USA, December 7, 2020 /EINPresswire.com/ -- 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, eCommerce retailers need the right data combined with the right machine learning and customer scoring models.

Creating a 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 sites dynamically depending on who is viewing it. Each visitor sees a version that reflects their needs, wants, and behaviors. 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 anyone starts thinking about how to create a perfectly personalized site, they need to consider what kind of customers they want to target. This is where customer scoring models come in.

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

Data science for website personalization
With that in mind, it’s time to start thinking about how to create perfectly tailored online experiences that make products irresistible to the ideal customers. Personalization can’t be done properly without data science. In particular, machine learning underpins product recommendation engines and gives a way to build on predictive forecasting.

Personalizing your product recommendations
When Spotify builds 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 predictive models are based on segmentation by age, price point, intended use, and customer needs. There may already be a ton of website, marketing, and transactional data, which gives a pretty good picture. However, by adding external data to the mix, particularly demographic and geospatial data, this can be enriched for far more nuanced insights. One of Explorium’s 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 to 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.

What makes personalization difficult?
Figuring out exactly who each visitor is and what they want from a site is a fine art. To start with, it means knowing what kind of data is relevant and actionable. This means knowing where that information is kept (or how it can be accessed if it’s external) and how it will feed into your machine learning platform. There needs to be a way to track the impact of personalization efforts in order to keep improving strategy.
What’s more, there is a 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 that can be thought of if only a handful of those segments are ever likely to buy anything. And that takes us back to customer scoring, of course.

Final thoughts: getting the right data
As we’ve seen, there is no way to get to the point where the customer is understood well enough to make solid predictions without the right data.
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. 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.

Explorium offers a first of its kind data science platform powered by augmented data discovery and feature engineering. By automatically connecting to thousands of external data sources and leveraging machine learning to distill the most impactful signals, the Explorium platform empowers data scientists and business leaders to drive

Shelby Blitz
Explorium
+972 58-733-3765
email us here

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