Predictive Analytics: Staying Three Steps Ahead

Posted on Tuesday 8 August 2017.

by Michael Franchetti, Customer Insight Analyst, SDI

 

In this digital age, storing vast quantities of data has become easier than ever. Doors have been opened and decisions which were previously made on hunches can now be made through the analysis of patterns, trends and characteristics. Whilst there’ll always be a place for random ‘noise’, the application of predictive analytics within business can reduce costs, increase revenue and make for a far more fulfilling customer experience.

The recent data boom has given ‘Predictive Analytics’ a whole new importance. It’s an umbrella term given to forms of analysis which use what is already known to make decisions about the future. We’ve seen the rise of complex techniques such as propensity modelling – which looks at the likelihood of a given outcome occurring – and more recently machine learning.

The retail industry was one of the first to utilise predictive analytics. Rather than staring into crystal balls, retailers were able to use data to maximise the relevance of their marketing campaigns. Analytics were used to improve the timing of customer communications. Some techniques were simple such as identifying individual spending spikes – possibly due to family birthdays – and running campaigns offering discounts at those times of the year.

Other techniques were more complicated and involved the analysis of buying patterns. Customers who had recently bought bikes were sent adverts for helmets and gloves. A year later they’d be placed into a campaign offering tyre replacements or brake checks. The potential benefits may sound small but there are plenty of case studies with impressive numbers.

With the right data you can model any outcome and IT Service Management has had no problem embracing predictive analytics. Data collection is vital to meaningful analyses and service desks have long since collected a range of different customer and incident information.

One of the simplest ways predictive analytics is used in ITSM is to determine how much demand a service desk will be required to meet on any given day. More complex processes include the examination of incidents to determine the likeliest root causes. These analyses will lead to a wealth of knowledge and, ultimately, more efficient IT support.

Just as marketing teams yearn for customer insight, service desk analysts would benefit from an improved knowledge base. Understanding a user’s behaviour, preferences and history will all lead to improved service.

Predictive models can be developed to answer a number of questions long before problems arise. Several pieces of SDI Research look at how the future lies within proactive strategies and predictive analytics will play a vital role in maximising business value.

 

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