Hospitality has taken the full force of the pandemic’s impact over the past few years. With restaurants, venues, and accommodation reeling from the closures imposed on them, they’ve relied on recurring revenue to adapt. Pret-A-Manger’s coffee subscription being a prime example.
While subscription models are a great way to establish habitual purchasing and tap into consistent revenue streams, many hospitality businesses are nervous around shifting to a lower margin model, or are not realising their full potential.
A Model Shift
Adapting to a subscription model, involves a significant shift in operational model, and is not without risk, however other industries have successfully made a shift to great success.
A decade ago, it would be hard to imagine that gaming would transition to a subscription-based model, however looking at the massive success that brands like Microsoft have had with their Xbox Game Pass, the whole industry is now on the verge of a transition.
Hospitality venues even have a specific challenge which can be solved via a subscription model, levelling out the peaks and troughs of weekly or seasonal popularity. This is a major issue for the majority of settings, from gyms to cinemas, to theme parks, which can be addressed via subscription-based off-peak access for a reduced rate.
An example of this can be seen in Merlin’s annual pass, where you can pay a monthly subscription, with different pricing tiers, and access to match. This offers genuine value for families who want to arrange day trips throughout the year.
But why are subscription models not more common across the hospitality industry aside from a few prime examples?
A subscription’s potential weakness is that it is easily stopped and started, and where it is offered at a lower margin, the venue needs to know that they can retain subscribers for a significant period, to recoup their cost of acquisition.
The Answer?
The answer lies in what these companies already have – data. Hospitality venues collect lots of information on their customers, the problem is that it’s often left siloed and thus unused. This “dark data” has the potential to give organisations the insight they need to keep existing customers on the books as well as attract new ones.
Effective subscriber analysis will utilise data science techniques to interrogate subscriber data to look at common usage patterns and target subscribers whose usage is dropping, this can be developed into what is referred to as a “churn model”.
The ability to predict that a particular subscriber is at a high risk of churning, while there is still time to do something about it, represents a huge opportunity for businesses to retain subscribers. The capabilities to predict churn are only increasingly, in part thanks to the latest customer data platforms (CDPs), such as Microsoft Dynamics 365 Customer Insights, and the AI capabilities they bring to the table.
Where technology can make a real difference is by breaking away from human-based assumptions around causes of behaviour. By analysing thousands of different data points, AI-based algorithms can uncover patterns which would not have occurred to a human. For example, in the case of a gym subscription, a simple analysis may highlight those subscribers were visiting less frequently over a holiday period due to other commitments, and they were not at significant risk of churn.
But what if those subscribers start discontinuing their memberships?
By incorporating a CDP which ingests a variety of data including customer feedback, the churn model may identify a myriad of other factors, for example a popular instructor leaving, causing many members to stop attending their favourite class, and ultimately stop their membership.
Identifying causes of subscriber churn is only half of the battle, the bigger challenge is to intervene effectively to address those causes, and here a CRM strategy which includes high levels of personalisation is paramount.
Taking the gym example, a step further, sending out a monthly newsletter addressed “Dear [first name]” may include an element of personalisation but realistically it’s unlikely to build rapport or trust with the customer base, nor tackle the reasons why a segment of the base is leaving.
But, if as a venue you can use a CDP combined with behaviour analysis to group customers based on their wants and needs – and then you tailor your communications to meet them you stand a far better chance of retaining those customers. So, if the gym can target the users of a discontinued but popular class, with information on new alternatives, or even a targeted offer to retain this sub-set of subscribers whilst they make improvements, it’s going to stand a far better chance of improving the relationship with dissatisfied customers.
Personalisation in hospitality communications, just like the patrons can come in all shapes and sizes. Perhaps, at a ski resort, you’ve recorded that subscribers to an annual lift-pass are dropping off their visit frequency, analysis of their visit data may demonstrate that they visited on particularly busy days, with long queue times. An effective communication strategy could highlight future days when day pass bookings are lower and encourage them to visit then, or even up-sell a fast-pass for the ski-lifts.
The greatest barrier to this level of personalisation is the ability to piece all their data together in a way that they can draw effective insight from. Hospitality venues are often turned off by the cost and time of developing an effective customer data-strategy and focus instead on funnelling more budget into new customer advertising campaigns. But as the cost-of-living crisis increases and more consumers look to subscription-based models as a way of saving on the pastimes or treats they enjoy the most, an effective subscriber acquisition and retention strategy could be exactly what hospitality venues need to give themselves the upper-hand.
Are you keen to know more about the power of personalisation in hospitality? Read Edit’s latest report: The Price of Admission: Why the hospitality sector must focus on personalisation.
