Smart companies use Artificial Intelligence (AI) in Predictive Customer Analytics for several reasons. One is that AI can analyze huge amounts of data that humans simply cannot process in a short time. Machines working in tandem can find correlations between data sets that would take humans years, perhaps decades, to spot. For example, in the field of medical diagnosis, data scientists theorize that data as widely disparate as breathing, varicose veins, muscle mass, and a whole lot more can be correlated so that a combination of these factors can predict a medical condition well before it develops. What’s more, they theorize that a single test may be enough to predict the probability of a whole range of diseases. This can only be made possible because of the enormous number-crunching capabilities of modern computers.
In the field of Customer Management, this capability is crucial in:
- Collection and storage of data: Computers are now being used to store vast amounts of seemingly unrelated data on customers. Websites already track sites that customers visit – even sites that are unrelated to their own businesses – so that they can build up a comprehensive database of buyer behavior, including preferences in fashion, reading material, purchase of groceries over a period of time, periodicity of purchase of various items, etc. Customers have already noticed that sites like Amazon are able to suggest what to buy along with an ongoing purchase. This is because its computers have stored all the clicks one has made on Amazon as well as any site with which Amazon has partnered.
- Collation and correlation of data: Once captured, customer data is “crunched” by these machines to form correlations that may be invisible to human beings. This can be an unbelievably huge amount of data that human beings, however skilled and trained, will not be able scan in the short time available before a decision has to be made. What’s more, this data may be dynamic and changing even as the human is working. For example, those who use apps to suggest routes to travel may find that the route suggested at first changes dynamically as they travel. This is because the apps receive data on traffic conditions, road conditions, etc., dynamically and are able to use that data to suggest the best possible route at a particular time.
Over and above the reasons earlier discussed – capacity and speed – AI has another advantage over humans; it is entirely unbiased and undistracted. All vertebrates’ brains are hard-wired to take shortcuts. All animals, including humans, take shortcuts wherever possible so that a task can be accomplished sooner. While this has suited us admirably for millennia, this tendency to take shortcuts creates bias that interferes with the accurate collection, storage and processing of data. This is an easily observable phenomenon. When a video has sub-titles, viewers often tune out the audio and rely on the sub-titles, even if they are native speakers of the language. Also, upbringing, family atmosphere and social atmosphere, education and life experiences create biases in human beings. Some biases may even be physiological and uncontrollable by the human in question. For example, many people are color blind, unable to distinguish between colors that are close together on the spectrum. Many, for instance, are unable to distinguish between violet and indigo in a rainbow. These biases prevent human beings from collecting, collated and correlating data accurately.
In addition, there is another reason why AI is increasingly being used in Predictive Customer Analytics; its ability to see hidden correlations and use them to predict customer behavior. Gone are the days when groups of customers “representative” of the customer base and segments were subjected to test scenarios to determine customer preference. However good that tool may have been in its day, it is today, a most primitive tool in predicting customer behaviour. Instead, AI can analyze each individual customer and predict his / her behavior, as well as make an offering that is most likely to be snapped up by the customer. Departmental stores have known for decades, for example, that small, inexpensive items along the checkout line are more likely to be picked up in snap decisions by customers waiting to checkout. These small items often add up to a large turnover that would not have taken place had they been restricted to their own sections. This ability of AI to predict behavior is amplified by its ability to factor in data that may be completely ignored by humans. For example, by analyzing historical data, AI can factor in the business atmosphere, social atmosphere, cultural atmosphere, and a host of other factors. What’s more, it is able to factor these in for each individual customer. For example, one particular customer may be entirely uninfluenced by a festival, while another may consider it a most important event and yet another may even consider it a nuisance and just another ploy by businesses to guilt people into buying merchandise. AI is able to use these factors to predict the behavior of each one of these customers so that they are each served with what they are most likely to buy. In other words, AI is ideally suited to create that most desired of market segments, a segment of one, to which a unique offer can be made
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