Market Research

10 ways AI can help market researchers succeed

When we see representations of artificial intelligence in the media, it’s nearly always focused on the more attention-grabbing ways things can go wrong: driverless cars entrapping us (Minority Report); virtual assistants turning on us (2001); robots enslaving us (literally hundreds of movies).

But if Hollywood is mostly anxious about AI, market researchers are not. When we surveyed 250 researchers in early 2018 we found:

  • 93% described AI as an industry opportunity
  • 80% said AI will make a positive impact on the market

And globally, it’s often a company’s research department that is the biggest advocate for AI within their organization. Just look at Google renaming its research center Google AI.

So let’s look at why market researchers - both in-house and independent - have reason to be optimistic for the AI revolution. Here’s what they can do now and into the future:

1. Analyze open-ended text responses from across channels

Most of us can’t keep up with our emails, let alone manually review hundreds of thousands of open-ended survey responses, social media comments and contact center call logs.

AI allows you to dive into those millions of words and emerge with an understanding of what your customers think, feel and want with powerful text analysis. With natural language processing and sentiment analysis running automatically across tens of thousands of pen text comments, you can see trends clearly and understand prevailing sentiment, all in real-time. It takes our incredible ability to gather data and helps you actually process it and take action.

2. Ask the follow-up question, to the follow-up question...

You can build algorithms to ask survey follow-up questions you could never have thought of, based on what the algorithm has learned from previous respondents. It’s essentially helping you dig deeper into customer responses, without needing to predict every possible response ahead of time and map out convoluted pathways yourself. It makes gathering feedback and insights conversational - something that’s been proven to gather far more robust data from respondents.

3. Find respondents faster (and make sure they’re the people you want)

You don’t need to wait till you’ve gathered data to use AI. In fact, market researchers are seeing how useful it can be much earlier in the process. For example, you can use AI to review a wider pool of respondents, removing those who don’t suit your requirements and leaving you with a better shortlist of potential candidates.

4.Make use of data already collected

In the rush to use AI to optimize surveys and gather better data, a lot of people overlook the huge amount of data already out there, whether it be old company records or in the public domain. In effect, AI helps you unlock that operational data (O-data) that some in the industry may have written off as ancient history, but which you can combine with your own experience data (X-data) and mine for great insights.

 

5. Save time writing reports

Most of a market researcher’s time is spent writing reports, but AI can help you there as well. If you think of research findings as data points, then it makes a lot of sense. An algorithm can simply learn to make certain assumptions and judgements about that data, and then generate a report for you. It can free you up to focus on higher-value tasks like establishing hypotheses, validating AI-produced findings and communicating findings to stakeholders or clients.

6. Remove bias from the gathering and assessing of data

As a market researcher, you’ll know one of the biggest risks to data integrity is bias. Whether that’s a leading survey question, or one of your survey respondents remembering some things but completely forgetting others, therefore skewing the data. AI produces higher-quality data by removing unconscious human bias and remembering all things equally as simple data. We’ve recently built this into the Experience Management Platform™ through Survey Review - an AI-powered tool that analyses every question and makes real-time suggestions on how to improve your survey and gather better data. It’s a great example of how AI is helping market researchers work smarter.

7. Focus on the more rewarding parts of your job (not admin)

Automation is nothing new - just look at your computer for evidence. But automation only frees us from repetitive tasks, ones that can be performed by a computer the same way every time. With AI technology and machine learning, you can start passing over more complex tasks that are nonetheless tedious to most researchers: think localizing surveys for different regions, or data cleaning.

 8. Stop community members dropping out or disengaging

For an internal insights team, online communities or panels help you have a constant conversation with your customers and ensure they’re at the heart of your organization. AI can support you in maintaining engagement, reducing churn and getting higher-quality results. It does this by using predictive modeling, analyzing things like logins and dwell time, to identify members at risk of dropping out. It’s then up to you to incentivize them to stay.

9. Conduct extensive secondary research

Both small and large organizations use secondary research (or desk research) when they’re looking into new markets, working on pricing strategies or reviewing their suppliers. But it takes time to do this, so it often makes way for primary research. AI can analyze troves of secondary research in seconds and show you trends and themes in the data. And rampant digitalization means more newspapers, magazines and reports are online, so AI has a lot to work with.

10. Improve your surveys continuously

You won’t need any help in writing questions or need a lesson in how to design surveys - but AI can nonetheless act as a final QA to any surveys you send out, and a constant aid as the survey gathers responses. With AI, you can see where questions may need tweaking or reveal bias, and capitalize on machine learning to optimize your surveys based on past respondents.

Qualtrics iQ is a set of advanced intelligent features built directly into the Experience Management Platform. Powered by machine learning and artificial intelligence, it empowers market researchers to generate insights quickly and easily. Make AI a part of your day job and learn more about Qualtrics iQ

How Qualtrics is using AI to improve research

Below are 4 key areas where Qualtrics has been effectively using AI to predict and improve poor data quality.

1. Improving how you ask

Asking the right questions matter. Too many questions and it can lead to fatigue and disengagement from the respondent. The words and answer choices used in a question can lead to various biases in how users answer those question. The type of question - a choice based one or an open-end text question or speaking out the answer in a voice-based device - all of these impact the quality of the response. For example, Qualtrics ExpertReview - an AI based digital research assistant - can scan through millions of anonymized responses to understand the impact various questions and their content can have on the quality of the response. It can then suggest how users can improve the questions they ask to maximize the response rates and quality of these responses.

2. Improving whom you ask:

The audience matters. For example, if you are polling opinions of voters for an upcoming election, you need to be careful that you are asking the representative sample. If you were to only ask one group of people that does not represent the entire voting population, you will make wrong predictions on the outcomes of the election. This has been the biggest reason why pre-election polls and pundits often get it wrong. AI can match respondents answering the questions against their demographic information to make inferences on the representative sample that is being surveyed. AI can then predict how representative the current set of respondents are of the actual population and recommend any changes that are needed.

3. Improving where you ask

People are willing to provide insightful answers, if you ask in the right channel. If you ask questions over mail the response rates and even the answers will be very different than say asking a set of questions via the Alexa integration in many new cars. The medium matters. AI can predict the quality of the response based on the response channel and then use that to recommend the best medium that researchers can ask questions on.

4. Improving how they respond

The research industry is seeing a rise in ‘human abusers’ - folks who, for a few quick bucks, will answer surveys posing as someone else. Bots taking surveys is also on the rise. The answers you will get from these sources will clearly be invalid. Secondly, with increasing regulations, if respondents provide information that is sensitive, it could get the researchers who are collecting this data into trouble. Recent high-profile data breaches cost billions in shareholder value because customers personal data was compromised. And GDPR regulations is a reminder with teeth that when researchers collect data, it needs to be compliant. AI can help detect these fraud patterns or Personal Identifiable Information (PII) in the responses that come through and recommend changes to the survey or flag responses that contain sensitive PII information.

At Qualtrics iQ Research Labs, improving research data quality is our mission. We are actively applying AI in all the above four ways to help researchers all over the world collect data of the highest quality.

Our vision is to prevent bad economic and political decisions for the world, one high-quality answer at a time.


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