With football season upon us, stats-obsessed sports fans have spent substantial time and effort developing their ideal fantasy football teams. Understanding the importance of data-driven insights to predicting player performance, participants meticulously selected top lineups based on player data. In fact, 74 percent of fantasy users conducted research on four or more websites in preparation for the draft, and fantasy users dedicated 8.67 hours a week to fantasy sports.
The tremendous amount of time and resources used to leverage data and build a dream fantasy lineup is not unfamiliar to marketing teams, who look to data to assemble a pipeline of winning leads.
With the rise of big data, it can be time-consuming and complicated determining what information provides the insights needed to prioritize leads and, ultimately, close more deals. In the same way that football fans must look at different types of data -- expert predictions, past injuries, touchdowns, yardage -- to build their top-notch teams, savvy marketers mine various types of data to compile the most promising leads and prioritize those most likely to convert. For discerning marketers, the questions often become which measurements matter most and how can teams use these data to win buyers before they even enter the marketing database?
For smart marketers and fantasy football players looking to leverage analytics and save valuable time, there are a few useful tips and tricks of the trade:
Know your data.
Understanding all of the data at your disposal helps save time when making decisions about who to target. Just as football fans must conduct extensive research into different kinds of data on the league's individual players and team performance, taking into account various performance indicators to give themselves the best chance of winning, marketers must also evaluate different types of data to help their sales team win in the market.
There are three key types of data with which marketers must be familiar:
- Intent data: Helps identify prospective buyers earlier, provide greater accuracy on who's likely to buy what and when, and get deeper insight into buyer personae. Intent data takes into account factors like: keywords searched, blogs read, and content sites visited.
- Fit data: Includes demographic data collected about prospects, and includes a potential buyer's company size, growth, credit score, and industry classification.
- Behavior data: Consists of information collected about how prospects have engaged with a marketer's brand in the past -- this could include emails opened, research reports downloaded and content viewed.
Determine what measurements matter most.
Making the most of insights provided by behavior and fit data is standard marketing procedure. What really separates the pros from the rookies is mining intent data -- the data collected before a prospect engages with a brand -- from across the Web. This data is especially telling, because it helps marketers zero-in on who will buy what and when, so they can better target their efforts and time outreach to when a prospective customer is most likely to buy.
In fantasy football, incorporating intent data can be compared to taking advantage of expert predictions.
While it's crucial to understand that intent data and expert predictions are among the most important tools for marketers and fantasy football players, respectively, it is important not to fall victim to tunnel vision. Intent data and expert predictions alone cannot capture all of the insights needed to get ahead.
Just like the quarterback cannot play every position on the field, intent data cannot act without the support of behavior and fit data. The color provided by behavior and fit data can make the difference between targeting a campaign toward a high school football coach or toward the CIO of an Inc. 5000 company.
Evaluate your progress.
Finally, marketers and fantasy football fans must measure and track their progress and results to determine what's working and what may need to change. If your fantasy football team isn't winning out over your opponents or your players aren't performing as expected, it's time to make some trades. Similarly, if marketers and sales teams aren't finding success and closing leads, consider hiring an in-house data scientist or employing predictive marketing applications with easy-to-use dashboards and machine-learning systems to help with the marketing team's efforts.
--Nipul Chokshi, senior director of product marketing at Lattice Engines, also contributed to this article.