Customer driven marketing – a.k.a. customer directed marketing – can change your business. But first, you have to change how you look at your customers.
Customers have long been called the heart of every business, but they’ve moved into the driver’s seat when it comes to marketing. Forward-thinking companies are shifting to a customer driven marketing strategy. Using AI for marketing and implementing the digital twin concept is helping larger companies scale this new strategy quickly.
Responding to Customers as Individuals
It’s no surprise that consumers today drive the buying process; marketers have known for years that customer-centric approaches work far better than intrusive, one-size-fits-all commercial broadsides.
Now, though, the problem has become targeting each customer in a meaningful way. In the past, customers were segmented by static groups: age, location, employment, marital status, etc. Yet, if you think about it, that segmentation leaves a lot to be desired: the behavior, motivations, and purchase patterns for single male engineers in their mid-30s who live in Minneapolis are going to be widely different.
Clearly, a better type of personalization and segmentation is needed if we’re going to respond to customers as individuals.
In an ideal world, companies could use AI for marketing in a more personal way. Marketers could follow, understand, and target each individual customer. But even with the huge amounts of processing power and data available to us, this is not possible. And more importantly, privacy and security concerns make it undesirable. However, we can do better than the old static segmentation model.
We can use twins. Digital twins, that is.
What Is a Digital Twin?
The concept of the digital twin has been around for a long time. It’s not unique to marketing; in fact, you’re more likely to find it in manufacturing, engineering, and health research.
Essentially, a digital twin is a virtualized model of a physical entity. In research settings, companies use all the data at their disposal to create a virtual machine, process, person, or even body part. They then can run various test scenarios on the digital twin and see how it will likely react.
In the marketing world, digital twins represent the logical evolution of customer segmentation. Like other versions of the digital twin, the marketing digital twin is a collection of data that can be used to run tests and predict results. However, there’s a key difference in how the digital twin works.
Unlike static groups that only give a rough outline of what and where someone is, digital twins reflect the actions, behavior, thoughts, and feelings of the people in that group. Understanding these factors can make a powerful impact on the success of your marketing endeavors, starting with how well you personalize your approach.
Hyper Personalization with Digital Twins
What’s one of the key goals in customer driven marketing? The ultimate goal is enabling the customer to easily buy from you, when and where they are ready to make a purchase. And the most effective way to know when and how to best approach the customer is to understand:
- What they know
- What’s driving them
- What we can do to help them at that time
With the old segmentation model, this was extremely challenging. We could find the best message for that cohort as a group, but not for the individuals that make up the group.
A Digital Twin Example
Let’s return to our single male engineers for a moment. According to the old method, both should be receptive to the same message – say, buying a nicer car or TV. But if we looked closer, we might see that one of our engineers recently completed his degree and started his first engineering job. He’s probably not in the market for a big ticket item just yet. The other engineer, meanwhile, has been in his job for seven years – and he’s been looking at a lot of engagement ring information lately. Now there’s a guy who might be ready to upgrade his car or even his house.
Both of our engineers fit in the same demographic, but their digital twins are entirely different. That’s because digital twins are based on more types of data, including the treasure trove of insights provided by social media. They present a deeper, more accurate picture that considers consumers’ attitudes, habits, needs, motivations, relationships, social connections, and interests.
Not only are digital twins realistic, they’re also very dynamic. As people’s needs, attitudes, and behaviors change, they move in and out of digital twin groups. This provides a far more immediate picture of who someone is and where they are in their life.
Scaling with AI Bots
Understanding these digital twin cohorts means you can identify that special intersection of where someone is in their purchase journey, what they know, and what message will most appeal to them. You can also predict their behavior across key areas, such as conversion, purchase intent, brand choice, spend levels, response to various marketing activities, etc.
So, when you have digital twins set up, you’re in a much better position to pitch relevant messages to your customers at scale. You can understand their needs and drivers on a more personal level. But how is all of this information managed and delivered to you, the marketer?
To execute at scale, an AI-powered marketing bot is used.
Behind the scenes, the marketing bot is doing the grunt work – tracking and updating each dynamic cohort, modeling the next prospective action this group will take, and figuring out what stimulus will drive a desirable action. It also shifts the numbers of each twin – for example, if one digital twin goes from 122 customers to 400, it not only tracks this change but also reveals why it happened.
On a human level, the AI bot provides recommendations to the marketing team regarding what campaigns are most likely to succeed, how to prevent churn and other negative changes, and what actions to take to mitigate any migratory behavior.
In the AI-powered digital twin model, live campaign optimization and modification replace tedious manually-performed reviews and analyses. Rapid personalization, dynamic segmentation, and multiple targeted campaigns replace sub-performing advertising blasts. AI-driven recommendations, simulations, and analysis replace long, time-consuming test cycles.
Marketing hasn’t just become more agile, it’s also become a lot less labor-intensive.