Reduce waste and scale using data

How to reduce waste and scale using data

AJ and Meaghan recently appeared on the Nice Guys on Business Podcast. It was an awesome conversation that covered everything from tracking and gathering data all the way to how to use data to improve relationships.

Check out the full episode below along with our insights:

What is data, and how does it affect business?

A lot of people glaze over when they hear “data talk” coming at them. That is a natural response to something that seems very complicated and difficult to understand. For that reason, we want to simplify data and point out that data is just points of information.

Data isn’t just ones and zeros or spreadsheets; data is about helping you make better decisions in your business. Throughout history, people have looked up to their wise elders to help them make decisions. The wise elders were the ones who had the most data and information to draw on. Nothing has changed today; the smartest people and companies leverage data the best.

Data is everywhere and can be used for almost everything

As we move more and more of our lives into the digital world, we create more and more data. As businesses have started aggregating and analyzing that data, some have developed the ability to see into the future.

They actually haven’t; but sometimes it feels like that. From Target accurately predicting that women were pregnant before they knew, to Amazon knowing exactly what you want to buy next; companies that use data properly can hyper-target their customers at exactly the right moment.

Analytics, metrics, and KPIs -Is that data?

Yes and no; analytics and metrics are just names for different types of data; and KPIs are specific metrics of data.

When it comes to data, the first thing you have to do with data is track it. Most businesses have some tracking and data under their belts, the problem is that they have too much of it. That is where KPIs come into play. KPIs, or key performance indicators help businesses narrow in on the metrics that make the biggest difference for them.

So while all three of those things take part in the data ecosystem; data is just information.

I now understand data, now how do I use it in my business?

The Nice Guys focus primarily on sales, so we focused our answer on sales organizations; but the principles apply to all businesses.

No matter what your data initiative, the end goal is always to get someone to convert. For sales organizations, that means that you want more sales. In order to get more sales, you need to figure out what drives your sales.

Obviously, getting more leads should increase your sales; but leads are not created equal. You need to figure out what lead sources drive the best leads to your sales people, which sales people work best with which type of clients, what impact your marketing efforts have on the leads, etc.

Do I need to do all of that analysis myself?

Not at all. Becoming a data expert or data scientist should not be high on any business owner’s priority list. Praxis can help perform the heavy lifting, preparing and distilling the information down to the insights that you need to make changes in your business.

The last thing that we want to do is add more to your plate; so we work with business owners and marketers up front to determine what insights and data will help their organization the most. We start with metrics that can help you make decisions immediately to help your business.

Find the things that you need to track, not the things that other people tell you to track. Data does not have intrinsic value. Data is only useful in that it can provide insight into what actions you should take. If you don’t take action from the data, then it is not valuable to you.

Praxis helps you reduce waste

At Praxis, we like to say that we’re in the waste business. Our goal is to help other businesses reduce wasted time, effort, and money. Most waste in business comes from averages.

Averages are inherently evil because they mash together your highs and your lows and spit out one number. In order to reduce waste, you need to eliminate the lows and redirect those resources to the highs. That is the secret to scaling a business. By transitioning resources from underperforming areas to areas of overperformance, you can push your entire business into hyperdrive.

The fastest and easiest way to scale a business is to eliminate the things that aren’t working and shift those resources to the things that are working.

While running through that process of trimming the fat, you will find that your business and processes naturally simplify along the way. As you eliminate wasted time, energy, and money; you’ll naturally have more of all of those things.

Data is the key to rapid scaling

We live in dynamic times; in the past, businesses could rest on their laurels and remain successful. In today’s business landscape, industries can be disrupted by someone with a laptop. Businesses that don’t remain agile cannot compete with companies that shift and adapt.

The best way to know how and when to adapt is through data. Taking action from your data is the new competitive advantage. Those that capitalize on their data will scale, and those that don’t will fail. There are a myriad of case studies that almost anyone can cite of agile, data-driven companies taking on and toppling the incumbent industry leaders.

One of our favorite sayings is that every company today is a data company. If you’re not analyzing and taking action from your data, your competitor’s will, and they will eventually overtake you.

How does the lifetime value of customers impact my business?

