Measuring the ROI of a data analytics solution

Visitor attractions usually prioritize big data analytics to either power growth or efficiency. When the question is posed of which is most important, the answer will usually be ‘both’, with an emphasis either way. To translate what efficient growth means in monetary terms, calculating the return on investment for analytics is a useful exercise to simultaneously align the team before getting into the data details while evaluating the business case for investment. 

In our view at Dexibit, return is usually a game of two halves – the first concentrating on quick wins that are direct outcomes of implementing a data solution, the second shifting focus to strategic gains that deliver an exponential upside in value powered by more insightful decisions.  

 

 

The direct impact of quick wins

There are two quick wins that can be directly attributed to implementing a data solution – people’s time and legacy system cost. Previously wasted time spent on data administration, such as manual data collection and entry, spreadsheet manipulation, report writing and even forecasting can be automated to enable faster time to insight and democratized access without gatekeepers or bottlenecks. This means the attraction team can spend more time actioning insight than simply trying to generate it. This time won’t just impact one resource such as a system administrator – it’s likely time spent by just about every managerial staff member and their analysts, concentrated at month or year end. In a visitor attraction, it easily adds up to at least one Full TIme Equivalent (FTE) per million visitors, more where average revenue per visit is higher and the organization labors over the metrics and more still where the attraction business model is complex, such as with multiple lines of consumer business. This saving will likely be in efficiency as opposed to hard dollars, given these tasks are distributed amongst team members with other responsibilities.

Some visitor attractions may have an existing system spend in the big data analytics space which can be consolidated with a new data program. It may be that the organization has already attempted a home built solution and found the total cost of ownership too high and will be able turn off the various products and services stitched together (or general maintenance of this stack) or avoid continuing to sink cost into making a difficult build’s outcomes workable. Early data adopters may have already purchased licenses to visualization tools or similar products which can be consolidated into a comprehensive data solution. Or, the visitor attraction may have found itself with a growing piecemeal analytics landscape – an extra reporting module on a ticketing system, WiFi analytics products, sentiment analysis tools, social media analytics subscriptions, a forecasting consulting service – all which can be retired in favor of a central hub approach to data needs that centralizes access for all and more powerfully enables enriched insight between data sets. In this case, finance may need to review when old contracts can be cancelled to see how quickly cost savings can be realized.

The benefits of these directly related quick wins begins almost immediately and should deliver returns in less than 12 months. Early wins helps to rally the team, prove the data hypothesis and fund future investments in either further data advancements such as a Head of Insights hire, or more aggressive execution of actions taken from the insights themselves.     

 

The strategic upside of insightful decisions 

The real value in analytics is beyond saving time and system effort – it’s in the upside of making more insightful decisions, in increased topline income or decreased operating costs. In cultural institutions, there will also be an immeasurable benefit to engagement, education and transparency in helping the institution deliver on its social mission which in many cases may be of higher focus than monetary gain, however in an economic environment where every dollar counts, the addition of commercial gain through analytics initiatives helps the institution’s sustainability.  

In the business case for data, this strategic upside needs to be clearly positioned as indirect, as unlike the quick wins of automation and modernization, it won’t automatically happen simply as a result of implementing a data solution. To achieve these results, the visitor attraction team will have to listen to the data, take meaning from it and action the insights, most likely making additional investments to do so, such as the cost of signage in improving wayfinding, or the cost of a pop up stall and staffing in improving visitor spend. 

Top line revenue has two levers: visitation and yield. Tackling both together maximizes growth acceleration – more visitors, spending more and preferably with increased recurring revenue given its higher profitability. These revenue goals can take on a specific objective, like the conversion to upsell or an experience or exhibition, increasing the visitor’s basket size in the shop, or improving conversion to membership. At a basket level, an increase of even 50 cents in spend per visit will deliver good results at 100,000 visitors – and impressive outcomes at 1,000,000. For cultural institutions, funding through government grants or private donors can also be a data objective –  funders are more likely to be confident in committing funds and more likely to follow on with secondary funding, where they have confidence in a data informed leadership (especially so for donors with a business background who exercise metric heavy management in their business affairs). The same is true for commercial attractions such as Location Based Experiences (LBEs) with venture backing needing to manage the expectations of investors, or for joint ventures between a licensor or real estate owner and attraction operator.

 

Go from good to great 

When figuring out how aggressive to pitch growth objectives, stay pessimistic – the numbers quickly add up for the business case and create an achievable plan for the team to then set sights higher once initial results come through. Amongst our community of visitor attractions at Dexibit, high intensity insight inspired teams experience double digit growth, whereas the industry average for culture, tourism and entertainment in visitor numbers globally is approximately 4%. Basing the return on investment calculation just above that at 5% annually sets an attainable business case from which to build upon, with impressive numbers as this growth compounds year after year across a three or five year return, both visitation and spend levers acting accretively. If the attraction’s numbers are currently in decline, the value of this can then be calculated first in reversing this trend, secondly in returning to growth. 

Cost savings are harder to deliver percentage wise, so we recommend starting with 1% or less (still significant for attractions with operating expenditures well into the millions). On the plus side, these can be achieved without significant additional investment – for example, by using long term forecasts to more accurately plan seasonal front line workers during the high season, or hourly forecasts to refine resource and inventory scheduling on a day to day basis.

While business case evaluation and return on investment calculation is good practice for any investment initiative, it is an especially powerful planning tool in a data program to set the tone of using data in leading change. A good return on investment calculator will set out these targets in unit economic terms, a great leadership tool in introducing the language of data in accessible terms for the wider team. These numbers present a baseline, evaluation metric set and target future from which to return to in evaluating analytics return as the program progresses. 

 

Contemplating a data program and curious to see the return for your visitor attraction? Talk to us and have Dexibit’s team prepare a return on investment calculator based on your goals for efficient growth.