Brands continue to strive for unique, intimate and relevant communication with their customers, and there are several names that the consumer can now associate with ‘suggested content’. Netflix, Amazon and Google are all key players in the area of big data and put this to use by tailoring content to individual users. But events giant Ticketmaster has found its own niche for this important and adaptable tactic. Using Predictive Intelligence (PI), the brand has overhauled its email strategy to provide its users with a responsive, personalised experience, connecting them with their favourite – or new potential favourite – events, shows and concerts.
“While creativity is still phenomenally important for marketers, bigger companies place a great deal of focus on working with large-scale data, and learning how to leverage that to deliver great customer experiences.” explains Sophie Crosby, Senior Vice President Marketing, Ticketmaster. “We knew that we had scale, we had historical customer data and transactional data, and we wanted to do something good with it. So five years ago we placed a strategic bet, and decided that we would use this data to better serve our customers.” The mission was to develop and smooth the customer experience across channels, particularly driving engagement, traffic and conversions from email. “One of the key challenges was data governance – making sure we were treating the data in the right way. Then we got to a stage where we could draw on the insights of our huge database, and start to calculate scores like distance to venue, frequency, or monetary value.”
Distance to venue is an important factor unique to live events marketing, which Ticketmaster’s predictive intelligence would need to take into account in order to provide a relevant experience. “Fan dedication is important too- understanding the signals we get from fans- knowing who would travel to Dundee on a Wednesday night for example!” Ticketmaster’s vast database could be segmented in a huge variety of ways, lending itself to the diverse array of events on offer, but offering a truly individualised, high-quality experience to the customer was still out of reach. The huge amount of processing required to produce the relevant, adaptable and timely customer experience would require another layer of automation.
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The solution was machine learning, acquiring an automation tool to trawl the huge amounts of data and deliver personalisation and customisation at scale, bridging that gap between content saturation and the somewhat beleaguered consumer. The PI recommendation engine aims to understand the preferences of every customer – the artists they like, the genres they prefer, the towns and venues they are interested in. It does this by analysing customer profiles, purchase history and browse data, before comparing the profile picture with available tickets to match the two together. “80 per cent of all content streamed on Netflix is from recommendations. It’s not people searching, they’re just scrolling through and looking” states Crosby. She argues that a key feature of improving UX is shortening the timescale of that decision-making process, so that consumers don’t have to contend with the information overkill presented by the average inbox. “We’re getting lazy as consumers. We’re reaching that overload point- we’re having so much content thrown at us that we just want to have something recommended!”
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The live event space is an area that has even more need for these personalised customer recommendations, but it comes with its own separate list of challenges that Ticketmaster must take into account in order to provide the level of customer satisfaction they were looking to offer. “While Spotify and Netflix and John Lewis are doing recommendations, they don’t face the challenges we have, where timing and location are as important as the content.” Continues Crosby “We might have half a million people queuing for an event with 35,000 tickets. That volatility is quite difficult. If a user opens their email only to find they’re too late, that then becomes a bad customer experience.” And customer experience is a non-negotiable consideration for live events, where the transactions are fuelled not just by competitive pricing, but dedicated, emotionally-invested fans. Missing out on the chance to see one’s favourite band or football team could be an irreparable breaking of brand trust, so making sure that content is responsive, relevant and timely to sending and opening is vital for Ticketmaster’s position as a leader in the live event sector.
With over eight million active customers receiving weekly TicketAlert emails in the UK, there was an opportunity to boost the open and click rates through this enhanced targeting and personalisation. By understanding customer propensity for the different types of events, the first step was championing the most relevant copy at the top of the email, whether it be sports, theatre, music or art. “That alone gave us a 100 per cent uplift in clicks. So we knew we were on the right track, and that relevancy was the key going forward” asserts Crosby. Being able to send uniquely populated emails, based on user preferences drawn from click-through data, transactional data and data science has seen 35 per cent uplift in open rates, with conversions also increasing. “That click rate means we are delivering more relevant content.” And as customers click through the recommendations and hopefully move to purchase, this provides more data to influence the recommendation engine and therefore suggest more content. The machine learning element of the recommendation engine means a long term win for customer experience, engagement and relevance.
The creation of Ticketmaster’s platform-agnostic inventory store was a turning point for the brand, which is now able to pull together the availability and “recommendability” of the diverse events. With a variety of events and venues having different requirements, the segmentation needed to be able to identify realistic availability depending on venue size. “A hundred tickets left at Wembley Stadium is not really worth suggesting, but the same number of seats available at an intimate club is a great recommendation.” explains Crosby. All these nuanced details are fed into the recommendation engine, providing the individualised content for eight and a half million single customer views each week. It’s a feat that simply wouldn’t be possible without automation- not that it hasn’t had its challenges: “Trying to solve human patterns with numbers and rules is not easy- we’re continuing to test and check and validate this. Now we are looking at this in parallel with all of our customer sales cycles and lifecycle emails, looking at triggers where we see where people have been clicking.” As the tool continues to develop, Ticketmaster’s marketing team are able to refocus their efforts. “We can concentrate much more on creating super fan experiences- some really great content, or something that engages with the fan and shows we care about them. This extra time is a great opportunity for us to be able to reinvest in, and lift the fan experience.”