Modeling of marketing effectiveness is dependent on good historical data capturing, at a minimum, your marketing activities and sales performance. The quality of the data and the level of detail drive the accuracy of the modeling. There is a significant difference in having data on a weekly basis vs. monthly. We won’t get into the technical requirements for modeling but will establish that companies without quality data are not good candidates for marketing mix modeling.

There are additional considerations once data availability is confirmed.

• Do you have the resources – either internal expertise or budget – to complete the modeling? Using more sophisticated methodologies does have a cost, especially if there is work required to clean up and consolidate the data prior to developing the models.
• Do you have large volumes of sales each month? Modeling accuracy is generally better with larger volumes that can establish statistical validity. As volumes decrease and marketing mix modeling becomes ineffective, there are other modeling and analytic techniques that can be applied.
• Do you expect that marketing is driving at least a moderate portion of the total sales activity? For marketing organizations that play a minor role relative to sales-driven organizations or retail presence, it is possible that the contribution of marketing may fall below detectable levels.
• Will your data reflect marketing impact that aligns with your marketing objectives? For example, if marketing contributes to generating new customers expected to have long-term purchasing behaviors, modeling the impact on short-term sales volume will not reflect the complete contribution.
• Can you include data points for external factors that may also influence sales activity, such as competitor marketing, economic trends, environmental conditions (weather), or changes in your own operations and customer contacts? Modeling that includes more diverse and relevant data points will provide greater insight while a lack of analysis on external influences could leave gaps that distort the results.

What you should expect from a high quality marketing mix model in terms of both benefits and gaps:

• The analysis will identify correlations that indicate the incremental contribution to sales/revenues/profits generated from each marketing channel. More detailed models will show the impact at a promotion level with some insight into campaign strategy.
• Smaller-scale marketing programs are often below detectable levels; however, this should be acceptable since you typically want to put your strongest measurement effort against the larger initiatives. Other forms of measurement are more appropriate for smaller, strategic initiatives.
• One of the most appealing benefits from the perspective of marketers is that this measurement comes after the fact and does not require modifications to the marketing that is typical with measurements such as market testing.
• One of the greatest shortcomings is that modeling a full year of historical data will typically deliver results after three months of analysis, providing results over a year after the marketing initiative concludes. This limits the speed and ability upon which to act on the insight.
• Identifying the influence of external factors on your sales activity provides valuable insight on historical performance, but also lets you apply adjustments to your real-time performance tracking that follows as you see these external factors change.
• It is important to understand that factors such as seasonality effect sales even without marketing programs.
• Marketing mix models can be used to develop simulation models and optimization models that apply the calculations from the historical analysis in an interactive tool to support planning of alternative marketing programs.
• Marketing mix modeling plays a specific role in measurements – it establishes correlations between marketing initiatives and results (typically but not always incremental sales activity). What it does not provide is any diagnostic information as to what parts of the overall strategy are driving the marketing program’s success or perhaps causing it to fall short.
• Once the model is completed, there will be a large portion of the sales that are considered “base sales” in addition to the sales associated with your marketing programs. This is sometimes referred to as “brand impact” and mistakenly associated as sales driven by the value of the brand. What this base represents is the uncorrelated portion of the data, which includes a combination of impact not appropriately correlated to current marketing activities, sales activity from repeat customers not captured through modeling, and your base sales from your overall brand and customer relationships.