She left to go on a shopping trip, but she returned feeling misunderstood.
Her go-to drug store stopped selling one of her most frequently purchased products, and that caused her to look more closely at her store relationship. She realized the retailer didn’t seem to recognize all her other regular purchases. Volumizing shampoo and disposable razors, mints and contact lens solution—important items to her that revealed her unique needs. Yes, the retailer sent her occasional promotions, but they looked like the same thing everybody got.
It’s not an uncommon dilemma. As brands respond to perceived shopper behaviors and pivot their product mix accordingly, they must also pivot the way they engage their customers who are significantly affected by the changes. How can brands better understand and engage with their evolving customer base in a more human way, and what does that mean?
Let’s take a look.
Learning to Listen
Let’s say that in the case of our shopper, her favorite store’s inventory change caused it to reassess its customer data practices and messaging. In an effort to be more human, the retailer sought to learn who its shoppers are at precise moments and where they are in the buying process. To recognize unmet needs and, based on that understanding, engage its shoppers with empathy and value.
But where did it start? As marketers, we advise starting this journey through listening—listening to the data, the explicit and implicit data cues and customer signals. From there, they can understand what the customer values.
In this story, the retailer couldn’t hear the shopper because it didn’t have the right systems and processes to heed her signals. So, it installed a signal-based system to better listen to its customers’ cues, build predictive models that brought priority and focus to high-value customers, and engage them with hyper-personalized messaging around their unmet needs.
The result: Increased revenue through relevant messaging, marketing efficiency and new customer targeting and growth. Check out the full case study here: Fortune 100 Retailer Deploys Signal Hub to Drive Business.
This particular retailer may not be in the majority of organizations, however.
Most organizations want to understand their shoppers, but few have the tools to listen to the explicit and implicit data cues and customer signals. Just one-third of all U.S. marketers listed “improved customer understanding and targeting” as a top area of investment in the next three years, according to a recent report by Bain Capital. Less than one-third of marketing leaders (28%) included the integration of marketing and advertising technologies among their top-three priorities.
Here’s why this matters to you.
Listening Takes Humanity
Each customer has a different definition of what is valuable, and that value determination is directly related to the shopper’s place in the buyer’s journey. They aren’t seeking promotions or answers to needs they were trying to solve last week; they’re seeking answers to their immediate needs, and they have the technology to find those answers. But how do you find those customers seeking answers, let alone the answers themselves?
By listening—like you would to any individual that was interacting with your brand face to face.
What can you provide that makes the customer experience happier, easier, more gratifying? There are behavioral and data cues that indicate specific needs at different moments—such as a sudden frequency in shampoo purchases or the hour of day someone purchases candy—but you have to develop the intelligence to detect these subtle shifts, let alone deliver on the changing values.
In the buyer’s journey, the customer wants to be engaged according to his or her need at that moment in time in the journey.
3 Musts for Humanizing Your Brand and Understanding Your Customer
The challenge to achieving personalized communication, to understanding what the customer values, is humanizing all the incoming data. These questions bear the answer.
- Are you collecting the right data? Too much data benefits no one. What matters is data quality; it should produce intuitive patterns that tell you who the customers are, where they are in the buying process and their unmet needs. Screen out all that data noise and listen for small bits of behavioral insights, or “small data.” For example, thousands of signals, such as engagement, purchase patterns and preferred communication channels, can describe individual customers. Those signals can be organized into hundreds of cues that detect what these customers would likely do in different situations.
- How do you know if you’re listening? If you aren’t pulling from your data the intelligence upon which you can confidently act, then you do not have the correct listening systems and processes. Lots of organizations don’t. It’s a sizeable investment, so most rely on third parties. The most effective models include layers of artificial intelligence and machine learning that support rules-based engines. Good intelligence will reveal, for example, the probability of a customer accepting an email offer, changes in an individual’s lifecycle, or warning signs she may defect to another brand.
- How quickly can you act? The time between deciphering customer insights and engagement is tight, and the relevance of your communications diminishes rapidly if you miss that window. That is why effective intelligence models also integrate advertising and marketing systems, so all players are positioned to act in a timely way. As the models develop and decipher more “small data,” specific patterns emerge that enable the teams to anticipate needs, so they are poised to act in real time. A retailer can, for example, use its data signals to suggest the next best actions to take, sending customized offers to shoppers who were cutting back on trips.
Some of those shoppers may be debating whether to continue buying with a store that stopped carrying their favorite brands. What matters is the store starts engaging them like it genuinely cares—not simply see its brand through their eye, but see what they value through their eyes. And to hear it, loud and clear.
To learn how to extract intelligence from large amounts of data quickly, for human-quality engagement, you can read about the Harte Hanks relationship with Signal Hub.
About the Author
Scott Rhodes serves as head of Digital Delivery at Harte Hanks and a 20-year passionate veteran of the digital marketing and business consulting space. In his role, Scott oversees the global marketing platform teams, including Salesforce Marketing Cloud, Adobe Marketing Cloud, Eloqua, and Marketo. He also provides Harte Hanks clients with thought leadership and guidance on implementing digital marketing solutions to become best-in-class digital marketing organizations. Prior to Harte Hanks, Scott worked to support fortune 500 Brands’ digital transformations through other top agencies and startups in the marketing space, including Sapient | Razorfish and TMi (McCann Erickson). He has also has served as an advisor to technology firms, including Salesforce Marketing Cloud (ExactTarget).More Content by Scott Rhodes