Marketing-mix models measure the impact of
marketingactivities, competitive effects and market environment on sales of a product. This method is extensively used in Consumer Packaged Goods(CPG) industry, although it is now being adopted in retail, telecom and financial services industries. Evaluating the effectiveness of marketing activities is an important task in the market strategy for any consumer product. Measuring the effectiveness enables marketers to determine the return on marketing investment, but more importantly, it also enables them to ascertain if one marketing channel is over-saturated, so that resources can be more efficiently deployed in under-saturated channels using optimization techniques. Marketing-mix models decompose total sales into two components:
Base Sales: This is the natural demand for the product driven by economic factors like pricing, long-term trends, seasonality, and also qualitative factors like
brand awarenessand brand loyalty.
Incremental Sales: Incremental sales are the component of sales driven by marketing and promotional activities. This component can be further decomposed into sales due to each marketing component like
Television advertisingor Radio advertising, Print Advertising(magazines, newspapers etc.), Coupons, Direct Mail, Internet, Feature or Display Promotions and Temporary Price Reductions. Some of these activities have short-term returns (Coupons, Promotions), while others have longer term returns (TV, Radio, Magazine/Print).
Marketing-Mix analyses are typically carried out using
Linear RegressionModeling. Nonlinearand lagged effects are included using techniques like Advertising Adstocktransformations. Typical output of such analyses include a decomposition of total annual sales into contributions from each marketing component, a.k.a Contribution pie-chart.
Another standard output is a decomposition of year-over year sales growth/decline, a.k.a ‘Due-to charts’.
Marketing-mix models can also be used to simulate the sales impact of changing investments in different elements of the
Media Spend OptimizationMarketing budget can be optimally distributed across marketing tactics by iteratively adding marketing dollars to each tactic that maximizes total ROI. It is important to remember that ROI of a marketing tactic is not constant but changes as investment levels are changed. This is because of the nonlinear relationship between most marketing tactics and sales. Marketing optimization will always distribute the next marketing dollar to that tactic that will yield the highest total ROI. It is also important to remember that tactics like trade promotions that are usually included as a linear impact on sales cannot be included in the optimization, since the linear relationship will result in the ROI for that tactic never decreasing for any level of spending. Also most marketing-mix models in industry and academia use a preset nonlinear form like logarithmic, exponential decay or s-curve. The actual shape of the relationship for different marketing tactics and sales may differ from tactic to tactic and the correct approach is to empirically determine the correct shape by iteratively testing various logical shapes.
Forecasting With Marketing-Mix ModelsMarketing-mix models determine the impact of historical marketing investments, pricing, competitive activities, economic and industry/category trends and environmental factors on sales. Once these relationships are established, marketers can use 'univariate time-series' forecasting methods to predict future values for these factors that drive sales and score them against the output of the marketing-mix model to generate sales forecasts. Marketing-mix model have confidence intervals for each factors impact on sales and these intervals can be leveraged to create a 'confidence interval' for sales forecasts.
Proliferation and Application of Marketing-Mix Modeling To Non-CPG Industries
Marketing-mix models were more popular initially the CPG industry and quickly spread to Retail and Pharma industries because of the availability of Syndicated Data in these industries (primarily from
Nielsen Companyand IRI and to a lesser extent from NPD Group). Availability of Time-series data is crucial to robust modeling of marketing-mix effects and with the systematic management of customer data through CRM systems in other industries like Financial Services, Automotive and Hospitality industries helped its spread to these industries. In addition competitive and industry 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:
*Interpretation of promotional activities across industries for e.g. promotions in CPG do not have lagged effeects as they happen in-store, but automotive and hospitality promotions are usually deployed through the internet or through dealer marketing and can have longer lags in their impact. CPG promotions are usually absolute price discounts, whereas Automotive promotions can be cashbacks or loan incentives, and Financial Services promotions are usually interest rate discounts.
*Hospitality industry marketing has a very heavy seasonal pattern and most marketing-mix models will tend to confound marketing effectiveness with seasonality, thus over or under estimating marketing ROI. Time-series Cross-Sectional models like 'Pooled Regression' need to be utilized, which increase sample size and variation and thus make a robust separation of pure marketing-effects from seasonality.
*Automotive Manufacturers spend a substantial amount of their marketing budgets on dealer advertising, which may not be accurately measurable if not modeled at the right level of aggregation. If modeled at the national level or even the market or DMA level, these effects may be lost in aggregation bias. On the other hand going all the way down to dealer-level may over-estimate marketing effectiveness as it would ignore consumer switching between dealers in the same area. The correct albeit rigorous approach would be to determine what dealers to combine into 'addable' common groups based on overlapping 'trade-areas' determined by consumer zip codes and cross-shopping information. At the very least 'Common Dealer Areas' can be determined by clustering dealers based on geographical distance between dealers and share of county sales. Marketing-mix models built by 'pooling' monthly sales for these dealer clusters will be effectively used to measure the impact of dealer advertising effectively.
Added Impetus from Marketing Accountability
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. Marketing-mix modeling presented a rigorous and consistent approach to evaluate marketing-mix investments as the CPG industry had already demonstrated. A study by American Marketing Association pointed out that top management was more likely to stress the importance of marketing accountability than middle management, suggesting a top-down push towards greater accountability.
Marketing-Mix Model Limitations
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 [brand equity] 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.
Also, just 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.
Another area in which marketing-mix modeling needs development is expanding to include new forms of media like Sponsorship Marketing, Sports Affinity Marketing, Viral Marketing, Blog Marketing and Mobile Marketing. As media gets more fragmented, it is critical that marketing-mix models be correctly deployed to measure the impact of all of these tactics accurately. Most approaches to marketing-mix models try to include these tactics in aggregate at the national or regional level, but each of these tactics are targeted to different demographic consumer groups and their impact may be lost by aggregating even at the regional or market level. 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 is the arch-nemesis of marketing-mix models.
Not a Tool To Manage New Product Marketing
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.
*Berk Ataman, Harald J. van Heerde, Carl F. Mela, (2006) ‘The Long-term Effect of Marketing Strategy on Brand Performance’, Journal of Marketing Research, Forthcoming.
* [http://www.bevnet.com/news/2007/01-04-2007-Coke_Zero.asp Coke Bets on Zero to Save Cola Category]
* [http://enumerys.com/branding_roi.pdf Branding ROI White-paper]
* [http://www.boozallen.com/media/file/153550.pdf Booz Allen Hamilton Article]
* [http://www.imediaconnection.com/content/11502.asp How to Know What Media Are Working?]
* [http://www.tmcatoday.org/InsideTheIndustry/Marketing/PDFs/Marketing_Accountability_Study_AMA.pdf AMA Marketing Accountability Study White-Paper]
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