Years ago, I worked as a waiter to put myself through school. I met some very demanding customers who insisted on their soup being very hot. In most cases, restaurant soups are pre-made and stored in large vats inside countertop warming tables. So, being a good waiter, I would go the extra step to heat up the soup to a full boil in a pot before serving them. That satisfied most customers but there were a number of customers who would automatically reject the soup when served. They insisted that the soup was still not hot enough! Why? The reason was that the soup bowls were stored at room temperature and the customer was simply touching the bowl to gauge the soup’s temperature. No amount of persuasion would change their minds. More experienced waiters then taught me a trick - dip the soup bowl in hot water before serving. That usually worked. (I did have one rather obstinate customer who still rejected her soup; I ended up holding the bowl with tongs over a grill – she definitely felt that it was hot enough after that touch test.)
This was my first lesson on customer inferences. In the words of MIT Sloan Professor, Duncan Simester, customer inferences are “Using observable cues to infer unobservable product features”. Whether true or not, the restaurant customers inferred that touching a hot bowl meant that the soup was also hot.
Duncan Simester cited another example of inferences – McDonald’s encouraged their franchises to maintain a clean parking lot since people inferred that a clean lot meant that the kitchen and restaurant were also clean.
People infer when making buying decisions – the solid thump when closing a car door infers that the vehicle is solid. People usually associate a higher priced item as being a more premium product of higher quality. Many digital cameras and smart phones advertise high megapixels counts because people believe that more megapixels means better photographs (not necessarily true). Brand names are constantly extended to other products if they infer quality and value to the buyer. It’s also how a small one-person outfit can outshine a multi-national corporation by creating a fancy company website with stock photos of good looking professionals – the internet can make all companies appear the same.
In fact, one can argue that the user experience after purchase and quality impression is also based on inferences.
Being innovative and having a great product is not enough for success – do not expect that the customer will recognize that a product meets their needs. Professor Simester states that people are selective and interpret information based on prior experience. We interpret information in a manner that supports our beliefs, and retain information that supports our past beliefs. One of the world's top automobile manufacturer visited my company to select a vendor for a major contract. In addition to technical specifications, their decision factors included observing how clean our factory was and whether the employees appeared happy and friendly.
It is important for firms to address these inferences and understand how customers learn about and choose products. In some cases, it could be through educating the consumer. In other cases, the customer bias is too ingrained. That means properly satisfying their inferences, i.e., make that car door sound solid when closing or torch that soup bowl over a grill.
Frank Lio is a Product Manager, Strategist, and Change Agent in the Hi-Tech industry. His growing track record of successes include creating 3 winning software products, leading nationwide seminars, and turning around a failing business unit. He is currently serving a dual role as Product Manager and Business Team Support Manager at Instron ITW.
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