Published: November 06, 2013 | Comments
Traditionally, companies have used data to drive cost efficiencies. However, an exclusive focus on cost reductions is an enormous missed opportunity. Management of metrics such as average handle time (AHT), first call resolution (FCR), and call reasons certainly results in cost savings (and in some cases can support the development of a good customer experience). Focusing on these metrics alone, however, does little to improve customer loyalty, increase sales through cross and upselling, or deliver brand identity.
Analyze the right data
Customer experience indicators are valuable sources of insight. It is useful to measure the customer experience from the customer’s perspective, as industry insiders often misunderstand what’s most important to their customer base. The best way to ensure this approach is to conduct customer satisfaction surveys on every contact channel, including self-service channels. You can take this a step further by attaching each customer satisfaction measurement to a particular individual interaction. This allows you to ultimately narrow down results to particular processes, and provide actionable outcomes.
There are many factors that influence customer satisfaction. Service processes, product features and costs, technology, and customer service agents are just a few examples. Without an understanding of the context of a particular survey, it is difficult to assess which of these might have influenced the results. By linking a survey with a particular interaction, you can understand the impact of the agent, service processes and technology.
Customer loyalty is directly related to customer satisfaction
We have seen that customer loyalty is directly related to customer satisfaction. Once you have data tied to interactions, you can start to look for trends in the granular detail. For example, if all interactions where a customer used self-service technology (eg: the website or IVR) scored lower, this is a clear indicator that there is an opportunity for improvement. The same methodology holds true when looking at product types, calls related to certain processes/policies, and even agent interactions.
If you warehouse this data over time, you can begin to look at former customer’s interactions and survey results. This can shed light onto some of the reasons for customer attrition.
The main indicators of churn
Leading indicators of customer attrition typically vary by industry and product type. In situations where a customer complains, or where there has obviously been a bad experience, it is relatively easy to see the connection.
There are other less obvious causes of customer attrition that should be addressed. Analysis of former customers and their interactions, transactions and behaviors before they stopped using the product or service can help to define a set of risk of churn factors. For example, in the financial services industry, a sudden drop in transaction volume or a change in account funding habits is an excellent indicator of impending attrition.
Once these customers can be identified, they can be routed to specialized retention agents when they initiate contact. You can also launch an outbound contact campaign to proactively reach out to them and attempt to save the relationship.
Here are some tips to help you make sure the data you measure will lead to initiatives that improve customer experience and retention.
- Capture customer satisfaction feedback on every channel and down to an interaction level wherever possible.
- Invest in the right systems and processes. One of the primary barriers of measuring the right data is that much of it, if tracked, is stored in isolated systems and databases. In many cases, there is not a common key that links all of the data together. This is an area where a good service provider can add significant value to overcome these challenges.
- Build for your own environment. In an ideal world, you’d build everything from the ground up. You’d have a data warehouse with a common key linking every contact together from start to finish, and this would be linked to a particular customer record. All of this would be linked to a customer satisfaction survey mechanism. However, in most cases, this is impractical. Therefore, the best course of action is to look at your own environment and determine which data points can be easily linked together. Once you start providing information and analysis, even if it’s limited, you’ll find it’s typically much easier to get needed resources to complete additional IT work needed to expand data collection and analytics. If you don’t already have one, adding a customer satisfaction survey mechanism is essential.