Businesses must comprehend what their buyers are thinking and experiencing to flourish. Companies invest a significant amount of time and money in gaining a deeper understanding of their clients.
Despite this significant expenditure, however, most businesses are not particularly effective at listening to their customers. It’s not through a lack of trying; the tools they’re employing and the data they’re trying to collect just aren’t up to the task. Customer satisfaction (CSAT) and Net Promoter Scores (NPS), according to our research, fail to inform firms what customers truly think and feel, and can even disguise major problems.
So, Where does the Problem Lie?
Quantitative surveys have been the industry standard for years. Customers are asked a single question: On a scale of 0 to 10, how pleased are you with this company’s product or service? Or, how likely are you to tell a friend or colleague about this product? While these surveys are time and resource costly, and customers are becoming less willing to participate as they get more intrusive, they have remained a key component of organizations’ customer knowledge strategy.
The difficulty is that these surveys are unable to detect crucial emotional reactions, resulting in the omission of critical feedback. Customers frequently rate companies highly in surveys, even when they have severe difficulties with their products or services, according to our research — a critical reaction that they overlook. These surveys can also cause businesses to lose consumers without them realizing it because they disguise serious customer unhappiness.
The good news is that most businesses have the ability to rapidly fix this blunder. Now an AI-driven strategy has been created that practitioners may use as a template to tweak their customer feedback procedures.
The Role of AI in Decoding Real Time Respondents
Quantitative surveys are popular for a reason: they allow you to ask a large number of clients how they feel. Focus groups and personal reading and evaluating client comments, for example, were too time-consuming to expand. Now that technology has altered what is possible, tactics must adapt.
The first and most important shift that businesses should make is to refocus their investments on customer sentiment analysis. They should start with the qualitative feedback and then move on to the quantitative survey data. Firms could even consider abandoning quantitative surveys altogether if they have the right tools to analyse qualitative data (e.g – Customer relationship management systems, social media, customer reviews, emails, call centre notes, chatbots, and so on), as these allow them to hear what customers are thinking and feeling across multiple touchpoints in real-time.
AI models and tools can aid in this situation. Marketers and customer experience managers are still hesitant to use AI technologies, and those that are accessible tend to show just positive or negative opinions.
We at Maction use a customer-focused framework to extract and map 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, or situations that affect the experience (e.g., weekend); interactions (e.g., calling, chatting, etc.) and customer role (e.g., calling, chatting, etc). (e.g., provides suggestions or neutral).
Finally, without using quantitative survey scores, AI produces and translates crucial information into predictive variables that may train the model to forecast whether customers are satisfied, neutral, or have a complaint.
Customers’ specific vocabulary may be captured by AI algorithms, which can then be combined with traditional rating systems to provide deep insights. These insights can have a direct impact on both short- and long-term consumer retention strategies.
Six Benefits of Using AI in Market Research
1. AI shows you the missing bit
Finally, without using quantitative survey scores, AI produces and translates crucial information into predictive variables that may train the model to forecast whether customers are satisfied, neutral, or have a complaint.
Customers’ specific vocabulary may be captured by AI algorithms, which can then be combined with traditional rating systems to provide deep insights. These insights can have a direct impact on both short- and long-term consumer retention strategies.
2. Train your employees
Understanding how your customers work with your firm allows you to build a customised training programme to educate employees on how to empathise more with customers, care about their issues, and interact with them seamlessly.
When confronted with customer complaints, one company’s employees were frequently inflexible and uncaring. The company used this information to train employees in customer experience workshops to deliver key messages about customer care, customer empathy, service recovery strategies (what to do when things go wrong), and corrective action. By following these customer experience actions, firms saw an increase in customer satisfaction and an improvement in retention.
3. Identify root causes
To solve a problem, you must first comprehend it. When it comes to customer experience, companies can use AI-generated insights to figure out not only where problems exist, but also why they exist.
Communication was a major issue in one case. The knowledge gained was put to good use in mending relationships with customers who had been identified as likely to defect. The business took decisive action. First, account managers began following up with these selected consumers to learn more about their issues. The company then invited key customers to a business event to address the reasons for the service failures in one-on-one discussions.
4. Capture responses in real-time
In real-time, businesses should record how customers feel about the service through discrete emotions — pleasure, love, surprise, anger, sadness, and fear — and extract cognitive reactions, which are conceived through customer evaluations (complaints, compliments, and ideas). Real-time feedback is critical since emotional and cognitive responses fade over time, and aspects of the interaction are likely to be forgotten. Companies might reconsider their present customer experience measuring programme using AI analysis.
One of the companies, for example, was testing three crucial touchpoints and embedding feedback mechanisms into each of them so that our AI model could analyse data in real-time.
5. Identify and eliminate decreasing sales
By combining NPS with customers’ emotional responses, businesses can classify clients based on their monetary value, allowing them to notice declining sales. This knowledge could alert businesses to predict sales declines and assist them to cut costs related to client loss and acquisition. The organisation is able to intervene to avoid losing a customer by detecting when a consumer drops to a lower category score.
6. Improve customer experience
Finally, businesses may utilise these insights to diagnose the underlying causes of consumer distress and prioritise which fundamental causes need to be addressed. This allows managers to stop doing particular actions (complaints), start performing new actions (suggestions) and continue performing existing actions (actions) (compliments).
The Bottomline
Firms may monitor the customer experience in real-time and produce insights by deploying an AI-driven model, allowing them to provide a flawless customer experience and intervene in a timely manner for optimal service recovery. As a result, firms can use data from both internal and external touchpoints to meet the core goals of continuously and proactively implementing customer experience in order to retain consumers, achieve customer loyalty, and achieve long-term success.
