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Intuition can take you only so far. You’ll be more successful at retaining audiences and reviving lapsed attenders if you use data rather than instinct to develop customer loyalty, says Sarah McAleavy.

Ticketsolve speaker addressing the Dublin Forum

Working on instinct and gut feel is a common strategy in the arts: ‘we know who our audiences are – and what they want – what more do we need to know?’ But based on what we’ve seen working with a diverse range of arts and culture organisations across the UK and Ireland, hunches just aren’t good enough any more.

It isn’t so much that your gut instincts might be wrong, it’s more that data can help you go a lot deeper. And when that data is readily available, easy to monitor and simple to decipher, it can provide key insights into two critical audience areas: engaging lapsed customers and customer retention.

Metrics that matter

The first question is, what data to analyse to give yourself the very best chance of improving sales and customer engagement. In an ideal world you would start with a full data audit, but the essential building blocks you need are:

  • Split between online and box office, including app growth rates
  • Ticket sales analysis, ancillary sales analysis and conversion analysis
  • Frequency of attendance and genre analysis
  • Demographic analysis
  • Days of week of purchase

Retaining customers

Broadly speaking, customer retention needs to focus on three main segments that need to be approached in different ways:

  • First-time customers (the objective is a repeat purchase and build loyalty.)
  • Loyal customers (make them feel special, show them you care, move them to high-value.)
  • High-Value customers (focus on deepening the relationship with them, creating a personal relationship and encouraging them to become a donor or member).

In general, loyal customers account for only 20% of all customers but they are drivers for 80% of revenue, so focusing here is a great place to start.

To encourage repeat customers you can do two things: encourage cross purchasing or higher value purchasing during a transaction, and/or create targeted, behaviour-driven campaigns post-purchase to attract them back for more.

Your data can establish useful baselines. For example, you might ask 'what percent of customers have more than one item in their cart at check out?' Suppose aggregate data shows that only 10% of customers buy more than one item. Using this data, you can experiment to see what types of cross purchase offers work best and where in the customer journey to best present this information. Approaches that have worked well for Ticketsolve customers include incremental discounts such as ‘buy tickets for two shows and save 10%’, or 'buy for three and save 15%’; exclusive offers, for example online only offers, or online-only merch/products; and membership/donor offers.

Data can also drive targeted post-purchase campaigns which focus on a variety of segments and can be used for different ends: thanking a first-time customer, encouraging a repeat purchase, recommending another show, extending an offer or discount, membership offers etc. By tracking sales data – from opens to click throughs to purchase – you can also tweak your post-purchase campaigns as needed to make them even more effective.

Harlow Playhouse runs a membership scheme and the team decided to send an email to thank their loyal attenders (those who had attended three times or more), and to mention how they could benefit from the membership scheme. 538 emails were sent, and the campaign achieved an open rate of 22.9%, with a click-through rate of 2.2%. More importantly it led to an additional 58 new members for Harlow Playhouse.

Defining ‘lapsed’ customers

Those who have made purchases before but not recently need a different strategy.

First we need to get a sense of when a customer really is ‘lapsed’ by determining the average time between customer purchases. This may be tricky depending on buying patterns. For example, you might see spikes in purchasing, especially if some customers buy for the season while others buy on an ad hoc basis.

Whatever the average time between purchases for your patrons, those who fall outside of these parameters can be sent a targeted message.

You may want to consider segmenting further. Repeat customers may have a shorter time frame between purchases than new customers, for example, and this analysis would enable you to determine more specific times to send messages. Some customers may buy infrequently but buy a lot. They are not necessarily lapsed, but just purchase differently. Others bought a lot once, but haven't bought anything since. Analysing the data enables these different types of patrons to be identified and targeted.

What to say?

While even a simple ‘We've Missed You’ campaign is useful, and using data on purchase history or timing can let you target your message more effectively. Recommendations based on previous purchases or perhaps discounts or even complimentary items are excellent ways to welcome back lapsed customers.

For example, Hertford Theatre found that 74% of their audience members had attended just once in 12 months, so they used an automated email campaign to draw in lapsed customers who hadn’t visited for 12 months. In the first month of testing the campaign, the email was sent to more than 4,500 customers and achieved a 33% open rate plus a 10.5% click-through rate. However, the main performance indicator was the number of customers who returned to Hertford Theatre after receiving the email. A staggering 287 customers out of the 4,258 bought a ticket after receiving the email – quite a result considering these patrons were considered lost forever.

As Ticketsolve users know, what determines data success is good audience segmentation, being flexible and creative in your thinking and implementation, and letting the data guide your gut instincts.

Sarah McAleavy is Customer Success Manager at Ticketsolve.

This article, sponsored and contributed by Ticketsolve, is part of a series looking at the power of box office data to inform strategic decision-making.

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