June 28, 2017
Analytics is all about searching for patterns in data and, hopefully, doing something useful with the results. In our last post, we explored analytics for Convention Centres. This time, we'll shift focus to Destination Marketing Organisations. For the last decade, DMOs have made huge efforts to implement technologies that collect data and eliminate paper. However, all that data represents a largely untapped resource in many DMOs.
So what secrets lie in DMO data?
That answer really depends on which system is being examined. Customer Relationship Management (CRM) systems such as iDSS, Simpleview and Ungerboeck contain a wealth of sales, meeting, and member/partner information. Social media platforms like Facebook, Twitter and Instagram as well as website tracking tools such as Google Analytics also provide detailed data streams. systems. In addition, there may be even more insights from other data sources such as market research, Point of Sale (POS) systems in visitor centres and even various sensors within a city.
How do we attempt to understand this data?
Once the sources of data have been identified, a strategy is really required as to how best to leverage this data. Gartner has a nice model which defines four levels of analytics:
1. Descriptive, "What happened?"
2. Diagnostic, "Why did it happen?"
3. Predictive, "What will happen?"
4. Prescriptive, "What should I do?"
The first level of Descriptive analytics may include examining such things as historical trends and seasonal patterns of your meeting/event data or looking at social or web traffic. A diagnostic analysis may explore subjects such as why meetings were lost/canceled or why the Facebook engagement is declining.
Moving to the higher levels of predictive and prescriptive analytics opens up possibilities such as room night forecasting and lead scoring. Room night forecasting / management is an emerging area where techniques that have been leveraged in the airline and hotel industries are now being applied to the DMO industry. The goal is to understand "gaps" in future meeting demand early so that sales efforts can be mounted before it's too late. Automated lead-scoring is a powerful way to ensure sales staff are spending their time on the best leads. Watercooler Analytics is currently developing a machine learning algorithm that assigns the probability of a lead closing (i.e. a lead score) based on historical data. This approach was validated in a joint research project with the University of Victoria.
What tools can help analyze this data?
From a technology perspective, many DMOs tackle analytics with tools like Excel which, while useful for some preliminary descriptive analytics, really start to frustrate users when trying to manipulate larger data sets and undertake more complex analysis. Some DMOs purchase "generic" analytics software which appears to be a bargain price, but then realize that connecting to industry-specific data sources and implementing industry-specific business rules and algorithms quickly translate into significant consulting expenses.
Management plays a pivotal role in the adoption of analytics. The three critical attributes these executives must possess are curiosity, evidence-based decision making and a desire for continuous improvement. Curiosity is the lifeblood of analytics -- when the questions stop, so do the insights. Evidence-based decision making is vital and sets a tone for staff that decisions are made using facts and data. Finally, a culture of continuous improvement within a DMO creates a demand for analytics as an important tool in boosting revenue, making meeting planners happier and creating an engaged and productive workforce.
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