History of the problem: OIS solves a problem first articulated by department store mogul, John Wannamaker, in the 1880s.  Paraphrasing: “I know half my advertising is wasted, I just don’t know which half.”

Confirming Wannamaker’s problem, Professor Kotler et al in the 15^th edition, 2020, of their iconic marketing text, Marketing Management, page 551 described the problem as: “One of the most difficult decisions is choosing how much to spend on the marketing communications budget (MCB).”

Professor Kotler et al spent 55 years and 16 editions of their text trying to solve the problem. Initially, a prescriptive approach was proposed; subsequently, a predictive approach was adopted.  For a summary of the predictive approach, see a summary of the 15^th edition’s proposal. Neither approach was practicable let alone capable of providing the best possible solution.

The latest illustration of the problem was illustrated by the results of a recent CMO magazine’s survey:

“Demonstrating the impact of marketing actions on financial outcomes is #1 C-suite communication challenge (CMO survey 2019-2022). 11% of revenues are in marketing investments, yet only 41.6% are able to quantify its impact.”

Finally, Wikipedia underscored Peter Drucker‘s importance to marketing by commenting”His writings have predicted many of the major developments of the late twentieth century…the decisive importance of marketing…”

In conclusion quoting him directly:

”…the business enterprise has two–and only two–basic functions: marketing and innovation.  Marketing and innovation produce results; all the rest are costs.”

Solution to the MCB problem: The solution is OIS, a model of the firm’s income statement. It is however, a very significant departure from the traditional income statement and creates very significant new advantages for the firm including the most profitable forecast.

Summary of OIS’s solution:

1. Modeling Technique: OIS’s modeling technique is a prescriptive technique. It answers the question of what is the best possible solution.
2. The specific modeling technique OIS uses is a mix of integer and linear math programming (MILP). This modeling technique works because it establishes the causes and effects that exist in the income statement.There are three:
   1. OIS’s COGS + G&A costs model what the firm does that drives customers’ value; it performs operations (a.k.a., activities, flow, processes). These relationships are called cost functions and describe how the forecast’s volumes cause the operations’ costs.

This portion of the OIS model is created with a supply chain design application.  The vendor is Optilogic and this model has a fixed forecast.

2.OIS’s S of SG&A costs model what sales and marketing do that cause the forecast. These relationships are called response functions. They describe how marketing expenditures cause the product volumes that make up the forecast.  marketing mix-modeling data are used to create OIS’s response functions and are integrated into the supply chain model, described above.

This data relaxes the supply chain model’s assumption of a fixed forecast. The vendor is  ZS Associates.

3. Profit is the third causality. It is OIS’s objective function (i.e., that which OIS determines to be the best possible).

The OIS model uses the causalities described above to determine the best possible profit. This best possible profit was attained by the data in (2) above determining what was the most profitable size and allocation of the S of SG&A which, in turn, created a new most profitable forecast.

The data in (1) was used to determine what were the most profitable COGS and G&A costs to make and fulfill the new most profitable. In fact, the new forecast was necessarily the most profitable because:

   best possible(bp)(COGS + SG&A)> bp(COGS + G&A) +bp( S)