Marcio Rodrigues, Customer Proposition Director, Vizolution.
Soldiers across the world and down the ages will tell you that time spent on reconnaissance is rarely wasted. When lives are at stake it pays to find out as much as you can about the enemy’s strength, deployment and intentions.
The general receiving information from his scouts, spies (and, today, drones) needs to have the right mindset to understand what the signals-in-the-noise actually are. That is easier said than done.
A story told to me by my friend Alan Newman who was in the army tells of a cavalry commander who sent out scouts. He was clear about the information he required. Being a cavalry man he wanted to know how many horses there were, did they look strong and healthy or tired, how many of them were equipped to pull pieces of artillery, and, from the uniforms, were the enemy forces elite regiments or not.
Just after dawn the scouts road off.
Early that afternoon, from different directions, the scouts were seen returning at the gallop. “Well?” enquired the cavalry commander, “how many horses do they have? What regiments?” Wide-eyed and breathless and with a single voice the scouts shouted, “Tanks!”
The cavalry commander seemed perplexed. “Answer my question!” he said, “how many horses do they have? What regiments?” “That doesn’t matter sir”, said one of the scouts. “The enemy is sending TANKS!”
The commander wasn’t willing to tear up his plans for a battle involving horses, artillery and infantry. With his decades of experience he knew that this could be his finest hour. As he was considering how to make sense of his scouts’ new information, or ignore it, he was killed by a shell exploding a few metres away. It had been fired by a tank.
Below we continue our series of blogs on the management of the customer experience in financial services based on insight from a white paper produced by Dr. Frederic Ponsignon from Exeter University.
Best Practice 2: Use transaction data to personalise the customer experience
Author: Frederic Ponsignon, Researcher in Service and Process Management, University of Exeter
Service organisations usually rely on their staff to understand customers and deal with them in a personal way. Front-line employees are required to constantly adapt their interpersonal attitudes and behaviours to meet the specific needs and preferences of individual customers. Unfortunately, a personalisation strategy that relies on employees may be difficult to implement because of what Frances Frei calls “subjective preference variability”. According to her, customers vary in their opinions about how they want to be treated. For instance, she writes, “one diner appreciates the warmth of a waiter’s first-name introduction; another resents his presumption of intimacy”. This may give a false impression of service inconsistency, although it is the inconsistency in preferences from one customer to another that is to blame here.
However, in information-centric industries, like Financial Services or Telecommunications for instance, the systematic collection, storage and analysis of customer transaction data makes it now possible to personalise the experience of individual customers based on hard facts and quasi scientific evidence. Transaction data, readily available at the level of individual customers, offers deep insights into the customers’ own context, in terms of how they use a firm’s products and services. For instance, the nature, frequency, time, location and amount details of all credit card transactions are recorded. Additionally, historical transcripts of telephone conversations or complaint letters are often held in internal databases. This constitutes a huge amount of data that can be leveraged to predict future customer behaviours and identify new opportunities for value creation. Although many organisations are still pondering how to best utilise their data resources, we found several examples of successful applications. For instance, a large UK retail bank is able to identify customers who have bought a holiday and when and where they will go on the holiday using credit card transaction data. They contact those customers to find out what might concern them around the holiday. They can then offer a specific travel insurance package or a discount on foreign exchange as appropriate. Another, albeit more controversial, illustration of the power of big data is provided by a global bank who claims it is able to predict divorces before they happen by detecting patterns in credit card transaction data of married couples. It is unclear if the divorced tend to stay loyal to this bank!
We hope you enjoyed this blog and please stay tuned for our next installment that will focus on: