Marketing Mix Modeling - Limitations

Limitations

While marketing mix models provide much useful information, there are two key areas in which these models have limitations that should be taken into account by all of those that use these models for decisionmaking purposes. These limitations, discussed more fully below, include:

1) the focus on short-term sales can significantly under-value the importance of longer-term equity building activities; and

2) when used for media mix optimization, these models have a clear bias in favor of time-specific media (such as TV commercials) versus less time-specific media (such as ads appearing in monthly magazines); biases can also occur when comparing broad-based media versus regionally or demographically targeted media.

In relation to the bias against equity building activities, marketing budgets optimized using marketing-mix models may tend too much towards efficiency because marketing-mix models measure only the short-term effects of marketing. Longer term effects of marketing are reflected in its brand equity. The impact of marketing spend on is usually not captured by marketing-mix models. One reason is that the longer duration that marketing takes to impact brand perception extends beyond the simultaneous or, at best, weeks-ahead impact of marketing on sales that these models measure. 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. On the contrary, it is very possible that in the short term sales and market-share could deteriorate, but brand equity could actually be higher. This higher equity should in the long run help the brand recover sales and market-share.

Because marketing-mix models suggest a marketing tactic has a positive impact on sales doesn't necessarily mean it has a positive impact on long-term brand equity. Different marketing measures impact short-term and long-term brand sales differently and adjusting the marketing portfolio to maximize either the short-term or the long-term alone will be sub-optimal. For example the short-term positive effect of promotions on consumers’ utility induces consumers to switch to the promoted brand, but the adverse impact of promotions on brand equity carries over from period to period. Therefore the net effect of promotions on a brand’s market share and profitability can be negative due to their adverse impact on brand. Determining marketing ROI on the basis of marketing-mix models alone can lead to misleading results. This is because marketing-mix attempts to optimize marketing-mix to increase incremental contribution, but marketing-mix also drives brand-equity, which is not part of the incremental part measured by marketing-mix model- it is part of the baseline. True 'Return on Marketing Investment' is a sum of short-term and long-term ROI. 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). Short-term marketing-mix models can be combined with brand-equity models using brand-tracking data to measure 'brand ROI', in both the short- and long-term.

The second limitation of marketing mix models comes into play when advertisers attempt to use these models to determine the best media allocation across different media types. The traditional use of MMM's to compare money spent on TV versus money spent on couponing was relatively valid in that both TV commercials and the appearance of coupons (for example, in a FSI run in a newspaper) were both quite time specific. However, as the use of these models has been expanded into comparisons across a wider range of media types, extreme caution should be used.

Even with traditional media such as magazine advertising, the use of MMM's to compare results across media can be problematic; while the modelers overlay models of the 'typical' viewing curves of monthly magazines, these lack in precision, and thus introduce additional variability into the equation. Thus, comparisons of the effectiveness of running a TV commercial versus the effectiveness of running a magazine ad would be biased in favor of TV, with its greater precision of measurement. As new new forms of media proliferate, these limitations become even more important to consider if MMM's are to be used in attempts to quantify their effectiveness. For example, Sponsorship Marketing, Sports Affinity Marketing, Viral Marketing, Blog Marketing and Mobile Marketing all vary in terms of the time-specificity of exposure.

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. For example, Mountain Dew sponsorhip of NASCAR may be targeted to NASCAR fans, which may include multiple age groups, but Mountain Dew advertising on gaming blogs may be targeted to the Gen Y population. 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. As media become even more fragmented, it is critical that these issues are taken into account if marketing-mix models are used to judge the relative effectiveness of different media and tactics.

Marketing-mix models use historical performance to evaulate marketing performance and so are not an effective tool to manage marketing investments for new products. This is because the relatively short history of new products make marketing-mix results unstable. Also relationship between marketing and sales may be radically different in the launch and stable periods. For example the initial performance of Coke Zero was really poor and showed low advertising elasticity. In spite of this Coke increased its media spend, with an improved strategy and radically improved its performance resulting in advertising effectiveness that is probably several times the effectiveness during the launch period. A typical marketing-mix model would have recommended cutting media spend and instead resorting to heavy price discounting.