With the continued pressure of a down economy, marketing executives are driven to not only expend marketing budgets more effectively, but also to ensure business growth from those budgets. Any request for additional marketing funds requires a solid business case analysis that illustrates a direct impact on revenue growth. With the explosion of various marketing channels in the last few years (web, mobile, email, social media, etc.) has come a tidal wave of related data. As these marketing channels are usually distinct entities, data related to each channel is also distinct, and often not integrated. While fully integrated marketing channels that track customers across channels from "first touch" to sale may be the Holy Grail of marketing analytics, the reality is that many organizations do not have systems in place that can serve up data this way. Investment in marketing automation tools can provide part of the solution, but this potentially costly investment can be a tough sell to for the marketer that is trying to justify additional marketing funds in the first place! Leveraging available data across disparate channels, with the right mix of historical data analysis and statistical modeling can provide a business case for a growth strategy that requires additional marketing spend.
Since a common customer identifiers are not available across disparate marketing channels, analytics at this level of detail is not realistic. Spend justification relies on illustrating relationships between marketing spend and revenue, however. This relationship need not be defined at the customer level, but instead could be defined across some common time interval such as days, weeks, months, quarters or even years. Naturally, the more granular the time period, business variability related to marketing spend will be better understood.
The example below illustrates this relationship for an online retailer that wished to justify increased sales through an expanded marketing program budget. This retailer used a multi-channel marketing program that includes Facebook, Google Ad Words and Search Engine Optimization (SEO) to promote their products. The chart below shows daily spend on Facebook to ad clicks that lead to website visits. The strong relationship between these two factors indicates that Facebook spend is leading to website visits (naturally with a pay-per-click campaign). Similar relationships are seen with Ad Words and search.
Given the multi-channel nature of this retailer's marketing program, and the fact that data from Facebook, Ad Words and SEO were neither integrated nor tracked at the customer level in their sales system, direct insight of channel spend to sales was not possible. Instead, this retailer looked at daily website visits (total) to daily sales (total). The chart below tells the story that higher daily web site visit volume leads to higher daily sales.
This insight, while valuable, does not justify an expanded marketing program budget, however. In order to make the business case for additional marketing spend, with the constraints of un-integrated and customer blind data, the retailer built a simple marketing forecast model that drew upon the relationships seen between spend and web visits, and web visits and sales. This model projected web visits from each source (Facebook, Ad Words and SEO) based on the known relationships and multiple future spend scenarios. It then aggregated projected visits across all three channels and used that result to project sales, again based on known relationships of visits to sales. The result was the model below which provides a sales forecast for two separate growth scenarios. Both scenarios assume a baseline spend for each marketing channel that is equal to historical levels. Scenario 1 assumes that spend will not grow from this baseline over then next 15 months. Scenario 2 assumes a 2% monthly growth from the baseline for each marketing channel. Based on the underlying relationships, the model projects expected sales for each scenario. The model displayed below was a live, interactive model that allowed for creation of various scenarios (View the live version here).
Although the results are based on a rather simplistic model of a multi-channel marketing program, they do provide a sufficient business case to justify increased spend. This simple model could be easily adopted to more complex programs, to include more channels or seasonality, for example.
While having well integrated customer level data across marketing channels is a preferred starting point for marketing analytics, the reality is that this unattainable and costly endeavor for many organizations. Leveraging available data with common attributes does have the very real potential to provide insights that can bootstrap a marketing program for growth and success.