ANALYTICS 2.0 – The Future of Advertising

Ad Analysis 2.0

The days of correlating sales data with a few dozens discrete advertising variables may be over. Today many of the world’s biggest and leading companies now deploy analytics 2.0, a set of capabilities that can chew through terabytes of data and hundreds of variables in real time to reveal how advertising touch points interact dynamically. The results achieved range from 10% to 30% improvements in marketing performance(HBR, March 2013).

Data Deluge (“Big Data”)

Enabled by the recent exponential leaps in computing power, cloud-based analytics, and cheap data storage, the predictive tools of Analytics 2.0 are able to measure the interaction of advertising across media and sales channels, and they also identify precisely how exogenous(external or environmental) variables (including the broader economy, competitive offerings, and even the weather) can affect the performance of an ad. These models are able to quantify cross-media and cross-channel effects of marketing, as well as direct and indirect effects of all business drivers, and the software employs cloud-computing and big-data capabilities.

Analytics 2.0

It is advised that marketers move onto Analytics 2.0 instead of the Swim Lane approaches of measuring performance of each marketing activities. Swim-Lane measurement involves measuring the performance of each marketing activites as if they work independently of one another or a rather “Silo” approach of measuring the contributions of each marketing effort. This practice often results in significant over- or underattribution  of advertising revenues because ads in one medium can exert a powerful influence on , or assist, those in another. Swim-lane “silo” measurement ignores those assisted effects. For instance, Data analysis of one campaign revealed that swim-lane measurement grossly underestimated the revenues attributable to social-media marketing and display advertising while overestimating PR and paid-search revenue.

Source: HBR, March 2013

Analytics 2.0 provides new insights into marketing effect on revenue. It comprises of three broad activities, namely ATTRIBUTION, OPTIMIZATION and ALLOCATION.

Attribution involves the process of quantifying the contribution of each element of advertising while Optimization involves the “war gaming” or the use of predictive analytics tools to run scenarios for business planning. Allocation is the real-time redistribution of resources across marketing activities according optimization scenarios. 

Ad analytics 2.0 model . Credit HBR

5 STEPS TO IMPLEMENTATION OF AD ANALYTICS 2.0

Technology consultancy Gartner estimates that within 5 years, most CMOs will have a bigger technology budget than chief technology officers. According to Wes Nichols, technology though necessary but is not sufficient to move an organization to analytics 2.0.

The initiatives require five(5) steps

First, a Company desiring to explore the efficiencies of analytics 2.0 must embrace analytics 2.0 as an organization-wide effort that must be championed by a C-level executive sponsor.

Second, an analytics-minded director or manager should be assigned director or manager to become the point person for the effort. Such a person should be one with strong analytical skills and a reputation for objectivity. This person can report to the CMO or sit on a cross-functional team between marketing and finance. It is also recommended that as the project expands, this person can help guide business planning and resource allocation across units.

Third, equipped with a prioritized list of questions you seek to answer, conduct an inventory of data throughout the organization since the intelligence that is critical to the success of analytics 2.0 efforts is often hidden among many functions beyond marketing, from finance to customer service. You need to identify and consolidate those disparate data sets and create systems for ongoing collection. You need to treat data as you would do intellectual property, given its asset value.

Fourth, you need to begin with proof of concept (POC) involving a particular line of business, geography, or product group. Build limited-scope models aimed at achieving early wins.

Finally, you need to test aggressively and feed the results back into the model. For example, if your optimization analysis suggests that by shifting some ad spending from TV jingles to online ad display will boost sales, attempt small local experiment and apply the results to refine your computations.

Usually when businesses have multiple sales channels such as retail, online, value-added resellers, or multiple products and geographies, analytics 2.0 tends to become more complex than internal teams can handle. It is in situations like this that the expertise of vendors with specific analytics and computing capability can be sought or procured.

Marketing is rapidly becoming a war of knowledge, insight, and asymmetric advantage gained through analytics 2.0.

An example of such tools based on Analytics 2.0 is Adobe Analytics.

Culled from Wes Nichols article on page 59-68 of HBRMarch 2013.