Data analytics can be a game changer for marketing organizations. McKinsey’s DataMatics study showed that firms in the top quartile of analytics performance were 20 times better at attracting new customers and more than five times better at retaining existing ones than firms in the bottom quartile.
But many companies struggle to realize the full potential of their analytics efforts. In a recent survey of senior marketing executives, only 10 percent of respondents said their organizations were very effective at applying analytics-generated insights about customer behavior.
In our experience, companies often address this problem by trying to analyze ever greater data sets in an effort to uncover a killer insight. Or they look for a tool that can solve every problem. But the core issue is that many analytics efforts remain disconnected from key decision makers. What tends to happen is that a small set of brilliant data scientists off in an isolated environment create impressive models that no one without a PhD in analytics can understand or use.
To add real value, marketing analytics solutions have to answer the questions on the minds of important decision makers and be part of their day-to-day work. And what decision makers want is a full and clear picture of what’s going on so they can make better decisions.
The example of a European grocery chain highlights this necessity. The company’s senior leaders told its marketing group to cut spending by 10 percent. But the set of marketing analytics tools they had at hand were so difficult to use and understand that the team couldn’t be sure what to do. Only after they adopted an integrated analytics solution that evaluated the return on investment of all aspects of the firm’s marketing spend in a simple way could they figure out what to do. What they discovered was that they could reduce the budget by even more than the initial 10 percent target, while maintaining revenues at prior levels.
This principle lies at the core of uncovering the benefits of marketing analytics. Through the development of Marketing Navigator, McKinsey’s marketing analytics capability, and working on over 500 engagements, we’ve found that applying analytics effectively can allow companies to free up 15-30 percent of their total marketing budget. This amount can be re-invested in marketing, which typically increases sales 2 to 5 percent, or saved without compromising the top line.
Three keys to successful adoption of marketing analytics
We have found that these three elements are critical for realizing the marketing analytics payoff:
Decision makers in marketing organizations continually face questions like: What level of overall spending is right? How should that spending be allocated across geographies, brands, and categories? What specific messages should we send to whom and via which channels?
What decision makers need is a consolidated view across the entire marketing portfolio and available analytical approaches. But that view needs to provide high-level insights into how to maximize returns on the overall spend and, at the same time, offers granular analysis that can help to optimize specific campaigns by audience, geography, week, etc.
Many analytic tools, however, focus on more tactical issues, such as the relationship of sponsorship to brand awareness or A/B testing. And different methods for measuring marketing return on investment (MROI)—such as reach-cost-quality (RCQ) heuristics, econometric marketing mix modeling (MMM), and digital attribution modeling (DA)—are each suited to particular circumstances. Such tools and approaches are valuable, but when used separately, on a stand-alone basis, they can provide conflicting insights that are difficult to reconcile.
Building an integrated view requires normalizing across multiple analytics approaches in a way that brings all the measures onto a consistent footing. By translating various MROI metrics into a single “currency,” the impact of all marketing activities can be compared and viewed on one dashboard, which evaluates ROI for each element of the spend and projects the anticipated impact of changes – both in the long and short-term.
At a global consumer goods firm, a hodge-podge of marketing processes and systems had emerged organically, over decades, across multiple geographies and categories. Because of the diversity of practices, managing marketing activities across the organization as a whole was nearly impossible.
The central marketing group successfully implemented a unified solution that allowed managers to finally compare the ROI of its marketing spend across all geographies and product lines. After several successful pilots, the solution was rolled out across more than 60 country/category groups. The new analytics system has also made the marketing organization more adaptable and agile. Innovative marketing practices developed in one geography can now be migrated more readily to others, and staff can work together more effectively across countries and product lines because they have a common frame of reference. Efficiency of spending has increased overall, and the group can now take advantage of growth opportunities far more quickly than in the past.
Easy to use
Few would dispute that the world of advanced analytics can be mind-bogglingly complex. For this reason, any marketing analytics solution has to treat simplicity as a “must-have” rather than a “nice-to-have” feature.
A simple screen, relying on graphical displays to show average and marginal ROI for marketing spending in total and by channel, brand, and category is a good starting point. But today’s tools need to be more than a nice display of a report. They need to allow decision makers to “play” with the information. For example, a good solution should support running simulations to project the anticipated outcomes of various actions and spend decisions. The system should also be able to suggest an optimal marketing mix that maximizes ROI across the portfolio.
The analytics solution’s interface should also let decision makers drill down and view data in more granular detail if they choose. In addition, solutions should provide enough information so the rationale behind the analysis they offer is clear; black box approaches won’t be credible.
It’s important to note that the burden for achieving this shouldn’t all be on the data scientists. CMOs and business leaders need to be able to communicate their priorities and needs clearly enough and then be involved in the process so that the end result is what they need.
In many settings, analytics are the province of a separate group or are outsourced. In situations like these, the experts with the capability to report on the impact of marketing activities are removed from key decision makers. Requests for reports typically are sent to a far-off group, and a lag of several weeks can occur before a response arrives. Decision makers are effectively held hostage, unable to know the impact of their actions quickly enough to make adjustments.
In high-performing marketing organizations, a marketing analytics solution should be able to return simulation, campaign assessment and reporting queries in as little as a few seconds. Only at that kind of speed can decision makers compare options effectively, fine-tune marketing outlays on an ongoing basis, and accelerate decisions so that decision makers can shift resources during a campaign and bring good ideas faster to market.
For the European grocer cited above, once the new marketing approach had been in place for a time, the solution could show quickly that actual results were in line with projections. The marketing team was then able to run simulations that projected that if the company were to restore marketing spending to prior levels, with the newly optimized allocation, the firm could grow its top line revenues by 2 percent.
Armed with this information, and with the validity of prior projections now proven, the firm’s senior leaders made a surprising reversal: they cancelled the cuts and restored the marketing budget to prior levels. And the grocer’s revenues beat the target, growing by 3 percent rather than 2 percent. The result was an increase in the company’s net profits that was more than three times larger than it would have been under the budget-cutting scenario.
Focusing on the needs of key decision makers brings marketing analytics out of the quants’ labs and embeds analytics-driven insights in everyday routines and ongoing decision-making processes. Solutions that do this can drive lasting, sustainable change in marketing organizations.