The famous department store mogul, John Wanamaker, is reported to have said, "I know that half my advertising works, I just don't know which half."
This statement typifies the "feel-good marketing" paradigm. But in today's scarcity economy with layoffs and radical cost cutting, companies must acquire the tools and processes to move beyond itâ€”to a new, "accountability marketing" paradigm.
Marketing response modeling (MRM) is a tool that applies science to a company's sales data and mathematically determines the precise contributions of each marketing element; it enables a company to truly understand the returns on investment from its marketing spending. In other words, these tools are specifically designed to tell you which half of your ad budget is working, and which is not.
This article will explain what the adoption of MRM entails and how it can help large or small companies to move toward this new paradigm of accountability marketing. Above all, this article will try to articulate a process whereby firms can harvest the benefits of these tools and plug them into their formal business planning processes.
We stated that MRM is a technology that applies science to marketing data. In effect, it begins with the collection of end-user sales data over time and links to that various marketing initiatives such as TV advertising, promotions, direct mailings, call center leads, pricing and external non-marketing factors that have an impact on the business, such as the economy or weather.
All of that is designed to work together for the analyst, who will build cause-and-effect statistical and predictive models of a firm or product's end-user sales.
So, the process of doing an MRM project first starts with data collection, all of which needs to be customized around each firm's unique business model. Not all firms use the same marketing tactics; some firms' end-users are businesses, whereas others are consumers. Design, and due diligence in collecting the information up front, is step-one in completing a successful MRM engagement.
Once that is done, competent modelers are plugged in to construct valid and predictive mathematical models.
Take the first step (it's free).
You may also like:
- What You Need to Know About GDPR and Data Privacy: Lisa Loftis of SAS Talks to Marketing Smarts [Podcast]
- The Marketing Metrics That Matter to the Bottom Line
- How Content Intelligence Can Empower Your Business [Infographic]
- Data-Driven Marketing: How (and Why) to Move From Siloed Reporting to Data Ownership
- How to Use Your Marketing Analytics Smartly