What are the best measures to use to track the internal customer experience?

[tasmih1234]

Unfortunately, there are no easy answers to that question. The best measures are those that reveal your true performance to target. So while “speed of delivery” might be the right measure for one group, “ease of doing business” may be more appropriate for another. I will say that “proactive, anticipatory partner” is rising as a key critical predictor of internal customer satisfaction. Just make sure to have a good balance of both leading and lagging metrics.

In your upcoming presentation you note there are 5 key tactics to get results in the next 30 days. Which tactic is your favorite and why?

Think small, act fast. I believe the number one issue keeping organizations from achieving more success is scope. Our appetites are larger than our capacity to execute. I like thoughtful, limited scopes that are executed quickly. I’m a big fan of 20-day touchpoint re-design sprints. Building a rapid, ongoing pace of quick change is important for our ADD culture.]
Do you have any advice on points someone should include if they are making a business case to move to using predictive analytics?

Siegel: A strong business case for implementing predictive austria mobile numbers list analytics should include the following three elements:

Value—an estimation of the impact on the bottom line or on other key performance indicators that will be achieved.

Cost—all anticipated resource requirements. This empowers decision makers to balance the resource investment against the potential value.

Risk—for example, the risk that the predicted model will not perform as well as expected. Include information on how you can mitigate the identified risks, such as deploying the predictive models in a regimented way (e.g., only using it for 10 percent of decisions at first).

Q. What are the common pitfalls you see most organizations face when they move to a data-driven decision making culture and how can organizations overcome these pitfalls?

Siegel: The most common pitfall is not effectively defining the deployment objective in the first place. You can spend a great deal of effort conducting robust analysis, but it will all be wasted if you don’t have an agreed upon plan for how the output will be used. This requires a commitment across the organization to change the way decisions are made so that predictive scores are made use of. Predictive analytics projects are usually more likely to fail due to an organizational failure to do this than because of analytical or technological shortcomings.