The saying, “that which is measurable can be improved,” remains truer than ever. The key to that saying, however, is to understand how to interpret each measure in order to place the proper value on the metric and insight it generates for one’s business.
In today’s business environment, we are overrun by data. In the quest to continuously survive and excel in competitive markets, we place emphasis on metrics with any potential to provide guidance, instead of those proven to make a difference. We overstate the value of certain metrics because of their trendiness in industry periodicals, or use by companies that we admire, in the belief if it is good for another’s organization, it must be good for mine.
Yet, in the midst of marketplace trends and competitive landscapes, the measure defining “Quality” continues to play a role in most corporate scorecards. We have witnessed the “Quality metric” playing roles within organizations as valuable lead measures of customer satisfaction and lifetime value as well as gateways toward employee incentives. We have also witnessed these same metrics reported as abstract corporate goodwill values with no link to business or employee performance.
The learning from the above is that the concept of ‘Quality” is lasting, but like any concept can be either valuable or non-useful depending on its application and design.
To Understand is To Measure….
Measures involving a quality delivery of your corporate message or properly executed issue resolution paths can be useful lead measures for your company if they are assessed through the “eyes” of your customer. That guiding principle transforms ‘Quality’ into the ‘Customer Experience’ measure that can transform your business into a customer-centric organization.
What Customer Experience IS…
- A clear lens to place on your business defining product receptivity success or the effectiveness of processes and employee skill proficiency
- A leading indicator of customer satisfaction, retention likelihood and lifetime value.
What It Is NOT…
- An arbitrary measure of vague principles that cannot be clearly conveyed or replicated
- A metric without relationship to financial health and business results
Identifying the proper elements to measure begins with realigning some previously held “quality” beliefs.
- Are your measurements actionable and clearly identifiable or simply a “feel good” instinct of what transpired?
- Do you quantify the customer’s reaction to the interaction to provide perspective or is it simply an ‘agent checklist’?
- You’ll know the answer to this by listening for a robotic, distanced agent delivery even though you don’t have a script guiding your dialogue!
- Is there data integrity in the measurement diagnostic used to drive your business?
Building trust and respect for your new CEM process requires that the following pitfalls be avoided:
- Findings appear inconsistent and vary without explanation
- Findings do not appear to relate to operational/business objectives
Each of these pitfalls can be avoided by incorporating the following preventative measures in your CEM initiative.
Pitfall #1: Findings appear inconsistent and vary without explanation
Data Integrity Factor #1: Sample Size to understand the margin of error that exists
Data integrity begins with sample size. Ensuring that you sample enough calls to generate a statistically valid diagnostic of your customer experience is the first step toward protecting the integrity of your findings. The desired sampling strategy generates a 95% data confidence with a 5% interval (margin of error) for the population (data set) that is being measured. The key to this process is to understand the margin of error that exists in your dataset and match that against your corporate objectives. A sampling strategy that generates a 10% margin of error may require a longer sampling cycle before the database contains enough data points to conduct a statistical study.
Pitfall #2: Findings do not appear to relate to operational/business objectives
Data Integrity Factor #2: Statistical regression analyses to understand the CEM dataset
in relation to your business metrics
When Customer Experience metrics are not aligned to corporate financial metrics, their value is disputed and their use is not consistently leveraged. All stakeholders need to have a singularly-focused objective involving Customer Experience Performance Improvement. Such focus allows operational, marketing, product and service delivery functions to unify their functions in the pursuit of serving the customer. The most effective method of unifying such teams is through statistical rigor identifying the relationship between Customer Experience metrics and financial outcomes. Such rigor, coupled with the predictive modeling simulations, allows all stakeholders to visualize the relationship between customer advocacy and business success as well as forecast the additional financial gains to be achieved through an improvement program roadmap. That degree of rigor will not only prevent the ‘pitfalls’ identified above, but will transform Customer Experience into a primary initiative to serve both customers and shareholders.