(AI helps you get market research right)

Market research is the main tool for businesses to get information about potential and current customers, the competition, and the market. In recent years, new technology was introduced into the Market Research playing field – Artificial Intelligence (AI). Unlike traditional market research, AI market research accounts for the entire customer journey, from the first click to the satisfaction surveys. This is a major game-changer.

What is Market Research

Market research is a data-driven evaluation of the potential of a new product or service or an understanding of what changes can be done to make a brand more attractive. This research allows businesses to identify their target audience, collect market information and analyze customer insights. Moreover, the data helps overcome potential marketing challenges and is the base for marketing strategies that improve brand innovation and success.

Key takeaways:

• AI can help contextualize your data and allow you to make better business decisions quicker
• To get the most out of your data-driven decision-making framework, you should start with a clear business strategy
• You can use AI and analytics engines to uncover new insights and build a strategy that can outperform your competitors
• While AI can assist with your business decision-making process, remember that it won’t replace human judgment entirely

Market research is often conducted in two steps:

Primary Research – This initial data collection is a combination of qualitative and quantitative research. businesses reach out to their consumers through surveys and questionnaires. The research can collect either exploratory or specific information.

 Exploratory research uses an interview format with open-ended questions, usually with a sample group.

Specific Research is more focused and the aim is to solve problems identified in exploratory research.

Secondary Research – This research uses data organized by an outside sources such as government agencies and the media. This information is collected from reports, studies, newspapers, and other publications.

Why is Market Research Important For Business?

According to Forbes, doing quality research before launching a new product or service provides you with information needed to make data driven decisions. The data inform pricing, branding, marketing design, and potential brand innovation. It will also allow you to know if the new product or service is sought after by consumers.

Identify potential consumer markets and industry gaps

Quality market research will also help you identify potential consumer markets. it’ll become clear who your target customers are, what are the best marketing channels to reach said audience.

Stay in touch with your customers

Market research keeps businesses connected with their customers.

Using surveys and questionnaires, brand can obtain consumer insights that provide valuable knowledge of consumer desires, needs, and motivations. This way a brand can improve customer experience and increases sales.

How to conduct Market Research

Identifying current and potential consumer markets is the first step of primary market research. After finding the wider market, it is important to divide customers into smaller groups based on shared characteristics (similar needs, interests, lifestyles or demographic profiles).

Identifying customer sub-groups prior to performing CSAT surveys or Social Listening can provide a more accurate analysis and help businesses have a better understanding of their customers.

After identifying the consumer market, there are three main research methods:

Online Customer Surveys – Polls and surveys have become the most popular tool to gauge customer attitude. Surveys provide insights into the customer experience with a product or service.

Social Media Listening – Monitoring and analyzing social media provides valuable Social Sentiment – customer satisfaction or critique of a brand.

Focus Groups – One of the most traditional ways of conducting market research, focus groups are formed of 6-10 diverse individuals. They are asked a few questions about new services, possible product launches or innovations, marketing ideas, and more.

The information extracted helps to better understand what customers are missing in the current market, and aggregate customer sentiment.

How will Artificial Intelligence transform Market Research?

So, we know Market Research is great. And we already know that AI is great for eCommerce. But let’s talk about how AI will make it even better in 2020:

Faster Research Delivery

Market research needs to be done ASAP in order to maintain relevance. Delay in delivery often results in outdated insights and inaccurate sentiment analysis.

Most of a market researcher’s time is spent writing reports. And it takes time. A lot of time. This results in delayed delivery, and often outdated data. AI-based market research delivers results in near real time. The AI technology collects the data from a selected target audience and automatically monitors and scans keywords or topics. And it does it all a lot faster than a human would.

More Flexible Solutions

For the Research to be effective and provide quality information, the data collection tool has to be designed with the target audience in mind. For example, Customer Satisfaction Surveys that aren’t user friendly often cause low response rates that alter scoring or provide inaccurate information.

AI technology makes surveys more interactive and pliable by using customer’s answers. Computer learning technology allows for a more dynamic analysis, and helps modify the existing tools to better suit the customers.

Improve CX Quality

The research has to provide a highly granular analysis for marketing campaign design and resource allocation. One of the biggest risks to research integrity is human error or bias. For example, leading survey questions that will skew the data, and provide misleading analysis. Inaccurate data analysis results in unsuccessful marketing and lost sales.

AI enhances traditional CX analysis methods. Research results are provided quickly, accurately, and free of any human errors or biases. The end product is rapid, highly granular, spot-on market research.

Accurate Text Analysis

It is impossible to keep up with all the emails, open-ended survey responses, social media comments, and call logs.

AI-powered algorithms dive into those millions of words, perform quality text analysis, and provide insight into what customers are thinking, feeling, and looking for in the current market. Using both Natural Language Processing (NLP) and Sentiment Analysis, AI technology runs through thousands of text comments, reviews, and survey answers, and provides real-time quality analysis.

Organizations that use a data-driven approach when making business decisions can outperform competitors and leverage new strategies for improved results. Most businesses depend on a sound business strategy to survive in challenging business climates, and adopting a data-driven decision-making process can make all the difference.

The advent of artificial intelligence (AI) to analyze large sets of data and make intelligent inferences about your business is becoming common practice in boardrooms around the world. AI is helping thousands of organizations to optimize functions like marketing, sales, and customer service.

Today, we’ll look at five ways that you can get your market research right using AI and data-driven decision-making in your business.

5 ways to use AI for data-driven decision making

Advances in AI technologies are helping modern organizations discover new insights into all elements of their business. By using AI to do much of the heavy lifting, your business can make intelligent business decisions quicker than ever before.