As we talked about earlier, averages are evil. This goes doubly for the lifetime value (LTV) of your customers. We find that many businesses track the average LTV of their customers and think that they’re set with that.

One thing that we always ask clients before we build out any dashboards or metrics is what they’ll do with the information once they have it. If you knew today that your average customer subscribes to your product or service for 5 months, what would you do with that information?

Would you contact every client right around the 5 month mark in order to try to keep them on with you?

The problem with that is that by the 5 month mark, you’ve already lost half of your clients. And those that stuck around probably weren’t looking to cancel right at the 5 month mark.

In order to make the data truly actionable, you need to know when individuals purchase or leave. You need to know what affects those individual’s purchasing decisions, and then try to impact them on an individual level.

How do I get started with all of this?

The best way to get started with data is to schedule a data strategy session with Praxis. During that call, we can walk through your technologies, goals, and data needs.

After talking with you, we can help you create a data roadmap of how to move forward with your data initiatives.

All of our data strategy sessions are completely free. At Praxis we are very purpose driven, so we want to make sure that we can help as many people as possible through data.

If you’d like to learn more about our products and services, you can see some of the metrics and dashboards that we can create for you here.

How ecommerce companies can turn data into growth

How ecommerce companies can turn data into growth

Meaghan and AJ were invited to speak with Alex Brown, founder of Ecommerce Rockstars. They cover everything from data foundations to predictive analytics, so don’t miss out! Check out the full interview and our insights below:

Data shouldn’t be scary

When businesses think about data, most of them think of massive data warehouses with AI and machine learning algorithms. While that may be something to strive for, that’s not data. Data is simply information. Every business has information; now more than ever.

Praxis wants to help businesses find ways to leverage the data that they already have to make better decisions. We always say that we’re in the waste business. We help eliminate wasted time, effort, and money.

The goal of any data project should be to answer your business questions. We want to help businesses answer the questions that will help them scale. Whether your ecommerce business is just getting started, or if you’ve been in business for a few years; this can help you figure out what next steps to take and how to grow your company.

The roadmap to data mastery

While the end goal may be to run massive data projects and collect granular data on every customer’s spending habits; we need to start at the beginning. The more information you have, the better decisions you can make; that means that the less data you have, the worse decisions you make.

That’s why Praxis built out the data maturity spectrum, to help businesses figure out where they stand, and then what to do next.

The data infancy stage

Most companies start in data infancy. They don’t have time or means to dedicate to data and analytics projects; so they put it off. We characterize this stage with a general “spray and pray” type of attitude. Businesses in this stage generally are just throwing ideas at the wall in order to see what sticks.

As they start to see what sticks, and what works and doesn’t work; they begin to lay the foundation for their data strategy. This moves them into the data foundation stage.

The data foundation stage

As businesses start to gather reports and notice patterns, they start to grow their data maturity. In the foundation phase, businesses start to track the cause and effects of their actions. Generally this involves manual reporting, pulling data from disparate sources into spreadsheets, and using complicated pivot tables to analyze the data.

We call this stage “spreadsheet hell”. Businesses in this stage generally have some automations when it comes to their reporting; but they often rely heavily on human input and data aggregation.

The data foundation stage is generally the phase in which businesses start to see explosive growth. Because they track what works and doesn’t work, they’re able to start replicating efforts and successes. In order to continue growing at the same rate, they’ll need to move up the scale of data maturity to the data optimization stage.

The data optimization stage

In the data optimization stage, businesses focus on automation. During this stage, businesses move away from manual reporting and begin to create automatic ETL processes. ETL stands for Extract, Transform, and Load. The idea behind this process is to extract the data from the different “sources of truth”. The source of truth is the place where the most accurate data on whatever you want to measure lives. For example, in the case of financial data, the source of truth would be your payment processor or bank account. For Source/Medium data, the best place to get that data would be Google Analytics.

Next, we need to transform the data as needed. Transformation of the data entails taking all of the data from your disparate sources, joining it and then cleaning it to make sure that it’s all tracking uniformly and the data is formatted properly.

From there, we load the data into a data visualization tool so that you can easily analyze and leverage your data into growth.

