How Big Data Could Facilitate Selective Perception Marketing

February 13, 2014
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Selective perceptions

This scenario should be familiar to the many of us. Let’s say you have just purchased a brand new iPhone. No sooner had you bought the phone, than you see brand new iPhones all over the place!

Another classic example is that when women get pregnant, they suddenly start seeing pregnant women everywhere. After childbirth, they stop noticing pregnant women and start seeing babies everywhere. This is called selective perception. 

This could be graphically illustrated.

Selective perceptions

This scenario should be familiar to the many of us. Let’s say you have just purchased a brand new iPhone. No sooner had you bought the phone, than you see brand new iPhones all over the place!

Another classic example is that when women get pregnant, they suddenly start seeing pregnant women everywhere. After childbirth, they stop noticing pregnant women and start seeing babies everywhere. This is called selective perception. 

This could be graphically illustrated.

 
Image

Depending on their age, some people recognize a young lady, whilst others recognize an elderly woman. This picture succinctly captures the essence of selective perception. In other words, people tend to see the things that reinforce what we believe, or the things they could relate to; the rest are usually disregarded.

Trigger events

Perceptions are triggered by events that we experience over the course of our lifetime. These actions, called “trigger events”, shape how we see or perceive any particular situation. These perceptions are gradually revised and changed each time we experience a set of trigger events. Hence, selective perceptions are continually created and modified.

This concept is particularly important in a marketing context. By concentrating on the set of trigger events, one could design marketing campaigns using text, visuals, media or scenarios that resonate with the target audience. In other words, this means designing communication contexts that capture prospects’ attention and maximize the likelihood of a campaign’s success.

How perception marketing works

In the book, “What the Dog Saw: And Other Adventures”, Malcom Gladwell describes seemingly usual incidents and how they are perceived by different people. 

For example, two people who witness the same incident may report the activities and incident differently when asked to do so. Such an exercise helps in understanding elements critical to selective perception marketing. This allows mapping the internal circuitry and how different audiences process and use information. The mapping results in a blueprint of internal mechanisms that is vital to decision making, such as whether to buy a particular product or service and so on. Selective perception marketing is about customizing messages that help position a company’s product or service in the mind of consumers by tapping into areas involved in purchase decision-making. 

This is especially crucial given the fact that customers are inundated with marketing materials over diverse channels that the divide between potential value and worthless buzz is nebulous. Customers’ tendency is to shunt themselves from the marketing brouhaha and seek other “values” that resonate with the products or services of particular company. To achieve this feat, marketing messages must survive the ordeal of getting past consumers’ internal filter that automatically shuts out the advertising overload and outright rejects information not pertinent to their lives.

How can Big Data help?

Most companies collect and analyze volumes of data about their customers and their shopping behavior. This is especially true of most online shops, such as Amazon, which sell a range of products. Users spend a considerable amount of time sifting through items and provide Amazon with valuable behavioral information a.k.a “Digital Exhaust” that is not normally available through brick & mortar channels. When a purchase is finally made, the algorithms could trace the actions, such as mouse clicks, product views, time spent, etc. that eventually led to a sale. 

Furthermore, by tracking a user’s past purchases, Amazon could correlate, predict and recommend items that are of interest to a user i.e. appeal to the customer’s selective perception. Thus, these items have a higher probability of a sale. Also, by collecting and processing similar purchases by other users, Amazon uses this information to refine the recommendation engine. As a result, the users find products of interest pretty quickly, which is tantamount to reducing the cost of information (in our case product) search. 

Big Data plays a crucial role here. With a single user’s behavioral and shopping information, algorithms of the recommendation engine are only partly effective. The margin of error is pretty high. However, with information about other users’ purchasing patterns, a user’s past purchases and behavioral information from a population of several million users, when used as training data sets, the algorithms in the recommendation engine iteratively adapt themselves so that the margin of error gradually reduces and becomes almost negligible. In other words, recommendation engines simulate the internal mental circuitry algorithmically and compute strategies to arouse the desired perception based on individual choice architecture – a concept that describes how decisions could be influenced by the way choices are presented. 

As a result, Amazon recommends only products of interest when a user logs in, which has the side effect of a cleaner and seeker web design that resonates well with the users. By selectively offering a limited set of products of interest, Amazon manages to eliminate the clutter and lead the shoppers only to those items that users perceived to possess substantial value.

Conclusion

Thus, it is easy to infer the potential benefits of employing Big Data and Analytics to supplement and bolster the traditional marketing concepts. Big Data and powerful algorithms help reduce the cost of information search and showcase products or services that are perceived by users to have substantial value. 

To close on a quote from Peter Drucker: “the aim of marketing is to know and understand the customer so well the product or service fits him and sells itself.”

Big Data proffers the means to achieving this end.