According to the Association of National Advertisers, accountability is the most important issue facing marketing executives this year, and it has been a recurring, top concern for the last six years. For those unfamiliar with exactly what this means in a marketing context, marketing accountability means demonstrating the value of marketing to top management through performance management and reporting; a popular shorthand term for this concept is ROMI which stands for Return on Marketing Investment.
Knowing this is a chronic problem for marketing executives makes big data specialists very excited—this is one area where big data analytics can make a huge difference.
There are three interrelated challenges with measuring marketing performance: causality, communication, and credibility. Causality is the most fundamental. Marketing is not like sales where activities are easily linked with results. Many marketing activities—such as advertising, positioning, and promotion—don’t have a direct causal relationship with revenues.
This turns marketing measurement into a complex process which compromises communication. So even if you are able to build a good model of marketing impact, it may be difficult for the rest of the organization to understand. Unfortunately, this promotes a credibility issue—complexity and confusion lowers credibility.
Big data analytics can be used to counter this effect by raising the confidence level in marketing measurements; however, for this to succeed, you must step outside of the marketing function and look at it from three critical perspectives: management, consumer, and critic.
The view that matters most
Don’t ever lose sight of the fact that top management is your key stakeholder in this exercise, and their confidence in your measurement system is what needs to be maximized. That’s why the very first step in building marketing accountability is doing research on your own top management.
Start by formalizing a qualitative research study on the level of confidence top management has in your marketing metrics, and the themes (both positive and negative) that form the basis of their perception. This should guide the rest of your initiative and act as a control mechanism to ensure its continued effectiveness.
As a general rule, you’ll find that top management’s main focus is on making good investment decisions and it should be clear what marketing’s contribution is to this process. As such, it’s best to align your performance metrics with the company’s business units and avoid metrics that ostensibly benefit multiple (or all) business units.
For instance, as sensible as it may seem to invest in promoting the overall corporate brand, it’s difficult to attribute these costs to the various lines of business that bring in revenues. As a result, these costs look like overhead—which is not the perception you want to build.
Looking in from the outside
The second perspective you must consider is your customers’. This is a great failing of many marketing performance measurement systems, as many marketing professionals approach this exercise from a functional perspective and not from a customer experience point of view.
The potential customer that you market to becomes a real customer when they buy your products and services. This is also the same customer that calls your service line when there’s a problem, learns from your educational services, and racks up points in your loyalty system when they take advantage of your promotions.
Having this holistic view of the customer is important for building a marketing measurement model. Here, big data analytics can be used inside the organization to profile your most valuable customers, then used outside the organization to target them.
Additionally, these types of big data insights will help you understand why your most important customers are leaving your company for the competition, thereby justifying marketing activities that help customer retention.
What’s wrong with this picture?
The third and final perspective to consider is that of the critic that’s reviewing your model and methods. When submitting research for an academic journal, there will invariably be a peer-review process. When these peers review your research, they will look for errors in your approach. They don’t necessarily need to agree with your findings; however, your procedures should be scientifically sound. If they aren’t your piece won’t get published.
In the same regard, you should conduct a formal audit of your measurement model once it’s complete. According to the CMO Survey, over 60% of companies do not formally evaluate the quality of their marketing analytics. It’s no surprise then, why executives have a credibility problem with their marketing performance reporting.
Provided your methods pass muster, the great advantage with big data analytics is the precision you can get with the confidence in your marketing metrics. By definition, big data deals with large volumes of data, and this usually translates to large sample sizes. As the sample size goes up, so does the quality of your marketing metrics.
The challenge however, as noted earlier, is how to communicate this level of quality. A big data scientist understands the relationship between sample size, alpha risk, beta risk, discrimination, standard deviation, and confidence interval. However, it’s not the data scientist that needs to understand this—it’s top management. This is where a great manager comes into play: one who understands the analysis and knows how to effectively communicate it upstream.
Big data analytics and marketing accountability go hand in hand to solve one the biggest challenges facing marketing executives today, as long as the right perspectives are considered. First is the view from the fore (top management), then the view from around (customer), and finally the view from behind (critic).
Having these three perspectives in mind, astute marketing executives can leverage the power of big data analytics to fortify their value in the organization. Take some time today to see how your executives feel about your marketing accountability. It will be the first step in a very valuable exercise.