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In marketing, we like to talk about customer journeys, but what we’re really talking about is customer lifecycle management (CLM)—the idea that a customer relationship can be broken into stages, from acquisition to engagement to purchase to loyal customer and, hopefully one day, brand advocate. Implicit in CLM is the idea that marketing will be in the driver’s seat for much of that journey.
In theory, customer lifecycle management is a great idea. In practice, it’s a bit trickier. Why? Let’s just say there are too many drivers.
The problem starts with the fact that many marketing organizations are siloed by channels. There is content marketing, paid marketing, email marketing, and so on. Each has their own unique set of data, objectives and metrics for success, and each is focused on their own performance and goals. The paid marketing team may be interested in return on advertising spend and improving conversion rates. The email marketing team typically obsesses about open rates and click-throughs. The content team focuses on audience reach and engagement measures like pageviews, conversions, social media shares, and time on page.
While each of these channel performance measurements is important, what gets lost is the importance of delivering timely, relevant, personalized experiences. Siloed teams with their channel-specific data struggle to target campaigns effectively because their decisions are based on the siloed data. As a result, customers end up living double (or multiple) lives, with each channel or team managing the customer differently.
To really get a holistic picture of a customer, you need to centralize your prospect and customer data from your website, emails, ads, and mobile apps. This is a critical step for effective CLM. Only when you stitch data fragments across sources into a single customer profile through identity resolution can you begin looking at the right things: behavioral data, first-party data, multichannel data.
Bringing this high-value data together from different silos into a single, smart hub is a big part of what a customer data platform (CDP) does, in effect allowing marketers to build customer segments in minutes instead of weeks. When high-value customer data is in one place, a CDP with built-in artificial intelligence and machine learning can add even more value by identifying which customers are engaged (and how much), separating loyal customers from those who are likely to churn, and building lookalike models based on your best existing customers.
Even more importantly, CDPs collect and analyze first-party behavioral data, which is pure gold for marketers. First-party behavioral data drives personalized experiences like those delivered by Netflix and Amazon and helps brands make meaningful content recommendations that move customers along the journey.
Journeys rarely follow a straight line and customer journeys are no exception. They can fork off unexpectedly, like when you get married or relocate for a new job. They can stop abruptly. (Covid-19, I’m looking at you). And those changes in direction manifest as changes in behavior. Certainly, the pandemic has radically changed consumer behavior, from how we buy our groceries (and what kinds of groceries we buy) to the kinds of books and articles we read.
Now, take those changes and view them from a strictly channel-specific perspective. Customer X (which I totally admit is me), might not be opening emails from Best Buy anymore. To the email marketing team, I look like a customer ready to churn. In reality, however, I’ve simply developed the habit of reading the subject line of the email and going directly to the website to research whatever product/promotion is mentioned in that subject line. In other words, I’m still very engaged with the brand, but I’ve changed my behavior.
To the online and email marketing teams, I look like two different people, in effect living out two very different lives. To a CDP, however, I’m a loyal customer who has simply shifted my behavior. So, instead of receiving email offers targeted to a customer winback segment or, worse, being sent back to the email prospecting bucket again, a CDP recognizes me across channels and allows all marketing teams to meet me where I am in my journey.
Companies that have success in customer lifecycle management don’t arrive there by accident. They’re guided by a desire to improve and change. Change can be scary for companies that have a set idea about how marketing should be done. I think of it as the inertia of experience. Email marketing teams learn to equate an incremental improvement in open rates with success. Paid marketing teams have historically leaned on data management platforms to optimize ad buys and improve targeting–a practice that is disappearing as browsers tighten their rules around privacy and data use. The changes that a CDP brings to CLM, whether it’s creating customer segments around behaviors rather than demographics or serving up customized slices of your website, can feel risky.
To remove that sense of risk, two things are needed: executive support and small, early victories. The first is self-explanatory. The second is more problematic, since marketing teams don’t know what they don’t know. In our experience, small but measurable wins can build trust and confidence in a CDP. For example, optimizing ad spend by suppressing known customers from an acquisition campaign that you’re running on Facebook or Google. Or setting up personalized modal experiences on your website to collect an email address and create that critical first-party relationship. Targeted projects like these show the value of a CDP in helping to support a more intentional and effective CLM strategy.
There is no golden rule for successful customer lifecycle management. Each industry is different, and each brand is different. The gold lies in collecting the right data, making it accessible to everyone and drawing meaningful insights from that data to create unique customer journeys for each of your customers.
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