Machine Learning in Market Research

Machine learning is a buzzword in the industry. This post explores how its adoption can help market researchers reduce timing and costs, and some of its challenges.


By Brooke Patton

Obviously with all the talk about machine learning, it’s clear it has a significant impact on a variety of industries. One in particular is market research, but you probably already knew that. Our previous post in this series outlined the basics of machine learning: with that knowledge, we can now begin to understand how it can apply to market research. Recall that machine learning uses a series of algorithms in an iterative process to gather, process, and interpret data into learnings. Does that last part sound similar to anything else?

Applying Machine Learning to Market Research

Market researchers actively gather and interpret information specific to consumers, so it only goes to show that machine learning and market research are a match made in heaven. Below is a more detailed outline of the machine learning process and where market research can play a role:

Chart explaining how machine learning related to market research

The most influential aspects of combining market research with machine learning includes the impact on insights and the bottom line for research productivity. While machine learning is great for data collection, its primary purpose is to quickly learn from data and adjust itself— whereas market research wants to take those learnings and apply additional action outside of the data. The ability to collect more robust data sets that can quickly gather the who, what, when, where, and how allows market researchers to focus on more important aspects like why, and spend less time on additional research timing and costs. Additionally, more data means better data quality and less bias.

Considerations

So while machine learning in market research certainly has its benefits, many wonder if market research is at risk for being replaced with machine learning. Machine learning is unable to take in external factors like politics, economics, or seasonality. So due to the fact that machine learning can gather, analyze, predict, and adjust itself all on its own doesn’t mean it can act on the findings or explain the business implications of them. For example, machine learning could tell you that someone who clicks on your ads at least three times is likely to purchase your product within the next month. However, what it can’t tell you is why they clicked on those ads and how the ads can be improved— that comes from the combination with market research.

There are also several challenges when it comes to machine learning: like data security, scalability, and cost of implementation. Data security is somewhat out of our control. With the influx of data and the difficulty in finding quality data, parameters will likely be put in place to control how much can be accessed and by whom. But it shouldn’t cause much of a slow down in the way of machine learning. Scalability on the other hand is dependent on how agile and prepared a business is before implementing machine learning. Some businesses, as we’ve seen with big data, are slower at adopting new tools than others. The cost of implementing machine learning practices is typically most expensive prior to implementing. And while the data will still come at a price, once the model and process are running smoothly, cost is less of a factor to the benefits received.

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