AI and Deep Learning: Impact on Market Research

The reality that customer behavior is shifting is undeniable. There are several things at play, but one of the most significant is the impact of technology in everyday life, which is speeding up and modifying the customer journey.

According to a study by online retailer, 46 percent of transactions in the Asian Market are now made using a smartphone (desktop and tablet purchases are dropping), and 88 percent of shoppers now conduct research on their phone before making a buy. As a result, the importance of analysis, insight, and market research in keeping up with consumers has never been greater.

Market researchers have the opportunity to revolutionise what has traditionally been a long and expensive process by combining big data, which enables businesses to discover what customers are doing, thinking, and motivating them, with higher and more cost-effective computational capacity. As a result, business decisions will be made with more agility, accuracy, and speed.

However, there are a number of issues that must be addressed. The first step is to dismantle the silos that exist in many businesses. Technology, research, data analysts, and marketing are all attempting to answer the same issues, but they are utilising different methodologies, thinking, and data, and as a result, they frequently arrive at different conclusions. The fact that each of these business groups uses distinct systems adds to the problem, as bringing divergent sets of data together has traditionally been a primary reason for the silos remaining in place.

It's also impossible to talk about data without bringing up GDPR. Data management has gotten more complicated as a result of the new legislative landscape. Whereas it used to be simple to fill a table with several rows of data, customers now have the right to dictate what firms can and cannot do with their personal information. Add to that the reality that data has a sell-by date and cannot be stored in a table indefinitely. There has been an increasing dependence on digital data to cover these gaps, but it appears that ePrivacy law will impact that as well.

As a result, data will become even more fragmented, as people will have more control over their digital data, and businesses will most likely be forbidden from tracking online customer behaviour without their explicit approval.

So, what are we going to do next? We travel into the future.

The evolution of segmentation through deep learning

Legacy thinking, technology, and systems are stumbling blocks for many businesses. These act like elastic bands around our past, pushing us back rather than allowing us to explore new possibilities. The ongoing use of out-of-date customer classification methods based on the notion that "birds of a feather flock together" is an example. While this was true when these segmentations were created 50 years ago, times have changed. Why are businesses still using technology from the 1970s while so many of us do not?

The answer lies in the advancement of these processes, which we call "deep learning segmentation" — a segmentation system that uses artificial intelligence to find patterns that are too complicated for humans to comprehend. This is a method for breaking down obstacles and realising the benefits of big data.

Deep learning can find patterns that are too complex for humans to identify

Traditional data analysis relies on linear trends and hypothesis testing, such as "Is this true or not?" Outside of the linear, however, there are significantly more predictive and informative patterns to be found in today's increasingly complicated environment. Deep learning also identifies patterns that are free of human bias or prejudices.

A prediction of a home move is an example of this. According to the linear approach, when a family requests for planning permission, they will stay put. They are concentrating on home improvement rather than moving. When it comes to elder citizens in coastal communities, however, the application for planning indicates the polar opposite. When people in this specific group of householders ask for planning clearance, AI has discovered a pattern that predicts they will relocate. This is due to the fact that they will be selling their excellent position property to a developer who would construct high-end homes in locations such as Sandbanks and Harbour Heights in Dorset, which are currently in high demand.

A modern, science-led approach to delivering actionable insight at speed

Traditional segmentation can help with a variety of issues, from determining the impact of seasonality to lowering customer attrition. However, one of the primary challenges is that the more generic a segmentation is, the less accurate it is at predicting individual behaviours.

Deep learning segmentation accomplishes this by layering information to create a more accurate perspective and knowledge of customers. For example, it combines transactional data with durable and stable data such as property, location, and demographics to provide better targeting. It also enables for a better understanding of spending patterns and lifetime value, which can help with acquisition and retention expenditures. Furthermore, real-time segmentations and automated replies can ensure that opportunities to engage with clients are not missed by analysing ephemeral data such as clicks, likes, and emotions that need to be acted on fast.

This method combines many forms of data and allows businesses to deal with complex and missing data. Not only that, but it also breaks down organisational silos by unifying heterogeneous data and providing a consistent language for describing consumers that can be used across various business divisions and levels of seniority.

Researchers may analyse the value and behaviours of each segment, predict where each is headed, and then decide exactly what this implies for the business by having a better grasp of what customers are doing and their motives. The extra benefit is that these types of unique segmentations can be created in a short amount of time.

The insight process used to describe what the consumer was doing, but thanks to AI and deep learning, researchers can now predict exactly what the organisation needs to do next to better serve its consumers, resulting in better business outcomes.