This is one of those things that just keeps going and has as many points of view as an ugly political issue, Marketing Mix.
Below is a dry explanation:
The term marketing mix was developed by Neil Borden who first started using the phrase in 1949. “An executive is a mixer of ingredients, who sometimes follows a recipe as he goes along, sometimes adapts a recipe to the ingredients immediately available, and sometimes experiments with or invents ingredients no one else has tried."
According to Borden, "When building a marketing program to fit the needs of his firm, the marketing manager has to weigh the behavioral forces and then juggle marketing elements in his mix with a keen eye on the resources with which he has to work."
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 awareness and 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 advertising or 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 regression modeling. Nonlinear and lagged effects are included using techniques like advertising adstock transformations. Typical output of such analyses includes a decomposition of total annual sales into contributions from each marketing component, a.k.a. Contribution pie-chart.
It really doesn’t matter the type of business, the bottom line is to make money. Any business that doesn’t won’t survive long. But increased competition, shrinking advertising budgets, inflation, the need to grow, and the always-increasing mountains of data have fueled the need to measure the impact of every advertising dollar. While this isn’t a new topic, over the past decade or so, data has become the driving force in all decisions for ad spend.
Now the downside,
a. Competition – competition is never going away and will always be an issue.
b. Tight advertising budgets have probably been a fact of life since the very first advertising budget was established. Understanding how and where it’s spent to be most effective is the challenge.
c. A degree of inflation is normal. “Inflation is the overall rise in the prices of goods and services over time. The annual inflation rate in the United States averaged 3.27% between 1914 and 2022. So moderate inflation has been a fact of life and the natural economic state for more than a century.” Unfortunately, periods of high inflation have, and will probably always, supersede other factors.
d. There can be inherent flaws in data and its analysis if not processed carefully and objectively. Here are a few of them:
Sampling bias
Cherry-picking
Disclosing metrics
Overfitting
Focusing only on the numbers
Solution bias
Communicating poorly
e. Base sales vs. Incremental sales. I believe there is an inherently flawed assumption with this thought process - the idea that base sales are a function of natural demand with qualitative factors like brand awareness and brand loyalty and incremental sales are the component of sales driven by marketing and promotional activities.
f. Analyses include a decomposition of total annual sales into contributions from each marketing component.
Points E and F require some fairly significant assumptions. What portion of base sales, if any, would be affected by a lack of advertising? Is brand awareness not, at least partially, a function of advertising? Can you definitively confirm that every marketing component is 100% attributable to a sale unless it was the only channel used in a campaign?
Where do we go from here?
Plan for the tough times, they’ll rise and subside but they’re not going away. (Plan for the worst, hope for the best.)
Use data carefully. Make sure of the validity of the data and that whoever is interpreting it is interpreting it carefully and objectively and in your best interest. Don’t allow data to be used to fit a preconceived plan.
Don’t get trapped in an attribution model. The debate over first-touch, multi-touch, and last-touch attribution modeling may never end.
Rather than committing 100% of your resources to one channel or one model, a more reasonable thought process may be that some combination of the right channels used for their strengths, with a whole lot of common sense added to the decision will be an effective and impactful means to reach your goals.