Signals: Identify and Get to Know Your Customers Better

April 10, 2017

identify anonymous visitors with signals


A lot of our conversations lately have been focused on providing the right content to the right person at the right spot in the buyer’s journey—marketing in the moment. This is not a simple task, given the explosive growth of customer data we have at our fingertips, driven by the various touch points across digital, social, mobile and traditional channels.

One of the challenges to marketing in the moment is bringing this data together across online and offline channels and across devices to identify individuals and the context in which they’re interacting with us. In other words, we need to take all of this big data we have available to us and turn it into small data—specific information that helps us to be more responsive to each individual.

It sounds complicated (and there are complicated algorithms and model training involved), but the concept is actually quite simple.

The Evolution of Matching

We are essentially talking about the evolution of matching. Anyone that works with marketing data is very familiar with this process. We’ve been associating individuals to households and contacts to businesses for decades, fixing truncations and abbreviations, filling data gaps in the customer profile, etc. The difference now is that, thanks to our partnership with Opera Solutions, makers of Signal Hub, this matching can be done across online and offline interactions, all in one platform, in the time requirements the business demands. This broadens what we know about our customers, allows us to identify them more quickly and in new ways, and improves our confidence in the match.

Your goal is to make sure you understand who your customers are, where they are in their buyer’s journey, and what needs they have so you can talk to them relevantly. For example, if you provide a content marketing platform, and you see a prospect Googling about how to manage content development, hitting your website, interacting with your social platforms, reading a Forbes article on managing a content team, etc., you can identify this prospect in your CRM and add the data to the record. Or, if the person is not in your CRM, you can save the information for later to make the connection when you are able.

Now, you know who this web visitor is and have a more accurate picture of the context in which she is interacting with your company. It’s probabilistic—you won’t know the identity of this person with 100% certainty until she self-identifies in some way—but you can have enough confidence in the match to speak to her contextually.

In this case, the data may tell you this person matches to Jane Smith at Acme Company A in your CRM. Since she last interacted with your brand, she has moved on to Widget Company B and moved up in title, acquiring a small content team. You’ll probably want to provide her with some content geared toward helping her learn about best practices in managing a content team, how to improve efficiencies in her development processes, etc. She’s not ready for, say, pricing information or case studies geared more toward helping her to evaluate specific solutions. Without the probabilistic match between your anonymous web visitor and Jane in your CRM, you would have none of this information with which to provide relevant messaging. Or, with a slow turnaround time on the data matching, you could miss the opportunity to speak to Jane when she’s actively looking for information.

Respect the Data

You might be thrilled to match an anonymous prospect with an email address (and you should be), but don’t jump the gun and email this person too soon. An email address is a valuable piece of data that will help you to match further valuable data points to the record—use it to this end. Your smart data matching does not mean it is a good idea to actually email this prospect. Remember: the goal is to be able to interact with individuals in a more human, relevant way. Emailing a prospect when he has not explicitly given you his email address is equivalent to cold calling him (ugh!), and he likely won’t appreciate it.

The same goes for other pieces of information you have gleaned about your customer. A good way to turn him off is to appear to be stalking his web browsing behavior, for example, and sending him a bunch of articles relevant to that activity. Respect the data, and use it in a way that helps your customer to achieve what he’s looking to achieve without being intrusive.

But…Hold Your Horses

This all sounds great, and you may want to jump right in. But the reality of it is, a lot of marketing organizations are not ready to implement this technology and these processes. Why? They’re not collecting the right data in the first place. This is referred to as the “sparse data” problem. There is a lot of data out there coming from many sources that, when combined, can provide us with great insights—but we’re not collecting or combining it today. Many marketers collect the obvious data points—name, email, etc.—but not all the available data points that would be useful in achieving their goals, such as geo location, browser ID, device ID, etc.

Not surprisingly, this problem can be remedied by increasing your data collection according to your specific goals—but don’t underestimate this endeavor. It’s a complex process that requires clearly-defined metrics and a strategic plan. Prioritize your data strategy, starting first with data housed or produced from internal platforms and applications, before moving on to partner and third-party data.

Simple, right?

The whole idea of recognizing customers across devices and identifying “unknown” to “knowns” has been somewhat overcomplicated. It’s a natural struggle to understand everything that technology can do considering all the choices and what that means for marketing. At the end of the day, signals and the Signal Hub platform make it easier for us to understand more about customers, including who they are, which helps to interact with them more contextually, in the moment.

If you want to learn out more about our approach to bringing human interaction back to marketing, including building an effective data and martech ecosystem, check out the 5 Pillars of Best-in-Class Marketing.