Many people are talking about “big data’ today. Organisations have been accumulating massive amounts of data that they are unable to capitalise on for business benefits. Today’s businesses are operating in a fiercely competitive world with explosive growth of data volume and complexity. Organisations are working to develop strategies to deal with large quantities of data, such as how to capture it, manage it, and process it in ways that will benefit the organisation.
How did big data get to be so big? Over the years, organisations have built up massive silos of data that reside in disparate databases with little thought to how such data could be made useful to the majority of the business. This big data was accumulating in data warehouses across many enterprises. This included ever-increasing customer interaction data from transactional systems, as well as a dramatic increase of data from websites, social media, call centre records, and various unstructured sources. Every customer touch point or interaction was more data being fed into systems.
Every time a new channel was launched to help customers (web, chat, social media, etc.), it was done so in isolation and treated as its own division or operation. Customer service staff rarely had access to a complete view of this abundance of information, let alone how to make it actionable. Business intelligence (BI) solutions were able to solve part of the problem by accessing the data and pulling it together, but call centre representatives were left to sort through it and find the right answers to best serve customers.
As business intelligence matured, organisations were able to pull data together in near real-time, but staff were still left to interpret and act upon that data, which is not a model for consistent customer service or effective marketing and sales. Organisations now had greater insight into their big data, but the missing piece to the puzzle was how to make this data actionable in order to best serve customers and improve their overall experience with organisations.
Organisations who have hit this wall have begun to see the light with decisioning solutions that can make big data actionable and use such information to drive smarter decisions within customer service organisations. Adaptive, self-learning predictive models can generate significant insight from raw big data in real-time. Organisations that have been able to do this right have enjoyed a formidable competitive advantage due to the superior, relevant experiences they are able to offer their clients.
Predictive models can be used to monitor the vast amounts of big data within organisations and then offer a next-best-action for call centre agents to recommend to their customers based on all of the historical information that an organisation has on their customers. Next-best-actions that are not only more relevant because they are based on predictions of customer behavior, but also consistent with recommended actions in other channels. Organisations are able to continually incorporate data from multiple information sources, and their predictive and adaptive models can actually learn faster as more data gets fed into them. This yields better predictive recommendations in real-time, improving the information that call centre agents have available to them to best serve customers at the point of interaction.
Predictive and adaptive models are a perfect match for big data, as the more data these models can access, the faster the system learns and yields predictive recommendations that anticipate the needs of customers. With real-time capabilities, this is even possible for a call centre agent during the point of interaction. This puts a tremendous amount of power at the hands of today’s customer service organisations, because providing a more personalised service experience, driving relevant offers for clients, or aiding in customer retention are just some of the benefits that can be realised by leveraging decisioning and analytics with big data. Point in case: imagine that upon contacting your mobile provider, bank or insurance company, your request is handled easily, then you’re simultaneously offered a product or service that not only fits your needs, but even saves you money. While always balancing this with business objectives like bottom line growth and risk reduction. No need to imagine. Some well-run organisations are already making this a reality today.
By embracing big data and using decisioning technology to make it earn its keep, organisations need not fear it, they should instead think of it as an opportunity they should act on soon. Decisioning and analytics is a proven method for predicting and projecting the actions of customers. A change in the way that we perceive big data can create tremendous possibilities for a business.
If we are able to better understand our customers, and use that insight at the moment of truth when we talk to them, wouldn’t we be able to facilitate better relationships with them moving forward? Organisations that are able to use and leverage big data effectively will drive greater success and outperform their competition – ensuring a place at the top of the standings for their business.
Luke McCormack is APAC Vice President for Pegasystems