Building brand loyalty in the age of hyper-personalization

With the constant evolution of consumer behavior and the ‘on-demand economy’, we are in an enduring race to keep up with shifting expectations. B2C and B2B customers alike have come to expect streamlined convenience and experience across all industries. We can meet these expectations with the aid of technologies that grant access to valuable information about customers. However, this puts the onus on marketers to demonstrate real added utility to customers in exchange for their willingness to share their data.

Power of personalization

One of the primary ways in which data-driven marketing will distinguish itself is with the fruition of hyper-personalization. We can already see personalization to a degree, even if we don’t create an account on a website or join any loyalty schemes. Tracking individual’s internet behavior with cookies allows companies to see what they are looking for, where they are looking for it, when they add something to their cart, and when they abandon their cart rather than carrying on to checkout.

Google ads uses programmatic bidding to choose which company’s advertisement is placed on your screen each time you use their website (often as retargeting ads), however individual companies should adopt their own version of this by building custom ads from within their own product offering that deliver contextualized content to each customer, such as customizing the artwork to their name, location, and interests. This can be delivered using dynamic content optimization software.

DCOs allow marketers to quickly adapt ads by separating assets within the design into individual components that are stored on a digital asset management platform. This permits the dynamic assembly of ads rather than a rigid design template, meaning thousands of variations of an ad can be delivered without expensive and time-consuming manual design. Using machine learning to identify where customers are in their journey, means that creative content can be highly contextualized in real-time for precise micro-moments within the customer journey – but on a massive scale in order to maximize the conversions. This can complement marketing efforts such as dynamic pricing and can also be used to adapt content for various channels and platforms, maximizing efficiency and speed-to-market.

Types of personalization

Two common forms of marketing personalization that we see are ‘recognize me’ and ‘help me’ marketing. ‘Recognize me’ marketing is the answer to ‘who am I?’. This can be done with basic content customization, such as saying “welcome back” to return customers, using the customer’s name in communications and discount codes shared with them on their birthday.

Today, being recognized is the minimum that most customers expect. ‘Help me’ marketing offers more assistance and anticipation of the customer’s needs. Many companies fail to demonstrate any real added value from the amount of information that they have about their customers. For example, if you regularly purchase clothing from a website, an AI product recommendation engine could build an idea of your preferences. This then gives the opportunity to customize the landing page that each customer sees with tailored recommendations, improving the likelihood of purchase and providing a smoother browsing experience for customers.

AI systems can take a training set of data that exemplifies the patterns you are interested in, and as more data is added they learn the meaning of it to eventually identify hidden patterns that provide new insights. Another example of them being used in retail is in natural language processing (NLP) chatbots. Rather than programming a customer service chat with a set list of questions and answers, NLP is a type of AI that learns from previous interactions with customers in order to build its knowledge of how people talk, the sentiment behind their questions, the common questions and concerns that people express, and how to answer these questions more fittingly. This gives customers the impression that they are speaking with a real person rather than a rigid machine and improves the chance of their query being answered satisfactorily.

AI chatbots also present the opportunity to deliver omnichannel customer service. Whether the customer makes contact on an app, website or in-store, these systems should all be connected to a central database that makes information about previous contact available wherever the customer next makes contact. This recognizes the customer’s previous experiences with the business and makes cross-channel interactions more streamlined.

Market leaders in personalization

Amazon has been at the forefront of personalization for years. It developed a collaborative filter in 1998 that identifies products that a customer will like based on the item ratings by similar customers. By taking a data set of items and users who have reacted to some of the items previously, Amazon can predict an individual’s like or dislike for products they have not yet seen. This means that they can recommend items that the customer is more likely to click on and buy. This can be done when browsing, on a product page, and after an item has been added to the cart. Amazon’s access to a large enough dataset to make this filter system is a huge advantage over competitors and is further bolstered by their range of products across various departments. However, many large companies have a high enough customer to product ratio to develop a similar recommendation engine. And yet, they still rely heavily on customers searching for products themselves and filtering thousands of results until they find something that they like. This is not only more arduous for the customer but also wastes the opportunity for the business to increase basket size and customer satisfaction.

Sephora has a similar level of personalization to Amazon, but a different method of delivery as a result of their physical and online presence. Their email marketing campaigns demonstrate how to reach out to customers to bring attention back to their stores – both online and physical. Sephora sends periodic emails to remind customers to replenish products that they have bought previously, to let them know about complementary products that they might like to try, and to retarget customers who have abandoned their cart at check-out, as well as proactive emails notifying customers about loyalty rewards. Sephora also utilizes mobile app services to provide a multi-channel experience. When app-users are in a Sephora store they can receive offer notifications on their phone, and they can scan products with their phone to read online reviews from other buyers.

Digital transformation is an integral element of successful hyper-personalization, and yet Adobe has highlighted the difficulties companies are having to make this a reality in-store. Deloitte has similarly emphasized the call for a ‘phygital’ experience in their recent report on the Digital Disruption of Retail, which blurs the lines between physical and digital spaces in order to build a cohesive omnichannel experience. This would not only provide a more engaging experience, but also bridge the information gap for businesses with customers who expect recognition across all channels.

Value of loyalty programs

Today’s savvier and more discerning consumers are less interested in loyalty programs that fail to create relevant offers and rewards. Although the retail industry has moved towards more personalized communications, coupons, and channels based on vast datasets, many loyalty programs are simply not keeping pace with the needs and expectations of today’s shopper. Segmenting your audience is no longer enough. Now, we are looking at calls for a segment of one approach that tailors communications to each customer.

According to the KPMG Customer Loyalty Report, 81% of millennials who sign up to a loyalty program end up spending more with that company, however, 78% of those surveyed would switch to another company if they offered a better loyalty program. Evidently, the value and relevance of the program rewards are deciding factors for potential members. Sephora’s Beauty Insider program shows how popular a tier-based system that recognizes customer loyalty by offering more impressive rewards can be.

With tailored rewards that members appreciate becoming more commonplace, companies must meet these standards or risk being disregarded by today’s consumers who have more programs to choose from. This is especially true during the pandemic, when we can see several loyalty programs simplifying their terms and conditions. For example, several companies (especially airlines) are extending expiration of points in order to keep up levels of engagement during quiet periods. Maintaining customer commitment to loyalty programs is crucial for understanding the behaviors that motivate customers and using this to drive repeat purchases and strong customer bases.

Maximizing on data

Whilst there is much talk about optimizing customer experience, customer-centricity and data-driven approaches, many companies are yet to make these concepts a reality. According Google, only 5% of brands believe they are using first-party data to create more relevant experiences for their customers, and 46% of brands said that their main barrier was a lack of understanding of data and how to use it. Customer data is the key to successfully delivering hyper-personalization, and yet we still see untapped value in many companies with the data that they already have.

Unquestionably innovation of personalization is a competition in which large conglomerates with larger datasets have the upper hand, but smaller players can still adopt leading best practices in order to measure expectations against their own offerings. Regardless of business size, bids to personalize the customer journey should be designed with an end-to-end overview. In-depth knowledge of the customer experience and journey is vital to building a truly impactful connection with each customer.