The New Age of Customer Loyalty
Yes, big data can help brands acquire new consumers, but it can also mitigate churn, writes Leo Burnett’s Chadi Saab
Marketing professor Byron Sharp’s maxim that brand penetration is the main factor in brand growth has been an indispensable resource for marketers for the past five years. While expanding penetration is a smart tactic for any marketer, successful brands have always tried to optimize both penetration and loyalty metrics. A recent report from GfK shows that less than half of the overall brand growth was explained by an increase in new customer acquisitions. The majority of brand growth, GfK concluded, was due to increased customer loyalty. Since this is a circular argument that will never go away, it is safer to focus on loyalty as well as penetration to achieve brand growth.
While the notion of brand loyalty is straight forward, not all loyalty is created equal. According to McKinsey & Co, loyalty comes in two forms: “active loyalty,” wherein customers not only to stick to, but also recommend your brand; and “passive loyalty,” where customers keep purchasing the brand out of habit, but without committing to it. It goes without saying that we all aspire for our customers to be active loyalists. The major question is how to achieve that.
In order to nurture active brand loyalty, we’re required to find ways to build a deeper connection with our customers and provide them with more value. Thanks to the digital revolution, we can aggregate knowledge and cultivate a deeper understanding of our customers through data. But as you have guessed, data in its own right is rarely a solution to anything; it needs to be accompanied with the right strategy that allows us to take advantage of the data in hand.
Below are three strategies in which data can help us build more loyal customers.
Create Deeper Connections With Your Customers
By understanding each individual’s needs, we will be able to find ways to improve our communication and be more relevant to consumers. Many brands today have rich databases brimming with specific customer attributes. Now imagine combining a brand’s CRM database with third-party data sources to reveal even more psychographic details and insights about those customers. The combination could help us customize and serve them messages tailored for their needs and interests.
For example, a food company is trying to lure infrequent brand buyers with a message that its product tastes delicious. This effort could increase short-term sales. But what if we aggregated these customers’ data with a third-party data source? We might learn that a significant percentage of this audience is interested in health and nutritional benefits. So highlighting the product’s health benefits could increase adoption and relevance and therefore increase brand loyalty.
Help Your Customers Make Better Decisions
Providing customers with information that helps them save money, and feel more certain about their next potential purchase, is essential for building loyalty. Word of mouth is still the most popular method of recommendation for consumers, and customer reviews drive sales on e-commerce and review sites. But as online reviews become more abundant, and their reliability sometimes called into question, the impact of these broad user reviews could start to fade.
Data today can help us deliver the right review, or even connect similar customers to one another, to help spread the word about a product or service. For example, we can connect an owner of an older smartphone model who is a heavy user of the camera feature with like-minded users of the most recent device. How? By tailoring the web experience through dynamic content; creating a reward-based customer outreach program; or even by simple social profiling. Our scope as a creative agency today expands to social community management and interactive websites. We can integrate this approach as part of a larger social media strategy that impacts the lower part of the funnel.
Proactively Identify and Address Customer Dissatisfaction
Big Data can also help us identify specific customer behaviors that signal when a customer is likely to shift to a competitor. Let’s say a smartphone brand is able to predict churn if it notices consumers are not updating the operating system, or are not using their instant messaging services as often, or have visited the product-support section on its website. By correlating this data, the brand would be able to determine the relevance of these activities on customer churn. Spotted early enough, the brand could find ways to mitigate this churn by delivering value to these customers.
While big data continues to play a bigger role within marketing solutions, if you can close the loop on the customer-decision journey by providing value and creating stronger connections with your current customers, then not only will it mitigate your customer churn and increase repeated purchases, but it will have a spill-over effect on your market penetration strategy, which will ultimately lead to growth.
Chadi Saab is a strategy director at Leo Burnett.