The Risk of an Unsuccessful 1:1 Personalization Effort
Knowing a customer—their purchasing patterns, likes and dislikes, and propensity to use discounts and rewards—has never been more fundamental to marketing strategies than it is today. In fact, 91 percent of consumers state they are more likely to shop with brands that recognize them by name, remember their preferences, and provide them with relevant offers and recommendations than those that don’t.
On the other hand, having too much customer-specific data can also steer people away. According to research by Gartner, each added data dimension used to curtail an offer increases relevance by 5 percent, but simultaneously increases the risk that the message will be perceived as creepy.
But what is ‘too much’ data. The reality is a failed personalization effort often stems not from the amount of data an enterprise uses, but rather a failure to identify the right data points to use to appeal to a customer. For instance:
Dan walks into his favorite retail location. The day before, he had scrolled through their fall jackets online, and placed a few options in his cart. When he enters the store, he receives a push notification from his customer loyalty app:
“Shoes are 25% off. Check out our latest offerings.”
Irrelevant to his current need, he disregards the notification and heads to the outerwear department. After searching around for a bit and failing to find what he was looking for, Dan decides to try somewhere else and exits the store without making a purchase. Three days later, he receives an email from the company:
“Looking to bundle up this fall? Buy one coat, get one 50% off.”
However, Dan already purchased a jacket at a different store, and so disregards the message.
Dan’s story is one that’s familiar to many customers and shows the risks of personalization gone wrong. While his customer loyalty app was able to recognize Dan was in the store, it failed to connect his earlier searches online to his current purchase desire, thus serving up an irrelevant offering. When the appropriate offer came up a few days later, it was too little too late: he had already made the purchase elsewhere.
Identifying “The Right Way” to Personalize
Humans have an average attention span of 8 seconds, and so it is critical for enterprises that every interaction entices a customer, and none contribute little value—or worse, negative value—to the customer. Luckily, the right approach to personalization can make a world of difference. Following are alternate approaches to personalization and their varying levels of effectiveness:
- The Segment-based Approach
Market segmentation has been around for decades and involves grouping consumers who share common traits and patterns so that enterprises can serve relevant content to smaller portions of their consumer base. However, with the surge of personalization technology, generalizing large groups of consumers does not satisfy the modern customer’s wants and needs.While market segmentation is a step in the right direction, it misses a key facet of true, 1:1 personalization: relevance. A recent Gartner survey found that 62 percent of consumers would unsubscribe or stop doing business with a company who sends irrelevant or annoying emails—a common occurrence when enterprises target large segments and deploy mass emails. Rather, an effective approach to personalization allows enterprises to curtail outreach to customer’s specific history and in-the-moment context to deliver value at every interaction.
- The All-In Approach
Moreover, enterprises can use technology solutions to leverage customer data and deliver individualized experiences; however, applying every possible data dimension to each interaction creates a sense of weariness and distrust between the enterprise and the customer. About 27 percent of consumers say retailers have communicated with them in a way that felt too invasive, and almost two-thirds of those consumers claim that the brand had the information they didn’t share knowingly. While a big part of personalization is the ability to understand how gender, location, age, and behavior play into each customer’s buyer persona, it is equally critical to provide customers full transparency into efforts and protect their data. Such efforts can go a long way to build loyalty, rather than distrust, amongst customers.
- The AI-driven Optimization Approach
Alternatively, brands can choose to use an AI-powered solution that empowers customers to opt-in to personalization services, maintaining trust and transparency while simultaneously breaking down customer data silos. As long as the brand is transparent with how the information will be used, 83 percent of consumers are willing to share personal data to enable high levels of personalization. A successful brand-to-consumer interaction includes tailored help, which is when an enterprise leverages real-time, historical, and environmental data to effectively personalize the customer journey. Through AI-powered personalization technology, enterprises can provide each and every customer with a unique 1:1 engagement based on their unique needs and preferences.
With ZineOne, enterprises can start off their personalization efforts on the right foot. By removing data silos and empowering enterprises with insight into a customer’s historical, environmental, and real-time data—all delivered via a secure, transparent platform—our AI-powered Intelligent Customer Engagement (ICE) platform brings greater value and relevancy to every customer interaction. Schedule a demo to find out how we’re helping enterprises get personalization right the first time around.