Marketing Measurement – Analytics is Changing the Mix

"Half the money I spend on advertising is wasted; the trouble is I don't know which half."

John Wanamaker, a department store merchant and marketing pioneer in the late 19th and early 20th century (as well as Postmaster General from 1889 to 1893), is reputed to have made this statement and advertisers have been wrestling with the question ever since.

Enter the science of marketing measurement. In the early days the questions revolved around the effectiveness of newspaper advertising and then radio advertising and fairly rudimentary techniques were utilized by consumer packaged goods companies which had good data, large advertising budgets and managed multiple brands to measure how this advertising performed. The business of marketing measurement matured during the 1990’s as consulting businesses and econometric modeling groups within advertising agencies emerged and focused on developing better insights into the increasingly complex array of marketing spend options. Companies such as Hudson River Group, Marketing Management Analytics ("MMA") and consulting firms including Mercer and Bain Consulting developed practices that typically served Fortune 500 companies.

Today, this discipline has become known as Marketing Mix Modeling (“MMM”). This term encompasses the use of statistical analysis such as multivariate regression on sales and marketing time series data to estimate the impact of various marketing tactics on sales and then forecast the impact of future sets of tactics. It is often used to optimize advertising mix and promotional tactics with respect to sales revenue or profit. Today, brands are embracing these advanced measurement methodologies to determine the true impact of all interactions across all devices, channels, and campaign tactics.

Until recently, marketing mix modeling was directed mostly towards optimizing marketing programs at an aggregated level across various channels. As such, they tended to be backward looking and results were delivered via powerpoint ex-post.

With the growth in online advertising in the early 2000’s, advertisers faced a similar conundrum - how to measure the effectiveness of their online ad spend. Today, advertisers have access to much more data and a clickstream pattern of individual consumer action to analyze the effectiveness of marketing costs. New metrics such as cost per click, cost per acquisition as well as new attribution models have evolved in response to this new consumer commerce channel. Attribution methodologies range from simple last click attribution to fractional and probabilistic attribution approaches which seek to more accurately assign credit to differing touchpoints.

Today, the term revenue attribution has come to encompass the process of quantifying the influence of each advertising impression on a consumer’s purchasing decision. This allows marketers to optimize media spend for conversions and compare the value of different marketing channels, including paid and organic search, email, affiliate marketing, display ads, social media and more. Unlike the more traditional MMM approach, this analysis can be performed in real time at the individual user level. Companies such as Adometry (acquired by Google in 2014) have become major players in this space.

Online commerce has grown rapidly in the past decade or so and now accounts for 7.2% of total retail sales, while digital advertising accounts for close to 30% or $52.8 billion of the total advertising spend in the U.S. of $187 billion, according to Strategy Analytics. Digital advertising is the fastest growing segment of ad spending, growing at a 13% rate in 2015. However, television advertising remains the strongest single avenue for ad spend and will remain so for the foreseeable future. Not surprisingly, paper-based ads are the only category to show a decline.

The age of the customer is now upon us. No longer can companies view customer interactions through discreet silos - to maintain the loyalty of the customer, companies must be able to interact with them across multiple channels, whether online or offline, and multiple devices. Accordingly, as advertisers must now analyze their marketing spend across all channels, so are the disciplines of revenue attribution and marketing mix modeling becoming a single analytical tool. Forrester defines cross-channel attribution as “the practice of using advanced statistical approaches to allocate proportional credit to marketing communications and media activity across all channels, which ultimately leads to the desired customer action."

Some companies have already recognized this convergence and have developed cross-channel attribution solutions. MarketShare, Visual IQ and AOL Convertro are all considered leaders in the cross-channel attribution space and all appeared as leaders in the Forrester Wave for Cross-Channel Attribution Providers in Q4 2014. All vendors have a strong econometric modeling foundation but appear to have slightly differing strategies and/or source data expertise.

As a result, the analytic heritage of the new breed of cross-channel attribution vendors influences the positioning of their current offerings. For example, in my view Adometry is influenced by Google’s expertise in mobile data, while AOL/Convertro has developed deeper expertise in user level television viewing data. Marketing Evolution creates survey-based industry benchmarks against which it can compare individual marketing data. MarketShare’s Decision Cloud platform and Visual IQ take a more holistic view incorporating multiple approaches that are targeted to more sophisticated marketers.

Notwithstanding such differences, these vendors all naturally tend to focus on the Fortune 500 customer base. Given that these vendors are typically seen as strategic advisors to their clients and that their offerings tend to have a significant services element, the average deal size remains high and consequently are more easily justified by large clients with large brand portfolios and advertising budgets.

Given these realities, smaller companies and those businesses with smaller marketing budgets have been less able to justify the cost of a marketing measurement solution, notwithstanding the potential ROI benefits available to them. Newer companies that are not constrained by their legacy or their own conventional wisdom are emerging and building solutions from the ground up, tailored to their assessment of market needs. As with many segments of the business analytics markets, the data collection front end is becoming more efficient and the back-end analytics are becoming more automated - consequently solutions that leverage these elements are becoming more economical for smaller companies. Emerging companies such as Beckon (and its data wrangling capabilities) and Marketing Decision Science (and its automated SpendMetrix platform) are meeting these needs. For example, Marketing Decision Science offers a subscription based SaaS multi-channel marketing solution that is largely automated. By limiting the number of models that are evaluated and adopting an optimization approach that focuses on the key actions that drive ROI, the platform can provide almost all of the benefit of more expensive solutions at a fraction of the cost.

These companies are bringing marketing measurement analytics to the broader market – cost is no longer a constraint. Predictive analytics is becoming democratized. However, buyers will still need to assess their priorities and the vendors relative strengths in making the optimal purchasing decision.