In Part 1 of this series, we discussed how organizational objectives may change the implementation of a media mix and optimization platform. Once the objectives have been set, it’s time to formulate a plan around how to measure the efficacy of your efforts. Much of that is centered in appropriate distribution of marketing dollars, and it’s important that those decisions be well-informed. Today, we’ll discuss the collection and normalization of media spend data.
First, companies must determine what data can be collected and at what level. Media data exists at many levels; capturing that data and integrating into a platform at a useable level is at best challenging and at worst impossible. Complicating factors include:
Data reporting varies. Digital media placement (display, social, etc.) agencies restrict the data they return to advertisers and varies at the level of placement. Targets can range from granular like consumer and household to more broad targets like geography, making specificity in reporting complicated.
Search advertising is casting a broad net. Search, both branded and unbranded, must be tracked separately from other digital as it is effectively 21st century telephone book/yellow pages (print). The targeting is a dynamic guessing game and is often difficult to attach to other digital channels to get a clear read on how they interact.
TV and radio are slightly less broad nets, but still. TV and radio ads are both placed at the DMA/market level and placement data requires subscription that is managed by a couple of vendors. Navigating the relationships in this space can be complex.
Using spend wisely. Measurable TV advertising is happening, but relatively expensive and doesn’t command a significant portion of advertising spend. It’s important to measure and assess your ROI here.
Direct mail is best supported by quality in-house data. Direct mail is placed at the household level in cases where the marketer maintains a prospect file.
Outdoor estimates are based upon exposures and depend upon consumer migration patterns.
And traditional print media is not only tricky to measure, but can also be a challenging landscape to navigate.
Second, companies must not confuse sales channels with marketing media. Retail, inbound salesforce, inbound customer care, e-commerce website, door-to-door sales, etc. are sales channels. They manage response driven by marketing media placement. Their key responsibility is to generate sales from that marketing-generated response.
And sales channels should not be confused with media. Often in our practice, we conduct analyses that map marketing media-generated customer response journeys through sales channels. Not surprisingly consumers often touch multiple sales channels during their order journey – often both online and offline. Note, this is important, as consumer behavior is not singularly aligned to a specific sales channel. Each purchase decision may require a different path – understanding this eliminates errors of moving to digital too fast in order to reduce channel order costs.
Third, marketers must determine the period of measurement they would like to control. Data can be collected at the daily level and rolled up to media weeks or months. Ultimately, the marketer will make investment decisions for each media and agencies will need to execute those wishes. Understanding the cost of managing media placements on a daily, weekly, monthly, quarterly, etc. basis is important to generating ROI. The cost associated with those changes may not bear the return required by the business.
Once a business understands the limitations and possibilities of how each media is placed and how consumers interact with their sales channels, the marketer can detail specifically the requirements for capturing, storing, and managing the data. They can set expectations with current and new vendors in order to receive the data in the format and frequency required to support the business objective and processes.
In the next part we’ll discuss the pitfalls and opportunities for collecting consumer response and order/sales data.
