Regardless of whether you’re in radio or television, sales or programming, we’ve always used analytics, often without even realizing that’s what it’s called. So what is it? According to Techopedia, “Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.” Perhaps you’re familiar with the movie, Moneyball, in which the assistant GM of the Oakland A’s calculates which prospects are worth the money based on their ability to hit singles, and thus increase the team’s likelihood of more RBI’s. The purpose is to use as little money as possible to acquire the greatest gain.
This sounds a lot like our goals as broadcasters and salespeople…maximize our tangible numbers (ratings, budgets, and revenue) to produce the best possible outcome for the future.
As a salesperson, you’ve used analytics to maximize campaigns for a client and even to forecast yearly or quarterly revenue. If you work on the content side of our business, you’ve used data science to study ratings and possibly predict future trends of your audience.
While all industries and companies use analytics in the descriptive and inferential senses, predictive analytics is gaining momentum in the business world. Many industries engage in this practice and some even employ full departments for this purpose. However, broadcasting is an industry that’s falling behind in this trending topic. We often think of the here and now, as it pertains to our industry, and when we do consider our future, it’s often regarding equipment upgrades or simply meeting next year’s budget.
Why not do more with the numbers we have to progress even further rather than simply keeping our terrestrial broadcasting heads above water?
So what can we learn from data analytics and predictive analytics in particular? The fascinating part is that we can literally discover or predict any business problem we can think of. We just need to creatively think of how to use data that we have to make any situation more successful. Some cases are simpler to solve than others. Regarding sales, we can calculate when one department will outpace another in revenue or profit.
From a completely different angle, we can determine which clients are likely to discontinue or become dissatisfied with results based on multiple factors (both numerical and categorical). Systems and algorithms exist that will allow us to combine inputs such as price, length of relationship with the station, ROI, ad schedule frequency and whatever factor you choose to investigate to decide which factor(s) influence the customer’s choices the most and even to which degree. This is done much like banks determine how likely one is to default on a loan.
This is only one example, and while it may be one you’ve already considered, the same process can be used for any business question imaginable. The likelihood of reducing employee turnover is another issue that can be addressed with this system. Once the exact issues of why have been pinpointed, we are better prepared to determine correct actions to mitigate the problem.
For an example in programming, let’s say we want to increase the number of phone calls. We can collect data from the calls we receive, determining age, gender, location, number of calls per week, etc. Maybe we’d learn that most callers are women over 40 from a specific region or zip code. From there we can take action to find a way to increase calls from other demographics or cater more to those specific callers depending on our goals. Words can also be calculated using specific systems. This can aid in situations such as determining how linguistics in your email blasts or on air ads affect consumer action.
Data analytics is wonderful and even required for understanding our trends and goals, but as an industry, broadcasting is underutilizing this science. The downside is the time (and sometimes software) these analyses require, as we all have “day jobs” to attend to, but the findings in the results can save budget money (ex. employee turnover) and increase revenue even further than we would have simply using our data in only simple observational manners.
The most important strategy in using analytics is to think about our business questions creatively and hit even more RBI’s!
By Heather Storm
