One of the first considerations is base sales: the number of sales that you can reasonably assume are just going to happen on their own.
Incremental sales volume is the target here, marketing through channels to guide the customers to the brand, whether for brand awareness or acquisition. All those channels have their own means of measurement to indicate success, finally linking up for total, measurable, incremental sales.
The goal is to capture the marketing history and align it to current sales. But there are plenty of other considerations along the way and blind spots to be revealed.
Let's say a multi-channel automotive service provider has had pretty good success with a weekly flyer, maybe it's a newspaper insert, and it traditionally claims a significant chunk of the marketing budget. But they don't really know what their other options are. Is the flyer as effective as they once believed? Should they move dollars into other marketing channels? They might try CRM (customer relationship marketing), leveraging customer data to do a targeted mailing that will be measurable while building up their customer knowledge.
Maybe they've also consistently run TV spots and know the total rating points but can't really correlate the impact to sales projections. Modeling can tell them that and whether that impact is degrading over time. Maybe they are over saturating their TV presence, wasting money for diminishing returns. Modeling can tell them the ROI sweet spot. At the same time, MMM may recommend diverting some of those dollars to radio because it has revealed the efficiency of their occasional radio spots.
Maybe the brand would also discover that despite 40% of their targeted customer base being women, their media spend doesn't align enough with that important group, prompting a shift within the channel or a jump to another.
Or perhaps the MMM reveals that a younger population has grown more important — and responsive — than the brand realized and prompts them to double down on social channels that skew to this group. This modeling gives marketing decision-makers the dials to fine-tune their marketing campaigns for the best signal, allocating spend in percentages that have been shown to increase profitable return.
At its core, MMM helps automotive services providers eliminate guesswork and gain deeper insight into what's working and what's not.
Backed by data science that quantifies their decisions — and lets them defend their budget — these businesses gain high-level analysis across all their media spend, giving them the long-term strategic planning insights they need for smarter spending.
Rick Muldowney is the chief analytics officer for digm, the marketing agency that combines complex data analysis and marketing technology to create a holistic customer view with insights that drive businesses forward.