Does Our Past Data Still Apply?
As we mentioned in a post last year, as well as one from last month, our knowledge (regardless of industry or discipline) has been based on our understanding of what has happened in the past coupled with our understanding of what is happening at the current time. We then use these two sets of inputs to make a prediction of what will happen in the near future. Typically, the better the inputs and understanding of these two data categories the better the predictions of future events will be.
However, there is one big challenge with both of these inputs, especially our historical data.
Change is always happening, but rarely does change affect the entire globe the way COVID has over the past year and a half. Customers disappeared overnight, children were in school at their kitchen table, and working from home became the norm for the vast majority of office workers. Naturally this led to a new set of behaviors during the various levels of lockdowns and reopenings. It is safe to say that some behaviors over the last year are products of the pandemic and should go back to a more normal state of being (looking at you toilet paper hoarders), but what about the vast majority of changes? It seems naïve to think that everything goes back to a predictable normal with the current data connecting with the past in a nice neat package.
This is not to say that pre-COVID historical data is worthless, instead there is going to need to be a bridge from the past to the current understanding. Organizations have two choices on how to build this bridge: 1) be patient and get a year or two of fresh data/information through post-purchase engagement, learn what changes are permanent, which are temporary, and then connect the two points of time and move forward with a new understanding and game plan; or 2) engage with your critical audiences now and moving forward, gain an understanding of what changes are likely to be permanent, and then connect past with current data. Both will work, but one of these can be done in a much shorter time period and will set up the organization for future success. The other is just a rehash of an approach that is outdated and based on a world that no longer exists.
We have long argued that the old way of doing research no longer captures our current world. The pandemic has put a much finer point on this fact, and many organizations do not seem to be adjusting to the fact that the same old way of approaching their understanding of key audiences is just not going to cut it. The wait and see approach to the data bridge discussed above is not only going to cause a lot of organizations to make poor decisions, but it is also likely to create a situation where the investments in historical data collection and analysis will be mostly for naught.
As an example, let’s say there is an international hospitality group trying to determine what their 2022 and 2023 are going to look like. Pre-pandemic this was a relatively easy task given the historical data available to them. They know how their frequent guests behaved in the past, number of business trips, travel with family, etc.
In our new pandemic-induced reality, they know that some of their customers have started to travel again, but most have not. What they don’t really know is whether their customers are planning on rediscovering past levels of travel, or have their future behaviors likely changed permanently? The challenge that exists for this hospitality company is that inputs from their customers have relied almost exclusively on post-purchase surveys. A customer books a room, they check in, they leave, they are sent a (typically) dull survey about their stay that may or may not be filled out. There is a key problem with this approach in that a customer needs to stay at the hotel before they are engaged.
Moving into today, this approach to engagement leaves a massive black hole of information since most travelers have not been staying at their properties. A traveler who typically has stayed 30 nights a year and has not traveled since March of 2020 has been likely given zero ways to share what their likely future behavior is going to be.
This hotel group can move forward with plans based on guesses over the next couple years and rebuild their understanding of how many fewer stays the average customer is going to have moving forward, or they could rethink how customer information is obtained and shift from a post-purchase approach to an ongoing engagement.
The same is true for companies that have a subscription-based model. These can be organizations that have annual, seasonal, or monthly subscribers (think big ski area companies, gyms like Orange Theory, or streaming services like Netflix) all of these companies are in a state of data noise right now. For some it has been good (streaming and home gym subscriptions for example) while others have not been as lucky (in-person gyms), but what both have in common now is the difficult task of trying to connect the two points in time for better predictions of future behavior. Is Disney+ going to stay once the kids are back in school full-time? Are they heading back to the gym or continuing to workout at home? Was that 70-ski day season the new normal or an abnormality? These are all answers organizations could have if they moved away from the post-purchase model of engagement.
Building a better bridge from past to present is key for organizations who want to be able to make the best decisions possible moving forward. If your plan is to rely on post-consumption information, you are already too late. Your understanding of the future is going to be based on assumptions at best for the next few years.
The other choice is for organizations to rethink and modernize their research and move to a regular engagement with their key audiences, learning what is likely to change before you make strategic decisions.
Two years from now, the successful organizations are going to be the ones that adapt to our new world, while those that shun change are likely to move closer to becoming more and more obsolete.
