Dear Market Researcher, don't get left behind with AI

 
don't get left behind
 

Artificial Intelligence was one of the hottest topics of 2017.

A.I. in your life. While many think that AI is just the trend of the moment, it is much more real than they believe. You might have spotted it in many of the applications on your phone or in our computer: virtual personal assistants like Siri (Apple) or Google Now, online customer support and music or movie recommendations.

 
 

AI and your job. When it comes to the workplace, the general belief is that AI will kill jobs. While this is true for certain sectors (e.g. customer service, web marketing, etc.), we are far from a scenario where AI can replace humans even in very simple tasks.

We think that within the next 5 to 10 years, AI will change the workplace for better.

AI in Market research. Let’s take an example we are very familiar with: Market Research. At the moment, AI is being used to automate simple and tedious tasks such as coding data for further analysis. It’s also commonly used for the likes of detecting the sentiment of reviews left by consumers about products or services they purchased.

A few comments are worth mentioning here:

 

1. A one-fits-all approach towards AI does not work

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Let's go back to the example of Sentiment Analysis for coding data.

As market researchers, we want to capture a representative set of self-expression across demographic groups and psychographic consumer groups. How do we capture the nuances of a specific domain (e.g. air travels questionnaires vs. haircare products reviews) with generic Sentiment Analysis? It simply would not work!  

 

2. Train your AI to deliver what you need to understand

train your AI

The most challenging part of effectively using AI in your existing work is embedding it. Now you can tailor AI to the distinct question or problem that a particular brand wants to understand.

How to use it?

Focus on the exact question that you want to deliver on, rather on focusing on the methodology. Leave this part for an AI expert to train it to work on that specific question.

 

3. Which bits of data are interesting and which ones are not?

Which bits of data are interesting and which ones are not?

Asking people how they behaved in the past is quite unnatural because people can't always remember how they behaved. As well as misremembering past behaviours, another significant problem is simply that people often say one thing and do another, even if it’s unintentional. Subconscious biases can easily distort a respondent’s view of themselves and others, thus affecting the resulting data.

Now we have a different issue on our hands: how can we trust the respondent and ensure accuracy? Fortunately, there is a modern solution. We can use social media listening to uncover insights and to understand human behaviour.

However, social listening can be very noisy, so how can we find out which parts of the data are interesting and which aren’t?

The value of AI is that it can cut through the big data and get to the quality data.

 

Conclusions

Social media listening can be more accurate, more in-depth and can reveal insights that respondents simply aren’t aware of. Artificial Intelligence can work through huge amounts of information and has a great collection of features and capabilities with which to harvest insights. As a new year begins, don’t get left behind! Innovate your workflow with AI and new methodologies.

How does it work in practice? Find out how we used AI to uncover insights from social media in one of our case studies.

Discover how we can help you get started with AI: schedule a quick call to discuss one of our tailored solutions for you.