Predictive Analytics: Utilising client data to boost business
We are in an age of data explosion.
With the advancement in technology, companies can now harvest a massive cache of data in the most cost-effective manner. Big tech giants like Google, Amazon and Netflix are leaders in the B2C sector thanks to their mastery of analytics knowledge and its effective application in understanding the needs of customers. If B2C organizations can do it, why shouldn’t B2B marketers develop and adopt similar analytics-based strategies?
All serious players in the market know the importance of data. Yet even so, many companies are not utilizing customer data to the best of their ability. They are still depending on the so-called “proven and tested past experiences” to make critical business decisions.
There are some positive signs, as some companies have started utilising analytics. However, the adoption rate is still wanting, as analytics is also in itself still evolving. Like any other marketing tools, the total application of results from analytics will take time. Moreover, when it comes to predicting human behaviour, it is impossible to always get it 100% right – we do remain the ever-elusive, unpredictable species.
Having said that, it is the right time to start adopting predictive analytics as companies can quickly and easily harvest the necessary customer information. Predictive analytics is similar to a weather forecast; it is the science of forecasting the company’s future based on data analysis. Its mechanism involves data mining, machine learning and artificial intelligence, which will help in processing the available data. This processed data can be then worked to predict the future buyer-pattern of existing clients, by analyzing their buying behaviour. Predictive analytics also studies the complete customer lifecycle journey and presents valuable information about them.
However, this predictive analytics should not be confused with buyer segmentation analysis, as purchasing information is much more dynamic. Buyer segments are somewhat constant over a period of time, whist an individual’s purchasing behaviour can shift any time. As the customers’ ‘reasons for purchasing’ change, companies should ensure that their offers are aligned to meet customer needs. This is why predictive analytics is gaining importance. And yet even so, despite the wide acceptance that customer data is essential for most business decisions, many companies have not yet fully utilized it when it comes to engaging buyers.
If data is available, why not use it?
To understand buyer behaviour, predictive analytics uses those data which can give comprehensive information at the individual customer level. Modern businesses which use predictive analysis effectively are thriving, and they will continue to do so based on their capacity to hold digital dialogue with customers and other stakeholders. In a digital environment, companies are listening with data, and at the same time respond through a variety of digital interactions
Marketing departments should make significant headway into predictive analytics and worry just about ROI. Studies have shown that companies should start using those data right from the interactive systems like click streams, video views, or search engine searches; today’s businesses have over 40 types of interactive technology at their disposal. By incorporating information from other systems including CRM, logistics, and finance, your company could stay ahead of both the competition and your customer.
The future outlook is simple, albeit cutthroat: start using analytics, or perish.
If you look around, you will see that top business and analytics vendors are working to develop algorithms for future success. Look at top companies like Amazon, Netflix, or even Facebook, and ask yourself: why are they way ahead of the pack? It is because they have learnt to use the available data to predict future sales.
Predictive analytics is ideal for addressing complex business situations. It can identify and interpret signs that provide information on buyer behaviour, along with key events. Furthermore, it can employ behavioural data that is already available within the company to predict figures that may help with future growth strategy and planning. In simple terms, predictive analytics is not an additional investment, but a way to analyse data you already have – data which can boost your business and keep you ever ahead of the game.