How AI Transforms User Feedback Analysis
Customers in the digital era expect businesses to not only understand their wants and needs but also to anticipate and exceed them. Customer feedback, therefore, is a critical business growth tool. Traditionally, businesses have used surveys to gather information regarding customer experiences. Organisations have tried to gauge customers' sentiments using this collected data, but the efforts have not yielded much in return.
Two of the most widely used feedback measures have been customer satisfaction (CSAT) and Net Promoter Scores (NPS). However, these are quantitative scores and do not indicate customer emotion. Customers increasingly find the surveys for these measures intrusive and score them randomly rather than as a reflection of their opinion. The scores might be high even when customers are highly dissatisfied.
A large pool of customer sentiment data is available in survey comment boxes, emails, chats, product reviews, calls, and social media channels. Third-party data sets covering data on customer attitudes, purchase behaviours, and preferences are also accessible. IoT-generated data like store navigation and device-usage data can reveal detailed information about customer preferences. The content of these qualitative feedback avenues is a much more dependable repository of customers' views.
Businesses need to rethink their strategy and focus on analysing qualitative data. Organisations need to process and gather the multi-source transactional, behavioural, demographic, geographic, and psychographic qualitative data into a data lake. The confluence of this array of data sources and AI-powered analytics lets businesses know customer thoughts and emotions across a range of touchpoints in real time and make quantitative surveys obsolete. How can companies combine AI and customer data to create great customer experiences and maximise growth?
AI analytics helps companies determine what the customers want and avoid misjudgements. AI can identify themes and sentiments in feedback through labelling, categorisation, and clustering. AI calculates predictive scores for customers based on their journeys. Organisations use these scores to predict customer satisfaction levels, revenue, loyalty, and service cost, thus facilitating ROI estimation and linking initiatives to business outcomes. AI algorithms can identify the touchpoints that matter to the customer and ensure sufficient allocation of resources to those critical touchpoints. AI tools provide insights into the impact of customer interactions with staff. This visibility allows organisations to create customised training programs that educate staff to better allay customer concerns.
AI analytics can detect issues and their root causes in real time. The root cause analysis helps organisations prevent issue recurrence. With AI, feedback capture mechanisms are inserted within the touchpoints to run real-time analyses of customer sentiment. AI-powered predictive customer experience (CX) platforms generate a precise and quantified assessment of the elements influencing CX and business outcomes. They enable the linking of CX to value generation and justify CX improvement. These platforms have a range of applications, from strategic planning and performance management to real-time customer engagement.
Leading global brands are taking CX to the next level through an AI and data-powered hyper-personalised customer experience. Hyper-personalisation is about setting up customised and targeted experiences using data, analytics, AI, and automation. Through hyper-personalisation, organisations can go beyond customer satisfaction to spur brand loyalty and increased spending. It maximises revenue through data-driven content, individualised pricing, and product targeting.
Traditionally, customer segmentation was the mechanism to send relevant messaging and offers. AI-based hyper-personalisation tailors communication and offers at the individual level by using data specific to the customer. Personalised product recommendations or exclusive discounts are generated using AI solutions and individualised customer data such as psychographics or real-time engagement with the brand. It is a segment-of-one approach that allows optimised messaging, offers, and channels. Organisations can hyper-personalise the customer journey through customised webpages, tailored products, individualised pricing, personalised after-purchase services, and contextualised loyalty programs.
Hyper-personalisation utilises real-time browsing, purchasing, and other behavioural data, making it more complex and attuned to customer needs. The insights from co-relating multiple data sources provide a 360-degree view of the customer experience. The business should have a precise vision of how the hyper-personalisation strategy aligns with customer needs. Hyper-personalisation is a consolidated technical implementation and impacts multiple areas of the company. A roadmap that clearly delineates the personalisation deployment stages, keeping business priorities and estimated costs and returns in mind, is essential.
In this era of high and generally unforgiving consumer expectations, combining AI, enriched and co-related data, and automation can ensure a memorable, highly-personalised, and consistent omnichannel brand experience.
*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.