Marketers have a surplus of data available, much more than can ever be used. To harness the right data, they need the right insights and tools to help them meaningfully drive their business. This article explores three of the biggest challenges in today's data-centric world, solutions to help marketers succeed, and references to key brands that are currently doing a great job of activating data to their advantage.
When you consider all the data that reaches a marketing professional from a broad range of sources, it can become overwhelming. Successful companies that support marketers correctly know how to synthesize data to a level where it is "crunched." They're able to then present the marketer with easier-to-read results that clearly state what's working and what isn't, so they can quickly and strategically decide which message to push, which audiences to go after, and what their value is. Most importantly, such representations of "crunched" data need to be formulated in the language, context, and framework of the business -- and not in some analytic geek-speak format.
Today, marketers tend to be focused on response and interaction with ads, when what they really care about is driving revenue for their company. In many cases, looking deeper (beyond the CTR), means not just looking at revenue lift driven by a campaign, it's about looking at the margin contribution of the products that are bought and sold. So the question to ask is:
Am I generating traffic that drives low-value revenue, or am I generating traffic that drives high-value, high-margin revenue?
It's key to link back to what the results mean to the business. When considering campaigns, one must consider both the raw stimulus response data as well as the big data. You have to look at how people interacted with a campaign, who these people are, and how they're connected. Today, there are very few tools that help bridge that information gap to help marketers operate at a level where they can be more effective. It's a game of simplifying and speaking the language of the marketer.
There are some "data as a service" (DaaS) solutions that can solve this today by offering deeper, clearer, and more meaningful analytics to marketers. These solutions will become even more robust and more readily available in the near future.
Unfortunately, the majority of solutions focus on the easy way out -- click-throughs and CTR. These are far from revenue and profitability, and from business rules regarding priorities like clearing inventory, perishable goods, and dated SKUs.
Algorithms vs. business indicators
A marketer's goals generally map back to business indicators, which start out as the basics that businesses care about. In a simple world, where interactions are limited, the flux of those interactions isn't overly complex and the need for algorithms to make business decisions isn't strong. In today's world, where we can measure and correlate so much, the only way to link it all is through big data and complex algorithms. We are moving into a world where not only measuring, but knowing how to impact these metrics, will require fairly sophisticated knowledge and utilization of advanced and predictive analytics algorithms that operate over big data. Such deep skills are essential if an organization is to figure out how to simplify, reduce, and abstract the crunched insights into clear business actions.
The companies who hand off business metrics and tell a marketer to "get it done" will be handicapped relative to companies that know how to utilize algorithms correctly, and know how to connect data flows with results. That represents a very critical support function for business management and for marketing -- a sophisticated data infrastructure with many algorithms that can be run on increasing volumes of data with a wide range of variety including structured, semi-structured, and unstructured data (free form text, images, audio, etc.).