The end goal

The end goal of this entire process is getting you data that you can take action on. Data for the sake of data won’t do anything for your business; you need to take action from it. Having data and not taking action from it is like having an expensive race car and then never putting gas in it.

Going with the race car analogy, if you want the car to perform optimally, you need to put only the highest quality gasoline in the car. Your output is only as good as your inputs. The same is true with data; in order to get amazing insights from your data, you need to have clean data coming into your systems.

If you don’t know how to make sure that everything tracks properly, we recommend using a process called “Metrics Mapping”.

Metrics Mapping

The process of metrics mapping is actually pretty simple, and helps you gain clarity in what you need to track and how to use the data once you have it.

You start metrics mapping by defining your goals. As you can see in the example below, this company wanted to double their revenue year over year.

Metrics Mapping

From there, you need to figure out what questions you need answered in order to attain that goal. In this case, they need to know how to increase the conversions on their website.

Once you know the questions that you need to answer, it’s time to figure out what metrics can help you answer those questions. In this case, they decided that the metrics that would help them the most would be the conversion rates for each of their funnel stages, customer LTV, allowable CPAs, and channel profitability.

From there, you need to decide on a source of truth for each of those metrics. You can find funnel stage conversion rates through Google Analytics goals, enhanced ecommerce tracking, or event tracking. Lifetime values would be through your ecommerce platform. You would need to calculate allowable CPAs for your business based off your margins, COGS, and LTV. And finally, you can find channel profitability by tracking your CPAs, LTV, and COGS.

From there, you want to validate the data across as many sources as possible and make sure that your sources of truth align. Then you can begin the process of applying your calculations and loading it into a data visualization tool.

If you’re not able to track any of these metrics, then you can know exactly where you need to focus your tracking and figure out a platform that will help you track those metrics.

Lead with revenue

Every data project should help you make more money. If you’re running a data project to get a metric that is “nice to have” or “nice to know” then you’re likely wasting your time, energy, and money.

As we talked about before, you need an action tied to your data. If something changes, you need to know what you’ll do, and who will do it. Once you have action tied to metrics, it becomes much easier to determine the value of that metric. For example, if you can get a 10% increase in the lifetime value of your customers, you can easily calculate out the value of that kind of change for your business.

The key when determining KPIs is figuring out which ones are the most feasible and deliver the maximum impact. As shown in the chart below, we want to focus on the things that drive the highest business value and are the easiest things to implement for your business.

The key is to make sure that you don’t work on data projects just because you can. Those belong in the bottom right quadrant and should be treated as the second to lowest priority for the organization.

Praxis Metrics Feasibility Quadrant

The beauty of this chart and this process is that as you implement your data projects and improve your data processes, you can increase the feasibility of future projects.

The big data secret

The biggest secret when it comes to data projects is that no matter the size of the company, everyone wants the same information. They want to know how to decrease their waste and increase their bottom line. The easiest way to do that is to ask the right questions, you can just run down the rest of the metrics mapping process.

Too many SMBs think that they don’t have enough data to compete at scale with large companies, but today everyone’s cell phones have big data. We had a client that had 4 million rows of data stored in the back end of his payment processor; and that was just a couple months worth of data.

Almost every tool the businesses use store data, and every data point can help deliver valuable insights. We have found that most small businesses have a treasure trove of data available to them, but they don’t realize it.

Every company is a data company

If you’re not looking at your data and finding ways to better optimize your company, your competitors likely are. We have seen massive giants fall by the wayside because they failed to take appropriate action off their data.

The time to start taking action off your data is now. At very least, start setting up your tracking, or aggregating data. Even if you’re not ready to use it yet, you’ll be grateful to have it when you are ready to tackle big data projects.

Another great place to get started is with your North Star Metrics. These are metrics that all other metrics rely on. For Airbnb, their North Star metric is nights booked on the site. The more nights they have booked, the better their overall business does. For Facebook, they look at active daily users; this allows them to keep their finger on the pulse of usage of the site and retention over time.

You may not have time to run down and figure out all of the KPIs that impact your business; but you can figure out the one. Take the time to figure out your North Star Metric, and start tracking that. You can start to map out the trends, look for causation, figure out what drives it up and down. This is an easy way to get started with a data project, and helps establish value for future data projects.

