In recent months we’ve been seeing a rise in the amount of interest in predictive analytics from our clients, so we’ve decided to run a series of blog postings to explain what predictive analytics is, how it’s used and so on. This posting is the third in the series.
In our last posting we looked at an example of a company in the telecoms and media sector who use predictive analytics to optimise their marketing campaigns and increase revenue. We’ve come across another great example of a travel company mining their data to get better use of the information it already holds and we thought we’d share it with you.
But first, let’s focus on a couple of well-established applications: ‘customer segmentation’ and ‘next best offer’ – and look out how the data you already hold can be used more effectively.
Customer segmentation involves breaking up a population into ‘segments’. The segments are often based on demographic data and are derived by data mining. Each segment contains clusters of data points which all show similar behaviour. For example, in the travel industry, such a model may show a strong correlation between people in the age range of 21 to 30, with an income above £40,000, who are married or in long term relationships and the successful historical sale of long-haul package holidays with a luxury resort in exotic locations. If a marketer knows that many people with these characteristics have been shown to purchase luxury holidays, then they can target this segment of the population with luxury holiday brochures and expect to have a higher degree of success in selling more holidays than if they just sent the brochures to the population at random.
Secondly, a much newer application: real-time calculation of ‘next best offer’. This is applicable to any industry in which interaction with customers is via multiple channels in near real-time. Typically the channels would be a call centre and the Internet. Let’s say that someone has just rung your call centre and booked two return flights from London to New York, flying out on Friday and back on the following Monday. Based on historical bookings of similar flights, the predictive analytical system may score the next best offer as a hotel room in New York for two from Friday until Monday. The call centre can immediately offer to book a hotel in New York for the customer but let’s say that in this case the customer declines and politely ends the call.
The couple then access a website that the travel company sponsors and are recognised as the person who recently made a purchase on the phone. The
predictive analytical system knows about the flight purchase and the decline of the hotel and scores the next best offer as the hire of a car from JFK airport,
so in real-time it makes a discounted car hire offer in the advertising banner on the website.
The process of re-scoring the “propensity” of that person to buy continues, and a further offer is made in real-time with other personalised and relevant offers on the next web page. As you can imagine, this is only now possible because of the recent improvements in master data management; if that’s of interest to you then click here.
Now we’ve looked at a few applications, the next predictive analytics posting will look at how predictive analytics compares with other future-oriented techniques such as strategic planning and forecasting.