Munich, September 2019 – Fall is fast approaching, which means that it is almost time for hotel managers to begin planning for the upcoming year. That’s right – it’s budget season. Budgets must be allocated to different departments, investment decisions must be made, strategies developed and aligned, etc. For some, this may feel like a painful and time-consuming routine. But a bad decision during this process can come with major risks. I’m not talking about the possibility of a distribution channel performs slightly differently than expected. I’m thinking of major, extremely costly moves that can impact a hotel’s entire business strategy or profitability.
Hotel managers cannot leave these decisions up to gut feelings or even past experiences. Analytics and numbers must be combined with a strong vision and understanding of where the industry is headed. These things provide security and future-proof the hotel.
Is our industry making smart decisions?
The reality is that the situation is dire and extremely risky. Owners and asset managers must ask themselves if investment requests from operational departments are really necessary, if they are unnecessary, or if they are just flat out wrong.
Ask how a decision was made? What was the basis for it? From my point of view, 50% of all strategic decisions are questionable at best, and many are basically a waste of money. So when it comes to data analytics for decision-making, C-Level managers should be able to ask the right questions to make sure that the reports in front of them are a real help, or if they are just a basic orientation with questionable quality.
Number one when it comes to decision making is the quantity and the quality of the data used. This is crucial to understand. The more data I have available, the smaller the error margin. Just think about the Trump election in 2016. Most people had no doubt that Hillary would win, but then the tragic results came in. The reason for this severe error in the projections was that the polling sample sizes were too small and did not accurately reflect the opinion of US voters. Or let’s take a more current example from Germany. In August 2019, the German party SPD in Brandenburg, a state in the north east part was prognosed at 17%. At the election a couple weeks later, the SPD got 26%(1) The reason for such huge error margins are too small of sample sizes. For cost reasons, the market research companies cannot afford to ask more people.
For the hotel industry this does not count. Through post stay messages and feedback systems hotels get feedback from about 30% of all of their customers. This means the sample size is pretty good, and that the error margin should be close to 1%. But this is not the case.
Quantity aside, the quality of the data poses a further risk if the analytics are to be reliable and trustworthy. But, here we have a big problem in the industry. The “Global CRM and Data Management” study by the German consulting company h2c revealed that data cleansing has the most room for improvement; it is a key issue for hotels. Even when a hotel company uses a CRM system, 56%(2) of the time, it is still cleaning data manually. The data cleansing specialist dailypoint™ analyzed about 4.5 million stays in 2018 and found that returning guests have on average 2.3 profiles in the PMS. Just imagine what this means for the quality of any analytics.
Hotels need a fully automated cleansing process! Ask a vendor to clean, over-right and if possible, merge records in the PMS system itself. If a CRM or any other vendor is not doing this, then the marketed data quality management processes are not good enough.
Manual data cleansing is a no go!
Let’s assume quantity and quality of the data is good. The next hurdle is asking the right questions. Each hotel is different therefore a hotel needs an individual questionnaire. Everything else offers no real value for decision making. It can be important for customer acquisition, like TripAdvisor, but not for decision making.
So how can a reliable questionnaire be built? Let’s talk about the methodology. I am glad that today more and more companies use a 10-point scale for their questionnaires. When we introduced this to the industry about 10 years ago, almost all hotels used a scale of 5, which is fundamentally wrong.
Are we in good shape now, with big and clean data, as well as a “good” questionnaire with a correct scale? Many managers might believe so, but this is not the case.
Knowing the satisfaction index of different services is nice, but it is not a base to make investment decisions on! You must know how important the services are for your clients. As part of its feedback system, dailypoint™ offers the so-called two component approach. The result is a matrix with two angles, one for satisfaction and the second one for importance.
This alone would be great if a decision maker would know not only how satisfied the guests are with different offered services but also how important they are. But even this is not enough to make a sustainable decision.
You need to know who gives the Feedback!
The number one rule when analyzing data is, you have to know who gives you feedback. For a decision-making process, this means the beforementioned analysis must be sliced down further. Not all feedback givers are relevant. Just think who your key target groups are and who are your premium customers. For them, you want to offer a perfect experience which means you should align your product and services to fulfill their expectations. In dailypoint™, everything is linked to a clean central guest profile. Meaning managers can not only ask the right questions and get the right answers.
The appearance of Big Data is a boost for so many things and also for better decision making, but only if it is used correctly.
Einstein said: “God is not guessing”.
With Big Data, managers can limit the risk of making the wrong decisions down to a minimum if they know the secrets. Happy budget season!
(1) https://www.welt.de/politik/deutschland/article199601976/Landtagswahlen-Warum-lagen-schon-wieder-alle-Wahlumfragen-daneben.html; retrieved on 15.09.2019
(2) C.F. h2c Global CRM Study, 2019, Düsseldorf, P. 27 ff.