You don’t have to reinvent the wheel

Dashboards and data visualization tools have been a hot topic as of late. Lots of businesses jump in to the world of data visualization and end up getting an expensive platform that ends up just displaying data that was readily available on other platforms, or they get a powerful business intelligence tools that they can’t fully utilize.

Praxis helps businesses incubate their dashboards under our umbrella. We offer several pre-built dashboards that can answer some of the most important business questions. Once you have gleaned value from those dashboards, we want to help you graduate into custom dashboards that answer questions specific to your business.

If you’re not sure where to start, we offer free data strategy calls where we can walk through and help you diagnose where you are now, and help you figure out how to get to where you want.

How to use data to rapidly grow your ecommerce business

How to use data to rapidly grow your ecommerce business

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.

Metrics Mapping

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.

Metrics Mapping

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.

Getting started

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.

How to turn your data into explosive sales growth

How to turn your data into explosive sales growth

The co-founders of Praxis Metrics, AJ Yager and Meaghan Connell, recently went on the Brand Secrets and Strategies Podcast with Daniel Lohman. It was a great podcast episode that we wanted to share here.

Check out the full episode along with our insights below:

What is Praxis Metrics?

Praxis is a data analytics agency. Essentially, they help companies that are scaling but that don’t yet have the resources to build out a full IT or Business Intelligence team. Praxis helps them to leverage their data to scale, at a fraction of the regular price. Once the businesses have scaled to the point of having the resources to hire a team, Praxis moves to support them with the backlog. This allows businesses to remain adaptable and agile, even as they scale.

So how did Praxis get started?

Praxis Metrics started as a department within a marketing agency. Meaghan and AJ ran a successful marketing agency, but found that one of their best employees had to spend all of his time creating reports for clients. They starting researching ways that they could automate their reporting, and stumbled into the world of business intelligence.

They rolled out their reporting solution to their clients, and the clients immediately started asking if they could roll it out for their whole business. AJ and Meaghan decided to launch Praxis as the “data division” of their marketing agency; but the data division expanded so quickly that they had to decide which business they wanted to focus on. They found that businesses desperately needed data services, so they decided to pivot into that. The rest, as they say, is history.

Every company a data company

If you’ve followed Praxis for any length of time, you’ve probably already heard that data recently became the most valuable resource on the planet. In today’s competitive market, your competitive advantage often comes from the insights that you have on your customers. The more data that you have on your customers, the greater an advantage that you have.

Small businesses often have a treasure trove of data, but don’t know how to access it. Most businesses use a myriad of systems, none of which communicate with one another. This causes data-silos, which small businesses rarely have the time to dig through in order to capture valuable insights.

In today’s business landscape, lots of businesses are encouraged to focus on raising money. They’re told that the best way to scale is to raise money, then raise more money, and then some more money. While they focus on raising money, they’re supposed to hand the operations of their company over to an agency.

Dan believes that it’s much more important for business owners to understand their business inside and out. They need to know what drives sales, the pitfalls and struggles of the business, and the key factors to success. Because the landscape today is so competitive, you need to know the ins and outs of your business and your customers.

The challenge of today

The business landscape constantly evolves and changes; now more than ever. The only way to prevent getting left behind now is to develop an omni-channel presence. It used to be that you could stick strictly to wholesale, or retail, or ecommerce; but now you need to talk to your consumer everywhere. In today’s digital economy, even businesses that run exclusively brick-and-mortar stores need to advertise their products and services online. And not just online, but everywhere online.

When advertising online, especially in an omni-channel fashion, you need to make sure that you’re tracking your efforts effectively. That means that you have to have UTMs set up for all of your marketing efforts, your attribution models fine tuned, and your KPIs well defined.

Data is a big reason for the sudden push to become omni-channel. In a traditional wholesale business, you sell your products to the retail stores, and that concludes the transaction for you. The retailer collects all of the data on who purchases your products, what other products they purchase,and how often they purchase. Because everyone has started to recognize the value of data, traditional wholesalers now want to make sure that they also gather this data. That’s why we see so many businesses pivoting into the B2C and ecommerce models. This transition allows them to also gather data on their clients, and reduces their reliance on the retailers.

