It’s a great challenge to accurately measure the effects of advertising, packaging, distribution channels, media expenditures, social media Likes and Tweets, and sales organizational structure on brand share or sales revenue. Is marketing solely a game of chance, or might there be a way to bring scientific methods to the table?
Let’s draw a distinction between the micro and the macro.
At the micro level, the various pieces of the marketing puzzle can be, and should be, optimized on an on-going basis:
- The overall positioning and strategy should be evaluated
- Every ad and commercial should be tested for effectiveness
- Products should be tested and optimized
- Promotions should be tested
- Package designs should be tested
- Brand names should be evaluated
These micro-level tests must be a constant and ongoing process of evaluation, tweaking, and re-evaluation, to continuously improve the gears, bearings, and levers that make up the marketing engine. Optimizing these micro elements of marketing typically yields improvements in sales revenue and market share. But this is only the first step on the optimization stairway.
At a macro level the following types of questions should be asked:
- What happens when all of these elements are put together?
- How should the budget be allocated among the different marketing elements?
- How should the budget be allocated geographically? By different media?
- What is the optimal pricing strategy?
- What’s the optimal level and timing of media advertising?
- How much money should be spent on extra salespeople versus increasing media advertising?
These questions cannot be answered by copy testing, product testing, or other micro-testing methods. The workhorse of macro optimization is marketing mix modeling (MMM).
Business magazines and websites are abuzz with news about the value of marketing mix modeling as a way to help companies maximize their return on marketing investment (ROMI). The topic is of growing interest partly because of the interest of companies in growing topline revenue. The last couple of decades have witnessed unparalleled cost cutting and staff reductions among numerous companies. However, the opportunities for further cost reductions are diminishing in number and scale, so the pressure for long-term financial performance from public markets can only be met by renewed emphasis on new products and revenue growth.
A second reason for the growing interest in MMM is the proliferation of new media (i.e. new ways to spend the marketing budget), including the Internet, online communities, search engines, event marketing, sports marketing, viral marketing, cell phones, and text messaging. No one knows how to accurately measure the potential value of these many new ways to spend one’s marketing dollars. To grow revenue and profits, corporate executives need to understand the types of marketing investments that are most likely to produce viable, long-term revenue growth. That is, what combination of marketing and advertising investments will generate the greatest sales growth in order to maximize profits? MMM might provide some answers to these challenging problems.
So, what exactly is marketing mix modeling?
The term is widely used and applied indiscriminately to a broad range of marketing models used to evaluate different components of marketing plans, such as advertising, promotion, packaging, media weight levels, and sales force numbers. These models can be of many types, but multiple regression is the workhorse of most MMM.
Regression is based on a number of inputs or independent variables (e.g. TV ad, digital spend, price discounts, promotion) and how these relate to an outcome or dependent variable (e.g. sales, profits, or both). Once the model is built and validated, the input variables (advertising, promotion, etc.) can be manipulated to determine the net effect on a company’s sales or profits.
For example, if the CMO of a company knows that sales will go up $10m for every $1m he spends on a particular advertising campaign, he can quickly determine if additional advertising investment makes economic sense. But, in a broader sense, a deep understanding of the variables that drive sales and profits upwards is essential to determining an optimal strategy for the company. So, MMM can assist in making specific marketing decisions and trade-offs, and also create a broad platform of knowledge to guide strategic planning.
From a conceptual perspective, there are two main strategies to pursue in MMM:
- Longitudinal: In longitudinal analyses, the corporation looks at sales and profits over a number of time periods (months, quarters, years), compared to the marketing inputs in each of those time periods.
- Cross-sectional or side-by-side analysis: In the cross-sectional approach, the corporation’s various sales territories each receive different marketing inputs at the same time, or these inputs are systematically varied across the sales territories, and are compared to the sales and profit outcomes.
Both methods are sound, and both have their place. Often, some combination of the two methods is the most efficient.
Benefits of marketing mix modeling
MMM offers several important benefits:
- Better allocation of marketing budgets: This tool can be used to identify the most suitable marketing channel (e.g. TV, digital, print, radio, etc.) to achieve the marketing objectives and get maximum returns.
- Better execution of ad campaigns: Through MMM, marketers can suggest optimal spend levels in highly effective marketing channels to avoid saturation.
- Business scenario testing: MMM can be used to forecast business metrics based on planned marketing activities and then simulate various business scenarios like increase in spend by 10% or levels of spend required to achieve 10% lift in sales.
Who should do the modeling work?
Some large companies have internal modeling departments, but most companies will outsource the modeling and analytical work to media agencies. However, management consulting firms have been taking a portion of the pie as well.
The modelers, ideally, should have an in-depth understanding of marketing and marketing research, so that they really understand the complexities of the marketing variables they are trying to simulate. True, the model builders need statistical and mathematical skills, but without the marketing knowledge and marketing research experience, the modeling effort is not likely to be successful.
In the second part of the article we will cover what is essential in building a successful marketing mix model.
Jason Oh is a Senior Consultant, Strategy & Customer at EY with project experiences in commercial due diligence and corporate strategy planning. Previously, he was a Management Consultant at Novantas with a focus on the financial services sector, where he advised on pricing, marketing, channel distribution, digital transformation and due diligence.