Data collection for Hospitality, can you do it, or is it a waste of time?
One of the cores of most businesses is guest or consumer satisfaction leading to loyalty and to be able to fully understand behavior of the guests, there are numerous ways of collecting data, data that should be used by management to adjust the marketing and sales strategies.
Now whether the information is used in the right way, is an entire different story. I would like to focus on the collection of the data.
Image 1. How did you experience our service today?
Rather than starting with the hotel industry I would like to take an example of another industry. Recently I walked into one of the largest if not the largest Scandinavian consumer electronics shops. On my way out there was something that caught my eye, my mind and put an instant question mark on reliable data research.
On the way out there was a machine, offering an option to rate the customer service that you received today. Interesting right, and a pretty nice way of showing that there is an interest into what we think after we shopped there.
4 Buttons, the one on the left was dark green, with one of those super smiles on it. The one next to it, light green, that green that reminds you of something natural and hygienic, still smiling, just not as much as his green brother, then there is the orange warning sign, not smiling, a little sad in fact. The red button on the far right is clearly the unhappy guy. So the machine invites to hit one of the buttons on the machine. Boom, I am a generous guy, I hit the dark green button, or should I have?
In data collection there a 3 phases that should be recognized and followed through.
The pre-data collection
The pre-data collection is the base for a good end result. This phase should start with defining goals and objectives of the data collection. Also the means and the method of the data collection should be agreed. There are many different methods and tools that can be used; each of them can be applied to different ways. Note as well that within the hotel industry there is incredible data available from all the internal systems, like front office or accounting systems.
Within the first phase also ensure that data is accurate, stable and can be reproduced. In my example how many kids do you think walk by and play with these colorful buttons, making the data collection totally inaccurate?
1. Define goals and objectives.
Collecting data starts with a description of the project. What is the project, is there a problem, is there something we can do different to improve a customer experience, is there sudden revenue decrease, are there unexpected expenses, or is the payroll going up following revenue increases?
What specific data needs to be collected? Is there data that is already available, and needs cleaning up to be understood or do we need to collect new data. What data can be used to best reach solutions to ensure we reach our goals.
What is the reason or purpose of collecting that exact data? There is an incredible variety of data available, which one serves us best in reaching our goals?
Changes to be made to reach our goals are always a process, and the data collected needs to make sure it reflects and provides insight to how that process can be improved. An example is luggage handling upon guest check in. The issue often is that when the guest has checked in and has entered the room, they would like the luggage to arrive immediately, as they are ready to go out, or take a shower. There is all kinds of data you can collect in this process, finding the source of the issue, to ensure that it can be improved. It turns out that communication, or to be more exact the telephone call, between the bellboy and reception is often the issue, relating to the ticket that has been given to the guest.
Besides collecting useful data, before it actually is collected, you need establish what you will actually use the data for, to avoid mistakes, confusion or unnecessary collection.
Finally and perhaps a combination of the above, some typical stratification groups should be established:
- Who: which people, groups, departments or organizations are involved?
- What: relevant machines, equipment, technology, products, services or supplies
- Where: the typical location of the defect or problem
- When: what time of the day, day of the week, or step of the process is involved?
In the example of the buttons at the Electronics shop why are the buttons there? Are we measuring staff friendliness, speed at check out, availability of products, the interior? What do we get out of people pressing those buttons; in fact do we want to catch all of them, or just the ones who received personal service? What insight can this data provide us about the customer who just walked out? And finally can this data be used at all and what is the objective, what are they trying to fix, what part in the process is broken?
2. Define the methodology and create clear operational definitions that are equally understood by all involved in the data collection as well as the interpretation of it.
Operational definitions literally mean to define something specific, like variables, or objects in terms of a process. That sounds pretty funny, but when we go back to the example of the Electronics shop, I keep talking about the machine with buttons. These buttons can be visualized in many different ways, they can be small, hardly visible, could look a like a computer. I think you know what I mean with buttons, I just need to make absolutely sure that we are talking about the same machine, in a data collection process.
The operational definitions tell us how we should measure something, what tool to use and how to document it. This is an important step for the improvement team. Remember all of this data collection is aimed to deliver a final improvement to your organization or hotel, solving a failure in a process, or creating a new or additional service.
This is immediately related to the guest or customer. So it might be a good way to ask what they have available. Why not ask a decent sample size of customers how they would like to give feedback on a specific service, perhaps it is an online survey, or an app, perhaps they want to share comments or an evaluation with other customers.