Every transaction, customer interaction, social engagement, or microeconomic indicator you can think of is available to use in your analytical framework. While this increases the reaction speed with which you can make decisions, it also removes any cognitive biases from the process.

Modern business intelligence (BI) frameworks now regularly deploy AI and data-driven algorithms as part of the analytics process to classify, segment, and contextualize data into actionable information. Below, we’ll go over five tips that you can use to get the most out of your AI decision-making framework and make data-driven business decisions.

Firstly, you need to formalize a strategy for when and how you’ll need to use AI in your business decision-making processes. A better way to make data-driven business decisions is to use machines for the entire process and only use human intervention where necessary.

AI that uses a data-driven decision workflow can process non-linear relationships that transcend a human’s cognitive biases. To set your strategy and determine which workflow will work best, focus on your specific objectives and establish clear goals.

Start developing your strategy by answering questions like:

What do we want to achieve with this framework?

Where is the biggest growth potential for our brand?

Are we setting our prices at the right level compared to other businesses?

When is the best time to engage our customers about a specific product or service?

If you set out your specific goals with enough detail, you can avoid being overwhelmed by the sheer amount of data that you gather during your normal business processes.

Map your datasets to your business goals

A formalized strategy sets out the goals you want to achieve with your data-driven decision-making framework. After you’ve set your business goals, you can map your available datasets and identify any gaps in your analytics process.

You may have mounds of sales data but not enough insight into how your marketing efforts are resonating with prospective customers every day. Identify the data you have, what it means in a strategic context, and what else you need to make better business decisions in the future.

For the best results, you’ll need to frame your business goals with enough detail so it enables you to answer specific questions using a variety of data sources and analysis models. You can then experiment with different strategies to find an optimized framework that suits your business.

Collect only data that inform your business decisions

More data doesn’t necessarily mean smarter business decisions. If you have gaps in your current datasets, only gather additional data that will add value to your business goals. Organizations often make the mistake of thinking that bringing in more data will naturally start providing greater insight. By setting out your business goals and connecting only the data that helps you answer your strategic questions, you can reduce the workload.

You can assign your framework’s goals into three main categories:

Intelligent insights – These questions are what help the C-suite make strategic decisions for the benefit of the organization. Intelligent advisors based on real-time data can assist the boardroom to invest in the right ideas and tools early.

Operational excellence – For technical teams, the goal is to improve incrementally during each business cycle. Frame questions to uncover where you aren’t meeting your operational goals establish your baselines of performance, and understand when to make strategic interventions.

Hidden potential – Finally, AI’s capabilities continue to evolve and machine learning (ML) algorithms become smarter over time. You can use these features and capabilities to understand macroeconomic trends and position your brand for future success by uncovering hidden potential in your organization.

Determine the value of your AI and data-driven decision-making framework

Review your strategy, datasets, and insights regularly to understand what value the framework brings to your daily decision-making processes. The ideal outcome is to move from a descriptive-analytical model to a prescriptive AI-decision making framework.

By regularly revisiting your strategic business goals, you can establish which of your efforts yield real results, and where you can trim your approach due to little or no value. Prescriptive analytics can generate multiple courses of action for business decisions and rank these according to their statistical probability of succeeding. You can also use the outcome of these models as input for future data-driven decision-making.

Don’t discount the value of human intuition

The aim of AI-driven decision-making frameworks isn’t to automate the entire business process. To get the most out of your models and analysis, you need to combine the intelligence you gain from AI and data with the expertise of your human resources.

Constantly reconciling business decisions with the framework and extracting the best insights from both human resources and AI gives your business the best chance at success. By remaining curious and constantly experimenting with different strategies, you can drive engagement that outperforms your competitors.

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

1. Analyse 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 the 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 judgments 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.

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 from 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.

The Future of AI in Market Research

Of course, progress never stands still. We are still very much in the absolute infancy of artificial intelligence, and it is a technology that will have a much wider impact on market research in the years to come. Although there is little way to predict exactly what this impact will be, the ideas highlighted here are already in development – and may arrive sooner than we think.

Virtual Market Research & Forecasting

Recruitment is expensive. In fact, depending on sample size and the length of a task, it can quickly eat away at a research budget. One proposed idea to reduce this cost and stretch insights budgets further is to build a virtual panel of respondents based on a much smaller sample.

The theory is that sample sizes naturally restrict a brand’s ability to understand the behavior of every potential consumer and customer. Therefore, taking this sample, representing it as clusters of behavioral traits, and building a larger, more representative pool of virtual respondents from the clusters offers a more accurate prediction of behavior.

There are abundant limitations to this method, such as the likelihood that virtual respondents will be limited to binary answers in the first instance. However, there is still value in this – especially when combined with the ability to run a massive number of virtual experiments at once. This could be used to find the most appropriate price point for a product, or understand how the reaction to a change in product attribute might impact sales.

Chatbots & Virtual Moderators

As Flex MR CEO Paul Hudson highlighted in a paper presented at Qual360 North America, a question still hangs over whether artificial intelligence could be used to gather conversational qualitative research at scale. Today’s research chatbots are limited to pre-programmed questions, presented in a user interface typical of an online conversation.

However, as advances in AI continue to develop, so too may these online question delivery formats. The ultimate test will be whether such a tool could interpret answers from respondents in a way that allowed the following questions to be tailored and interesting points to be probed. This will signal the evolution from a question delivery format to a virtual moderator.

Conducting Secondary Research

Not all research is primary research. In fact, for many smaller organizations, secondary or desk research is the most cost-effective option. But it can also be a valuable tool in larger insight teams when seeking to enter new markets, develop new products, analyze competitor performance, or review supply chains.