I have no quibble with quants. Quants are those experts you are reading more and more about who sort through volumes of data to arrive at outstanding insights. They are the people that are working with the technology that is analyzing Big Data. Big Data is the net result of all the information that has been gathered into databases about you. Yes, long before there was concern about emails and voice traffic privacy issues, organizations—like your own—were collecting data about you. Marketers call it consumer insights. Small business owners might call it a "gut feeling."
If you lived in Eau Claire, Wisconsin in the 1990s you saw TV commercials that no one else saw. You were a test market, and new products found their way into stores (or not) based in part on how the test market responded. That sort of pre-launch activity continues today. However post-launch data analysis has been getting all the recent press. While post-launch activity has also always existed, the ability to track and analyze this information today—massive amounts of data—has never been more in vogue.
For instance, Netflix recently launched a series called House of Cards. They relied on internal data metrics to guide their decision making. Imagine the planning session:
Executive: "What kind of series should we produce?"
Creative: "A romantic comedy."
Quant: "Statistically, political-intrigue dramas are streamed 4 to 1 over romantic comedies."
Executive: "Great, a political drama it is! Who should be our star?"
Creative: "Tom Hanks?"
Quant: "The actor that our data shows to be most popular based on a view algorithm is Kevin Spacey."
Executive: "Kevin Spacey it is!"
As bizarre as this may sound, elements of the new series were developed based on quantifiable insights from Big Data. Every business has some data warehouse: inventory counts, sales data, customer databases or mailing lists. But not every business has decided to dedicate staff and infrastructure to support the task. It may be time to change that mentality. Here's why:
Data storehouses are only getting larger, and the cost of data storage continues to get less expensive. It is common to purchase terabytes of storage for less than $200. Twenty years ago a small business might run for years on 100 times less storage; plus, they had paid 10 times more for that smaller slice of storage.
Quantitative data is more reliable when it comes to making business decisions.
If the data storage is getting cheaper, it means the data analysis is getting easier. Still, few mid- to small- organizations are dedicating any time to the effort. Think about this a moment: It is easy to know where your customers are geographically. However, many organizations continue to spend effort and money in attracting prospects from areas that statistically are proven to be unproductive. Why? Because they are not allowing data to guide their decision-making.
It is also easy to know what products and services your customers desire, and yet many organizations continue to develop products and services that are not desired and are not profitable. They should know better—so why do they do it? Because they’re not being guided by rational truth; they’re being guided by emotion. We love a good emotional business decision, but we recommend that it be made within a context of quantifiable data as well.
In our experience, when you ask a business leader who their customer is, they will respond to that question with a story from the edge of the common experience. Something like this:
Marketer: "Who is your typical customer?"
Business Leader: "Our customers come from all over. Just yesterday we had a customer stop in from Alaska and they said that our place was the best they had ever visited and they would be sure to stop back the next time they were in town."
Marketer: "That's great, now, who is your typical customer!"
This answer is elusive because business leaders often like the stories (narratives) more than the cold hard facts (quantitative data). Both are instructive, but quantitative data is more reliable when it comes to making business decisions. That's why I have no quibble with quants, and why Big Data is a big deal.
Here’s what you need to know:
- Big Data is here to stay. Develop talent for quantifying your decision making.
- Test your organization. Identify all the data warehouses that are storing information related to customers—what they buy, where they live, etc.
- Tell new stories. Take your ability to narrate great brand stories, and merge this with your newfound ability to ground these stories in the numbers.
Of course the argument against quantifiable decision making is that it narrows your opportunistic growth, the serendipitous choices that reveal new markets. This is true. I recall talking with a market planner for Sears years ago. Sears would stock the young-men’s department with suits for young men based on geographic information. Flashy colors and cutting-edge fashions were sent to urban areas, and drab, conservative items were sent to exurbs. But this leads to the question, was it supply or demand that drove “Sunday Best” clothing choices?
Perhaps we’ll never know on that point, but today’s marketers should be much more capable of supplying what customer’s are demanding—if they listen to both the customer and the customer data.
Want to learn more about how this applies to your organization's marketing? We'd love to let you know.