From Segmentation to Individualization – Part 1
When someone asks a marketer how to go about personalizing customer experiences, their first answer is likely to be targeting. For example, special promotions targeted at “new users”, or additional incentives on in-cart products for “cart abandoners”.
This kind of targeting works at a user segment level. Segmentation refers to the grouping of users into different cohorts with similar parameters. These parameters could be based on demographics (age, gender, location, income level, etc.), technographics (device, browser), behaviors (purchase history, search history), or psychographics (affinities, preferences, attitudes, values).
Standard personalization platforms work on rule-based targeting: you add rules to combine and create segments and target the right customer with the right products and services.
However, this does not allow for a deep understanding of the customer. Some percentage of individuals in each segment will be different from the rest in many ways, and to convert these users into customers, you need to go beyond rule-based segments.
How Traditional Marketing Approaches Personalization
Marketers can no longer ignore the power of personalization.
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The purchase decision of 86% of US consumers have been influenced in some way by personalization. At the same time, almost three-fourths of retailers say personalization has increased their sales.
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A whopping 91% of customers in North America and Europe are more likely to shop with brands that personalize experiences for them.
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In fact, 83% of consumers in North America and Europe are willing to share their personal data for a personalized experience.
A look at early adopters like Amazon is enough to tell you why users prefer personalized digital experiences.
It’s close to impossible to survive in today’s market if you are not using any personalization strategies. However, traditional marketing strategies have a blinkered view of personalization.
A common misconception is that adding product recommendations at various stages of customer journey is enough. But in reality, product recommendations are just one of the many aspects of personalization.
So, in the hurry to get on the personalization bandwagon, companies end up using quick-deployment options such as product recommendation widgets and audience segmentation-based personalization tools.
An important factor affecting the executive decision of purchasing personalization engines and related platforms is the cost. An AI-powered personalization engine may be considered expensive against widget-based options that can be as cheap as $9 per widget per month. It becomes easier for business executives to justify poor ROI against low costs than to invest more and place higher trust in one software.
In addition, most large businesses also suffer from legacy systems and poor tech stack consolidation. Multiple tools are purchased at different points of time for different reasons, and the potential of each of these tools is not exploited fully.
A quick look at the tech stack of a leading US fashion brand (source: BuiltWith) shows that they use 4 marketing automation tools, 3 analytics tools, and 2 personalization tools. Instead of spending money on 9 platforms, if the business consolidates and optimizes its tech stack, they would be able to not only save on the software overhead but also achieve higher ROIs from the platforms they actually use.
How Do Segmentation-based Personalization Tools Work?
Segmentation tools work on the simple principle of analyzing user data and putting each user in a segment with other users who display similar traits. These segments are based on demographics, technographics, interests and affinities, onsite behavior, relationship level, etc.
A single user can be in multiple segments—for example, a 42-year-old male interested in golf can be in 3 different segments (gender: male; age group: 36-45; income group: $100,000-$200,000, interests: golf).
At a simplistic level, this kind of segmentation works—there’s no denying that. So, when this individual comes to a website selling golfing accessories, they get targeted ads or offers based on their age group or gender or income level (the interest being irrelevant here as they are on a site of their interest already). However, when they visit a website selling t-shirts, their interest segment is also given equal weightage, and they may be shown t-shirts with golfing references first.
The above strategy can, of course, yield positive results in conversion rates in general. But what if the individual has no affinity to golf-reference t-shirts? What if they like to keep their sport interests separate from their fashion interests? That personal information cannot be understood by tools that simply use segmentation based on third-party data.
Why Segmentation Is Not Enough
Take Monica, a 28-year-old looking for evening dresses. She searches for “evening dresses” on a brand website and clicks through to the product page of a classy purple dress. A possible segment she has been grouped into by the site’s personalization tool would be: “age: 25 to 34” + “search for evening dress”.
The recommendations Monica receives on the product page will probably be different types of evening dresses, based on the most popular ones on the site or from the same brand as the product she is looking at—and these recommendations wouldn’t be wrong. This strategy still has a good chance of converting the user into a customer.
But consider this: Monica’s favorite color is purple. In fact, she has shopped for purple attires and accessories from the same website a few times in the past. This changes the whole way the recommendations should have been personalized for her, doesn’t it?
Imagine a personalized recommendation panel of visually similar products reading: We know you love purple, so check out these evening dresses!. And then, to add more layers to the probability of conversion, a second set of “complete the look” recommendations containing complementary products and accessories, and a third recommendation panel with trending or popular products from the same category.
This deep personalization strategy with multi-pronged product recommendations and content will have a higher chance of helping Monica find the right dress (and even some accessories to go with the dress) than the previous segment-based mode.
Don’t Let the Limitations of Segmentation Hold Your Business Back
An average personalization engine fails to process and integrate all data points available about an individual user, is dependent on segment rules, and is not capable of delivering the individualized experiences shoppers of today expect.
Individual-level data unification and 360-degree view of customers can only be achieved through ML-based platforms such as the Algonomy Personalization Suite. You need an algorithmic foundation and real-time analytical horsepower to provide 1:1 personalization that will facilitate better customer experiences and customer expectation management.
Read more about Algonomy Personalization Engine and how it can help your business.
This is part one of a three-part series on the importance of individualized customer experiences in eCommerce. The second part discusses why hyper-personalized customer experiences are the key to survival in eCommerce today.