A rising tide lifts all ships

Meaghan points out in the video that Praxis has clients that will run 6-figure ad buys in specific geographic areas in order to drive people to their retailers. While we recommend owning as much of your data as possible, you can still find ways to gather consumer data and strengthen your business relationships. These businesses are able to gather data on the people most likely to interact with their ads; while simultaneously driving more traffic to their partner’s store.

This creates what we like to refer to as the “Lift-effect” or “expanding the pie”. The lift-effect is when everyone wins based off one smart decision. Rather than creating a zero-sum situation where the only way for one group to win is by taking from another, you can create scenarios in which everyone benefits. You should check out our real-world example of this from Organifi.

The cost of data

With all of the free tracking tools available, you can get a massive amount of data with very minimal investment. Some people maintain the mindset of yesteryear that data and big data is reserved for large, enterprise companies; but today everyone has massive data in their cell phones. You can easily gain a treasure trove of insights on every visitor to your site with almost no up-front costs, other than time.

Additionally, you can get creative with your data gathering. We had one client who created a massive database for their social media posts. They simply went through all of their previous social media posts and tagged them with meta-data on the location of the image, type of shot, color of the product, etc. By creating this database, they tracked what types of posts were most effective across different seasons. This allowed them to dramatically increase the effectiveness of their social media marketing.

Syndicated marketing data can still be extremely expensive; however you can still get massive amounts of public data for very cheap or even free. For example, Walmart knows that when hurricanes are reported, they see a spike in strawberry Pop-tarts sold. While Walmart tracked the sales of the Pop-tarts, it wasn’t until they joined that data with the public weather data that they saw the correlation.

Data as an investment

Most of what Praxis does for clients is education. We have to change the way that people think about their data. Too many people either live in fear of the data, or they view it as a cost center. In reality, it’s an asset. You wouldn’t view a gold vein as a cost center just because you had to mine it in order to get the value out of it.

Praxis has yet to have a client that couldn’t turn their data into exponential growth. There more information that you have, the better you can run your business, target and acquire customers, and increase your profitability. Many businesses think that data isn’t sexy or fun; but it may be the most sexy and fun part of a business. The best way to scale your business is through data.

Praxis specializes in turning the raw, boring numbers into the sexy insights that can help transform businesses into powerhouses. We do this by delivering metrics that actually drive results rather than vanity metrics. The best way to identify the metrics that can drive your business forward is through a process called “Metrics Mapping”.

Metrics Mapping

Metrics Mapping

As you can see, the process of metrics mapping starts with establishing your goals. From there, you need to figure out what questions you need to answer in order to hit that goal. In this example, we need to know how to increase conversions for the site.

From there, we drill down to see what numbers will help you answer that question. In this example, we need to know conversion rates, customer LTV, our acquisition costs, and profitability by channel. These numbers will help us figure out how much we can spend to acquire new customers, where we should spend that money, and how soon we can expect to see returns on our ad spend.

The next step of metrics mapping is determining the “source of truth” for each of those metrics. The source of truth is the place where the most accurate information on that metric will live. For financial data, that would be your bank account or your payment processor. For traffic source data, Google Analytics is your best bet.

From there, we focus on a process called ETL. ETL stands for extract, transform, and load. We extract the data from the many sources where it lives; transform it by making sure that everything is tracking the same information, and that they’re all on the same scale. From there, we load the data into a data visualization tool.

By following that process, we’re able to take data from raw numbers into insights that allow businesses to scale off their data. This allows businesses to change the way that they behave, decrease their waste, and increase their revenues.

Move beyond what happened, and into why

Once you have a solid understanding of what has happened in your business, you can start moving into the why. This is the most important shift that a business can make. Once you gain a solid understanding of your customers and their behaviors, you can start to look into why they react the way that they do. Once you understand that, you can start to move away from looking to the past, and start looking to the future.

This is the goal of all of your data projects. We want to get you to the point where you know that if you do x, it will yield y. At that point, you can start printing money.

Predictive analytics don’t happen overnight. You need to have an extremely robust data set in order to make accurate predictions. That means that your tracking and data storage need to be put together properly so that your insights are accurate.

