As most online retailers today recognize, a personalized shopping experience is a must-have. Nearly nine out of 10 marketers (88%) say their customers expect individualized experiences. And according to Gartner, by the end of this year, organizations that have fully invested in online personalization will outsell those that haven’t by more than 30%.
Yet even though personalization is becoming the norm, particularly in e-commerce, we’ve all had those obnoxious experiences where a brand that should “know” us completely misses the mark. Your favorite retailer may email you about a big sale in a category you’ve never shown interest in (e.g., baby clothes if you’re not a parent, or lawn care if you live in a high-rise apartment). In your work life, you’ve likely gone to a company’s website, only to be hit with a promotion for a report you’ve already downloaded.
Dear Amazon, I bought a toilet seat because I needed one. Necessity, not desire. I do not collect them. I am not a toilet seat addict. No matter how temptingly you email me, I’m not going to think, oh go on then, just one more toilet seat, I’ll treat myself.
— Jac Rayner (@GirlFromBlupo) April 6, 2018
In the past, shoppers might not have batted an eye at these lackluster experiences, because all of their experiences looked like that. But that’s not the case today. With a wide array of options, consumers today can (and do!) take their business to places where they feel recognized, appreciated and valued as an individual.
So retailers have to do better.
Moving Up the Personalization Maturity Curve
Applying personalization across all relevant channels – so shoppers are recognized and can pick up where they left off – should be the goal. To do so, companies need to be able to: 1) track an individual’s behavior across different channels; 2) merge that information with pertinent customer data from other systems; 3) automatically interpret the data to determine affinities and intent; 4) house everything in a central place – creating a single, unified profile for each person; and 5) act on all of the data in real time.
But no company will be able to do all of this from the start. It’s important to crawl before you walk, and walk before you run.
The most important element – and an essential part of all five steps – is, of course, data. You can’t personalize an experience for an individual if you don’t know her. Get started by leveraging a next-generation personalization platform to start bringing in deep, contextual, real-time, accurate behavioral data from one digital channel, like your website. You can use this data to start ensuring that your shopping experiences in that channel are truly individualized.
Then, you can begin incorporating more data sources like in-store purchase data. And, tie in other digital channels like email and mobile. And, when you have the strategy, process and technology in place, move ahead to personalizing your call-center and in-store interactions.
Machine Learning Powers 1-to-1 Experiences
At Click Summit 2018, I’m looking forward to discussing the nuances of a winning personalization strategy — particularly when it comes to using machine learning to provide a unique experience for every shopper. It’s a topic I am passionate about; in fact, I devoted my new book to exploring this topic in detail.
When we discuss machine learning in the context of personalization, it means using computers to process vast amounts of data, in milliseconds, to make the best decision about what to show each person. Machine learning puts the vision of “The One to One Future,” which renowned customer experience experts Don Peppers and Martha Rogers, Ph.D., predicted in 1993, truly (and finally!) within reach – accessible to businesses of all sizes and across industries.
This is not something reserved solely for advanced personalizers. It can be a part of your crawl/walk/run strategy.
E-commerce marketers should already recognize the value of algorithms for product recommendations. Today, many retailers recommend products based on what other shoppers viewed or purchased. This may be helpful, but it is not individualized. These same retailers are accumulating a ton of information about each of their shoppers all the time.
By observing what a shopper is viewing and how she is engaging with pages on your site, you can infer her favorite brands or categories, her preferred price points, the colors that tend to grab her attention the most, and more. Machine learning can take all of this information, interpret it, and use it to determine the most relevant product recommendations for her specifically — not anyone else.
But machine learning can be used for more than just product recommendations on PDPs. Search results, category pages, site navigation and more can all be powered with machine-learning algorithms.
And with the right platform, algorithms can be created, customized and managed by marketers and other business users – no need to cede control to a “black box,” and create a large, time-consuming IT project.
I look forward to discussing 1-to-1 personalization and machine learning more at Click Summit 2018 in the session, “Goals, Use Cases, Techniques: How Personalization Changes Things.” In this session, you’ll learn about the nuances of a winning personalization strategy, and how to put it into practice with a group exercise on a sample website.
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