Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn’t happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a significant communication and knowledge gap between data scientists and marketers, owing to their relative lack of familiarity with the other side’s perspectives and paradigms.

The successful marketer of the next decade is someone who is conversant with management theories of Kotler[1] as well as machine learning advances by Hinton[2]/LeCun[3]/ Ng[4]. As a step towards that goal and to alleviate the current gap between data science and marketing, I propose an Aim-Lever-Data-Implement (ALDI) approach below.

1. Aim

The first step in this paradigm is that the aim of the analysis needs to be fixed by the strategy teams, before any data scientists gets involved. An aim or an objective can be of different shades. Sometimes objectives can be more direct like increasing market share, improving profitability, or increasing targeting accuracy. At other times, objectives are more subtle or indirect. At all times, though, an organization must have a vision for whatever it is trying to solve for. This step is typically owned by either corporate strategy or marketing strategy.

2. Lever

It is very important for an organization to know what levers they have in the game. It is more common for marketing strategists to know the answer to this question. Examples of levers could be: We will exercise control over suppliers to drive costs down; or We will optimize placement of products in retailers’ aisles; or We will increase our advertising budget; or We will acquire downstream players to achieve synergies.

It is important to note that by lever, I refer to the actions that an organization is going to take as a result of the analysis, not what the organization’s strengths are.

Many organizations jump into “analyzing” data before they spend time understanding their aim and identifying their levers. This is a sub-optimal approach to problem solving because at best, it leads to descriptive analyses. There must be an aim, and one must know where the levers lie, and a subsequent analysis could tell you which lever to move, how much to move it by and in what direction to move it in, leading to a more prescriptive solution. Descriptive analytics is far less valuable than prescriptive and predictive analytics. As an example, analyzing where and among what population cancer rates are higher (descriptive) is less useful than analyzing how to prevent cancer in the first place (prescriptive).

3. Data

Once the objectives have been defined and once we know how we bring about an intervention in the game by understanding our levers, the logical next step is then to gather the appropriate data and then perform the analysis. This is where data scientists would really come in. Some important considerations for companies to consider are the need for clean and complete data, need for more structured datasets, differentiating categorical and numeric variables, being mindful of outliers, to name a few. I plan to cover more details on this step in a future post.

4. Implement

This is the final step in the ALDI paradigm where the results from the analysis help drive action. Depending on what levers are going to be pulled, the marketing strategy groups must engage and hand off the actual implementation to other groups like Sales, Operations or IT. Even if marketing strategy doesn’t get to drive associated programs, they will remain key stakeholders because of their contributions in the Aim and Lever stages.

Conclusion
ALDI is a systematic approach for integrating prescriptive analytics into marketing strategy. I see two important consequences of adopting the ALDI paradigm.

• One, ALDI is deeply connected with an organization’s inner dynamics. Every step must be driven by an internal team. And the emphasis on data-driven insights (generated from data gathered and analyzed by internal teams) makes organizations rely less on intuition/ advice provided just by external consultants.
• Second, this data driven approach shakes up the traditional management theory practice, which is primarily based on soft intuition gained by many years of experience from being in the trenches or by talking to visionary leaders. Intuition alone is difficult to quantify, but ALDI provides a framework where intuition can now be backed with prescriptive data analyses.