- Improving the quality of feedback data collected
- Improving the speed and depth of text analysis
- Improving how client feedback gets actioned
- Improving the volume of feedback collected in a year
AI helps improve the quality of feedback data collected
Does AI finally herald the end of tick box feedback? I’m not going to be quite that bold in my predictions, but it certainly reduces the need for it.
We’ve all suffered through surveys packed full of multiple choice questions. The logic is that they are easier for clients to answer, and to a point that’s true. But often they are used to make the data easier to analyse, rather than to make the user experience easier.
The most effective tick box questions are very specific, and most feedback is nuanced. So we end up battling through several questions asking variations of the same thing.
Ironically, AI can make feedback collection more human! When friends talk to other humans, we don’t ask about experiences with a list of closed questions. Instead they ask a couple of open questions and let people explain their particular emotions and experiences.
The same can now happen when collecting client feedback. The old way was to ask questions ‘inside-out’, creating multiple choice options based on what you think clients will say. Now you can ask questions ‘outside-in’, using open questions that let clients tell the story from their perspective.
My two favourite questions are ‘what are we doing well’ and ‘what could we do better’? Simple, open, human questions that give clients the space to share what’s driving their experiences with your firm.
AI improves client feedback by not putting words in the client’s mouth. Instead it empowers you to ask open questions – and you can do that because the text analysis now happens automatically…
AI helps improve the speed and depth of text analysis
Natural language processing (NLP) is a proven form of artificial intelligence that’s perfect for analysing unstructured client feedback. Unlike large language models (LLMs) that generate new text to explain the original text, NLP focuses on what clients are actually saying. This empowers your people to define the ‘so what’, based on your strategic priorities, brand values and the context of a specific client situation.
For example, the NLP algorithms used by MyCustomerLens can identify:
- What clients are talking about.
All the comments related to each relevant topic is clustered together so you can see what they’re saying about your competitors, thought leadership, brand values etc - How you make clients feel.
Client experience is not a process, it’s how you make clients feel about interacting with you. Sentiment analysis can reveal these emotions within open questions, as you discover what delights and frustrates your clients. - Why these experiences are driving client behaviours.
Clients evaluate their experiences by relating them to a series of ‘value drivers’, which are similar to the ways that firms seek to differentiate themselves. Responsiveness, approachability, expertise, value for money etc. You or your clients may use different words, but the underlying value is the same.
As Aileen Leahy at Shoosmiths put it, AI enables you to retire your highlighters. There’s no more need to manually trawl through raw text comments, looking for common themes. AI can search for 100s of thousands of themes simultaneously and instantly – humans can typically spot 7-10, depending on how much coffee they drink!
As a result, AI-powered text analysis can reveal insights in the silence. Analysis using NLP algorithms is not a black box, it’s consistent and predictable. That means if client’s don’t mention particular topics or value drivers, it can be a significant signal that humans would miss. For example, client testimonials are often more insightful when you look for the brand promises and experiences that clients don’t mention.
AI helps improve how client feedback gets actioned
Having asked rich open questions, and then applied fast and consistent text analysis, you still need your firm to do something with what’s been heard. It’s time to turn insights into actions.
Always-on client listening platforms use AI to deliver the right insights to the right people at the right time. Being hit in the face with a firehose of feedback (metaphorically of course) isn’t fun. Marketing, BD, client teams and the Board all have different insight needs.
AI helps each team to see just the real-time actionable insights that they need for their role. With an up-to-date view of how well services, experiences and brand promises are being delivered, each team can quickly see the ‘so-what’. They can use their experience to discover root causes, prioritise and then take action.
This is where the AI enables, rather than replaces the humans. AI doesn’t know your priorities as a firm,as an individual or for his client. So it shouldn’t be telling you what to do next.
What it can do is then track and analyse those actions. AI analysis can tell you what actions are being taken and why, and where they are having the most impact.
AI helps improve the volume of client feedback collected
I tend to think in threes and draw circle diagrams. So while this is a 4th point, it’s the one that completes the circle! Specifically, when you apply AI to client feedback you can create a flywheel of collecting and acting on feedback.
AI takes away most of the manual work that slows client feedback down. As a result, you can collect and action more feedback, which in turn boosts engagement and enables even more feedback to be collected. The voice of the client goes from an individual whisper to a collective roar! For example:
- You can create shorter surveys by asking a couple of open questions, rather than lots of closed questions.
- You can send short surveys at more stages of the client journey, rather than one long one at the end
- You can respond to feedback – positive and negative – much faster, showing client’s you are genuinely listening.
By considering the user experience and showing how you are listening, AI-powered feedback reinforces the message to clients that it’s worthwhile sharing their experiences because something good comes from it. It makes the experience of sharing feedback a positive one, dissolving the myth that “my clients don’t want to give feedback”.
