As the professional services sector adapts to the post-pandemic world, forward-looking firms are challenging how they have traditionally listened to their clients. When Boards and Managing Partners/CEOs had to make fast strategic decisions, the “I know my clients” approach is no longer enough. Agile decision-makers are demanding continuous feedback intelligence.
The pandemic and hybrid working have shifted client needs and expectations, and how firms deliver their services. To keep up, firms need to listen to more clients more often and do more with the data they receive. By having their finger on the pulse, firms can create the rich feedback intelligence they need to make faster and more informed decisions. However, our recent law firm benchmarking study identified four common barriers that currently prevent many firms from scaling up their client listening programmes.
Barrier 1 – Disconnected feedback data is stuck in different silos
Client-related feedback is shared in many different places and formats. From interviews and surveys to online reviews and complaints. Sometimes it’s a passing comment in a client meeting or email. As a result, feedback gets stored in many different places. Gathering it all up is hard. This creates blind spots across the firm, as different teams have different information.
To overcome this barrier and generate more feedback intelligence, firms need a single source of truth for client listening. A central place to collect formal and informal feedback.
Barrier 2 – Analysing and reporting on feedback is a manual process
Closed questions produce data that’s easy to graph, but hard to action. By closing down the range of options, multiple-choice questions limit the ability to understand ‘why’. In contrast, open questions give people the space to share what’s on their mind. They can reveal new needs, emotions and expectations. But how do you make sense of all that unstructured data?
Many firms are still having to manually review verbatim comments. It is a slow and subjective process. The ability to ‘tag’ responses helps a bit, but it’s still not consistent or scalable. With clients sharing more verbatim feedback in more places than ever before, a new solution is needed.
To scale up client listening, firms are seeking ways to automate text analysis. That means taking advantage of AI. Dedicated machine learning algorithms that instantly turn raw feedback into actionable insights. Automating text analysis is central to creating more feedback intelligence.
Barrier 3 – Requesting feedback is a subjective process
“Now’s not the right time to ask for feedback”. We get it, fee earners would rather clients respond to emails than surveys. But when is the right time? Only asking when the work is done, or when the client is in a good mood, is a false economy. At best it limits the breadth of feedback. At worst, it generates feedback when it’s too late to respond. As a result, only 35% of the feedback collected gets shared across the business where it can shape firm-wide decision-making.
Forward-looking firms are moving to an always-on approach to client listening. They want to make it easy to share feedback when the client is ready to share, not just when the firm is ready to ask. This continuous listening becomes possible when feedback analysis becomes automated.
Barrier 4 – Experiences are only seen in the rear-view mirror
Manual feedback analysis takes time. When it’s combined with periodic feedback, the insights lose their impact. In the post-pandemic world, decision-makers need to have their finger on the pulse. They want to see emerging trends, new needs and expectations. They want to see the impact of the decisions and actions they’ve taken. But no matter how good the insights are, if they’re out of date they struggle to compete against recent anecdotes and assumptions.
One of the attractions of always-on client listening, is the fresh insights. Client listening teams are seeing what clients are experiencing right now. This makes it easier to align the business and deliver a differentiated brand experience.
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