Analytics are essential in every industry, including real estate. As new-build developers, you spend a great deal of time in the data. With any luck, at the foundation of your company’s marketing team, there is an accessible analytics platform that is set up to provide insights which you can actually take action with. You should always feel that the data is just a log in away. You should feel as though you have the data to make great recommendations, troubleshoot issues, and forecast our efforts accurately. Most importantly, you should feel totally in control of our analytics, and use them daily.
But in reality, this kind of organised and consistent analytics is not the case. Ever.
There may be a handful of development companies who are lucky enough to have marketing teams dedicated to doing all they can do as it relates to analytics. Some of you may even have staffed your team with a handful of full-time analysts. More likely, you may be trying to use data in your market research and campaign analysis, but not doing it as thoroughly or as effectively as you wish you were. I believe the number one reason marketing teams aren't as data-driven as they should be is because data is intimidating. However, the more you know, the more comfortable you will be to put your analyst shoes on. So let's talk about different types of analytics and common places to start with them.
The 3 Different Types of Data Analytics:
The ultimate goal of your data analytics is to leave you more educated than before so you can build better developments in the future and know exactly what the market demands. However, this sounds a lot simpler than it is. A common misconception among businesses is that all analysis is equal, which isn't actually the truth. There are three types of analytics; predictive, prescriptive, and descriptive. Usually businesses spend the majority of their time on only one of them: descriptive. As you can imagine, that leaves a lot of great data and innovation off the table.
Descriptive analytics is when we data mine our historical performance for insights. Often, we are just looking to get context or tell a story with the data. This is most certainly at the heart of what most marketers do on a daily basis, particularly in their web analytics. We look at how we are doing, and we try to understand what is happening and how that is affecting everything else.
Typical questions include: "How did the last development campaign do?" "What sort of sales performance did we see last project?"
Predictive analytics takes that one step further. It's less about the questions, and more about the suggestions. It involves looking at your historical data, and coming up with predictions on what to expect next. This is most readily used in our industry when we try to predict how the next new- build development will go based on the previous projects performance. While it seems like an obvious next step for analysis, it's amazing to me just how many companies stop at descriptive, and fail to push into this arena of predictive analytics. Often, it's because this involves predictive modeling which can, again, be very intimidating.
Typical statements include: "Based on the last development projects data and our consistent growth, we can expect to increase another 25%," or, "Knowing our seasonal drop trend, we can expect to slow down by 10% in the next 6 weeks."
Prescriptive Analytics. This is where things can get fun. Prescriptive analytics takes forecasting and predictions a step further. With prescriptive analytics, you automatically mine data sets, and apply business rules or machine learning so you can make predictions faster and consequently advocate a next move. Marketers tend not to think of this "as their responsibility." That is for someone else to think about and solve. I think that is a super dangerous mindset. Prescriptive analytics can be a very powerful catalyst for success at a company.
Typical questions include: "What if we could predict when customers were having doubts about the homes, what could we surface prior to that to change their minds?" "What if we can predict when they are ripe for purchase?" "What if we can predict what they would be most likely to share with a friend, how would we surface that?"
So, the question remains, are you doing enough?
I ask this because somewhere along the way, marketers began to believe that descriptive analytics was their job, and "that other stuff" was for someone else to figure out. At Blueprint Effects, we are working hard to have each team working on all three types of data analysis in a variety of capacities. It's not easy. There is a stereotype out there that you have to break through. Data can be fun. It can be accessible, and it can be part of everyone's job. In fact, it really should be. Imagine this for a second: just think about how much could get done if every team felt empowered to tell a story with the data, make predictions off of it, and then brainstormed ways to operationalize that data to prescribe next steps for the biggest gains. That is what being an analyst means and I believe we are all becoming more of an analyst as this industry continues to evolve. The platforms out there make it easier than ever, and the competition is more intense than ever. Why not be a part of something more than just telling a story with the data? Why not suggest the next move? Why not create crazy ways to use the data? We think it's time we all put our analyst shoes back on and had a little fun with it.
New-build developments have to make sure they stay on trend with location, demographics and branding. It is obvious that marketing analysis plays a huge role in the success of each new project. Hopefully, breaking down the types of analytics above is a great reminder that there is more than just descriptive analytics to help you achieve your goals. At the very least, you can share with your team to inspire them to be more innovative with the data in front of them.