McCarthy's four Ps look at marketing from the perspective of the marketer.
It describes what variables marketers have to work
with, and hence is sometimes referred to as a marketing
management perspective. Robert Lauterborn (Lauterborn,
R. 1990) claims that each of these variables should
also be seen from a consumer's perspective. This transformation
is accomplished by converting Product into "customer
solution", Price into "cost to the customer", Place
into "convenience", and Promotion into "communication".
He calls these the four Cs
A modeling matrix that pulls your qualitative brand tracking data and purchase intent, consideration and awareness into an integrated marketing mix model. What the model will show is how your marketing tactics influence end sales, but then the route by which those sales are influenced, through awareness to purchase, intent and so forth. Now, if done right what you get is an output. It looks a lot like a subway map, so that you can actually see what I call the hierarchy of effects of the different media on the precursors to sales
Most CPG and retail clients use a technique referred to as marketing mix modeling to help establish the relative effectiveness of their various marketing investments. This statistical modeling approach tells the marketer which tactics are working well and which are falling short, and indicates how to best manage budgets to get the most impact for the advertising dollar spent. Today, there are three things marketers and clients can do to help their Internet efforts register better in their marketing mix models.
First: Spend enough to be meaningful. The whole reason for developing a Marketing mix is to tease out the impact of separate marketing vehicles. It can only identify those marketing elements that are large enough to be meaningful contributors. Depending on the amount and quality of your data overall, it can be quite sensitive. But it has its limits. If you only spend one half of a percent of your marketing overall on interactive, don't expect it to register in your model.
Second: Stagger spending in short bursts. If your interactive budget is light, you may get a better handle on its contribution if effort is pulsed in short, strong bursts. These bursts should both align and counter align with other efforts. The statistics behind mix modeling are influenced by periods of change, so the bursts allow you to see the impact your having so much more clearly. If programs are very consistent over time, it is harder to read their impact. Further more, try breaking it out, if interactive is always "stacked" on TV in the media plan, it is harder still to determine its separate impact.
Third: Don't watered-down digital programs. Aggregate marketing vehicles and tactics. If you are trying to define the impact of Web site page views, ad exposures and click-throughs separately, you are splitting some very fine hairs. Instead, define a common metric such as "digital points," and assign point values for each digital interaction. By aggregating these points, your digital efforts in total will be more strongly represented in the model.
This approach might make interactive planners cringe. It might not be the most logical or efficient use of the interactive budget.
However, I would argue that this trade off is essential to help marketers get a common-basis read on the Internet's dollar-per-dollar impact relative to the rest of the marketing mix. Across a large marketing organization, it will not take very long to gather some meaningful data points. With this insight in hand, marketers might be willing to allocate more spend. Or maybe not.
Either way, marketers will feel more confident in
an Internet budget decision based on quantitative
apples-to-apples evaluation, within an analytic structure
that is understood and accepted in the company