Creating clear operational definitions are also important in reducing waste. This means that the data needed for a change or pilot is true data, supplied by the right source. Data collection should always be cleaned from any waste of information that cannot be used.
In the example, we would have to remove all the touches of the buttons by kids, as that is not our target market, they are not the right source, and they do not are most likely not informed about the purpose of the data collection in the first place.
The reliability of the data is also important for future data collection or additional data collection that is needed for a change. Often teams go back to old data and adjust the way to collect data, as a part of reaching the ultimate desired input.
3. Understanding the measurement variation
More weird terms, measurement variation, it is actually quite simple. To be able to measure and collect data you need data that is accurate. Accurate information is needed to establish correct end values. Accuracy can slightly differ for example when another person collects the data, or a different tool is used. That is totally fine, but the difference between the two occasions of data collection should be as small as possible.
On many occasions the team looking for a solution needs to go back or wants to go back to collect more data, in exactly the same way it was collected the first time. Sometimes however the data needs to be collected in another way, to make sure that defects are removed.
An example is the color of the buttons on the machine at the Electronics shop. I am not sure if you have noticed, but they start the dark Green (positive) button on the left. That is rather unusual, as I can imagine most people would expect that on the right in stead. That seems more logical, and in this case can cause confusion. They could switch the buttons to make the actions repeatable but most do it in such a way that variation is small, and is explained well.
There are clearly benefits in continuing data collection, or going back to data, tools or means of collection. Again the deviation should be as small as possible. Further data collection can show variations because of some kind of action that has happened, and can be both positive and negative. The beauty of measuring data is that you can react fast and make the appropriate changes, repeating the great and the positive and removing the root cause of problems.
During data collection
Data collection is a vital step in the path to making the right decision, and creating or adjusting a company strategy. Data collection is a valuable process, and to ensure that the results are 100% accurate, the data collection should be followed through totally, in an allocated time span.
The actual data collection is very often a challenge. After you have established who should be asked and how the data should be collected, the actual collection commences.
4. Taking the path of collecting data
What is vital in this step is to inform all members of staff, what is going to be done, why it is important to support the data collection and what will be done with the information after. The support of your staff will help the process tremendously, but also they can assist in handing out forms, explaining machines and tools. They are the ones facing the people they collect from, or are implementing the collection. They will be able to explain reasons for the data collection and answer questions about the survey, form or machine.
Within the hotel industry, data collection is a very normal and accessible activity. Every day people are checking in with a lot of the personal data, passport, email address, phone numbers, credit card details, and much of such data. When you Google data collection for hotels, over 32 million search results appear. Interesting in that is that most large hotel chains have updated privacy policies to protect their guests and clients, and also clearly indicating what the data can be used for internally.
I have been part of numerous data collections since 2001, some more challenging than others, mainly because of cultural differences. Measuring productivity for example can be an interesting exercise when a union member follows you around, or on one instant even a security guard. What is also amazing is that data collection is such a beautiful and useful exercise, and yet it is done wrongly so many times.
Quite a while ago, I came across a restaurant questionnaire. The guest had to fill out ten questions, some open answers, and mostly multiple choice. Personnel handed them out at the end of a dinner. If you have followed me so far, you will start asking questions already, about the reliability of this tool. Indeed when I looked further into this type of collection the data was totally useless, and even more so with whatever statistical tool I tried, I could not find any correlation between any of the questions on the form.
For example the question was you dinner value for money, was totally unrelated to the scoring of the service. Again I am not saying that every form is like that or has to be like that, but it is a warning sign for the use. Other questions you can ask are, is the guest happy to fill this out; can every answer be read the same; is the questionnaire only handed to happy guests; are they also hotel guests; where are they from and you can continue forever.
The result of this data collection or survey was astonishingly useless. No correlation, no education, no target subject, no clear operational definitions, no solid objectives, no calculated data collection, no description of the guest or customer and much more. And I remember the boss of the hotel said in front of the executive team; this information is of extreme importance ‘it gives us a good indication of how we are doing’. Until today I am trying to understand what that meant.
Post data collection
Now here is the most interesting part of the data collection. In the many years of hotel consulting, data has been collected; solid data, from systems like Micros Fidelio, accounting systems, loyalty systems and revenue systems, mostly existing data. Follow through of results of data collection is a requirement of success.