The waste business

Praxis is in the waste business. We work with brands to help them eliminate waste and increase their revenues. Too many businesses only focus on the top-line revenue and increasing that. They don’t look into the ways that they can boost the bottom-line profitability without increasing their spending. Praxis helps businesses find the 80% of things that aren’t driving results for the business. Once they cut that waste, they can redirect that waste to the 20% of things that are driving their revenues.

Just doing that can drive exponential scaling. We’ve seen clients explode off just that information alone. Whoever can spend the most to acquire a customer will win every time. You can see how just this information helped Danette May transform their business.

Optimization vs elimination

So many businesses focus on doing more of what’s working; not enough focus on the elimination of waste. If you can reduce your overhead or reduce your COGS, you can increase your profitability across the board.

Praxis Metrics ran through this issue a few years ago. We were stuck working in the business rather than on the business. Thankfully, AJ and Meaghan set aside 2 weeks every year to review the data for the company. We were operating at a 3% profit margin, despite the fact that we had glowing customer testimonials and good top-line revenue.

They looked into the company to figure out what was eating up our margins, and discovered that Praxis was marking too many hours as non-billable to the client. This allowed us to deliver amazing results to the client, but the company was unable to profit off our amazing work. As soon as we found this out and started to focus on charging for the hours that we worked, we went from a 3% profit margin to a 30% profit margin in 3 months.

Every business is just one data-driven decision away from exponential growth. You just need to find the number that will drive the results that you’re looking for.

Don’t cut your ham

Dan gave a great anecdote about a radio announcer who visited his son’t house for dinner. The daughter-in-law cut the ends of the ham before cooking it, so the announcer asked her why she did that. She responded that her mother always did that before cutting ham. The mother happened to be there and responded that she did that so that the ham would fit into her small pan.

Too often we do things just because that’s the way that it’s always been done. By getting deep into your data, you can find the areas that you have sold yourself short, or cost yourself opportunities.

One way that we can proverbially cut the ham is in the amount of data that we collect. Too many businesses just settle for basic tracking items. As we discussed earlier, we had one client create their own database of the different styles of social posts that they use. Another one of our clients used data in their branding commercials. This client wanted to track the effectiveness of humor in ads and when they needed to place the humor in order to maximize the effect.

They ran split tests on the different variants of the ad to see which was the most effective with time tags of when they used different types of humor. They then overlaid this data with watch times, click through rates, and purchase behaviors in order to see how humor impacted their consumers.

Talk to your customers

One thing that every brand can work on is making sure that they are talking to their customers and not at them. Often times, marketers get wrapped up in the story that they want to tell; but it’s important to remember that your product is not the hero of the story. Your customer is the hero of their story; you are just the guide, leading them to a better life.

In order to lead them, you need to make sure that your message is extremely clear. You need to tell them exactly what they need to do in order to get the most out of your products and services. Give them explicit directions on their next steps. You want to make their life as easy as possible, so guide them on the things that they should do and the things that they need to do in order to get the most out of your product.

Where to start

The first thing that every business needs to do is figure out where they stand and where they want to go. You need to run an audit of your systems and processes as they currently stand and then outline where you want them to be. That will fill in your goals section of your metrics mapping journey.

Once you know where you are and where you want to go, the next step is simply asking how you’re going to get there. Figure out the action steps that you need to take now and map out the journey that you want to take. Once you know your journey, you should ask for help where you need it.

So many professionals feel like if they ask for help they have somehow failed. That is not the case. It’s much worse to settle for what you think is possible than to ask for help and achieve the impossible.

Find your tribe

The first thing that you need to do is figure out who you want to target. Who are the customers that won’t just use your product, but that love your product, use it frequently, and will recommend it? You need to find the commonalities between those consumers, figure out the persona, and then figure out how to talk to and engage them. If you can nail that down, then you’re well on your way to success.

In conclusion

This podcast was very dense in ideas and action steps that you can take. If you need help taking action on any of the things that we covered here, we would love to help. If you fill out this form, we can help you figure out where you are, where you want to go, and help you set up that map.

How ecommerce companies can use data for better decision-making

How ecommerce companies can use data for better decision-making

Data is now the most valuable resource on the planet.

