Measuring Customer Satisfaction with Big and Small Data

We all want a “magical sign”, that will guides us to make the right decision. We can either wait for that sign to show-up or we can take the necessary steps to unveil the truth.

Big-Data in the form of behaviour and small-data in the form of surveys complement each other and produce insights rather than simple metrics. Meaning there is a “higher story” of the situation and there is a “lower story” like a specific event.

So how do we effectively measure customer satisfaction?

There are many strategies but let’s take a look at the 5 practical fundamentals that combines both big-data and small-data:

1. Response Time

Customers expect you to respond to their issues in a timely manner. If we fail to meet expectations we’re likely to bring down our customer satisfaction considerably.

2. Problem Resolution Time

Companies that measure how rapid and accurate “issues” can be fixed have far higher customer sat ratings than companies who do not take this measurement seriously.

3. Proposed Solution Tracking

Keeping track of whatever or not your solution to their problem was able to satisfy their initial reason why they called you. A recent study as shown that 53% of loyal customers, commit to that company just because they have a feeling of assurance of their problems always being taken care of.

4. Overall Customer Experience Rating

Measurement of this crucial metric usually happens on-going through relationship, transactional surveys and feedback channels.

5. Contact Volume by Channel

In big-data there is Variety, Velocity and Volume. The volume metrics is a great indicator of when and where you should be devoting the most amount of customer service resources.

The beauty of having both Big-Data and playing a complementary role in modern research is that in one hand you have the behavioural data of the “higher story” and in the other hand the “emotional data” which is a “in-the-moment” contextual value of the “lower story“.