Exclusive: The merits of path analytics – now all roads on the customer journey can be redirected to the profit pot
by Tony Brown, Vice President of Marketing and Business Development, Teradata
It is a paradox – many large enterprises with millions of customers know they will have to devise new data-driven marketing strategies but are frequently lost about how to achieve it.
Gartner research1, to give just one example, suggests that by 2018, organisations globally that have invested in online personalisation will outsell those that have not by 30 per cent.
So it is not surprising that retailers, banks, telecommunications and media organisations are looking to use their ever-growing mountains of data to make marketing much more successful by interacting with customers in a far more personal and timely way. Once they have advanced analytics solutions to drive value from the data, enterprises can make offers to individual customers that relate not just to all their preferences and previous history of transactions, but also to how they are interacting with the business now, whichever channel they are using.
Enterprises that do not acquire these capabilities quickly, must realise they will rapidly lose their business advantage as their customers are tempted away by competitors who have grasped the opportunity.
However, many organisations are prevented from achieving these advances by reliance on traditional, old-school analytics and an inability to integrate disparate data in real time. The result is that they can only market to groups segmented very crudely by age, location or other broad-brush factors.
To achieve effective, truly personalised and real-time marketing, they need to bring together integrated data, advanced behavioural analytics and automated decision-making so they can exploit every data source and point of contact including websites, mobile applications, call centres and chat.
Fast and on a massive scale
New data and analytics solutions are being created for companies within weeks by people who understand their business requirements and use their own intellectual property and field-experience to make it all work.
Today, organisations with millions of customers can make very personal interactions in real time on a massive scale. It means, for example, that when a call centre log, email or website query subjected to text analysis reveals that a customer has bought a new house or is getting married, a retailer can respond immediately with a meaningful and relevant offer, using automated multi-channel marketing.
Similarly, by analysing abandoned baskets and other behaviours on its website, a retailer can follow up with highly personalised offers to customers, boosting response rates by 5-20x or even more, when compared to emails targeted using demographic segmentation or propensity models.
Making banking more transparent – an omni-channel view
Advanced analytics are being used to follow the events that make up customer journeys through time and across all touch-points, to build not only a 360 degree picture of each customer but also to uncover common problems and streamline processes.
For example, text and path analytics revealed that many customers calling a bank to ask about changing their credit card limit had first tried to do so online. The lack of online functionality to support limit changes presented both a poor customer experience, plus additional cost to the bank as they had to resource contact centres to handle these queries.
Armed with this knowledge, the bank made it simpler for qualifying customers to increase their credit limits online, saving call-centre time and improving customer-satisfaction. Only by understanding the complete multi-channel customer journey was the bank able to understand this issue, and resolve it. There are many, many more examples like this to be found.
Similarly in web-chat, banks or retailers previously had to rely on anecdotes from the staff to find out how their customers were feeling, whereas today, text and sentiment analytics allow the rapid creation of a Top Ten list of reasons for calls. Continual monitoring means that these problems are resolved more quickly, with specific customers sent offers where necessary.
Solving problems in real time
In another example, a well-known high-street bank overhauled its online mortgage and loan application processes when path analytics revealed that only 20 per cent of customers managed to complete the forms. Path analytics uncovers not only how often customers fail to complete processes on websites, but where they abandoned it and why.
In this case, the analytics revealed a range of issues and bug within the online process, the removal of which led to vastly improved application procedures and far more satisfied customers. Of course, process changes take time to implement, but the insights delivered meant that real time triggers could be configured quickly so that customers falling out of these application forms could be spoken to immediately.
The customer journey delivers
By mapping each customer’s journey through path analytics and having the means to intervene meaningfully in real time, banks, telecommunications and media organisations stand to deliver enhanced customer satisfaction and ultimately enjoy huge financial benefits.
Marketing spend is infinitely better focused, yielding a greater return on investment (ROI). On the other hand, quick wins are available within weeks, while ROI can be achieved in months without the need for the installation of new systems or major CAPEX investments.
Enterprises that grasp the importance of understanding the individual customer journey and enable real-time customer data integration, advanced behavioural analytics and multi-channel marketing automation, will soon reap very significant rewards, keeping them ahead of the curve and leaving behind competitors with old-school and unresponsive systems and tactics.
1 Market Guide for Customer Journey Analytics” published June 9, 2016 by analysts Jason Daigler, Brian Manusama, Gareth Herschel, Jim Davies and Shubhangi Vashisth