By Graeme Grant

In one of his recent articles, Forbes contributor Howard Baldwin outlines 4 “gifts” big data needs in 2015:

  • better marketing
  • more relevance
  • perspective, and
  • patience

As I read his piece through a retail lens, I realized the retail industry has already received these gifts and is leading the way toward making big data an even bigger asset in 2015.

Better Marketing

Take Baldwin’s first gift, better marketing, which he explains by stating practitioners of big data need to market its value better than they have. I don’t see this problem in retail, where 2 of the biggest priorities are omni-channel and personalization. Every retailer I speak with knows that leveraging big data is vital to both. Consumers know retailers collect data based on each interaction, and every consumer expects this information to be put to good use to provide a relevant and valuable shopping experience. While it is definitely true that many retailers are struggling to meet these expectations, they at least know that big data is the key to doing it! I fear for any retailers that are not convinced of the value big data provides them on a practical level.

More Relevance

The second gift is relevance, which refers to the need for the output of big data initiatives to be “tangible insights” as opposed to “science experiments.” Said another way, big data has to be actionable, not just interesting. I see this problem come up frequently when retailers try to use last generation approaches with big data. For example, many retailers embark on big segmentation studies to become more customer-centric, and though the output does glean some insights to better understand their customers, it doesn’t help the marketer who is about to send out an email to their list. There are still too many questions that exist, like: Does it make sense to send the email? Should I send it to just a select group? What about those who aren’t in that select group? How should the content be different for each recipient?

The impulse behind the segmentation is the right one – to understand the customer – but the output is too intellectual and not tied closely enough to the tactical decisions marketers make every day. The real power of today’s predictive intelligence solutions is that they ARE directly tied to action, in real time and at scale.

What if you could send an email to everyone on your marketing list (a.k.a. a “full blast”), but have the content be uniquely tailored to each recipient so the content is as compelling as possible? Sound fanciful? Just ask Men’s Wearhouse or The Children’s Place how real and powerful this approach is!


Baldwin also discusses perspective, explaining that, “as with any technology, big data’s promise should be applied to business goals, not big data goals.” In the retail industry, one of the most common goals is to – obviously – meet and exceed consumer expectations for a personalized, relevant and valuable experience in order to drive sales. And with big data, retailers are looking for where personalization can move the needle. They have found many of these areas already – website personalization, email, mobile – but there is much more to come.


Lastly, Baldwin asks for the gift of patience, explaining how “two of the three most common problems in big data deployments are false starts and drains on IT resources” and that these issues can “taint the value of big data.” I have seen this dynamic play out in retail over the past several years. Many retailers have launched in-house big data initiatives because they don’t want to – as one retailer explained to me – “outsource their brain.” That sounds like a compelling argument, but it glosses over how hard it is to turn big data into more sales. The technical challenges are wildly different from what a retailer’s IT shop has faced before, the analytic challenges are daunting and require highly specialized and scarce resources, and tight alignment with the broader business goals is required in order to move the needle. Knowing this, it’s not surprising that these efforts inevitably fail.

However, I’ve seen this dynamic start to change over the past year as retailers increasingly realize that to do this right, they need an outside partner that focuses on predictive intelligence, has the technical and analytical chops to solve retailers’ problems, and will be laser-focused on those things that will drive a return on investment for them. Retailers that take this path are finding they don’t need “patience.” Rather, they need to move faster to widen their lead against their competitors.

Do you agree that retailers have received many of the gifts that Baldwin outlines? What do you think the best gifts are for big data in 2015?