Data is the driver
When Boparan Restaurant Group launched a 50% discount on food for emergency service workers this week to support police, firefighters and NHS staff during the winter, it triggered the simplest of reward schemes that targets specific customers.
To qualify, I suspect these essential workers simply have to show their ID card at the group’s Giraffe, Ed’s Easy Diner and Harry Ramsden’s restaurants. This scheme represents the most basic reward mechanic but at the other end of the spectrum we are also seeing some more complex, algorithmically driven initiatives that not only reward select individuals but can also be used to exclude others.
Uber recently introduced a system in Australia and New Zealand that bans people from using its taxi service if they have been awarded a low rating by its drivers. A spate of unacceptable behaviour resulted in the company bringing in measures to remove access to its service for six months for offending individuals who fall below a certain level when scored by the drivers.
Such data-driven mechanics are going to play an increasing role in the leisure and hospitality industry, helped by the fact it has become increasingly easy to accumulate data on individuals. From this a business can determine exactly the levels of service it chooses to give to individual customers. It makes sense for a business to look after its best (most valuable) customers by allowing them access to the best service, potentially more competitive pricing and other exclusive perks.
Collating this data would have previously required a margin-eating loyalty card to identify the customer following a transaction and then monitor their activities, but this is no longer necessary as people leave a trail of activity-led data on social media platforms. At base level this can be simply “likes” and “follows”. This information is easily accessible and can reveal rich insights on an individual’s preferences and intentions.
This data is coming into play regarding gig tickets, where solutions have been sought to address the major problem of touts. An enormous secondary market exists for the most popular artists and the industry wants to get its hands on more of the revenues without “ripping off” genuine fans via high ticket prices.
Finding these fans is at the heart of Ticketmaster’s Verified Fan solution, which has been deployed by artists such as Taylor Swift. It uses an algorithm that seeks to identify true fans by assigning points based on the number of videos they have viewed and merchandise they’ve purchased. Tickets are offered to true fans at an affordable base price.
This targeted allocation operates alongside a general release of tickets, which are priced much more highly than the base price and at a level that seeks to suck out all potential profit margin for touts. Although there has been scepticism about the capabilities of such algorithms, Ticketmaster is claiming victory. It said while almost one-third (30%) of tickets for the 2015 Taylor Swift tour ended up on the secondary market, this was cut to 5% for her 2018 tour when the Verified Fan technology was deployed.
The other finding from the early days of this solution is that events have failed to sell out as not all the higher-priced tickets ultimately found buyers. It looks like a more dynamic pricing model also needs to be introduced to ensure all the tickets find a home.
Such experimentation with data-driven solutions, algorithms and dynamic pricing will inevitably feed into all parts of the leisure and hospitality industry because there is arguably no sector that can’t be improved by using the increasing mountain of data that is now available.
By intelligently deploying solutions that use insights gained from this data, companies can make better decisions, improve the efficiency of their operations and ultimately drive up profitability.
Glynn Davis, editor of Retail Insider
This piece was originally published on Propel Info where Glynn Davis writes a regular Friday opinion piece. Retail Insider would like to thank Propel for allowing the reproduction of this column.