In Analytics
Summary: How does big data help marketing? Use the customer data you already have in support of your high-level marketing strategy and planning.

This project, applying big data insights to help marketing strategies, was a collaboration between Daniel O’Neil from The Understanding Group and Eric Kushner from Project Two Paths, Ltd.

It has never been easier to get or analyze data for your organization. Massive data sets that once were available only to the Fortune 500 are everywhere now. They are collected almost by default—and at minimal cost—by the systems that we use to run our companies. From Google Analytics to our merchant and fulfillment systems, customer-centered marketing strategies are just a spreadsheet away.

One potential downside to this explosion of data is the temptation to move away from story-driven marketing. If data is not applied wisely to the human narrative, marketing departments can be pushed into reactive, tactical activities against tiny bits of information.

Big Data Helps Marketing

We see this big data explosion as an opportunity for marketers. You can put data to work in support of your high-level marketing strategy and planning. It’s this simple:

  • Marketing teams know their audience and can tell stories about them.
  • Those stories should be testable with data.
  • Marketing teams can collaborate with data analysts to uncover compelling user stories that drive focused, specific marketing campaigns.

Recently Daniel (data analyst) and Eric (marketer) had an opportunity to apply this framework to a gaming company we’ll call GamerCo. GamerCo is for players who want to find opponents for online games. They asked us to determine how to maximize player engagement.

Getting Started: Creating Initial Theories

We started by examining the mechanics of GamerCo’s customer acquisition and revenue model. Revenue for GamerCo is largely driven by match frequency, so if customers aren’t playing, GamerCo isn’t making money. GamerCo had ten years of data about their customers, including player info, the games they played, and win-loss records.

We found that players who sign up for GamerCo have a wide range of usage patterns, from playing multiple times a week for years, to trying only a game or two. Our biggest initial finding was that a significant portion of accounts on GamerCo were inactive—that is, the player had signed up but had never actually played a game.

What did these players need to continue playing? And how could a marketing strategy help engage them?

To learn more about GamerCo’s users and their behavior, we interviewed stakeholders on the marketing and customer support teams. We also created accounts and played a few games ourselves. Based on our findings, we created several customer segments representing a kind of player behavior. We gave them titles that were human and memorable, for example:

  • Hey Big Spender: Players who deposit larger amounts initially are more likely to play again.
  • Power of First Impressions: The outcome of the first three games has an impact on long-term player value.
  • Interval Training: The shorter the pause between the first deposit and the first game, the greater the long-term player value.

The core concept behind the marketing analysis for GamerCo is an engagement funnel, where long-term, repeat game play was the desired end-state. We chose our theories with an eye towards inputs that could be turned into specific messaging campaigns. Our aim was to improve performance at targeted points in the engagement funnel, from initial site visit through early game play.

The Acid Test: Checking Theories Against User Data

Once we had our hypotheses, we examined user behavior data through three lenses:

  • How many theories could we actually test given the state of the data?
  • Did the data support or refute the theories?
  • What additional patterns emerged from the data analysis that can be applied in actionable marketing campaigns?

With the high quality of the data on the player platform, we could test almost all the theories. We found that about half were true. Three findings were particularly interesting:

  • The size of the initial deposit had a negligible impact on a player’s lifetime value.
  • A sizable number of depositors had never played a game.
  • There was a significant correlation between a player’s long-term engagement and their win-loss record in the first few games.

But the most notable finding was the discovery of an inflection point for lifetime value in new players. In other words, we found that players who played more than a certain number of games were dramatically more likely to become a long-term, engaged player versus someone with an inactive account.

Outcomes: Creating an Actionable Plan

Once we gathered the data, we used it to support initial hypotheses. We carefully crafted each hypothesis with if/then clauses that could be effected through marketing strategy. The answer to each question pointed to a strategy for optimizing revenue or acquiring customers. If it didn’t, we set it aside.

Based on what we learned about user experience obstacles for first-time site visitors, we helped develop a new user registration wizard. This wizard, combined with targeted email interventions, drove conversions up by nearly 4%.

The data also revealed that many users failed to deeply engage with the community, often because they just didn’t know what was available in terms of game play and social interactions. In response, we set up an email primer series. This tactic boosted game play from new users in their first two weeks by more than 14%, before stabilizing at a rate 8% higher than baseline 60 days out.

We took the time to identify variables that we were certain we could address with a marketing plan. This meant when we got the results from our analysis, we could quickly develop ideas on how the data would impact our marketing recommendations. With a few clear options in mind, we could act on them to further GamerCo’s goals. Where that was not the case, we tabled the hypothesis.

Tying it Together

Where did these notions come from? At the end of the day, there are only so many ways to drive more revenue. They are as simple as they are universal:

  • More prospects
  • More customers
  • More frequent transactions
  • Higher value transactions
  • More transactions over the life of each customer.

There are nuances, such as the cost of customer retention and customer service, but those are minor issues. Focused efforts to enhance the top of the funnel (customer acquisition) will compound on one another. This earns the marketer the time to work at the narrow end of the funnel (the revenue end), where the stakes are higher and the audience, narrower.

Final Thoughts

All of this information was available to the client. We just needed to help them see it. They were sitting on the answers for years but didn’t realize it until they looked.

They aren’t alone. Even after years of cheap, detailed, widely available data about user behavior on the web, most companies are not making the effort to address their marketing channels with this approach. By creating user stories with big data, companies and organizations of all sizes can better serve their customers, grow their company, and increase their bottom line.

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