If you are looking to grow your business, get more leads, simplify, or create more freedom, then you’re going to want to continue. AJ and Meaghan recently went on the Growth to Freedom podcast with Dan Kuschell to talk about data, automation, health, and relationships.
Check out the full podcast here, and out insights below.
Data for entrepreneurs
Most entrepreneurs think of themselves as left-brain individuals. They rely on intuition and instinct to help them make their decisions. Meaghan and AJ used to think this way as well, but someone helped redefine that for them. While talking to a mentor, Meaghan mentioned that she was the down-in-the-weeds person and AJ was intuitive and head-in-the-clouds. As an illustration, she talked about how she relied on data and AJ went with his gut.
This mentor quickly pointed out to Meaghan that intuition didn’t work the way that she described it. Intuition occurs when the brain processes data and recognizes patterns faster than we can perceive. That means that even those that think that they aren’t in tune with data really are.
Often these intuitive people think that they just get lucky, or they’ve just got good gut instincts; but in reality, they just connected data points in the back of their mind without recognizing it.
Data is just individual points of information, but it’s not useful like that. The value of data comes when you connect those data points together and find a pattern or correlation. When people say that they’re naturally intuitive, they have an ability to create those connections in their mind without even noticing.
The importance of LTV
We’ve talked a lot about customer lifetime value and how it important it is for organizations to track. What we want to make clear is the importance of not just using an average as your measurement for LTV. We always say that averages are truly evil because they don’t give you an actionable insight. Knowing a single, static number doesn’t do much for a business; the point is to take action from it. Businesses don’t just want to know what the number is, they want to impact it, to change it, and to increase it.
You need to examine LTV over time. Your business is constantly in flux, and so the value of your customers naturally will vary as well. What was the LTV of your customers last month, or one year ago, or even two years ago? You need to have multiple data points in order to create a trend or pattern. Once you have that trend or pattern, you can find the causes for the fluctuations, and then you can capitalize on the things that caused the upswings and eliminate the things that caused the decreases.
In order to do that, you have to get granular with your LTV. You need to know where your highest LTV clients come from, what they purchase, when they repurchase, etc. And on the other side of the coin, you want to know where the lowest value clients come from, what they purchase, etc. If you can double down on getting the high end clients and stop spending money on lower-value clients then you can dramatically increase the overall LTV of your clients.
Reduce waste to increase results
If you’re using an average and taking action off of that, you’re creating a massive amount of waste. Because averages mush together the highs and lows, if you just double down on everything, then you end up doubling down on some things that don’t work. That creates massive amounts of waste.
The best way to reduce and avoid waste is to get granular with your data. Rather than taking a shotgun approach, you need to take a precision, surgical approach. By taking the precision approach to your data, you can hyper-focus your efforts on the things that work, and eliminate the things that don’t.
Avoid wasting time and effort with a dashboard
Most businesses start looking at dashboards, and they don’t even know where to start; so they start with what they know, or what they’ve read. They look at dashboards for specific KPIs or specific metrics. They forget to look deeper into the why of the dashboard.
At Praxis, we don’t build out a metric without both us and the client understanding the “why” of the metric. That’s why we start all of our data projects with a process called metrics mapping. Metrics mapping is a process that helps you make sure that you’re only tracking things that are actually valuable to your organization.
The process of metrics mapping starts with establishing your high-level goals. What does your business want to accomplish? As you can see in the example below, this business wanted to double their overall YoY revenue.
The next step in the process is to determine what questions you have that you need to answer in order to reach your goal. Do you need to know how to increase customer retention by 30%? Do you need to figure out how to double your average order value? In this example, we’ll stick with how to increase conversion rates on the site.
From there, you need to figure out what metrics you can use to answer that question. In this example, the client needed to know the conversion rates for the different stages of their funnel. Additionally, they needed to know their customer LTV, allowable CPA, and finally their profitability by channel.
Once you know the metrics that you need to measure to answer your questions, it’s time to determine the “source of truth” for each of those metrics. The source of truth is the place where you can find the most accurate information. So, for financial metrics, we would recommend using a payment processor, or bank account. For source data, Google Analytics works best.
From there, you want to validate your data across sources and then plug it into a dashboard.
