With the utmost majority of growth and developed market research companies using Analytics to get their tasks done, it still poses a question if that’s enough. The growing technology world gets updated now and then and sticking on to old ways with no update can make your firm face a step down or lag at a point behind.
So what’s to be taken care of? As we know every successor has something to be carried on from its predecessor, with fewer and right changes made, now it is high time for market research companies to opt for Advanced Analytics.
According to Verified Market Research, the Advanced Analytics Market was worth USD 27.82 billion in 2021 and is expected to be worth USD 214.35 billion by 2030, rising at a CAGR of 21.1% between 2022 and 2030.
The autonomous or semi-autonomous study of data or material using complex techniques and tools, often beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or produce recommendations is referred to as Advanced Analytics.
Data or text mining, machine learning, pattern matching, forecasting, visualization, sentiment analysis, network analysis, graph analysis, simulation, and neural networks are examples of advanced analytic approaches.
Discussed in the blog are a few points to stress the importance of Advanced Analytics usage for market research companies.
Enhances Customer Experience
Advanced analytics refers to cutting-edge methodologies that employ AI and machine learning. While data analytics refers to insights derived from raw data, advanced analytics refers to profound insight derived from previously untapped or unstructured data.
AI and machine learning thrive on this gathered data. As models change, the amount of data from which they learn grow, and as algorithms improve, businesses employ increasingly more data to create even more economic value.
Take an example where a market research company that had previously worked with an Analytics approach but now benefits largely by deploying one of the Advanced Analytics methods.
A UK tech-driven market research company that gets verbatim data from 3.5 million ‘bees’ around the world who record product choices and consumer behavior on their mobile phones. The AI and NLP approach used by Streetbees analyze and arrange this massive collection of text and photos into strong consumer intelligence. Brands utilize it to acquire a deeper knowledge of natural, in-the-moment behavior to better satisfy the demands of their customers.
Predicts customer behavior
The most frequent ways of tracking consumer sentiments have a major flaw: they cannot detect crucial emotional responses. As a result, qualitative surveys such as Net Promoter Score miss out on essential feedback. Even if they submit a good score, customers frequently reveal their genuine thoughts and sentiments in the open-ended comment boxes included at the end of surveys, and AI can assist businesses in making use of this vital data to better forecast the behavior of customers.
According to an HBR report, AI models and technologies can be useful in this situation. They extracted and mapped keywords representing the customer experience (CX) to the following dimensions: resources (e.g., knowledge, system, product, skills, etc.); activities (e.g., fixing, ordering, service delivery, etc.); context (e.g., weekend); interactions (e.g., calling, chatting, etc.); and customer role (e.g., provides suggestions or neutral). They then identified touchpoints that provoked both client emotions (joy, love, despair, wrath, and surprise) and cognitive reactions (compliments, complaints, and suggestions).
Finally, without using quantitative survey scores, the AI produces and translates crucial traits into predictive variables that may train the model to forecast whether customers are satisfied, neutral, or have a complaint.
Helps in cost-cutting
Artificial intelligence (AI) has given rise to a new solution: Businesses may scan vast amounts of market research data in significantly less time than traditional approaches by leveraging algorithms developed with data from sources such as Amazon, Walmart, and Target.
A CEO of an AI-driven Commerce Platform company has disclosed in Forbes that his company used AI to scan billions of data points and generate 10,000 market reports. The cost savings provided by AI are astounding: while a group of analysts would take months or years to scan the same quantity of data, AI can do so in minutes.
These AI-generated market reports are free to be searched, in contrast to typical market research sources, where single reports are paid for and can cost up to $4,000. He goes on to say that AI can scan market data in any language and from a variety of sources which means that market insights are available to anybody, anywhere, and in any market segment.
Finally, market research used to be so costly that it was out of reach for many product teams. Market research insights are now more accessible than ever before thanks to the power of AI, allowing for increased product innovation.
Captures customer emotions
Emotional analysis of consumers is critical for measuring marketing strategies. Sentiment analysis employs machine learning and natural language processing to discover people’s feelings and emotions about a product, service, or brand expressed in words. Market Research Companies can use it to track social media discussions, emails, online reviews, and survey replies to gain a better knowledge of how the public perceives a brand.
Most firms are unlikely to be fully utilizing the technology, which means they are not utilizing advanced analytics in ways that truly put the customer first. According to Mckinsey, only 37% of firms believe they are using advanced analytics to create value, indicating a big missed opportunity.
There is a critical difference between using Analytics that many businesses have in place and the advanced analytics approaches and methodologies that are now becoming widely available. Companies may more precisely forecast what is going to happen by applying these new tools, allowing them to direct their future by deploying Advanced Analytics.