AI in Market Research - A Comprehensive Guide

(1) Omnichannel Open-Ended Text Analysis

No matter how smartly the businesses operate today, the customer still remains the king.That means, without the customers in place, there’s practically no value of a business and well, isn’t it enough of a reason why the target customers should be lent an ear to, even before they’re there?

Right, the obvious next thought is, manually attending which-customer-has-to-say-what is practically only as possible as dropping a jar of mustard seeds on the floor & handpicking each back in the jar. Surely not ‘impossible’, but tedious to the t!

AI, on the other hand, does it effortlessly when integrated to your market research workflow, here’s how : It takes each & every open-ended survey response from an array of communication channels - from the age-old emails to the trending social media comment boxes & more, and

deep dives into them to sift out the exact thoughts & feelings off those information.

While it may sound pretty black and white to analyze sentiment from the comments, it actually isn’t. Let’s look at an example! Suppose, you are performing a research on reviews of a particular hotel and two of the reviews go like this :

Review 1 : “In one word, wow! Everything about our stay was perfect, from the food to the clean linens to the courteous staff, loved it.”

Review 2 : “Breakfast wasn’t served on time, nobody bothered to clean the space, took 10+ attempts to reach out to room service on call..wow! could it be any better?”

Now, evidently, simply relying on keywords for sentiment analysis will not be a great idea in this case, as the ‘wow’ in first certainly doesn’t mean what the second ‘wow’ does! To ensure there’s no such cognitive hiccup in research, at BinaryFolks we build robust NLP and deep-learning driven sentiment analysis module to level up your market research game, as the model auto-runs to scan through free flowing texts in real time, to analyze the implicit sentiment instead of casually capturing literal meaning.

(2) Conversational Insight Collection

Suppose, you are collecting data to set-up the menu for a new cafe. Now, while running a survey with your target respondents i.e. youngsters who usually form the larger chunk of cafe crowd, you ask a question, “What are the top 5 items you usually order when you visit a cafe?”

Now, you get answers like, “Cappuccino, brownie, sandwiches, cake, croissants..”. Does that sound like enough information to jump straight into the menu drafting phase? Clearly, no.

But, if you further ask, “Any specific type of cappuccino?”, you’ll get answers like, “nutella cappuccino”,”java chip cappuccino”, “pumpkin spice cappuccino” etc. Or with a niche question like “Which type of cake?”, you’ll receive responses as “strawberry tea cake, lemon yogurt cake, fruit cake..” etc.

That’s the necessity of follow-up questions in market research - to clarify the information to add highest value to the research outcomes! However, it needs no clarification how difficult it can be to go about such a conversational follow-up survey trail manually, right?

That’s exactly where AI steps in to ease your struggle and does it, well, way better, with the highest possible efficiency & accuracy! AI-driven market research empowers businesses across industries to collect to-the-point, meaty information. Bespoke AI models closely monitor consumer interactions and thus, identify repeating patterns. Conversational chatbots can be trained with similar input to conduct an application set, for providing an advanced, more improved user experience. Guess what’s even better? These chatbots can actually self-learn from the conversations on-the-go! No more boring surveys for your respondents & better consumer market insights for you - good deal?

(3) Transform Operational Data Into Experience Data

Putting actionable insights into action is the second step, the first is still extracting the right insights! Let’s face it - there’s no dearth of data in today’s date, be it those present external to an organization i.e. across the globe, and that a business produces throughout its day-to-day functioning. But most of that internal operational data often goes unprocessed as they are stored somewhere & left untouched. But, while it’s a great idea to put your AI models to collect external insights, getting them to process your existing operational data is equally valuable.

Think of a second pandemic hitting the world in about a decade's time and you opt for AI market research that only considers the available data from that time. While you’ll surely get a grasp of the market scenario of that time, you’ll miss out on the crucial data points present in the historic data that holds information regarding the first pandemic’s impact on your business metrics & combat mechanisms adapted at that point including what worked then, what did not. This is where the power of historic data lies! A custom trained AI engine can help you unleash every bit of potential from your operational data and transform it to experience data for mining quality insights & strategizing the workflow modification in a better way.

(4) Auto Report Generation

What’s the easiest way to get to the ‘execution’ phase, after the plausible data is crunched into actionable insights?

By visualizing them into straightforward, visualized reports!

Artificial intelligence driven report generation modules empower businesses to generate crisp reports with customisable metrics. As a result, the different departments of an enterprise can mold the reports as per their need and feed the crucial decisions with what’s relevant, without any fuss!

