Five Common Mistakes to Avoid when Implementing Business Analytics

Posted: 4 December 2017

H.G. Wells once stated “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”.

As the use of statistics becomes more commonplace, clued up organisations are using analytics to make informed decisions to improve business efficiency. Yet, if implemented incorrectly results can be misunderstood and costly mistakes can be made. Here is a round-up of five common mistakes made when implementing Business Analytics and how they can be avoided.

Mistake #1: Looking at statistics in isolation to each other

In contact centres, there are many types of statistics that are reported upon. C-level executives may focus on service levels (calculated on the percentage of calls that are answered in 20 seconds or less) and First Call Resolution rates (properly addressing the customers’ need the first time they call and eliminating the need to follow up with a second call). Whereas, contact centre managers may prioritise Average Handling Time (the average duration of a customer’s call, including hold time, talk time and related tasks).

Standalone, these statistics do not provide an accurate view of the contact centre and the overall business. They may lead to statistics being used because they are ‘easy to understand’ or could force a culture of agents rushing through calls to maintain a good average handling time; not good practise.

The best way to avoid this is to use analytics software that doesn’t focus on one particular metric, rather shows the interrelations between them, thereby providing a true business perspective.

Mistake #2: Only focusing on statistics that you know

The contact centre has a service level of 80% – great! But how does a business know that it is really 80%? What if in reality, it is less (or perhaps even higher)?

Drilling down into the figures will provide a more accurate representation of the real service level. It will indicate whether only top performing campaigns (therefore skewing the figures to a more pleasing service level) are reported.

Service levels can be set for each individual campaign. Collectively, these will give an accurate service level percentage, with the ability to focus on one area if required.

Mistake #3: What information is missing?

High-level statistics do not provide an accurate representation of the information that is ‘missing’.

An example of this is the assumption that a good service level are all calls that is answered within 20 seconds. However, individual campaigns could have different service level. 50 seconds may be more realistic for some industries and call types.

What about call abandonment rates? Some systems do not report on these. Yet, these are valid indicators of happy customers and whether the contact centre setup is working for a customer base.

Understanding what information the numbers are providing is key to comprehending an overall picture of the contact centre and business. This can be achieved in a system that doesn’t focus on one element, instead, can be drilled down for ease of understanding and provide a true business perspective.

Mistake #4: Making assumptions from one statistic

It can be tempting to make conclusions based on just one statistic.

An example of this is looking at high average handling time and not factoring in other reasons for the occurrence. This may have been caused by slow computer systems, ineffective routing, poor agent training, high employee turnover, overly complex systems or lack of manager support.

Changing any of these factors may improve average handling time rates, however, there may now be an increase in the call volumes due to repeated call backs.

Dashboard access that allows users to focus on one statistic, yet have the ability to see the interrelations between statistics, providing an overarching view of the business is the best approach for organisations to take.

Mistake #5: Do the statistics make sense?

Common sense goes a long way when analysing statistics. Reliance on figures without looking at their true impact can be a common occurrence.

An example of this is 100% of an organisations’ customer surveys are positive, therefore, they don’t have any complaints. In reality, this is only a sample of the customers who have been in contact with a business.

This is a fairly extreme example. However, not using common sense in reporting can be an issue. Good understanding of systems can eliminate this happening.

Business analytics are highly useful and when used properly, can prosper a business. Avoid these mistakes to make the most of business analytics. If you are looking to implement business analytics software, contact ipSCAPE today and see how we can help.