Supercharging Brand Loyalty With Your Own Customer Data
Written by ActionIQ’s Tamara Gruzbarg and originally published on June 14, 2018.
In an age when consumers are a click away from competitor offerings, brands are struggling to reinvent their entire approach to brand loyalty. They can no longer expect that, by default, a certain portion of new customers will convert to loyal ones. They must win that loyalty over and over again, with each customer transaction.
Actually, there are glimmers of hope for those who believed brand loyalty was dead. A 2018 study finds that even Millennials — commonly considered highly fickle customers — are becoming more loyal to brands than they were just a few years ago.
Still, almost all the advantages of the digital age have flowed to the consumer, with instant access to a seemingly endless array of choices. But now, forward-thinking marketers are learning from the likes of Netflix and Amazon how to retain today’s distracted customers with an asset they already possess— their own customer data.
Playing Catch-Up With Consumers
It is a simple idea, but not necessarily an easy one to put into practice. Many Fortune 500 brands still struggle with customer data that is siloed in disparate systems, or gathered into generic data warehouses that require lots of time — and scarce, expensive technical expertise — when it comes time to actually use that data.
Ironically, brands have made it easy for customers to get the insights they need on a self-service basis, but they forgot to provide the same level of service to their own marketing teams. Imagine giving shoppers access to a bunch of massive spreadsheets, expecting them to hunt and peck for the products they want, and then forcing them to write code to complete the purchase — or hire an expert to help them with these tasks. They would be out of business pretty fast.
Yet when it comes to using their own customer data, marketers operate with these kinds of barriers every day. To make do, they tend to respond in two ways:
- By using a small subset set of customer data to “batch-and-blast” messaging, which increases costs and can end up harming the brand; and
- By investing in scarce, expensive data analysts whenever they want to create a highly targeted, multi-channel campaign.
Besides the costs, an IT-heavy approach like this takes weeks or months. Meanwhile, opportunities marketers wanted to seize in the first place are fading fast, as their freshest, most recent data — the kind that can most accurately predict customer behavior — goes stale before it ever gets used.
A Holistic, Self-Service-Driven Approach
To catch up with consumers, retail marketers require a holistic approach to customer data, linking everything from ID matching, segmentation, modeling and activation to orchestration, testing and measurement. Along the way, retail marketers should be able to do as much of the work as possible themselves, just like a consumer searching for a product online. This includes:
- Connecting and unifying data. Marketers need access to all the customer data, in all its granularity, in an agile way. And all that data should be automatically matched to individual customer profiles;
- Self-service audience modeling. Markets should be able to create the attributes that define customer segments — like “active customer” or “frequent buyers” — themselves. And they should be able to use them right away, without waiting on IT;
- Actionable AI and analytics. Data analysts should have access to all the latest data seamlessly; they should spend as little time as possible prepping that data; and the insights they discover — for example, early churn signals or drivers for a customer to make a second purchase — should all be actionable instantly; and
- Self-service activation & orchestration. Marketers should have the power to intuitively build segments by working with all the customer data, including the latest predictive analytics — and then quickly execute with precise, cross-channel orchestration.
Customer Data, From Acquisition To Retention
Now let’s consider how such a holistic approach can drive personalized offers and messages to support the entire brand loyalty lifecycle.
When you understand your best customers, you can target campaigns to prospects who specifically share those traits. Perhaps in the past you have relied on audiences made up of consumers who’ve expressed an interest in a certain kind of product. But even your current customers are not created equal. Imagine how powerful a campaign becomes when you target consumers who share the same behavioral and demographic traits of a specific segment of your best customers.
Imagine how targeted you could make the message, and how much it would resonate with someone with a high likelihood to become a true loyalist of your brand. You can only build such a campaign when you have deep, granular data about all your customers and understand them very well.
For many brands, acquisition may only be a “sign-up” process, for example providing an email or signing up for a credit card. Now, imagine that you can convert them into active customers with campaigns that leverage the data gathered in the acquisition phase — demographic information, the channel through which they were acquired, browsing history, abandoned carts, etc. Then you can quickly capitalize on their interest — before it fades — with attractive offers informed by the new data you just garnered about them.
3. Repeat Purchase
Common wisdom says that 80% of consumers are one-time buyers, while 80% of sales come from the 20% who are repeat buyers. In other words, a consumer’s first repeat purchase is perhaps the most impactful in the brand loyalty lifecycle.
If you can quickly incorporate the data gathered during acquisition, activation and first purchase — e.g. demographics, seasonality, price point, product type, whether the purchase was a gift, etc. — you can craft even more personalized messages and offers to drive that all-important second purchase. Again, speed is of the essence, since recent purchase activity is “hotter” than historic data alone.
4. Grow Lifetime Value
To drive lifetime value, you can’t just keep selling the same items to the same customers. You have to broaden the relationship to include new products or product lines. That is much easier when you can draw on insights from the full, granular history of your relationship with that customer. But it becomes even more powerful when you supplement understanding with insights that draw on all customer data. This helps you more accurately predict how customers with certain traits are likely to respond to specific messages and offers.
Retention may look very different for a subscription service versus a retail brand. However, in both cases you cannot measure churn risk accurately if you only rely on out-of-the-box models. For example, one retail customer may be a churn risk if they have not made a purchase in a month, while another may only be at risk after three or six months. It depends on their individual shopping cycle. And in the same way, a deep understanding of individual at-risk customers enables you to deliver the kinds of enticing offers that will keep them coming back.
Do brands have to do more these days to retain consumers? Absolutely. But with a connected, holistic approach to customer data, they don’t necessarily have to waste sweat and effort stitching together data and marketing tools with tech-intensive workarounds.
Originally published on Retail TouchPoints, and linked to this article: Supercharging Brand Loyalty With Your Own Customer Data
Latest posts by ActionIQ Editorial Team (see all)
- Supercharge your martech stack to achieve personalization at scale - January 8, 2019
- Foggy Conditions: Three Reasons Out-of-the-Box Artificial Intelligence (AI) For Marketers Falls Short - December 14, 2018
- Think Tank: Five Steps to Win on Relevance, Not Just Price, This Holiday Season - December 11, 2018