Customer experience, digital Marketing: The Most Profitable Place for Big Data Analytics
There’s currently a big talent war being fought between old school advertising agencies and big tech companies like Google, Facebook and Twitter.

As April Dembosky outlined in her recent Financial Times article, both sides are snatching up talent with technical skills and strategic marketing sense, and so far the tech companies are doing a great job poaching talent that would have previously defaulted to the ad companies.

But why are the tech companies winning this war? Are today’s rising marketing stars so easily swayed by the promise of free beer in the break room? Or do the tech companies have an edge because modern marketing and analytics go hand in hand?

Here’s a better question: Why are tech companies even poaching top marketing talent in the first place?

Marketing + Analytics = Huge ROI

Sure, tech companies are becoming increasingly ad-friendly, but these companies really want marketers because they've realized marketing is the ripest untapped area for data mining and analytics.

This shouldn't come as much of a shock. The key promises of Big Data align perfectly with the key desires of marketers: they’re both hunting for measurable ROI and reliable outcomes. And unsurprisingly, when you combine these two philosophically aligned approaches, you see ROI that you just can’t get tossing analytics at any other corner of your company.

Think about it, IT spends most of its time identifying and eliminating inefficiencies. They notice a bug in their email solution that keeps some customers from receiving their password reset codes. These customers then have to call support services to reset their passwords, and every time they do your support center sends you a bill. By fixing this problem, IT saves their company decent money -- about US$ 6 - 8 for every unnecessary support center call they've now prevented.

These savings certainly add up, but consider the fact that one large retailer used analytics to reduce their shopping cart abandonment rate by 1/10 of 1 percent and that miniscule action alone generated an ROI of US$ 3 million.

Will fixing any single IT inefficiency produce the 500,000 call center deflections necessary to generate US$ 3 million in savings? And won’t finding and fixing marketing-oriented inefficiencies, like a confusing shopping cart, also produce call center deflections at the same time?

A Few Sad Stats

What happens when you don’t apply analytics to marketing?

You end up like one of the 500+ companies responding to a recent Econsultancy survey, where 81 percent of respondents reported “limited” or “no understanding” of why their customers leave their site without converting. An astounding 78 percent of respondents had no clue why their customers abandon their shopping carts!

This sort of ignorance may have been acceptable at one time, but in the era of Big Data these companies should be ashamed of themselves. They either aren’t collecting data, or they don’t know how to handle the data they’ve accumulated. Neither excuse holds any water these days when affordable data storage and powerful analytics solutions can automatically generate accurate answers to these questions.

If you’ve used either of these excuses anytime in the last decade, then you need to get on the marketing analytics boat, and fast. The ocean of data under you sure as hell isn’t shrinking.

With Analytics: The Bigger the Data, the Better Your Answers

Instead of fretting over expanding data, embrace it! The more valuable, useful data you can accumulate, the better marketing can do its job. Not only can you answer questions about abandonment, with marketing analytics you can answer other vital questions quickly and easily:

  • Why do visitors come to your website?
  • What are they looking for?
  • What drives them to make a purchase?

Evolving Beyond Stone-Aged Single-Channel Evaluations

What’s more, hard marketing data multiplies in value when you combine it with other data channels, even qualitative channels.

Listen, I’m a hard ROI kind of guy, and taken on its own I don’t care how many “stars” a customer gives your shopping cart’s ease of use. You’ll paint a much clearer picture of how you’re taking care of your customers by tracking subscription renewal rates, customer recommendations, and most importantly, whether that customer buys from you again.

When you combine hard-ROI data channels with softer metrics, those qualitative metrics firm up and you can determine:

  • If higher shopping cart ease-of-use ratings translate into bigger sales;
  • if low shopping cart ease-of-use ratings translate into cart abandonment; and
  • whether experiencing certain site features, tools, or recommendation engines produce higher or lower ease-of-use ratings from customers

Different metrics provide insight on their own, but your customers only really come to life, and your predictive capabilities only kick into high gear when you un-silo your data and start searching out multichannel correlations, especially when you start tracking wider-reaching customer behavioral data that goes beyond what they do on your website.

Integrated Data = Integrated Results

Is it still any wonder that tech has taken such an interest in marketing and that the ad agencies are doing their best to adapt their old school methods for the era of Big Data?

Not at all.

What is surprising is the extent to which these ad companies are scrambling to rebrand themselves in IT’s mold. As Dembosky cites in her article, some agencies are going so far as jettisoning the word “advertising” altogether.

This is more than just a cagey PR move to swipe a few bright young minds away from Google. It acknowledges the fact that marketing and IT are so clearly aligned that sometimes it’s hard to know where one begins and the other ends.

Title image courtesy of Peshkova (Shutterstock)

Editor's Note: Want to read more about how marketers are using big data analytics? See Paige O'Neill's Are Marketers Using Data to Its Full Potential? No Way