For many organisations, the concept of using data and customer information to guide their decision-makingRead More
How to make the move from reactive to predictive analytics
For many organisations, the concept of using data and customer information to guide their decision-making is hardly a new one. Business intelligence technologies that allow employees to drill down into business data for insight have been around for decades, so many managers will be wondering what the big deal is surrounding the rapidly developing ‘new era’ of big data.
But one of the key changes is that whereas in the past, analysing data was a slow process that could only be performed well after the fact, today’s big data analytics can not only gather a much richer and wider range of data, but process it at hugely increased speeds.
This means that instead of merely reacting to events, businesses can get ahead of them and employ predictive analytics to anticipate or forecast what is to come – allowing them to ensure they are in the best position to take advantage.
Better revenue, market share
There are many benefits to be gained from developing a predictive analytics solution. For instance, it can give you a competitive advantage by spotting trends and future opportunities before they emerge – enabling an organisation to get the drop on its competitors and be well-prepared for any changes in the industry.
It also provides a big boost in areas such as customer service and retention. Under traditional reactive analytics, a business may well be able to identify why a customer left a company, then apply the lessons learned to its future dealings. But that will not help them with the customers that have left in the meantime.
Predictive tools, on the other hand, can highlight potential red flag issues before they occur, allowing customer service teams to step in and smooth things over, often before an individual realises they have a problem.
This plays a big role in meeting consumer expectations – something that has increased dramatically over the last few years. But it does not stop there. With predictive analytics able to forecast a person’s likely preferences and reactions, businesses will be able to reach them on a much more personal level throughout their journey – from initial marketing through post-purchase support and on to renewal(s).
Make the most of your potential
But how can these results best be achieved? For starters, businesses will need the right technology in place – and this means systems and tools that can process data in volume at extremely high speed, in order to deliver results in a timely manner. But these will not be much use unless the business captures and retains as much relevant information as possible in such systems and then focuses on asking the right questions.
In today’s increasingly-digital world, businesses are likely to have access to a lot more data than they realise – harnessing this is important to a successful predictive analytics deployment. For instance, when looking to create profiles of customers, social media data can provide valuable insight.
This can give companies indicators of customers likes, dislikes and opinions, as well as demographic indicators and life journey progress. For instance, if a retailer is able to segment the customer base for likely upcoming events in their life – such as engagements, weddings, child birth or graduation – they can tailor their marketing to reflect this.
But it is important that businesses invest in forming the appropriate questions they need to ask. For instance, in order to move from reactive to predictive analytics, analysts need to be asking not when a customer last made a purchase or how much they spent, but when they are likely to make their next purchase and what products and promotions they will find most attractive.
Use your assets
Finding answers to these questions is not an easy task, and will be a much more complex and intense process than traditional reactive analytics. And it will also require a large cultural shift within an organisation, from looking back to trust looking forward. But this does not necessarily mean organisations will have to tear up their old solutions and put in place entirely new approaches.
Businesses will still have large amounts of data they have collected over the years sitting in silos, this can be harnessed for longer term trending using modern predictive analytics tools. Effective approaches will leave no data unused in order to ensure they have a full picture of their customers and their interaction with promotions, products and services, providing the wider landscape view.
Therefore, you’ll need to invest in employees who have skills in traditional data warehousing and BI, as well as the new generation of more responsive real-time and predictive analytics technologies. Being able to combine this legacy data with new, unstructured sources such as social will be a challenging task, but the rewards for getting it right will be huge.
Key things to remember
In order to be successful in the move to predictive analytics follow this checklist, you’ll be well on the way to a responsive, fast-moving organisation that is well-equipped for whatever the future may throw at it.
- Clearly define your goals – Knowing what you want to achieve is the first step to a successful deploment.
- Understand your data sources – Assess breadth, quality, available time ranges and join keys to bring data together.
- Invest in tools to process data in volume and quickly – Fast processing speeds are essential to discovery, experimentation and timely results.
- Know your business response times – Setting goals enables you to better measure your overall performance and identify the most effective solutions.
- Gauge your resources – Do you have the skills within your organisation to meet these goals, or will you need to invest further?
- Identify a champion – Someone with the insight and knowledge, an influencer to drive managers to adopt and progress to get the most out of your investment.
- Question your questions – Even the best approach will be useless if you’re not asking appropriate questions – so don’t ask what your customers have done, ask what they intend to do?