Crucial customer insight with predictive analytics

The value is not in any specific analytic algorithms, nor in having everyone become an expert in statistics; it is in leveraging analytics and analytic applications for specific business goals. But with increasing customer demands, market competition, and budget cuts, there is urgent need for proactive rather than reactive management. This is where predictive analytics play a key role.

For analytics that has been widely deployed, it is all about identifying early warning indicators to predict when problems will occur, even while all the key performance indicators are still in desirable ranges. For example, when usage or operational process patterns change, analytics allow those changes to be better understood and forecast for improved operations.

Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behaviour patterns. However, predictive analytics is not a future-telling tool or a crystal ball, it can’t predict whether heavy equipment will come and cut a cable thereby causing outages. 

The core of predictive analytics relies on capturing relationships between variables and past occurrences, and exploiting that information to predict future outcomes. Predictive analytics allows companies to move from reactive to proactive management. 

 

Anticipating trends

Proactive management of the infrastructure is pivotal to protecting existing revenues and expanding opportunities. From a customer perspective, “slow is the new broke”.

Predictive analytics ensures that customers’ set of expectations are met regarding the quality of services and products. By creating an accurate view of business services, and supporting asset dependencies and status, organisations can predict the changes in service status to maintain optimal performance.

While widely deployed analytics can build patterns from the historical data, that approach may miss a newly evolving pattern. Analytics can build patterns without any historical data but this approach takes longer, especially to incorporate seasonality. The best of both worlds is to apply predictive analytics that takes advantage of all the data you have – history, subject matter expertise, known topology and the capability of an adaptive algorithm to learn from data in motion.

 

By analysing all the data, the system learns what the normal behaviour is for the resource’s performance. This includes the seasonality associated with the hourly and daily deviations of use. With this approach, the system learns the normal behaviour and adjusts this understanding over time. When the performance starts to deviate from the norm, a notification will be raised. This includes a deviation from the slow utilisation of a network to the busiest hour. A financial customer, for example, can now take advantage of the fact that seasonal trends are pretty consistent for trader transactions. When the data deviates from the seasonal expectations, a notification can be raised even before the thresholds are crossed.

Apart from understanding customers better, predictive analytics can help in identifying capacity levels of machinery usage or infrastructure. This will prevent degradation before it happens and can also identify where extra capacity is required in the components, so it can be ordered in advance of a service affecting event. However, keeping stock of unused equipment – whether it’s connected or not – is not financially prudent. This type of analytics can ensure equipment is ordered in time, and not stored on a shelf waiting to be used.

Today, we find problems through fault events and by managing thresholds, which have some important implications. First, you have to know what thresholds you need and want to manage. You may need to understand the relationship between key performance indicators. As the environment becomes more dynamic and more complex, more thresholds are needed. 

Predictive analytics can augment these traditional management techniques, providing new insight from data that’s already being collected, without adding new thresholds. As market environments and infrastructure become increasingly competitive and complex, and as customer and operational data continue to grow in size and complexity, analytics can help you make sense of it all.