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Why hyper-personalization is an essential marketing strategy

Using AI to personalize marketing strategies can be a business's golden ticket to improved customer experience. As a result, hyper-personalized marketing is essential.

Today's consumers expect businesses to personalize and craft messages and advertisements specifically for them. Customer personalization sufficed before, but now customers want hyper-personalization.  

Unlike its more generic predecessor -- customer personalization -- hyper-personalization depends on AI and real-time data to bring focus to content relevance, product information and service interactions into enterprise-customer contacts.

Hyper-personalization marketing is the next natural step in personalized customer relations, but what it specifically entails is vague.

An example of old-school customer personalization would be putting the customer's first name at the top of every email or text. That's fine, but with AI, personalization can go much farther and become hyper-personalization.

The data that feeds into AI platforms comes from customer website visits. When visitors browse company websites -- whether they buy something or not -- they leave a trail. Any browser activity tracking software -- Google Analytics, for example -- logs data on customers' page choices, lingering time and points of entry and departure. When customers make a purchase, those transactions become machine learning input living alongside previous transactions, all of which have timestamps. This can say a lot about the customer -- for instance, if they tend to browse and buy on weekends or whether they responded to recent marketing content targeted at the customer, which helps gauge its effectiveness.

Businesses can retrace the customer's product browsing via the tracking software to understand which brands attract the customer. AI can learn more about the customer's buying tendencies than the customer might be aware of, and a recommendation engine can use this data to further improve the customer journey.

Short attention spans

Hyper-personalization marketing is becoming essential because businesses inundate consumers with messages, and customers give each message less than 10 seconds of their attention, on average. Consumers are so overwhelmed with content that the less work they have to do to find what they need, the better.

AI can learn more about the customer's buying tendencies than the customer might be aware of.

When the customer receives an ad or a notification offering exactly what they're looking for, the deal is practically done. When they receive a message anticipating their need before they've even begun looking for it, better still. Businesses that reach out proactively to their customers, not just to sell something but to focus on what the customer really needs, leave the customer with a positive feeling about the brand. AI can now deliver this level of predictive marketing; touchpoints with the buyer can be the result of behavioral cues, establishing a stronger bond between the buyer and the company.

Hyper-personalization marketing isn't just competing with other messages with first names and reminders of past purchases; it's generating messages that echo the customer's thoughts.

Some hyper-personalization home runs

  • Amazon is the pacesetter in hyper-personalization marketing -- at least in emails. Its recommendation engine is second to none. It serves up spot-on, AI-driven content and purchasing options based on the customer's buying history. Amazon's tracking of customer browsing activity on its site provides more hints about what the customer cares about that make it into the recommendation engine. Millions of Amazon's customers have had the experience of seeking out products on the site without buying, only to find precisely targeted suggestion emails waiting for them soon after.
  • Stitch Fix is an online clothing retailer that focuses on personal style. Buyers don't select clothing directly; instead, they interact one-on-one with stylists over the internet who determine their tastes and then ship hand-selected items to the customer, who returns anything they don't wish to keep. The service is data-driven, as what the customer keeps or returns generates a score of the stylist's success rate, which becomes machine learning input. This generates an increasingly focused profile, not only of the customer's tastes, but of their optimum price point. Machine learning crunches these numbers so that the recommendations continuously improve -- and the customer feels very personally attended to by the stylist, even though they never entered a store.
  • Netflix also has a recommendation engine that generates targeted emails and push notifications to its more than 100 million users, who get recommendations as soon as they open the app. Its recommendation engine accounts for more than 75% of site activity. One part of the AI platform tracks user content ratings, and the other part of it combines predictive analysis with behavioral patterns to match those ratings with users who have very similar customer profiles.

Getting started

Software for hyper-personalization marketing addresses many of its moving parts. This software can include a customer data platform (CDP), a machine learning engine, browser activity tracking, analytics tools, a targeted messaging system and other personalization tools in different combinations.

For instance, a CDP called Evergage offers behavioral analytics for personalizing customer journeys in real time. A Salesforce subsidiary, Evergage is popular across a range of organizations, including banking, retail and technology.

The Bronto Marketing Platform specializes in marketing resource optimization. Its personalization focuses on SMS, both the cheapest and most intimate customer messaging. Text messages are the most immediate form of non-verbal communication today, and people treat it more personally than email because they receive more personal messages on that channel than any other.

Nosto, an e-commerce personalization platform, aims to increase customer engagement by updating product and shopper data in real time and personalizing the webpage specifically for the customer who is browsing. For example, if the visitor is a first-time, repeat or past customer, the webpage adjusts recommendations accordingly. Its machine learning component is an intelligence engine that trained exclusively on e-commerce data.

Software alone won't do it. The success of any hyper-personalization marketing strategy ultimately depends on quality data collection at numerous points, including e-commerce data from previous customer purchases, online behavior data from tracked site visits and feedback data from the customer at many points, such as query, purchase and service.

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