Organizations looking to digital channels to make customer service more cost effective need a holistic measurement framework.
Many organizations see the digital channel not just as a way of selling products and services, but also for servicing their customer base or providing additional product support. There are obviously potential advantages to both customers and organizations to allowing customers to be serviced online as well as through traditional channels such as the branch or the call center. For customers, it allows them to access their accounts at a time of their choosing from wherever they happen to be. For organizations, it generally provides a cheaper way of serving customers, though often considerable investment is required to develop and launch online servicing applications, so how do you know that you're doing a good job?
We've been working to develop a measurement framework for understanding the effectiveness of self-service and support strategies. Often the challenge with measuring the online channel is that the impact or benefits are often felt in another channel and organizations don't often have a holistic view of customer attitudes, opinions, and behavior across multiple channels. Our approach is to look at this from the customer's perspective and to look at their journey around the adoption and usage of online servicing and support capabilities. The framework is built on four key measurement areas: awareness, consideration, usage, and performance.
You might have the greatest online capabilities in the world, but do people know about them? Most people these days would expect most organizations to provide some sort of online capability, but the actual offer will vary from company to company. Do your customers know how they can interact with you online? Because if they don't, then they aren't going to be using these wonderful new applications that you've been developing! So the start point is to measure the degree that your customers are aware of the services and support that you can provide them online. What we are measuring here is the extent to which the promotion of your online services is effective and would typically be measured using survey methodologies.
People might be aware of the range of online services available, but would they actually consider using them? So the next step in the measurement framework is to understand to what extent customers would consider using the online channel for different types of services or support. Once again, this is probably best understood using survey methodologies and it will be important to also understand here whether there are significant differences between different customer segments. The aspect that is being measured here is whether the proposition is sufficiently compelling enough to get people to shift from their existing channel behaviors into new ones. For example, to go online to renew an insurance policy rather than to call the contact center.
The next step in the measurement framework is to measure actual usage. From a measurement perspective, this is where life gets a bit easier and is probably the point of focus for many companies at the moment. Usage of online services can be easily tracked using well-configured Web analytics systems and also back-end transactional systems. There will be some effort involved in determining what usage metrics are the most useful ones to track and to keep the framework focused around the most meaningful metrics.
The performance element of the framework is about understanding whether the online channel is achieving its objectives. As discussed in my last column, it's useful to differentiate between performance outputs and performance outcomes.
Performance outputs are those activities that are happening in the online channel that are considered to be "good." Effectively, they are the conversion points on the site. Performance output metrics might include things like the number of bill payments made online in the case of a banking environment or the number of policy renewals made online for an insurance company. For a support site, for example, output metrics may include the number of articles viewed or downloads made.
Performance outcome metrics are the strategic measures of success. These metrics are the real KPIs of an organization's self-service initiative. From an organization's perspective, these metrics will be looking at whether channel shift objectives have been achieved. For example, fewer bill payments being made in branches, less calls to the call center, and so on. Measuring something that is not happening is typically harder than something that is happening, and so careful consideration needs to be given to how these metrics are put together. Outcome metrics also need to be considered from the customer's perspective, such as whether they were satisfied with the online experience, whether they would use it again, and also potentially whether they would recommend other people to use the online channels.
At a time when many organizations are looking to digital channels to make customer service more cost effective, a measurement framework is needed that takes a holistic approach to understanding the effectiveness of those strategies.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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