Menu Close

News

Alleviate clients’ survey fatigue

share
tweet
share
alleviate clients’ survey fatigue

In brief

Instead of just giving clients more empty boxes to fill, analyze their experiences without disrupting them, consult them in an informed way, and discuss your findings and plans with them. Why? Because they are inundated with requests for feedback and it results in a poor experience for them and incomplete business intelligence for the asset manager. This article explains how you can alleviate clients’ survey fatigue with behavioral benchmarking.

Welcome back to our blog series on what you (the asset manager) can do with the new benchmark of client behavior and, conversely, why you cannot do without it.

Over the series, we will explore how you can use behavioral benchmarking to complete your business intelligence, cater for behavioral differences, manage and improve the different aspects of your client journeys, differentiate your distribution strategies, and alleviate clients’ survey fatigue.

Widespread survey fatigue

Poor client experience – requesting feedback is essential but, in 2015, Forbes warned companies that they could annoy clients by asking for too much.

“As company decisions increasingly become data- and customer-driven, it’s tempting to go into feedback overdrive and attempt to collect as much data as you can get your hands on. Everyone likes to feel like his or her feedback is taken into account, but at a certain point, it can annoy people and sour them on your brand.”

On aggregate, the asset management industry is in this situation. Clients can have relationships with multiple firms, which means they can get inundated from several dozen managers with similar feedback requests.

Incomplete business intelligence (BI) – the first indications of this issue are the low survey response rates we see across the industry. This poses a big problem because it can create statistically invalid sub-samples of data, e.g. per client segment, or per geography. Even though we should not draw conclusions from such data, it is easy to misinterpret the words of a few as being representative of all.

Worse, you can only ask for feedback so often. If like many firms, you ask once a year, you create low-resolution data and material lag times.

Moreover, even statistically valid feedback will contain gaps and inaccuracies. This is because humans are tricky creatures and you cannot rely on what they say: they forget less recent events, they avoid difficult conversations, and they can end up doing things that dramatically contradict what they said.1,2,3,4

The winners will complete their BI with behavioral benchmarking

Client experience is an ‘effect’ you ‘cause’ and it occurs in two ways – what your clients say (feedback) and what they do (behavior).

Yet, in 2020, Accomplish could not find an asset management firm that was measuring and comparing its effect on its clients’ behavior. This was an issue because actions speak louder than words, making behavioral data more reliable than feedback.

Our actions are universal, observable, and quantifiable. Information about them is digitally available in high-resolution, measurable over any timeframe, and obtainable without disrupting the subject. And, thinking of those tricky creatures, it is unaffected by our innate conflict-aversion and memory bias.

Thankfully, help has arrived in the form of behavioral benchmarking.

Alleviate clients’ survey fatigue

If requesting feedback is both essential and problematic, how can you strike the right balance and avoid being a burden or an annoyance? Here’s a worked example of what behavioral benchmarking has made possible for asset managers.

1. Compare clients’ input and output behaviors

  • Output behaviors – these are a small number of highly commercial strategic actions that you can ‘dollarize’. Were they persuaded by your sales processes (RFP and pitch conversion rates)? Did they stay with you (client tenure)? And did they come back for more (products per client)?
  • Input behaviors – these are the remaining tactical actions across the client journey that contribute to the strategic outputs above. They include things like podcast downloads, activity on social media, event attendance, meeting volumes, and query volumes. For example, if visitor numbers and dwell times on your thought leadership web pages decline, is your RFP conversion rate at risk?

2. Ask informed questions – to answer the question, you need to assess why their behaviors have changed. Have macro-economic changes shifted demand? Have competitors created innovative alternatives? Or, could it be you? Develop a theory and then, if necessary, consult your clients. This is what Forbes called ‘strategic surveying’ – fewer and more informed questions that have a specific purpose.

3. Give as well as take – this approach enables you to discuss your findings and plans with clients. What better way to demonstrate the importance you place on their experiences than to involve them in the solution and to shift your conversations from ‘how did we do’ to ‘could we do more’? 

In the digital era, the data is at your fingertips

Behavioral benchmarking is already creating unique and powerful datasets of the similarities and differences you can expect across the various stages of the client journey, between particular client segments, and over different regions of the world.

As a result, asset managers in growing numbers are exploiting this new source of BI to set and monitor their distribution strategies. And from the client’s perspective, they gain superior experiences for less effort.

For this reason, at Accomplish, we believe it is fast becoming a feature of the industry landscape.

The full blog series

We hope you enjoyed this article and that it got you thinking about how you can alleviate clients’ survey fatigue with behavioral benchmarking.

The full blog series will span the first half of 2022 and will explore the following topics:

Follow Accomplish on LinkedIn if you would like a notification when we publish them.

References

1. FeldmanHall, et al, 2012. What we say and what we do: The relationship between real and hypothetical moral choices.
2. Tversky and Kahneman, 1974. Judgment under uncertainty – heuristics and biases.
3. Shafir, Simonson, and Tversky, 1993. Reason-based choice.
4. Cooper, Heron, and Heward, 2020. Applied behavior analysis.

Adam Grainger

Adam Grainger

Behavioral analytics | Data science | Asset management