- Hunting down feedback data stuck in different systems and siloes
- Manually tagging a spreadsheet full of verbatim comments
- Duplicating powerpoint reports to share updated charts
But these are just 3 manual tasks that client listening managers find sapping their time and energy when they’d much rather be doing the fun stuff, like using their expertise to identify the ‘so what’ for their firm, or using their internal networks to drive collaboration and action informed by the insights.
So how can AI help scale the impact of your client listening programme?
You’ll be pleased to know this isn’t a post about how ChatGPT will save the world. Large language models are fun to play with, but they’re a long way off being able to summarise how specific firms should respond to client feedback.
Instead, client listening processes can benefit from more practical AI applications.
3 ways AI can help you today
1. Hearing the real voice of your clients
Have you ever battled through a long customer survey, full of multiple-choice questions that didn’t let you share what was really on your mind? These surveys have traditionally used multiple choice questions to avoid the cost and time-delays of manually tagging unstructured text data. In effect, they ask customers to categorise their own experiences within the buckets the firm has chosen.
A form of AI called Natural Language Processing (NLP) has changed this game. You’re now free to ask open questions, just like in a conversation, and the text comments get analysed instantly and automatically. NLP algorithms can scan every comment looking for thousands of relevant themes, emotions, and keywords so you can keep up with evolving client and market expectations.
This means you can replace most multiple-choice boxes with just two open questions – “what do we do well?” and “what could we do better?”. By getting out of your clients’ way, you give them space to share what they want to tell you.
2. Expanding the breadth and volume of feedback
Once your text analysis becomes automated, you’re able to reimagine what “feedback” looks like. You’re no longer limited to formal surveys and interviews. I’m now seeing firms discover additional client feedback in client portals, email inboxes, complaints, slack channels and CRM file notes.
The key to transforming all this disconnected and unstructured data, is having a single place to collect and analyse it. A place where you can quickly find the client insights you need, when you need them.
AI analysis is enabled by using modern ‘NoSQL’ databases that don’t have a rigid structure like the ‘relational’ databases used in traditional survey tools and CRMs. What this means for client listening is that these databases can easily expand to store new data formats, while also making it easy to analyse and report on the text in real-time.
3. Tracking your brand and reputation in real-time
Brand and reputation has traditionally been measured by bespoke research projects that due to their cost can only be conducted every year or two. In contrast, an AI-powered feedback flywheel can enable firms to monitor their brand and reputation continuously and to effortlessly benchmark the results across service lines and sectors.
This is possible with AI algorithms that are constantly analysing text comments, looking for examples of whether brand promises match client reality. Think about how your brand is positioned. Your firm may be looking to deliver stand out experiences based on responsiveness, expertise, innovation etc. Now imagine if you could see which client comments mentioned these brand attributes, which ones didn’t and what was driving those experiences.
With those kinds if insights at your finger tips, your firm could make experience your strategy for strengthening relationships, reputations and revenues.
