Customer Segmentation Is Not Personalization

A quick search on Twitter would reveal a string of hilarious and face palm-worthy #personalizationfail retail stories shared by consumers. From suggesting a collection of mosquito nets to someone who bought a net once to sending congratulatory messages for ‘bringing a new life into the world’ to people suffering from infertility, many brands have failed to crack the personalization code.

Source: Twitter

Sometimes a few brands fail to even insert the name of the consumer in their promotional emails even after storing a ton of personal data. And sometimes they would employ resources to track search archives, only to sell home loans and credit cards to those who searched these keywords for editorial research purposes only.

Should this be regarded as a failure of personalization strategy? Does that imply that it is a tool that might work for some brands and might not for a few? No, not really. Primarily because the concept hasn’t failed, it’s the implementation that did. The biggest contributor to these gaps in customer experiences is confusing segmentation with personalization.

Let’s debunk one of the biggest myths of the retail industry by breaking down how customer segmentation is NOT personalization.

Evolution of Segmentation

Marketing concepts such as segmentation and positioning were popularized in the 1940s and 1950s when professors like Wendell R. Smith started documenting marketing techniques through research papers, books and articles. But segmentation existed ever since trade practices started, albeit in an undefined form minus any labels.

The traces of modern marketing can be found in the 1700s and 1800s when retail shops offered private access to wealthy clients through backdoors & dedicated viewing rooms and kept the front display open to regular buyers. This was a way to target, segment and differentiate their businesses. In the 1700s, Josiah Wedgwood, an English industrialist used techniques like direct mail, free delivery, money back promises, discount schemes and physical catalogues in those times to expand his business.

The crude form of segmentation worked until the scope of business was restricted to the local market. As means of transportation developed and the effects of industrial revolution started showing up, the need for organized marketing techniques was realized. Thus began the era of demographic, behavioural, psychographic and geographic segmentation of consumers.

Source: Shutterstock

Car manufacturers like Renault, American Motors and Volkswagen started focusing on sales of small cars as psychographic need of people changed in the 1950s. McDonalds introduced ‘Speedee Service System’ in 1948 that paved way for self-service standardized modern fast-food chain of restaurants that are now mushrooming in every country. Starbucks started as a coffee bean seller in 1971. But transitioned into a premium coffee house in 1986 after changing its segmentation strategy.

These broad classifications worked for brands until the infusion of technology into retail. Access to internet changed the face of the retail industry dramatically. The growth of smartphones, digital media, and global ecommerce started influencing consumer buying behaviour. While retailers got access to an abundance of consumer data with just a click, buyers’ expectations climbed up exponentially. As a result, predictive personalization gained prominence in the retail industry.

Why Cohort Based Recommendations Don’t Work Anymore

While the term ‘personalization’ is thrown loosely around, not every retailer has managed to utilize its full potential. In most instances, ‘segmentation’ and ‘personalization’ are used interchangeably, despite both being two independent marketing techniques.

Traditional customer segmentation treats people as cohort – a group of people with similar tastes and interests. It focuses on categorizing, labelling and boxing people, instead of treating them as individuals with specific style preferences and needs. It offers a rather myopic view of categorizing based on limited data points that usually are just broad fragmented categories.

On the other hand, real personalization requires brands to invest in AI & automation and prioritize it (not just on paper) in order to individualize their strategy, understand shopper intent and provide relevant recommendations that add value in real-time.

When the two terms are interchanged, we hear cases like a customer being recommended mosquito nets after purchasing one a week back. Or a person waking up to a dozen of promotional emails about baking trays after buying an oven. These examples don’t imply that they aren’t ‘personalization’. It implies that they are ‘irrelevant personalization’, which would be regarded as a failure.

For example, out of the 100% oven buyers there might be a percentage that needs trays. However, sending all of them a prompt to buy ‘tray’ reflects that the brand has put everyone from that group under one category, instead of mapping their individual needs. This illustrates the outcome of confusing segmentation with personalization.

