Albert Einstein felt very strongly that all the complexities of nature could be understood through one theory. He spent 30 years trying to uncover what is now known as the unified field theory. Now, Einstein failed. Perhaps because maybe in nature, one single theory doesn’t exist.
While a single theory isn’t possible in nature, is it possible when examining other aspects of humanity? What about data? What about marketing? This is the question that drives the data team here at EverString–does a unified theory exist in the marketing world? Is there a formula or formula framework where multiple elements work together to predict a conversion event? We believe that there is.
To understand why we think this is possible, you first have to understand your data as a marketer.
The 3 Major Pillars of Marketing Data
Marketers naturally generate data. From activities in your marketing automation platforms, to the handoff in your CRM, to your content management system, you’re consistently generating massive amounts of data.
This data can be broken out into three pillars—content, audience, and actions.
- Content – What content did you send out? Who responded well to that content? Who didn’t respond well? These are the kinds of data points you can extract from looking at content metrics.
- Audience – This is data that is hidden in your CRM in different stages and in your marketing automation platform from different lead sources. This data can tell you what type of customers purchase from you and how they are engaging with your company.
- Actions – For each buyer, marketers are taking different actions. Maybe you put your buyers in a lead nurture campaign or maybe you serve them ads on multiple platforms. Perhaps you’re sending them straight to the sales team for a cold call. Whatever action you take, you then have data on how the lead responded to that action.
What to do with All of That Data
Marketers are sitting on a gold mine of data. But what can we do with all of this data? Therein lies the fun part.
At EverString, we brought in data-scientists with PHDs in mathematics and explained to them that marketing data was difficult to harness. We told them about MQLs, SQLs, and whitepapers. And then, they went to work on the data.
What is the job of a data scientist? The job of a data scientist is to find the hidden patterns that human beings are not able to find.
For example, as business minded humans, we have general intuitions about our target audience. Intuitions like “technology companies are the best fit for our product, or companies with this or that revenue can afford our product”. Anyone can come to an accurate conclusion around this. But what about looking a level deeper?
Marketers can look at the data we discussed (content, audience, and action data) and see patterns. You can identify that a series of converted leads saw this ebook or that ebook. You can see that your best sale executive was involved in the deal. And so on.
But what about the data patterns that humans can’t see?
What happens when you connect first party data (content, audience, and action data) and combine it with third party data—every single news event related to a company, what the individuals within that company are doing online, and the social activity of those people? What happens when you combine all of that data to look for patterns. We call this unlocking the data.
Unlocking the Data
Just as Einstein believed that there was one universal formula to explain nature, we at EverString think that there is a formula out there that can predict a conversion event.
Now Einstein failed, because, fundamentally in nature, maybe that single formula doesn’t exist. But can it exist in the marketing world? Is there a formula that can simulate a marketing world with multiple-elements to predict a conversion event?
I think every single data-driven marketer and every single company trying to help data-driven marketers should have this goal.
We’re moving in the right direction. With EverString, we are focused on defining an audience to predict conversions, and advising our customers to tailor their marketing efforts to the accounts that are statistically more likely to buy a product. But that’s only one variable.
To find this universal conversion formula, we are going to have to enter into a multi-variable world. We’re up to the challenge.