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Is Data-Driven Marketing Compatible With Agile Marketing?

  |  June 18, 2013   |  Comments

It's important to develop clear and well-thought-out strategies for turning raw numbers into insights that can be quickly applied to a real-world setting.

As countless marketers have argued (myself included), the age of big data brings with it a slew of insights that can help create more focused and personalized campaigns. What's not often said is that "can" is both a central and conditional word.

Big data left in its rawest form without effective tools and teams for processing it leaves many marketing teams feeling like they're drowning in numbers, or even like they're "clogged" with data, unsure what their next move should be. This leads to cherry-picking of data, confirmation bias, and just blatantly missing key insights, all under the guise of "smarter marketing."

However, data-driven marketing isn't a completely lost cause; in fact, data is still a crucial part of any marketing strategy, and it's becoming more so all the time. But to really understand what the stats are telling you, it's important to develop clear and well-thought-out strategies for turning raw numbers into insights that can be quickly applied to a real-world setting.

One way to begin developing a framework from which to analyze raw data is through useful agile marketing practices. Let's dig a little deeper into the problems marketers encounter when trying to master big data, and what agile marketing can do about it.

Problems With Big Data-Driven Marketing

When you're working with big data, your data set is, well, big. In fact, it may very well be the demographics and behavior of several million people. That's why even giants like Facebook and Yahoo evaluate data in clusters, in which data is broken into many different parts and processed by collections of powerful servers. But even that can still be overwhelming, and the sheer mass of numbers can lead to or stem from the following related problems.

  1. Data falling through the gaps and lacking context. The classic example for this has become New Yorkers' tweets during Hurricane Sandy. Analyses show that Manhattanites were the most active and descriptive on Twitter during the storm, which could indicate that this area was the most severely affected. But this neglects the fact that the worst hit places lost power, Internet, and cellphone service, thereby barring them from the social networking site. Without context, government agencies using this data could improperly allocate resources for the next storm, potentially putting lives at risk. This is what's called signal error - when large gaps in information are overlooked - and it presents a real danger.
  2. A lack of statistical education. Just because we have the numbers, doesn't mean the majority of us know what to do with them. In fact, there aren't even enough developers who know to navigate the big data storage program, Hadoop, to go around, let alone to translate data into actionable insights for every company out there. Without proper knowledge or guidance, many marketers fall into the trap of confirmation bias, seeking out only the small data points within the larger sets that validate their hunches - the exact opposite of the scientific process. That may make us look good in the short run, but it doesn't make for a richly informed, long-term strategy.
  3. Unreasonable delays. Even when data is properly analyzed, too often its sheer mass makes for take a look at what the ride sharing company, Carsurfing, did when it noticed many of its users were looking for rides to the Burning Man festival in Nevada's Black Rock Desert. Rather than continuing to bore on with its current marketing goals, the company dropped everything and focused all of its energy on arranging over 800 rides to the event. Because the app was brand new, this was a great introduction to the service, and it also provided a wealth of customer feedback the company could then use to further adapt its product.
  4. Do a post-iteration analysis. Finally, even if you're doing it as you go, make sure to do an analysis of your marketing and data collection strategy at the end of every iteration - a kind of mini strategy and data audit to further refine your next steps and reprioritize your goals for the next iteration.

The Takeaway

Big data improperly processed and managed can actually do more harm than good, as it can distract teams from more qualitative approaches. However, data-driven marketing is incredibly powerful when combined with agile practices, which forces marketing teams to focus their goals and work more closely with IT teams to curate and interpret smaller sets of data and act upon insights. In this new world, data and agility should go hand-in-hand to create the most highly personalized customer experience possible. Data-driven agile marketing is just the thing.

Image on home page via Shutterstock.

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ABOUT THE AUTHOR

Adria Saracino

Adria Saracino is the head of outreach at Distilled, a creative online marketing agency. When not consulting on content strategy or leading her team of outreach warriors, you can find her writing about style on her personal fashion blog, The Emerald Closet.

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