital touchpoint for its ability to both use and collect  customer data
  3. Take off those departmental blinders — think enterprise-wide
  4. Don’t try to “surprise and delight” your customers. Use digital technology to solve their pain points! 
  5. Adopt the right culture and organizational structure to support a data-led strategy

Taking the Next Steps Toward ML-Based Personalization 

It’s pretty clear from the webinar that machine-learning-based personalization isn’t just a trend or a fad — it’s here to stay. Having the right organizational mindset and analytical strategies in place to gather and act upon the data is only the first step. You need the right personalization tools to help you gather these insights and turn them into actionable recommendations powered by and led by data. 

At ZineOne, we provide this experience through AI-powered personalization which is relevant and engaging to users. Our intelligent customer engagement platform delivers real-time, dynamic personalization at every touchpoint, creating a true omnichannel journey tailored to the individual. 

Only by leveraging these insights across multiple consumer touch-points can you deliver the kind of value customers crave — the kind that leads to exceptional customer experience.

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