Focus on the needle-movers
Before you can understand how to scale your business, you need to understand lead indicators and lag indicators. Lag indicators are the easiest and most common things that people measure. They measure what happened after the fact. Examples of lag indicators are revenue, total sales, etc. Leading indicators are the actions taken that drive the results. These could be things like emails sent, phone calls made, ad spend, etc. These are the efforts that drove the lag indicators for the company.
When it comes to metrics, we divide them into 3 classes. Descriptive, prescriptive, and predictive. Descriptive analytics tell you what happened in the past, prescriptive analytics help tell you what you should and shouldn’t do, and predictive analytics tell you outcomes to expect when you implement the prescriptive analytics.
Each of those classes of data can be thought of as a phase of data maturity. In order to get to machine learning and AI, you need to have descriptive analytics that tell you what happened. From there, you can start to merge your data together and combine metrics in complex calculations to help you understand what to do next. Finally, you can move on to allowing computers to extrapolate models and forecasts based off the information that you have already gathered and tracked.
The most advanced AI can’t create models without data to rely on. That’s why it’s important to make sure that at every phase you have everything set up and tracking properly before you move on.
Leverage attribution to your advantage
Unfortunately, attribution will always be a war-zone. Every platform will leverage the model that makes them look the best, and there isn’t one attribution model that works best.
The easiest attribution model for the most businesses is last-touch. Since Google Analytics defaults to that as well, it’s generally the baseline for most companies. The ideal attribution model is one that can tell you what the best first-touch campaigns are (the ones that generate the most interest and awareness for your business), then the ones that tell you what the ideal middle-touch points are, and finally the best last-touch campaigns. That would allow you to optimize your ad spend across those campaigns and create a fully optimized customer journey.
Unfortunately, at the moment, such a model doesn’t exist. The best way to create such a model for yourself would be to use attribution comparison tools to compare each model and find the ideal journey yourself. This relies heavily on accurate tracking though; every podcast appearance needs to have a UTM link in the show notes, every email campaign needs to be tagged, and your website needs to have all of the tracking installed properly. If any of those fail to work properly, then the entire model can fall apart.
The un-sexy part of data
We’ve covered the best parts of data, turning your data into insights, and insights into revenue; but all of that requires the un-sexy, foundation. In order to get 6-pack abs, you have to sweat and look janky at the gym.
Tracking is the gym section of data. We have to pump some serious data iron in the back-end before your data is beach-ready. You need to make sure that you have UTMs attached to every single customer touch-point; additionally, those UTMs should ideally be standardized. You need to have every page and every funnel on your website tagged and tracked. You need to have event, goal, and ecommerce tracking in place to make sure that you’re tracking funnel steps properly.
Once you have all of that set up, you have to validate the data to make sure that everything fires correctly, with no duplicates or missing pieces of data.
Choosing a data platform
There are hundreds of data visualization tools on the market. The problem is that most of them are just visualization tools; and not business intelligence tools. Business intelligence tools can connect multiple sources of data together, whereas most of the platforms today are just single-source dashboards. While it may be helpful to see your data visualized, the best insights come when you can combine multiple sources of data together.
As we talked about with the foundation stages earlier, the first thing that you need to do is make sure that your tracking is set up correctly. Once you get your tracking set up, the next thing that you want to do is standardize your tracking. Make sure that all of the parameters are aligned so that you can get clean, standardized data across your platforms. Once you have that taken care of, the next step to take is automation.
Most of our clients come to us in between standardization and automation stages, in what we call “spreadsheet hell”. In that stage, you have tracking and data set up, and you’re trying to get all of the data together in one place; that lends itself to spreadsheets, and generally that turns into lots of spreadsheets. Once you hit that point, it’s generally time to start migrating to a dashboard solution.
Get creative with your data
As we’ve stated a few times, data can and should be sexy. One of the ways to make it sexy is to leverage it in creative ways. Meaghan and AJ decided that they wanted to quantify love and figure out how to optimize their love life. Once they started tracking the data on their relationship, they found gaps that were causing fights between them. Upon realizing this, they quickly made adjustments and now get more out of their relationship.
One of our clients, Fancy Sprinkles, had another example of how you can get creative with your tracking and data to make it sexy. They wanted to figure out what types of content they should post on social media. In order to figure that out, they went back through all of their social posts and tagged each one with meta-data. They tagged each post with information on whether the photo was inside or outside, a close-up or wide shot, and what colors they used.
When they mapped that data out across time with the engagement rates, they quickly found actionable insights that allowed them to skyrocket their social engagement.