Before embarking on data analytics, attractions must decide, at a strategic level, what they want to achieve for the business. If you jump in feet first, you can drown in information, so it’s important to refine the search to two or three key goals.

Most attractions face the same challenges – usually an inability to see basic information, like who's visiting and their concessions data. Getting a near real-time view of who’s coming and what they’re spending their money on will help an attraction shape its marketing.

Most attractions use blanket campaigns from a purchased list to attract new members. They'd expect a 1.8 to 2 per cent capture rate. At Cincinnati Zoo, rather than a carpet-bomb email, we did analytics on existing members, profiling them and asking how many children they had and how many cars they owned, for example. Then we identified other people who matched that criteria and emailed them. For a fraction of the cost of the email campaign we achieved a 10 to 12 per cent capture rate.

Data analytics boosted profits at Cincinnati Zoo by identifying a market for ice cream in the morning. More than $2,000 (£1,312, €1,750) was taken at one outlet in an hour. In one year, food sales increased by 25 per cent.

With data analytics, it's like going from blind to 20:20 vision, but it can be overwhelming. Attractions must evolve from being reactive to proactive, which means re-training staff. We usually provide this role and support for up to six months.

After the initial dramatic improvements the company evolves from reporting to true analysis, asking more complex questions, such as: based on Sunday's weather forecast, how many staff will we need?

Then, instead of looking backwards, operators can start using predictive analytics, so they move from the past to the future. This evolution takes one to two years. Then they start using it in other departments, such as how to save on electricity.

Cultural attractions have only discovered data analytics in the past two years. Retail is leading in this area. The next stage is to get closer to understanding the end user in order to improve engagement and become more personalised with marketing.

services.avnet.com

@Avnet_Services

We went live with data analytics in January 2013, for our zoo and aquarium, which receives 700,000 visitors a year. Our main aim was to understand the customer and improve communications with them, so we could provide a better service and experience. We now have a rich dialogue with them throughout the year.

One of the first things we discovered was that people were buying tickets online in the middle of the night. This changed the way we do promotions. Instead of sending out daytime offers that customers need to act on by 5pm, we started sending them out at midnight to redeem by 7am. This led to a 40 per cent increase in click through.

Our online sales have increased by 800 per cent in the last two years and 32 per cent are being bought in the middle of the night. We’ve also been able to inform our media buyers of visitors’ zip codes, so they can target those areas in promotions.

Our education programme manager has used data analytics to maximise capacity on our education camps. We now have the highest attendances in 10 years because he can look at the data, see which camps are selling out and add another, or if some aren’t selling well, he can do an email blast or offer a discount.

Data analytics allows us to predict attendance according to weather forecasts and history. On the last 4 July, we predicted attendance to within 100 people. This means, for the first time ever, we’ve been confident enough to cut the allocation for part-time labour and build next year’s budget accordingly.

We got our return on investment within six months. The biggest savings have been on labour and the rest of the benefits have come from increased earnings. Next, we’re going to use analytics to reduce utility costs, manage the animal records and to implement a customer loyalty programme to improve membership lifetime and the customer experience.

Before we started using data analytics, our point of sale information was closed. Now, we can query the system and get information in seconds. Anyone who wants to find out more about their customers can benefit from data analytics: that data is gold.

pdza.org

@PtDefianceZoo

We started using data analytics in May 2013 to get results more quickly and become more pro-active with our decision-making. Prior to this we were always a month out with our reports and playing catch-up.

Data analytics has helped with product sales, enabling us to determine what's selling, optimise our sales, produce inventories and decide on staffing levels at outlets.

It's a comprehensive data source to track attendance and produce visitor statistics, so we can make staffing decisions that keep costs down and improve the visitor experience.

Traditionally, we looked at revenue when making staffing decisions, but now we're able to look beyond revenue and focus on more accurate predictors like the number of transactions (regardless of how high or low the revenues are). We've found more transactions equals more guests. By further breaking it down by hour, we can proactively bring staff in or move existing staff at slower locations to the busier areas.

Overall, it’s made us much more efficient, cutting down the hypotheticals and “what ifs”. We’ve been able to drill down into what’s happening and draw accurate conclusions about why it’s happening.

For example, when our attendance revenues were up but parking revenues were down, we assumed that it was because there’s a finite number of parking spaces. However, analytics showed it was because the attendance was driven by more members visiting, and members receive complimentary parking. We also discovered a trend for buying tickets online, but a percentage of those going unredeemed.

The biggest challenge is learning to use a complex system. If you don’t have knowledge of all of the data sources, it can be hard to know which data to use for a report.

I create daily point of sale reports, and monthly reports on attendance, promotions and event sales. Geographical data allow the marketing department to measure the results of campaigns.

We’ll start looking in more detail at our members, so we can compare their behaviour with non-members. We'll also start trying to influence behaviour and spending in-park, for example, to see how far people will travel in the park to redeem offers.

fortworthzoo.org

@FortWorthZoo

When collecting data it’s important to have a strategy about what you’re going to collect and how you’ll use it. A current trend is for getting better quality data on the customer from ticket purchases and other spend. This information can inform targeted marketing, which brings down marketing costs. We’ve seen revenue increases of 7 to 8 per cent in the first year of using data analytics with joined-up technology.

Many attractions don't know who’s actually coming, so they spend too much money on above-the-line marketing campaigns. It’s much cheaper if operators can capture the data and do the marketing themselves. The more you can find out about the customer, the more you can design products and offers to encourage repeat business.

For example, the Snow Centre, the UK’s most profitable indoor real snow centre, discovered there was a market for people who wanted to ski in the summer. They didn't come in winter as they don't like it when it's busy, but they were happy to come in the off-peak months. The resulting summer skiing membership now accounts for 23 per cent of revenue.

We recommend “little and often” from a data collection point of view, so that customers aren't overwhelmed or put off. Initially we suggest finding out first name, last name and email address. One of our clients managed to establish that each email address collected is worth about $11 (£7, €9) to them. The more you engage with the customer, the more you can capture. For example, find out their birth date by tempting them with a birthday offer.

The Eden Project has 10 to 15 audience categories which it needs to engage in different ways, so it’s important for them to know enough about their customers to ensure they send them targeted emails. They don't want people to opt out of the mailing list because they are receiving too many irrelevant emails.

Data analytics can also be used to inform loyalty programmes, so customers can earn points or incentives when they spend money. When done correctly, a loyalty scheme can improve the customer experience, whilst giving a reason for further data collection. A card swipe at the point of sale, venue entry and other data collection points will identify the customer so that data is collected, without detracting from the experience. This is very popular in the retail and sports industries, but attractions are only just starting to to embrace this.

green4solutions.com

@Green4Solutions