Decoding the Customer Behavior – Numbers or Intuition?

Watched (actually re-watched) a movie recently. The movie was Moneyball, released in 2011, starring Brad Pitt. This got me thinking about one of the oldest & most discussed topics in business – Number-based decision-making vs Intuition-based decision-making.  

Let me first summarize the movie for those who haven’t seen it yet. In the movie, Brad Pitt is a general manager of a baseball team with very low budget. With his best players being poached by other bigger clubs, he realizes that the game is ‘unfair’ & optimal solution won’t be obtained by conventional means. So, he adopts an entirely new approach of scouting players on purely numeric basis, thereby assembling a team to satisfy the statistically calculated numerical requirements of the team. The approach proves to be a successful one & we witness a remarkable turnaround in the fortunes of his team. Over the course of the movie, before the success, we see many senior personalities in baseball warning him not to ditch the traditional methods of recruiting players based on a scout’s intuition & experience of the game. 

This got me thinking about one of the most fundamental debates in business – numbers vs intuition. In this article, let us have one more look at both the approaches from our perspective of decoding the customer’s buying & loyalty-related behavior. Let me state at the beginning itself that strong cases can be built for both approaches. There is no single approach that is universally applicable at all times.

In the number-intensive approach, most of the decisions are made purely on the basis of numerical data. Thought process of Billy Beane, the character of Brad Pitt in movie Moneyball can be used as a quintessential illustration of this approach. In the context of understanding customer behavior, customer data for various customer activities needs to be collected (you may visit our article on this topic here).  Then, we have to identify the trends & patterns that will guide us in the decision-making. This data can act as a solid backup to support your decisions. This is especially true if the decision backfires later on. The analyzed data proves to be of a great help when explaining the decisions taken to the stakeholders. Generally, such trends, patterns, percentages, ratios etc. are compared with the past calculations & technically analyzed at great length. Hence, it does tend to result in informed decisions. However, there are some downsides to this approach. Most of the times (especially when end-customers are involved) data is not collected for the entire population. Only sample data is collected which is a very small subset of the entire population. These samples are then extrapolated to get the picture of entire population. Thus, in effect, the preferences & choices of few people are applied to entire population. This could result in mismatch with a lot of expectations. Furthermore, it is impossible to measure every customer activity. Thus, numeric data may not be present for many important parameters, such as perceptual mapping of the product in consumer’s mind, tendency of the customer towards brand advocacy etc. Hence, number-intensive approach is useful when you want to measure tangible things & for existing products. 

In Intuition-based decision making, decisions are taken on the basis of the marketer’s knowledge of the industry. So, domain expertise of the decision-making team takes a prime importance. As discussed above, numerical extrapolation doesn’t always give a clear picture of the diversity of preferences. Furthermore, only market experts can get the hang of the complex social & emotional dynamics as well as other unusual market specific factors. This becomes particularly apparent when products/services for which customer behavior is to be gauged are of novel nature. In case of disruptive products & technologies, there’s really no alternative to intuition, since past data is either absent or inapplicable. Such gut-feeling based decisions are less mechanical & they tend to foster creative thinking, which is sometimes desperately required in customer engagement domain. As creative & righteous as this approach may seem, it has its obvious downsides. In intuition-based decision making, it becomes very difficult to explain the underlying basis of decisions since there isn’t really a scientific basis for decisions. If such decisions don’t work for whatever reason, it becomes troublesome to justify them. Also, objectivity of the decision-maker is a crucial aspect, without which such decisions may be subjected to personal whims or preferences of the decision-maker.

Now, if you were to implement a CEM solution, how should you select a platform that takes care of both the scenarios? You should always go for the platform which will give you all the data, trends, patterns, graphs & summarized dashboards plus other analysis tools to suggest the possible decisions. On the other hand, the chosen platform should allow for flexible decision making via manual interventions & highly customizable criteria.  So, while choosing a platform, ideally you should look for thorough numerical assistance (via dashboards, graphs etc.) as well as flexibility for customized decision-making with manual interventions. At Birdvision, we have developed our CEM platform on the foundations of similar thinking, handing over the control of the wheel to the user. By the way, the debate of Numbers vs Intuition still goes on. Do share your thoughts with us to add to this debate.