Customer Analytics

The definition of Customer Analytics varies widely but broadly speaking its using technology and data to better understand customer behaviour and using that information to improve business outcomes.

Through the use of Customer Analytics you’ll be able to:

  • Understand customer’s habits and preferences
  • Predict future behaviour and use that data to influence the outcome
  • Identify high-value customers

Put simply, using Customer Analytics increases your potential to reach the right customers, in the right place, at the right time, and in the most effective way to deliver the best outcomes for your business.

What is Customer Analytics?

The above all sounds nice and altruistic, but what exactly is Customer Analytics?

To enable customer analytics you need two key things:

  1. You need to capture, store, and organise your data into a single repository (single view of the customer).
  2. Analyse and make decisions with that data.

Data can come from a variety of sources including:

  • Purchases
  • Loyalty cards
  • CRM tools
  • Phone systems
  • Support tickets
  • Surveys
  • Registration fields

Customer analytics uses a range of techniques including predictive modelling, data visualization, information management and segmentation but if that all sounds too hard, you can use customer analytics software or platforms to interpret and present the data (a much easier option!).

Types of Customer Analytics

There are currently three key ‘types’ of analytics.

Descriptive Analytics

Descriptive Analytics provides an insight into the past and answers the question “What has happened?”

You can then use this insight to better prepare for a similar occurrence in the future.

For call centre managers this could be noticing a trend in the call volumes and enquiry types when a price rise is announced.

By understanding the impact you can ensure the call centre is better prepared when it occurs again.

Yes, it sounds a lot like WFM but rather than just worrying about call volumes and the number of staff rostered, your business can also review the customer advice (e.g. letters, website notice etc), media releases, self-help options etc.

Or, just not have a price rise đŸ˜‰

Predictive Analytics

Predictive Analytics help you understand the future by answering the question “What could happen?”

Using AI, Predictive Analytics can provide you with an insight into what can happen in the future based on machine learning from outcomes in the past.

Yes, it also sounds like another call centre function called forecasting but predictive analytics takes into account the entire lifecycle of a customer.

This normally requires users to ‘help’ the machine learning by selecting the right data to analyse and defining what particular element you want to predict.

Prescriptive Analytics

Prescriptive Analytics is the latest arrival using AI to help advise on possible outcomes by answering the question “What could we do?”.

It takes things up another gear from Prescriptive Analytics and will start modelling potential outcomes and ‘recommending’ how to get there.

An example might be automatically analysing customer’s who have had a complaint in the past and recommending ways to avoid the complaint in the first instance (what lead to the complaint) or how to best manage the customer by understanding what actions post a complaint lead to the most profitable outcome.

It’s still pretty new so I’m sure there will be some great customer studies in the near future.

If you’ve had a good experience, be sure to share your thoughts in the comments section below.

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