The story – Pádraig
I called my ‘mobile operator’ to cancel my subscription recently and I was put through to their ‘retention team’. This team eventually reduced my monthly subscription, and doubled the number of free minutes and free texts. They had had a data analytics tool that gave them the information on which to make the following decisions:
• Is Pádraig worth retaining as a customer?
• What offers will entice Pádraig to deliver the most value to us?
• What is the least profitable offer we are willing to make to Pádraig?
The data analytics
The operator has a variety of data sources available, providing information on almost every consumer in the UK. These data sources are derived from third parties, in addition to their own customer data.
They have used data analytics to maximise the revenue opportunity from each customer contact with their retention team. These data include information such as how old someone is, what types of holidays they like to take, what newspapers they buy, whether they live in an area populated mostly by affluent retired people, or by students, and many others.
They also include statistical scores, or segments, which attempt to condense a large volume of data about each individual into a few highly predictive numbers. An example of one of these numbers is known as a risk score. The risk score attempts to predict how likely each individual in the UK is to fail to pay for the services which they provide. Risk scores often used by companies to decide whether or not to extend credit to a current or potential customer.
The marketing analytics department at the mobile operator have taken all of these data, and used advanced statistical methods to predict the lifetime value of each of their customers, and the type of usage plan and phone they are most likely to desire. By combining this information, again using advanced statistical methods, they push the appropriate offers through to the dashboard of the contact centre worker taking the call from Pádraig.
The result is a win-win. Pádraig gets the price plan he wants at a cost he feels is good value. He wasn’t offered products or plans he wasn’t interested in. It was a quick call, focused on his needs. It’s a great customer experience, and Pádraig is more likely to return to the same operator for a deal in the future. From the mobile operator’s point of view, they sold the most profitable deal to Pádraig that he would be interested in, and now they have a chance to cross and up-sell (more of which later …)
April 7, 2009 at 1:19 PM |
padraig, what application does this have? can it be used by industry or companies or sector or country?
please explain…….
April 9, 2009 at 4:35 PM |
Great questions John. We will get back to you shortly.
Pádraig.
April 17, 2009 at 7:01 PM |
John,
Thanks for your questions. These approaches are independent of the scale of the organisation or business unit, and they have a very broad range of application.
In short, if the information is there, data analytics empowers a business to use it to best advantage.
The methods we employ allow a business to predict outcomes and control them. The key business inputs are:
1. Data: are there sufficient data available to capture the behaviour you are interested in?
2. Levers: are the business levers you can pull strong enough to create effective change?
These requirements can be satisfied for any size of organisation. As an example, ABC Co., a medium corporate wishes to set effective and economic staff rewards. The business inputs are as follows:
1. Data: ABC Co. have information on retention and rewards for their own employees, along with other HR data, including age, time with the company ,,, etc.
2. Levers: Bonus levels, share schemes, health care … etc.
ABC Co. can use data analytics to predict the probability that any employee will terminate (churn) for a given combination of incentives, They can then balance the cost to the company of these incentives against the benefit of retaining the employee, It usually makes sense to design incentives for groups of employees, rather than for individuals, and so the predictions and actions will be the same for, say, all middle managers aged 30-35 in group finance and accounting,
ABC Co. would employ a data analytic consultancy to supply a short feasibility study to estimate the costs and benefits of carrying out the data analytics, and prepare a project plan. They might also suggest that the incentives be used to target performance as well as retention if the data are sufficiently rich,
On a smaller scale, if a small hotel chain is sending out a mailshot to 100,000 prospects, they would expect low response rates. Data analytics can help make this process more profitable by ensuring that only customers that are likely to respond *and* bring significant revenue are targeted, that they are targeted with the right offer and at the right time.
A significant part of the technology we use was developed by governments (especially the USA and former USSR) for use either in defence or economic planning. These days, many governments act a bit more like corporates, and also use data analytics to forecast service take-up for customers in the health sector, for example.
Hope this has been helpful; let us know if you have any more queries.
Pete