I was recently reading a McKinsey thought piece titled “Using Marketing Analytics to Drive Superior Growth” . In the article, McKinsey rightly highlights that an integrated analytics approach can free up 15 – 20 percent of marketing spending. Worldwide, that equates to as much as $200 billion that can be reinvested by companies or drop straight to the bottom line.
As I read through it, I found myself agreeing with most of their main points. Disappointingly though, I didn’t come away with anything new around marketing analytics. Therefore I’ve decided to highlight the key points made by the article and add in what I believe is missing from the conversation:
• “Yet while advanced analytics provide the ability to increase growth and marketing return on investment (MROI), organizations seem almost paralyzed by the choices on offer. They quickly find that even the most advanced single methodology has limits.”
Missing element: This is true—especially when relatively old outdated techniques vs. next generation decision analysis methods are deployed.
• “Anchoring analytics to strategy – Without a strategy anchor, we find companies often allocate marketing dollars based largely on the previous year’s budget or on what business line or product fared well in recent quarters. Those approaches can devolve into ‘beauty contests’ that reward the coolest proposal or the department that shouts the loudest rather than the area that most needs to grow or defend its current position. A more useful approach measures proposals based on their strategic return, economic value, and payback window.”
Missing element: Marketers and general managers need to foster cross-functional relationships in planning that bring together finance and marketing functional disciplines. This is just good commonsense. If your company is simply rolling forward last year’s plan, you have a lot of opportunity to exploit.
• “Making better decisions – While new sources of data have improved the science of marketing analytics, “art” retains an important role; business judgment is needed to challenge or validate approaches, but creativity is necessary to develop new ways of using data or to identify new opportunities for unlocking data. These “soft” skills are particularly useful because data availability and quality can run the gamut.”
Missing element: New analytical methods are needed to take advantage of both the art and science arguments. I would have used this opportunity to highlight methods popularized by Nate Silver in Signal and the Noise. These methods are a tremendous improvement over data-only econometrics and data mining techniques used to build more predictive systems.
• “Identify the best analytical approaches – pros and cons of marketing-mix modeling (MMM), Heuristics such as reach, cost, quality (RCQ), and Emerging approaches such as attribution modeling”
Missing element: While I agree that it is best to identify the right analytical approaches, I wouldn’t necessarily highlight MMM, heuristics and attribution modeling. The first two are nearly 30 years old and MMM dates back to the advent of point of sale data availability to manufacturers. While a little “newer”, attribution modeling is already starting to show its weaknesses to capture cross tactic and channel impacts. What companies require are new systems that address the real need s rather than continuing to use outdated and ill-equipped approaches.
While this article asked all of the right questions, it failed to provide the answers. This is unsurprising given the nature of McKinsey’s business. Many clients today are far too willing to pay exorbitant fees to big consulting firms for their advice and benchmarking. Despite these high costs however, the overall results tend to be average. It’s not McKinsey’s fault though—they are often stressed across 400 engagements, most of which are your typical benchmarking exercises. They’re almost never wrong, but in today’s hyper competitive market, average benchmarked results are only as good as the next guy’s.