Brendan Witcher, VP & Principal Analyst at Forrester Research disclosed that 90% of organizations are going to invest in personalization but only 40% of consumers say that the information they get from a brand is relevant to them. He added that one of the reasons for this gap between brand’s effort and actual results is ‘segmentation’ in the garb of ‘personalization’ as it creates wrong experience for consumers.

Valuing Consumers’ Data

Consumers today are aware that their digital footprints are visible to brands that they interact with, directly or indirectly. After the Facebook privacy problems, people are more aware of their data privacy rights. They also understand that today’, a customized shopping experience can only be delivered if they allow brands to capture their data. So they happily give consent, allow mobile apps to access their personal data, accept ‘cookie’ policies, diligently fill out account information forms, rate shopping experience, provide feedback, share preferences and turn on their notifications.

In spite of doing all that, if brands fail to offer relevant recommendations, then it would lead to distrust and dissatisfaction. Consumers often wonder what exactly brands are doing with their data, if they are not using it to offer personalized service. The statistics mentioned in Accenture Strategy Global Consumer Pulse Research report released last year reflects this exact sentiment.

The report stated that US-based companies suffered a loss of $756 billion as customers switched to other brands. The reason for this switch was lack of trust and companies’ abysmal attempt at personalization. 79% of consumers were frustrated with the fact that some companies cannot be trusted to use their data correctly. Accenture’s Customer 2020 report also states that “the percentage of consumers saying their biggest frustrations with providers—failure to deliver on their promises, inefficient and slow customer service, and lack of interaction convenience—have remained consistent in the past few years.”

What is Real Personalization?

This shadow of frustration that’s hovering over customers can be swiftly removed if brands sincerely agree to invest in AI and technology to address customers’ needs. The emphasis is on the word ‘sincere’ because personalization can only bring good results if companies agree to prioritize this strategy and implement it properly.

Companies need to increase the value generated by customer experiences through automation and meticulously focus on metrics & initiatives that are truly relevant to customers with the help of analytics.

A good example that fits the above description is Adidas’ ‘Here to Create Legend’ 2018 campaign.

To celebrate 30 years of partnership with Boston Athletic Association, Adidas decided to generate 30, 000+ personalized videos (of each of the runners) in less than 24 hours. The sports brand used a mix of digital video technology, location-based technology and data to create these videos that were available for viewing after only few hours of the marathon concluding. Adidas relied on Idomoo’s Personalized Video as a Service (PVaaS) platform that made this amazing feat possible. Through video marketing and personalized content, the brand attracted 80,000 visitors on its website because of the campaign.

On paper, the very idea of producing more than thirty thousand videos in 24 hours sounds inconceivable. But Adidas managed to pull it because of the intent to deliver personalization to its users. It goes to prove that data and technology can assist in realizing any goal as long as brands have the foresight to envision a unique and relevant experience for its customers.

There was a time when recommending products was enough to impress buyers and convince them that the brand is at the top of their game when it comes to machine learning and AI in retail. But today companies need to personalize all point of contact, be it offline or online.  It is more than producing 30 search results for a burnt orange dress. It is all about providing custom-made relevant resolutions across all channels of sales, marketing and service. It is all about not letting irrelevant recommendations reach consumers’ inboxes. It is all about muting irrelevant recommendation based on an individual’s feedback. It is also about possessing the acumen to discard personalization that adds no value.

Optimum use of AI and automation enables brands to understand shopper intent and individualizing their offerings and service! If your interest is piqued, then get exclusive access to key insights on personalization and segmentation from this Vue.ai webinar on The New Retail Imperative. In this webinar, host Julia Dietmar, Chief Product Officer at Vue.ai and Guest Speaker Brendan Witcher, VP & Principal Analyst at Forrester Research talk about individualizing omnichannel experience using automation and AI.


Shweta is a freelance content contributor. An incurable introvert and professional sun-avoider, you'll often find her lost in thoughts, books, food or all three at once.


Increase SHOPPER ENGAGEMENT and reduce bounce rate through AI-powered Omnichannel Personalization