Marketing mix modeling
They are often used to optimize advertising mix and promotional tactics with respect to sales, revenue, or profit to maximize their return on investment.
Underlying MMMs is the concept of marketing mix, which is defined as the set of variables that a company can change to meet the demands of their customers.
[5] They added "people" to the list of existing variables, in order to recognize the importance of the human element in all aspects of marketing.
Mathematically, this is done by establishing a simultaneous relation of various marketing activities with sales using a linear or a non-linear regression equation.
Another standard output is a decomposition of year-over year sales growth and decline ("due-to charts").
Market mix modeling can determine the sales impact generated by individual media such as television, magazine, and online display ads.
For example, for TV advertising activity, it is possible to examine how each ad execution's market performance impacted sales volume.
MMM can also provide information on TV correlations at different media weight levels, as measured by gross rating points (GRP) in relation to sales volume response within a time frame, be it a week or a month.
While not all MMMs can definitively answer all questions, some additional areas in which insights can sometimes be gained include: 1) the effectiveness of 15-second vis-à-vis 30-second executions; 2) comparisons in ad performance when run during prime-time vis-à-vis off-prime-time dayparts; 3) comparisons into the direct and the halo effect of TV activity across various products or sub-brands.
It is possible to obtain an estimate of the volume generated per promotion event in each of the different retail outlets by region.
If detailed spend information is available we can compare the Return on Investment of various trade activities like Every Day Low Price, Off-Shelf Display.
When a new product is launched, the associated publicity and promotions typically results in higher volume generation than expected.
The variables are created from the marketing activities of the competition like television advertising, trade promotions, product launches etc.
[10] For example, a media mix model can help understand and optimize allocation on television spend to improve sales.
In contrast to MMMs, the goal of multi-touch attribution is to measure the impact of marketing activities at a granular levels instead of in aggregate.
The core question that MTA answers is, "What is the expected change in propensity to convert that was the result of an impression (or any form of interaction with the customer)?".
[11] Typical MMM studies provide the following insights Many Fortune 500 companies that are largely consumer packaged goods (CPG) companies, such as P&G,[12] AT&T, Kraft, Coca-Cola, Hershey, and Pepsi, have made MMM an integral part of their marketing planning.
The pioneers using this in full-scale commercial application were Marketing Management Analytics (MMA) in 1990 and Hudson River Group in 1989.
In addition, data availability through third-party sources like Forrester Research's Ultimate Consumer Panel (financial services), Polk Insights (automotive) and Smith Travel Research (hospitality), further enhanced the application of marketing-mix modeling to these industries.
Application of marketing-mix modeling to these industries is still in a nascent stage and a lot of standardization needs to be brought about especially in these areas:[citation needed] The proliferation of marketing-mix modeling was also accelerated due to the focus from Sarbanes-Oxley Section 404 that required internal controls for financial reporting on significant expenses and outlays.
Marketing for consumer goods can be in excess of a 10th of total revenues and until the advent of marketing-mix models, relied on qualitative or 'soft' approaches to evaluate this spend.
These platforms use techniques, such as adstock transformations and the modeling of saturation effects, which help in optimizing marketing budgets and strategies.
The open-source nature of tools such as PyMC-Marketing, however, helps alleviate these barriers by fostering a supportive community and resource sharing.
The other reason is that temporary fluctuation in sales due to economic and social conditions do not necessarily mean that marketing has been ineffective in building brand equity.
The fact that most firms use marketing-mix models only to measure the short-term ROI can be inferred from an article by Booz Allen Hamilton, which suggests that there is a significant shift away from traditional media to 'below-the-line' spending, driven by the fact that promotional spending is easier to measure.
But academic studies have shown that promotional activities are in fact detrimental to long-term marketing ROI (Ataman et al., 2006).
Further, most approaches to marketing-mix models try to include all marketing activities in aggregate at the national or regional level, but to the extent that various tactics are targeted to different demographic consumer groups, their impact may be lost.
Both of these tactics may be highly effective within the corresponding demographic groups but, when included in aggregate in a national or regional marketing-mix model, may come up as ineffective.
Aggregation bias, along with issues relating to variations in the time-specific natures of different media, pose serious problems when these models are used in ways beyond those for which they were originally designed.
A typical marketing-mix model would have recommended cutting media spend and instead resorting to heavy price discounting.
