How the right ID graph can bring intelligence to cross-channel marketing personalization
What would happen if you sat down with a group of people and asked them to buy a pair of sneakers?
It seems straightforward enough, but in today’s world, it’s actually an interesting study. Even ten years ago, everyone likely would have approached the task in the same way — by going to a store and buying the sneakers. A few forward thinkers might have done it online through a laptop, but that’s it.
In the future, however, I’d be surprised if more than one person in the group made the purchase in the exact same way. The options today are as unique as each person. Between the number of channels and the number of devices through which to access those channels, not to mention the combination of channels and devices through which you can cycle along the way, the modern marketer’s challenge is keeping track of a buyer’s behavior on the path to purchase.
Understanding Customer Behavior in a Multi-Device, Multi-Channel World
The multi-device, multi-channel world in which we now live has created limitless possibilities for retailers and consumers alike. We now have more paths for engagement, more ways to make a purchase and more opportunities to collect valuable data.
But as a retail marketer, in order to capitalize on the benefits that come from having more engagement opportunities and more well-rounded data, you first need to connect the dots — something that’s far easier said than done.
Enter the ID graph. An ID graph ties together various identities and their associated activities across channels in order to understand a single customer’s behavior. In other words, it helps you visualize the one-to-many relationship between individual customers and the various devices and channels through which they interact with your brand.
Without an ID graph, there’s no way to understand a single customer’s behavior across channels and devices. As a result, if a customer interacts with your brand on email, Facebook and your website, you won’t be able to connect the dots to identify all of those behaviors as coming from a single person. Instead, all of those interactions will appear to come from separate people.
An ID graph allows you to connect those dots. Perhaps the best way to think of it is like a wheel, where the customer sits at the hub and all of their actions across channels become spokes off of that hub.
Not All ID Graphs Are Created Equal
ID graphs have quickly become table stakes for retail marketers due to the proliferation of channels and devices. But even though ID graphs are relatively new to retailers, they’re hardly a new concept.
The Ad Tech industry has used ID graphs for years now in order to compile information about different audiences’ online behaviors and use that information to (a) determine which ads to show each individual and (b) properly target the same person across multiple websites.
The media-style ID graph can definitely help in the retail world, but to truly understand customer behavior across channels and devices, you need an ID graph built for retail. Most ID graphs connect digital identifiers to email addresses, but what you really need to take your understanding of customers (and therefore your ability to personalize their experiences) to the next level is an ID graph that can tie together onsite behavior (i.e. how customers interact with your product catalog) to digital identifiers, which you can then tie to email addresses.
While most retailers do have all of that data separately, it’s typically not tethered together in a way that makes it actionable. But an ID graph that ties it all together makes a flexible chain of data, and once you have that chain, the sky’s the limit.
Connecting the Dots With an ID Graph Made for Retail
Having a retail-specific ID graph that ties together onsite behavior, digital identifiers and email addresses creates a web of information on every customer. It means that you can connect activities like purchases, browsing and cart behaviors and even email interaction metrics for each customer, and that linkage allows you to start at any point and get back to any of the others.
I like to think of the power of this type of ID graph like playing six degrees of Kevin Bacon. For those who have never played before, the concept is simple: You can pick any celebrity and connect them back to Kevin Bacon within six degrees (although it’s typically less). For example, Leonardo DiCaprio was in “Catch Me If You Can” with Martin Sheen, who was in “JFK” with Kevin Bacon. And Courteney Cox was in “Scream” with Neve Campbell, who was in “Wild Things” with Kevin Bacon.
Bringing it back to the world of retail marketing, the right ID graph essentially allows you to play six degrees of Kevin Bacon with your data. That’s because you can start with any point of interest — an audience, a specific product or category, a channel, etc. — and get back to any of the other points. And that linkage creates numerous opportunities. Consider the following:
Start with an Audience
Determine a specific group you want to target (e.g. customers with a high predicted lifetime value or customers who are at-risk). Once you have the group of people selected, you can then personalize the outreach by determining the best products to showcase based on their individual preferences and/or the best channel on which to engage with them.
Start with a Product
Decide which product(s) you want to feature (e.g. high performing products, products with low views but high conversion rates, etc.), and then find the best group of people with whom to share those products. For example, you might target customers with a high predicted affinity for the selected product(s) and reach them on any channel.
Start with a Channel
Select the channel on which you want to focus (e.g. email, Facebook, Google AdWords, etc.) and determine the audience to target and the product(s) to feature. This option presents a valuable opportunity to take personalization cross-channel and reach customers in new ways. With the right connection of data, you can even use behavior from one channel to inform efforts on another (e.g. target customers who are unlikely to open emails through display advertising or look at onsite activity and build a Facebook campaign for customers who haven’t purchased in X days).
So You Have an ID Graph? What To Do Next
Having an ID graph made for retail that tethers together behavioral data across devices and channels, including customer interactions with your product catalog, comes with several benefits. Most notably, this type of data allows you to:
- Go multi-channel with ease: If you can tie together individual customers’ behaviors across channels and devices, you can easily launch a personalized, cross-channel marketing campaign that feels seamless for customers.
- Boost raw intelligence: Even if you only want to speak to customers on certain channels, having the raw intelligence that a retail-specific ID graph provides can still offer significant value. That’s because you have more data (both volume and variety) about customers, which can help you better understand what they might be interested in and how you can best communicate with them.
- Reach the unreachable: If you only have customers’ email addresses and they unsubscribe from your emails, that’s it — you can’t reach them anymore. But if you have their email addresses and behavior data tied to their cookies, Facebook ID and Google ID, then you can suddenly reach your previously unreachable customers on channels outside of email.
I’ll leave you with one final question: If you had all of the customer data as described above at your fingertips and could start with a product and get to an audience or start with an audience and get to a product, how would that change your marketing strategy?