Maxwell Turner

Understand what your current grand strategy is and what the potential risks are that will force a temporary or long-term pivot. Today, stated or unstated, many firms follow a concentrated growth strategy – they sell what they currently sell to their existing markets. Successfully following this model requires an understanding that there is a market for their service and that they can quickly sense and respond to environmental shifts impacting product quality, supply chains, channel management, and other factors. Their continuous dialog with stakeholders does not only focus on what is happening, but how the change or event impacts aspects of the business, how they anticipated the event, and how they will respond.

Factor Analysis is an important pattern recognition tool that can help us understand how a population or segments conceptualize services, products, and behaviors. Yes, the technique can be used to reduce the number of variables used in our analysis but, at least for me, it is more valuable in understanding how aspects of a topic are arranged in the mind of the market. As we study and name the factors, we may see attributes that we thought of as having little in common, group together – revealing unexpected motifs. These unexpected patterns reveal deeper insights into people’s experiences. This learning can help us pivot our marketing and better target our communications. Reframe factor analysis as a gateway into the minds of your customers.

Once you have confirmed that the stray data point is not an error, consider it a gift. Hiding in the outlier may be the first indication of changing market sentiment, an issue so important as to rapidly shift behavior, or a response to an event that presents future risk. Check for headlines, rumors, and operating issues that may have caused the fluctuation. The issue may not be yours but a competitor’s problem or an industry event. When a competitor has a problem, the market may assume that other companies in the sector will have the same problem. Importantly, don’t look at it in isolation. Understand the outlier’s relationship with other metrics, especially your financial metrics. Remember the outlier may be a leading indicator.

Assume we have the luxury of time and want to understand an aspect of consumer behavior. Start with secondary research - learn everything you can about the topic from existing sources. Move to observation, watch people doing what you are interested in. Now, conduct in-depth interviews. Listen for the ‘why’ behind the behavior. Craft a theory that explains the behavior. Construct your first set of survey attributes and conduct a test survey to understand how well the attributes explain the dimensions of the behavior. Select the smallest number of attributes that best explain the behavior’s dimensions. You should be at a point where you have a balanced and complete scale to test your theory.

Sometimes we can be so in love with our analysis, and the amazing techniques we applied, and our sudden lightning strike of insight that we completely miss that our audience does not care. The leadership team receiving our material will value our full grasp of the business situation, our empathy for the stress the business problem is creating, and our brilliance in cutting through the data clutter and providing a complete answer and, hopefully, an implementable recommendation to move toward the solution. Start by anchoring the story in the shared business challenge - something familiar and real. Then use plain, non-technical language to highlight your key insights. Focus on what the data means, why it matters, and what to do next.

Adults learn differently. Some people learn by listening, others by watching an activity being performed, and still others through a hands-on approach - learning by doing. Aim to incorporate all learning modalities into the teaching process. Provide reading material in advance of a lecture. Move from lecture to direct experiential exercises. Learning a consumer behavior analysis tool may require coding or qualitative research activities. In the former case, transitioning from a lecture to deep-dive articles with coding examples, then to programming and interpretation, can be effective. In the qualitative case, provide background reading and a seminar, but also send your team into the field to experience the process firsthand.