As marketers, we use all sorts of data to form our messaging, tactics, and our overall strategies.
We can use firmographic data such as a job title, name of the company the lead works for, and the sector or industry. Other derivative information might also include the revenue band of the company, the size of the company, where it is based, and so on.
We can look at product holding and transactional data. Anyone looking at this will know not just the lead’s contact details and the profile of the company they work for but also what this customer has previously bought.
Moreover, interaction data in the form of lead scores have become increasingly popular. But a lead score is just a sign of how engaged people are with your marketing, not why they are so engaged with your marketing.
All those data sources provide rich and valuable insight into your customers and prospects. However, marketers are missing out on the next level in prospect and customer data: interest data.
What is interest data?
“Interest data” is exactly what it sounds like. It is the interests of your prospect or customer—collected and stored in various ways (a unique profile or in a CRM database) for actionable use by marketing, sales, and customer service teams.
Interests can legitimately be just about anything: people, places, organizations, concepts, products, events, and so on. Right now, I am interested in marketing automation (a concept), Salesforce (an organization), and idio for Salesforce (a product). You might be interested in Marc Benioff (a person) or Dreamforce 2015 (an event).
Your customers’ and prospects’ interests can be captured both explicitly and implicitly. Brands often survey their customer base to find out what makes them tick. The advent of social media means that the social graph of platforms such as Facebook can be used to gleans insights into what our most vociferous critics and advocates think and like doing. In both cases, however, the interests identified rely on explicit input by each person.
Another more accurate way of capturing prospect and customer insights is to look at the content they are consuming. The content we consume as buyers is hugely indicative of our current needs and evolving interests. Think about it… The fact that you clicked on this article already tells us that you’re at least a little interested in the topic of “interest data.”
Of course, one article alone isn’t enough to build an accurate picture of you, the reader, but if we were to track your reading arc around MarketingProfs, very soon we could build up an increasingly accurate picture of which topics are interesting to you and use that to identify your current concerns and needs (say, “machine learning,” “B2B marketing,” “marketing automation”).
As I wrote previously, machine learning technology such as content intelligence does this by automatically adding descriptive metadata to content and use that to capture the interests of the prospects and customers that consume content.
How can you use interest data?
So, once this interest data is captured, how can it be used to power better marketing and sales?
Optimize content marketing. By understanding your audience members’ interest as they engage with your content, you can identify the content topics driving the most engagement and which topics are not resonating with the audience at all. Therefore, you can optimize your editorial strategy to focus on the content topics which your audience finds most interesting.
Sales conversations. So, you’ve got a hot lead with an engagement score of a billion—so what? That score notifies you that the lead is engaged, but it doesn’t tell you what the lead is engaged about. Interest data helps you know what to talk about once you pick up the phone to a lead, improving the likelihood of a relevant and successful conversation.
Customer service conversations. Possessing customer interest data in a customer service context is an excellent way to prevent churn and mollify irate customers. How would customer service change in a retail bank if the reps knew that the caller had just become a first-time parent? Or what if the agent was able to calm down an irate customer who had a product issue with tickets to see particular sports game because the agent knew the customer was an avid Boston Celtics fan?
None of these could be revealed by looking at purchase history logs or knowing the caller’s date of birth, but all of these would be revealed if brands were to understand the topics that each customer had been reading about recently—and therefore what their most current interests are.
Better segmentation. You might have segmented your database around job titles or industries. Both of these are quite crude buckets within which to parse your database. No two heads of Digital is alike; they’re both humans with differing needs and interests. Segmenting by interest data (or job role plus interest data) means you can make your marketing messaging extra-relevant now that you know what content topic is going to be of most interest to that segment.
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Interest data provides an exciting new frontier for marketers to explore as they seek to develop better messaging, tactics, and strategies. Customer interests can be gleaned from surveys and mining social feeds for insight, but the best way is to look at the emerging interests involved by their content consumption.
Capturing and analyzing the interests among your customers will enable your organization to connect with buyers in a more relevant and meaningful way.