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How to Use Data and Customer Scoring Models to Create Perfect Personalization for eCommerce

October 1, 2020 Explorium Data Science Team AI Education

For years, the retail sector has fretted over the decline of brick-and-mortar stores, but thanks to the global pandemic, in-person shopping may have come to an end sooner than anyone could have imagined. With stores closing en masse and internet purchases soaring during the shutdown, there’s more competition than ever to provide an online experience that’s not only seamless but optimized and customized to the precise needs of each and every customer. 

To get this right, you need the right data combined with the right machine learning and customer scoring models.

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Creating the personal touch online 

Today’s consumers are spoiled for choice and quality. If they arrive on a site and don’t immediately see something that grabs their attention or speaks to them in some way, they’ll be gone again in seconds. We’re now at the point where people expect — without even thinking about it — a digital experience that’s set up exactly for what they’re looking for, without them having to do much digging (or scrolling). 

After all, everything we see online is quietly tailored to our preferences, from our social media news feeds to our Netflix recommendations to the news. No wonder personalization is such a top priority for eCommerce businesses. And while it can be tricky to get right, the rewards are certainly worth it. Research shows that customers are not only 4.5 times more likely to add an item to their shopping cart when it comes to a product recommendation, but 52% of them are happy to share personal information in order to improve their personalization experience. 

What is website personalization?

Website personalization means adapting your site dynamically depending on who is viewing it. Each visitor sees a version that reflects their needs, wants, and behaviors. For example, media sites might offer articles that reflect a person’s past viewing history. A travel site might present deals that fit with public holidays in the visitor’s country. Retailers will target offers based on things like demographic data, location and season, and past browsing or purchasing history. 

How customer scoring models help

Before you start thinking about how to create a perfectly personalized site, though, you need to consider what kind of customers you want to target. This is where customer scoring models come in.

Customer scoring refers to a set of metrics that help you predict how valuable any given customer will be to you in the long term – both in terms of how likely they are to spend money on your site in the short term and in terms of their lifetime value (LTV). This means you can quickly and easily score any given lead or visitor based on how likely they are to make a purchase, how quickly they are likely to get to the point of sale and how much they are likely to spend. 

Combined with other types of data on customer behavior and trends, you can then determine very quickly whether this visitor is the kind of person you really want to become a customer. Are they a good fit for your business? A big spender? Given to customer loyalty? Or are they prone to canceling subscriptions? Returning products? Writing a ton of negative reviews over minor issues when things don’t go their way?

Data science for website personalization

With that in mind, it’s time to start thinking about how you’ll create the perfectly tailored online experiences that make your products irresistible to your ideal customers. Which takes us on to machine learning. 

You can’t do personalization properly without data science. In particular, machine learning underpins product recommendation engines and gives you a way to build on predictive forecasting. 

Personalizing your product recommendations

When Spotify builds you a personalized playlist, Amazon recommends a related product, or Asos suggests a shirt to go with those pants, these types of individualized product recommendations are all driven by data science. The algorithm predicts what product you will be interested in — or what other products you will be interested in — based on data collected on past visitors that share your traits, combined with other contextual and external data sources. 

For example, let’s say your predictive models are based on segmentation by age, price point, intended use, and customer needs. You may already have a ton of website, marketing, and transactional data, which gives you a pretty good picture. However, by adding external data to the mix, particularly demographic and geospatial data, you can enrich this for far more nuanced insights. One of our own clients, GlassesUSA.com, used this exact approach to boost conversions, leading to a 15-20% increase in per-session value for their target segment. 

Build on your predictive forecasting

Predictive modeling can help you work out what’s likely to sell, when, and to what kinds of customers. It takes into account a range of external data types such as market trends, online searches, demographic data, sales histories, and economic indicators. 

When you combine these insights with product recommendations, you can really lift your personalization efforts to the next level. Now you know what kind of products visitors might be interested in before they do, you can make suggestions that go beyond the obvious and really catch people’s attention. 

What makes personalization difficult?

Figuring out exactly who each visitor is and what they want from your site is a fine art. To start with, you need to know what kind of data is relevant and actionable. You need to know where that information is kept (or how you’ll access it if it’s external) and how you will feed it into your machine learning platform. You need to have a way to track the impact of your personalization efforts so that you can keep improving your strategy.

What’s more, you need to know which customers to prioritize. It isn’t worth the effort to create a website that can support infinite versions and customizations for every market segmentation you can think of if only a handful of those segments are ever likely to buy anything. 

And that takes us back to customer scoring, of course. Combining your customer scoring efforts with your predictive modeling and recommendation engines means you know who you should personalize your site for — not just how you go about doing it. 

Final thoughts: getting the right data 

As we’ve seen, you can’t get to the point where you understand your customer well enough to make solid predictions without the right data. That goes for product recommendations and website personalization. It also goes for credit scoring. 

Your machine learning models drive this entire process — and they are only as good as the data you feed into them. Make sure that you opt for a platform that facilitates fast, easy connections to high quality, relevant, external data. That the system takes out the heavy lifting by automating simple tasks and harmonizing these data sources. That you have a way to connect to the most up-to-date information. The past six months have proven why it’s so important to have your finger on the pulse. When all your customers have moved online, no one wants to be that last store dependent on Main Street. 

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