Are your customer relationships healthy? Savvy retailers know that nurturing customers as individuals is key to engaging, keeping, and increasing them.
The concept of personalization isn’t new. We’ve all received a coupon from a store we’ve shopped at before. Yet, in our modern omnichannel landscape hyper-personalization in retail is the next-level experience that customers have come to expect.
Customers want to be seen as individuals and have their specific tastes catered to in real-time so that they’ll continue to shop with their favourite retailers.
What is hyper-personalization in retail?
At its core, hyper-personalization in retail means retailers use technology like AI and predictive analytics, to anticipate the needs of customers–even before they have them.
As Steve Jobs once said:
“Our job is to figure out what they [customers] are going to want before they do… People don’t know what they want until you show it to them… Our task is to read things that are not yet on the page.”
You may be asking: “Is that possible for my business?” Well, many top retailers are already excelling at hyper-personalization. They are even sharing how it’s working for them within the retail industry.
Retail personalization examples
Let’s take a birds-eye view of some retail personalization examples where leading retailers use data, AI, and predictive analytics in the real world.
1. Website Analysis
Web analysis is the process of tracking activity on the website. This means that retailers are able to provide a targeted shopping experience based on an individual’s website behaviour.
Depending on an individual’s website behaviour, international cosmetic retailer, Sephora, shows users items they recently viewed or its most popular products when a user gets a “no search result” pop-up when looking for a product.
This strategy paid off. The customer didn’t leave their website right away and kept potential buyers in the conversion funnel. This resulted in 30% add-to-cart rates for returning visitors.
2. Loyalty Programs
Loyalty programs offer rewards, discounts, and other special incentives as a way to attract and retain customers.
Nike, one of the largest athletic-footwear companies in the world, provides a very personalized shopping experience through its loyalty program, NikePlus. As a member, customers get access to a locally tailored assortment of products. They can reserve items to be stored in pickup lockers and use QR-code scanning for the availability of individual sizes and colours.
3. Timing Personalization
Timing personalization uses customer data to prioritize which products to promote based on the individual’s age, stage of life, and overall place in the life cycle of a retailer’s product mix.
Enfagrow makes formula for infants throughout their growing stages. When moms and moms-to-be sign up for the brand’s mailing list, Enfagrow collects information about the due date or the age of their baby. The company then uses that information to send age-specific, relevant sales emails to each individual mother.
Enfagrow stays connected by anticipating customer needs based on data about the child’s age, type of formula used, and frequency of purchase.
When omnichannel retailers tap into their individual customers’ wants by harnessing volumes of data at the most granular level, both customers and retailers benefit.
Top 3 ways hyper-personalization brings value to retailers
There is a direct line between providing a hyper-personalized experience for the customer to seeing an increased GMROI.
Here are just three ways retailers can benefit:
1. Increased profits
While a certain level of investment is required to get your business up to speed. Companies that excel at personalization generate 40% more revenue from those activities than their slower-growing counterparts.
Consumers are simply more likely to make a purchase from a brand that provides personalized experiences.
2. Differentiation in the marketplace
A highly personalized customer experience using proprietary data is hard for competitors to duplicate. It makes your company stand out.
It’s also a sustainable advantage. As more data is collected and built upon, AI-based software becomes more knowledgeable; providing retailers with the information needed to make more insightful data-based decisions about customers.
These insights can inform planning, purchasing, pricing, and even fulfillment and returns decisions. Cutting unnecessary costs and fine-tuning operations.
3. Customer loyalty
Most everyone appreciates that feeling of a friendly, warm, knowledgeable salesperson who is familiar with you and your needs. That salesperson learns your needs as a customer, and can even predict those needs in the future.
If done right, retailers can “capture” the essence of that friendly salesperson with hyper-personalization. It’s something leading retailers want to achieve because 84% of consumers say being treated like a person, not a number, is very important to winning their business.
If you are able to fill your customers’ needs and offer them their preferred products, why would they want to go to a competitor?
The value is clear. So, what are the driving forces to make it happen?
Big data + AI-driven Analytics = Hyper-personalization
Hyper-personalization is a courtship between big data and AI-driven advanced analytics technology.
For a long time retailers were hungry for data. When the digital revolution hit there was a race to develop data-sourcing avenues and capabilities. Today’s retailer is data-rich. In order to make use of such high volumes of data retailers are turning to advanced analytics technology.
This technology helps compile, sort, and parse data providing greater insight into the retail business.
By leveraging this technology retailers are able to create detailed profiles of customers and gain a deeper understanding of their preferences and behaviours.
Machine learning capabilities of AI-driven analytics can then take this information a step further. Today’s retail analytics software uses this information to generate profitable, customer-specific recommendations.
The key to advanced analytics is that hyper-personalization takes place in every part of the retail journey, not just when marketing to customers.
Before retailers even engage with a customer, they should be leveraging AI-driven analytics to align key processes to their ultimate goal of meeting customer demand. Optimal hyper-personalization in retail can only be achieved through a unified analytics approach to the business.
Alt tag: Graphic overview of AI-based analytics impact on hyper-personalization
For omnichannel retailers, big data and AI-based analytics solutions are involved in “behind the scenes” retail functions. They predict demand by using, among many other streams of data, the customer’s past shopping history to optimize their personal shopping experience.
Are you ready to know your customer better?
At this point, you may be eager to include hyper-personalization in your business strategy, but first, consider your must-haves in order to make it work.
As a retailer, you’ll need:
- Leadership and stakeholder buy-in
- Up-to-date and accurate data
- User-friendly website
- Business-specific analytics solution
If these pillars are not in place, your best intentions for a highly personalized approach will be ineffective.
It can feel overwhelming to compete with the leading omnichannel giants, but if you know the right steps to take you will be surprised at the competitive advantage gained.
Looking to get started? Discover How to get started with analytics in 5 easy steps.
A customer-centric approach pays
Today, retailers who create more authentic interactions with customers become industry leaders!
Hyper-personalization is a customer-centric approach that pays dividends in customer loyalty, repeat purchases, and upsells. Increasing business for retailers willing to make the investment.
Have more questions about hyper-personalization and how AI-driven predictive analytics play an important role? Contact our team.
At Retalon we provide software for predicting the future, automating high-value tasks, and fixing your entire commerce process—from the factory to the consumer.
