Three simple steps for optimizing your marketing budget
For many marketing professionals, total marketing budget optimization is the holy grail, the rite of passage we get to after cleaning up and measuring as much as we can of the stuff that can be measured. Yet with marketing mediums growing and converging (think mobile, social, online) and consumers floating between channels at a faster rate than ever before (expecting us to recognize them wherever they pop up), marketers can't afford to wait while the measurement scientists evaluate each medium and each campaign and subsequently deduce or infer what that might mean for the aggregate performance of our overall ad budgets. Why? Because the whole in this case is likely no longer a sum of its parts.
With the complexity of today’s marketing budgets, marketers are tasked with managing across multiple mediums with various forms of above- and below-the-line marketing. Central Marketing Communications teams are often managing investment across multiple product lines. Maintaining optimal allocations is a feat, especially with constant pressures to trim. Yet the answer to getting at the optimal point of return is in the data.
There are more interdependencies in the data than ever before, and our campaigns are far more fluid and complex. Thanks to advances in data management and statistics, though, organizations can optimize full marketing budgets – and it can be done in a way that is logical, measurable, dynamic and operational within marketing departments. It's a three-step process:
IS SAS® MARKETING MIX ADVISOR FOR YOU?It is if your organization:
Two sources are commonly used: internal data (i.e., customer behaviors, profitability, prior campaign analyses, campaign spending and geographic reach, promotions, sponsorships, etc.)and external data (competitive information, econometric data, census statistics, market research, etc.). This exogenous information helps add context to the analysis and understanding of the market factors that have an effect on marketing campaigns, but are often difficult to comprehend and measure.
The kind of information will vary based on the business. Retailers, for example, given the obvious ties between weather and shopping behavior, may include weather patterns, whereas banks may find factors like housing starts and consumer debt ratios highly predictive of lending campaign success. The key is to collect the information in a way that allows it to be unbundled or, in data terms, disaggregated. This helps the marketer see relationships or correlations in the data and lead them on the pathway to optimization.
Bio: Lori Bieda is the Executive Lead for Customer Intelligence Solutions across the Americas for SAS.
This story appears in the First Quarter 2011 issue of