AI is helping professional services firms to listen to more clients more often, and do more with the data.
It does this by enabling firms to automatically collect, analyse and share client needs and experiences, regardless of when and how clients share their feedback.
As a result, firms can make more informed and agile decisions about how best to serve their client base.
Last week I joined Claire Rason
from Client Talk
for a great session with PSMG
members, talking about how AI is helping professional services firms to listen to more clients more often and do more with the data. Artificial Intelligence is such a broad topic, we drilled down into the specifics of how AI can specifically help professional services firms to automate and scale-up their client listening programmes.
AI and client listening – what difference does it make?
As is so often the case, the best learning came from the group discussions. While the specifics will stay “within the room” there were some great ideas and practices that are worth sharing here:
Why do you listen to clients?
Effective client listening programmes start with clarity around why listening to clients regularly is a good thing – for the firm and for its clients. This core purpose varies across firms. What’s important is that everyone understands the actions and decision-making that the insights must drive. Examples included:
– Client growth
– Picking up little niggles before they become bigger problems
– Driving strategic or relationship plans
– Continuous improvement
How are you listening to clients?
Clients are sharing their needs and experiences in many different ways, and at all stages of the client journey. Just listening to them at the end of a piece of work is too late. Instead, firms are using a combination of formal and informal listening:
– Relationship meetings
– Formal research
– Casual emails
– Social media comments
– Gathering feedback during events to track what people were talking about
Are you looking outside-in?
Overcoming blindspots starts by adopting an outside-in view on client experiences. This means being open to hearing what clients want to say, not just focusing on what firms want to ask. An example of this was asking how clients position the firm – where they see its strengths and weaknesses relative to competitors.
How do you overcome internal barriers to feedback?
Every firm has some people who are reluctant to ask for or share client feedback. From not wanting to bother clients, through to concern about what clients might say, these internal barriers make it harder for firms to achieve their goals. Ways firms overcome these barriers include:
– Promotion – not assuming people know why and how to collect client feedback
– Training – how to ask for feedback and where to put it
– Create internal competition – celebrating the people/teams who most regularly collect and use client feedback
Power of positive feedback
When most people hear the word “feedback”, they naturally think of “constructive feedback” – in other words, criticism. The human negativity bias, means we all have a tendency to fixate over what’s wrong. But it’s equally important to catch people doing the right thing and celebrate when clients have great experiences. Benefits include:
– Discovering when and where new things that are going well
– Boosting individual egos and team engagement
– Reducing feedback barriers by driving greater openness to clients being asked for feedback
Triangulation of data
Client listing data is far more powerful when it presents a single view of client needs and experiences. Achieving this means being able to triangulate different data sources. During the session we went from googling ‘triangulation of data’ to readily sharing examples of it in action. For example, combining feedback and operational data to compare the client experiences with the expectations that were set during the pitch process.
Summary – AI and client listening
AI, and related tech like modern databases, are helping client listening programmes to scale and flourish in 3 ways:
- The new technology is making it easier to combine and analyse multiple sources of feedback, even though the data is in different formats
- The algorithms are always looking for all the topics, emotions and value drivers – so positive feedback and new trends are automatically highlighted.
- Being able to automatically make sense of text data at scale, avoids having to trade off volume for insight. Now open questions and informal feedback can be instantly analysed and shared across the business, enabling faster and more informed decision-making.