For example, let’s suppose you are running a shipping business & you onboard AI in your market research wing to make more-informed decisions. Your operations team can generate reports showing relationships between empty miles, regional segmentation, package placement, freight scheduling etc. for safe & economic operation.

Your accounting team can look at reports showing expenditures & profits across a custom-set timeline to optimize future financial decisions.

(5) Bias Removal for Highest Data Integrity

Data integrity has been a constant concern for the market researchers since ages & bias has been the biggest reason. From a survey question being partial due to a biased questioner, or an answer being biased due to bias at the respondent’s end, chances of data skew remain high. AI has enough potential to combat bias to a great extent, as it generates high-quality data, mapping it against reasonable, standardized associated metrics. That way, data bias is minimized, ensuring that the research results in near accurate, close to unbiased data.

(6) Customer Success Automation

A customer's post purchase experience is just as important for your business’ wellbeing as new customer acquisition. While it can be a tedious task for a customer success team to stay on top of all the follow-ups and even the ‘stay-in-touch’ messages. But when AI takes over the same task, from ‘scheduling’ to ‘collecting insights on the message content & frequency performance’ to ‘better performing metric suggestion’, everything is done with far more precision & accuracy. While this takes the customer retention game up by notches, having a loyal customer base is undoubtedly a big plus. 

AI in market research enables effortless community maintenance through predictive modeling, i.e. compiling an array of attributes associated with customer’s time spent over your business’ digital presence. With such streamlined churn prediction, it becomes easy for your customer success professionals to prevent the same with timely measures.
 

(7) Advanced Secondary Research

Effective research isn’t a one step process and who knows better than you, how the dedicated unique purpose differentiates one round from another!
As the name goes, secondary research is the layer of deep digging performed over the information collected through the first round of research. In essence secondary research is a great exercise for companies operating at any scale, as it helps with understanding emerging markets better, strategizing pricing in the most profitable yet competitive way, reviewing their suppliers and more of such crucial aspects. Now, what exactly differentiates primary research from secondary? The extent of refining the information. 
Quick example, let’s assume you’re performing market research to chalk out a list of routes for a freight rental service company. 
Through primary research, you’ll be collecting all the information regarding what your competitors are offering, their pros and cons, the USPs etc.
Now, a secondary research should ideally be aiming to leave you with a crisp list of routes along with the logic that’ll justify your service specs definition. 
AI-based market research can take up your secondary research goals and finish it within much less time. With its striking capabilities ranging from factually analyzing the impact of trends to aligning the resultant with your specific goals, an AI based secondary research has become a contemporary Chief Data Officers’ choice for obvious reasons!
 

(8) Self-Evolving Surveys

What’s better than seamlessly getting surveys completed within deadlines?

Surveys that learn from the responses and self-trains to achieve more streamlined responses.

While it sounds like a dream, the late 21st century AI advancements have made it a reality & how! Drafting the right questions, the 0th step of market research, has always been a matter of concern - as the more mindful it is, the better responses are obtained. That’s the struggle AI has eased by not just shooting a pre-set questionnaire, but also closely analyzing the answers and altering the questions ahead accordingly, to get improved results reaching the core goal.

At BinaryFolks, we follow an advanced approach to build ML models that enable your business to evaluate the percentage match between past responses & standard responses of asked questions and tweak the next question based on the experience, increasing response quality on-the-go.

(9) On-Point ICP Filtration

Would you rather, reach out to 10,000 leads chosen matching ICP somewhat 60% on an average,

Or,

10,000 leads chosen matching ICP to exactly 100%? Clearly, quality wins over, making the second, the obvious choice! AI based market research digs deep into the customers’ previous purchase pattern & backtrack to evaluate the common factors among their socio-economic status along with the professional & personal details, deriving eligibility quotient. As a next step, it generates a detailed & logical ICP, showing whose response will do highest justice to your research. Further, AI recommends strategies to retrospect & modify the ICP based on its previous filtration accuracy & external factor evolution. This way, your human resource is effectively utilized on hot lead processing, instead of effort-draining, slow lead generation & workaround.

[a] Advanced Decision Intelligence for More Informed Business Choices

Market research primarily aims to back vital business decisions with practical data points compared to anticipate the future to the closest possible extent. That way, the next-course-of-action doesn’t come from the gut, but from data, with logical reasoning!

[b] Chatbots for Conversational Qualitative Response

If you analyze the AI-powered chatbots used in market research today, you’ll find that their capability is restricted to only answering questions which are pre-fed. But the AI practitioners anticipate that as AI advances ahead, conversational qualitative response might be a possibility. In simple words, the prediction is that chatbots may be able to auto-train themselves to answer different forms of questions encountered, through qualitative self learning.