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Your Big Data Treasure: More Valuable than Gold

Big data has been one of the most dominant marketing news stories of the decade. It always makes me think about that last scene in Raiders of the Lost Ark where the Ark of the Covenant is sent off to be stored in a giant warehouse full of thousands of crates. I’m sure we all cringe a little when we think of all the untold treasure wasting away in that mountain of boxes.

If your brand is like most others, it’s probably spent the last few years filling up little storage crates with all sorts of information treasure. And that’s great. But it’s time to break out the crowbars, open up all those little boxes, and consolidate the information in one big treasure chest. Because what that treasure can actually do when it’s all together might just exceed your wildest dreams.

If your data is in silos, it’s not as big as you think it is.

The information your brand already has is probably really great stuff. You get analytics reports for your website. You get a different report from Salesforce.com or Microsoft Dynamics CRM. Then you have your marketing automation tools like Marketo, HubSpot, or Silverpop, all with their own analytics. But the thing your brand is probably missing is the ability to aggregate and organize all that information in one place. Because once you do that, you’ll be able to make the critical leap from explicit decision making to implicit decision making.

Explicit versus implicit decision making

What’s the difference between explicit and implicit? Consider Amazon.com. At the basic level, it uses an explicit decision-making model: you click on the Books category, and you are taken to the Books homepage. But Amazon really succeeds in its implicit decision-making functionality that allows it to recommend books based on your viewing history. Rather than being tied to a very narrow filter, it can extrapolate information from a variety of sources. In this case, Amazon is implementing machine learning, using data science to make more accurate predictions to make your experience more relevant. Search helps you find; machine learning helps you decide and plan.

Implicit decision making used to be a playground reserved for big companies like Amazon and Netflix, whose analytic abilities and armies of data scientists set them apart from the rest of us. Data science technology advances will bring implicit decision-making models to your everyday marketing campaigns, giving your brand the ability to refine and optimize your conversion path in a more automated, accurate, and cost-effective way. Here are a couple of examples:

Dynamic newsletters

Let’s say your company has a highly segmented audience. When clients or customers sign up for your company’s newsletter, they establish their preferences explicitly: they choose what sorts of topics they want to hear about by filling out a form. The problem? Almost no one goes back to update their preferences, even if their interests change. With a dynamic newsletter, you could use your data to implicitly decide what content to serve which reader, based on a much larger and deeper information base (e.g., how many people came to the page from the same company, how long users engage with a particular piece of content, how often text is copied to a user’s clipboard, the behavior of the user on other sites, etc.).

Targeted calls to action (CTAs)

Let’s look at the website for this multi-segmented company. If you visit a particular page, you might find a short list of recommended content. Under an explicit decision-making model, those recommended pages would be set in stone, but if we applied implicit decision making, we could customize that list based on a much broader and more comprehensive database.

Why is this such a big deal?

Implicit decision making takes advantage of the most important truth about customers: That they are not X’s and O’s that only fall into one category or another. It recognizes that people’s interests and needs are spread across the spectrum.

Dynamic newsletters and targeted CTAs are just the tip of the iceberg. By combining existing data sources with machine learning tools, relevant content can be served up to your users at every stage of the customer journey. There are opportunities in e-commerce, advertising, videos, customer relations, call centers, social media…the list goes on and on.

Is your data stuck in a bunch of little crates? Ready to unpack those boxes, pour it all out onto the floor, and organize it? I would love to talk to you about it.