Measuring Brand Value and Convincing Finance

Over the course of the last couple months, I have spoken to many CMOs about coveying the value of a brand in terms of financial impact. The thinking really began at the MASB (the Marketing Accountability Standards Board) meeting this August in Chicago. MASB is a standards body for providing consistency on the way we measure marketing success for both marketers and finance folks alike.

But articulating brand value for both marketing and finance - and ultimately executive leadership - is a consistent challenge for many marketers across a multitude of industries. One approach we have seen be successful is for a marketing organization to work collaboratively to develop a "compound" brand health tracker. This tracker can be updated daily, weekly, or monthly as the industry and business requires. Below is a sample tracker:

Centralizing Marketing Data Management

Recently, the McKinsey Quarterly had an article on whether Executives should choose to centralize or not centralize a company initiative. https://www.mckinseyquarterly.com/To_centralize_or_not_to_centralize_2815. In the article they sight 3 questions leadership should ask itself to make that decision.
1. Is it Mandated by laws or government?
2. Does it add material value to the business?
3. Are the risks low?

Though the first question doesn't really apply in our case, except for consumer data, Questions 2 & 3 must be answered to manage marketing data effectively.

Adding Value
Centralizing marketing insights, consumer insights and marketing analytics data can provide a brand significant improvement in marketing effectiveness and efficiency. Take for example, something as simple as overlaying consumer insights with media insights to understand which media placements a brand can reach its target. This is beyond demographics and moves into real affinities, purchasing behaviors, and attributes that can refine a brand's media investment. In some cases, this improvement can be as high as 20% - 30% in a market. So Yes, marketing data should be centralized.

Low Risks
That is, are the business risks to centralize not disruptive to the lines of business (e.g., brands). Managing marketing data is extremely challenging. Data collection, for example, is the bane of many marketing analysts existence. Typically in mid-sized and large organizations data is disparate across a myriad of technologies, teams and reporting systems. Without the centralization of these data collection efforts, often work is duplicated across multiple brands and insights cannot be gleaned across brands. So Yes, again, marketing data should be centralized.

So we have checked off 2 of 3 McKinsey's hurdles and marketing data should be centralized.

If you like to learn more best practices for mastering marketing data, join us at the Data Management Summit in July in Park City.

Best Practices for Mobile ROI Measurement

In today’s business environment, many marketers are using mobile media to connect brands with consumers. For marketers, measuring the return of these mobile marketing programs is still elusive. Even with the several mobile monitoring tools there is the constant struggle how to link mobile marketing activities with overall business and marketing strategies. In particular, we see marketers are struggling with evaluating the return of mobile marketing initiatives relative to other marketing communication activities.

For the complete white paper email us at whitepapers@theammgroup.com

Stitching Digital Data Together Successfully

To date there have been several discussions around the appropriate way to provide attribution across various digital tactics to understand the return on digital investments. In fact, several advertisers have asked us for Best Practices for linking digital data together, across display, video, search, social and digital relationship marketing activities. But, before we model attribution correctly, we must make sure that data we are gathering is the right data collected in the right way to build out an effective attribution model.

Considering the effort we put into collecting, normalizing, and transforming these disparate data sources into a single data cube, we must provide two data cube outcomes. First, we must be able to use our data cube for digital optimization. Second, we must be able to use our data cube for mix-models or other attribution models to understand the role of digital in the overall marketing mix. Both are essential for the digital marketing and analytics leader and both must done using the same data cube to ensure any investement is not wasted.