Analytic managers feel it. Brand managers feel it. CMOs feel it. It’s the weight of big data and it keeps them up at night. Companies are getting flooded with multi-channel data they need—and data they don’t. Inundated marketers are tracking too many metrics and are unsure what key performance indicators (KPIs) are providing the most results. In short, data overload causes unnecessary data analysis, knee-jerk reactions, and planning paralysis. And this dynamic is rolling uphill, from the analysts to higher-level executives. Every day, marketers try to push their data up the ladder—in hope that the metrics are actionable and the numbers drive brands. Fortunately, there are five actions all marketers can take to tame their data overload.
#1 Understand Where Your Brand Is in its Product Life Cycle
When going to market, it is critical to be aware of the product life cycle matrix. Early in the life cycle, the brand’s goal will likely focus on new prescriptions, while later in the life cycle, the marketing strategy will likely focus more on compliance and maintaining share. Thus, throughout the life cycle, different types of data and performance indicators rise and fall in importance. As a brand matures in the product life cycle, continuing to focus on early performance indicators that helped to drive a successful launch not only adds unnecessary noise to the data, but may even hinder overall strategy and results.
#2 Understand Where Your Competitors’ Brands Are in Their Product Life Cycles
There are competitors and there are competitors. Marketers have to be like Kenny Rogers in The Gambler: know when to hold ‘em, know when to fold ‘em. There is no use putting big money against the category leader in the market if your brand is going generic in six months. It’s also a fool’s errand to go toe-to-toe with a competitor who is clearly outspending you. While it’s seductive to measure your brand against the category leader, you need to make sure your metrics and performance indicators reflect your overall market positioning.
#3 Architect and Know the KPIs That Matter
Big data produces an avalanche of noise. But what are marketers hearing? Let’s use this analogy:
In the scientific and engineering worlds, the signal-to-noise ratio is a measurement that compares the strength of a desired signal to the strength of the noise that surrounds it. Even on a good day, it can be hard to separate the essential KPIs from data noise.
The danger of noise with big data is that depending on how frequently brand managers or analysts dip into the data, they may hit upon an anomaly in the data and draw incorrect conclusions from it. One data point does not a trend make. Knee-jerk reactions to meaningless noise can be costly. In the millions. So it’s important to know the KPIs that matter so actual trends can be identified. There are only a few of them that drive the business. Once the KPIs have been determined, the data architecture and structure must be established before the campaign begins so that the essential data can be collected quickly and efficiently.
#4 Follow the 10% Rule
Unless a data point changes more than plus or minus 10% from your rolling average in a given period, or something significant has happened in the market (eg, new competitive entry, economic turmoil, etc), it’s not worth getting excited about. Don’t lose sleep. On the other hand, if data points keep moving north or south by 10% period over period, it’s time to investigate what’s gone awry. Or what you should take advantage of. Otherwise, stay the course and keep your eyes on the KPIs. Also, be sure to remember the words of English statesman George Savile, “A person who is master of patience is master of everything else.”
#5 Understand the Power of Course Correction to Keep on Track
During the course of your ongoing data analysis, when trends move above or below 10%, it’s important to course-correct as needed. These small changes will keep you on track, and build confidence—confidence to ignore the noise of big data. If data is managed the right way, there will rarely be a need for a hard course correction. Author J.S. Gilbert notes that “the ability for an airplane to go from one place to another though has almost nothing to do with its ability to stay on course. The plane does have a course heading that it follows, but on average, any airplane will actually be off course for 95% of its flight. The computers on board will tell the airplane that it has veered 5 degrees off course due south, for example. Sophisticated systems then allow the plane to make a correction. As the plane corrects, other factors now may place the plane off course by a few degrees southwest, and once again the plane makes a correction. And so on and so on.” Your marketing systems should operate in the same fashion, tracking when things have gone off course, and then making the necessary correction.
So when it comes to big data, use these five tips to narrow in on the data you need, and ensure you get your sleep. The goal is to focus on predefined KPIs in a regular cycle or cadence, paying attention to just enough data to turn your big data into small, actionable next steps. Your brand will thank you for it. What steps have you taken to turn big data into small data?