Measuring Results
By Perry Rearick, Chief Editor, Follow Your Buyer
The final piece to the Follow Your Buyer framework for B2B marketing and sales is measuring results.
In our modern digital, data-rich environment, the way organizations can measure results has undergone significant change, but it requires a dramatic shift in thinking from one that measures ready-to-buy leads and closed sales to one that has valuable business intelligence as an outcome.
Think Like a Data Analyst
When it comes to creating content that attracts and engages buyers through all stages of their buyer’s journey, I often encourage marketers to think like a publisher. When it comes to measuring results, I say, think like a data analyst.
Data analysts transform raw, messy and unorganized data into actionable insights and modern marketing communications delivers a lot of data. I’m not talking about vanity metrics like clicks and opens, but detailed target audience engagement data representing a real person interacting with marketing content.
Modern media organizations look more like technology companies than traditional trade publications, although there are still some holding tightly to traditional models. And if we’re working with a modern media partner, we can expect a great deal of engagement data. If we’re not getting it, well, maybe they’re not that modern.
The Data
Let’s take a minute to understand what engagement data is and is not. I recall a friend of mine once describing the results he received from trade pubs, those traditional organizational models.
After running a print ad in the magazine or a pop-up ad on their site, maybe even contributing an article, they would receive a list of subscribers along with company names and contact information. The number of names was contractually determined in advance, or at least expectations were established.
His marketing team would scrub the list from their own database to determine the number of new contacts they received. And they would continue to receive lists of subscribers and go through the scrubbing process until the publisher delivered the required number of new contacts. This is not the kind of data that delivers business intelligence, nor the kind to which I’m referring.
Engagement data that can be transformed into insights represents an action taken by a real person. In the case of marketing communications and advertising, the action is the person reading, listening to, or viewing marketing content.
When this event is captured along with a name, title, date and time, company, and yes, contact information, it becomes a building block for a database. When 100 or 1000 of these actions are collected, stored and organized, it becomes fertile ground for smart analysts to go to work. And this is when the fun begins!
Transforming Information Into Intelligence
The database I describe, even with all the details, is simply information. Marketing teams sharing it without explanation often leads to sales teams racing to contact the people with no context. Each contact is the same, a lead. When the contact is not open to speaking with a sales representative, someone must be blamed: marketing, the content, the data, the media company.
Transforming raw information into actionable business intelligence is both art and science. It is a process that starts by asking “what” questions but ultimately leads us to asking “so what” questions.
The “what” questions help us clean and organize the data and filter out non-persons, the bots that crawl through websites and open our email blasts. We can also remove anyone who doesn’t fit the companies we’re targeting, those outside our industry. And we can customize the organization as we like, by company, title, timing, content interest, and the list goes on.
Now for the fun part, asking “so what” questions. It’s important to put some thinking into our “so what” questions. To do this, go back to the original business outcomes that our marketing and sales are intended to support.
Solution providers should develop what I’ll call critical business intelligence requirements (CBIR). This is unknown information that they seek to understand that supports their business outcomes. It may be best explained with an example.
Let’s say we are a business that produces lab equipment that aids in gene therapy characterization supporting the early drug development process. Our goal is to grow the business by 20% in the next year. Our research shows that 5% growth will come from improved product offerings to current customers. We need to achieve 15% growth from new customers, but we’re not sure who they are. An assumption we have is that small, university-supported research laboratories are conducting a significant amount of gene therapy research manually and our products can help them. But the size of this market is unknown.
The CBIR is establishing the size of this research market and determining if this enables us to meet our 15% gap in reaching our sales goal. We can then use a content marketing strategy to engage this audience where they are in their buyer’s journey with the issues they have.1 The more engagement data we collect and apply “so what” questions to, will help us refine the content, revealing more of the market, and perhaps even outperform our goal.
Over time, the “so what” question may become, when should I contact a buyer because the data indicates they are ready to take my call?
Helping Hand From Technology
Earlier I mentioned how bots can be troubling by showing up in our engagement data. But technology that is enabling buyers in the search efforts as well as media companies in the capture efforts is advancing at a pace that is difficult to keep up with. In the time it took you to read this, a dozen more smart technology, AI tools have probably been developed.
So, the ”how” of measuring results is undergoing much change. However, the “what” will remain the same, but it will happen more quickly and in an automated way. Organizations will be able to query engagement data with CBIR in real time essentially having technology initiate contact with a prospect.