If you’ve read any of our other recent blog posts, you’re probably aware of the fact that data recently surpassed oil as the most valuable resource on Earth. While that came as a shock to some, to others this has been a long time coming.

Studies show that data-driven businesses are 23 times more likely to acquire customers, 6 times as likely to retain those customers, and 19 times as likely to be profitable.

As businesses have realized the value of data, the demand for more and more consumer data has exploded. Despite the general acknowledgement of the value of data, it’s estimated that 60-73% of data collected isn’t used in decision-making.

In this post I’ll cover a couple of ways that you can leverage data to make better decisions in your ecommerce business.

Understand your customers

Most marketers understand the importance of using data to drive their marketing decisions. The problem that most marketers face is getting accurate data that they can trust in order to make the right decisions. So that’s where we’ll start.

Overattribution

Truly the bane of every marketer’s existence, over-attribution is a constant in today’s marketing landscape. An example of over-attribution would be when you look at Facebook and they claim to have generated $10K in sales, and then you look at Google and they claim to have created $10K in sales, but you only had $15K worth of sales in that period.

Over-attribution occurs for a myriad of reasons. One of the primary reasons that it can occur is that the different ad platforms utilize different conversion reporting. Facebook currently utilizes a 28-day click and 1-day view conversion window. That means that if someone clicked on your Facebook ad and then came back and purchased from you within 28 days, they claim 100% responsibility for that sale. Google, on the other hand, utilizes a last-click attribution model. That means that they award 100% of the credit for the sale to the last click that someone used before purchasing.

UTMs

There are many solutions to solving over-attribution, but none are perfect. The first solution that we always recommend is UTMs.

UTMs are pieces of tracking information that you can append to a URL in order to improve your tracking. These can help you see exactly what ads, emails, or blog posts people clicked on in order to get to your site.

UTMs are amazing for increasing the granularity of your tracking and allow improved insights into what efforts actually drove people to your site. Unfortunately though, they don’t completely solve the issue of over-attribution. While they will allow you to see exactly what ads drove people to your site, you still have to deal with the different attribution windows in your reporting.

Multi-touch attribution

The best solution to the over-attribution problem is, unfortunately, also one of the more complicated ones. Multi-touch attribution most accurately reflects the client journey across platforms. By tracking the clients journey, these models can assign a portion of the total sale revenue to each platform that took part in the client’s journey. The reason that these can get complicated is because you need to model and decide how you want to assign credit to each platform.

Some of the more popular models that people use are: time decay, which allows you to decrease the amount of credit given to each touch point based off how long ago that happened; position based, which assigns 33% of the credit to both the first and last touch points, and then distributes the remaining 33% equally across the other touch points; the final option that we want to cover here is linear, which just assigns equal weight across every touch point.

Both UTMs and multi-touch attribution have their place in a marketers tool chest. We always recommend using UTMs, and multi-touch attribution can help with more advanced marketing initiatives.

Purchasing behaviors

Once you know where your customers come from, the next thing that you need to know is what they’re buying from you. Thankfully most ecommerce platforms readily provide this information. The important metrics to look at here are: average order value (AOV), lifetime value (LTV), and repurchase rates. Additionally, you should examine each of these metrics through the lens of how different products affect them.

In the early stages of a business, AOV is extremely important. We’ll cover more on this later, but the important thing to note is that if you can keep your cost per acquisition (CPA) below your AOV then you’ll always drive a profit off your ads. This will allow you to scale your advertising, and your company with it.

As you grow more advanced in your tracking and data, LTV becomes more and more important. As you grow in your understanding of LTV, AOV begins to matter less. Rather than worrying about driving a profit off the initial purchase, you can take a loss up front. Knowing the lifetime value of your clients gives you more freedom and flexibility in the acquisition of clients. This can lead to explosive results, just see what it did for Danette May:

The final important metric that you need to know about your customers ties in with AOV, and that’s repurchase rates. If you know when your clients will come back and repurchase from you, then you can accurately chart how long it will take for you to break even on your ads. Even more importantly, charting this metric over time allows you to see how your post-purchase marketing efforts affect your customers.

Understand your costs

In addition to understanding your customer behavior, you need to understand your operational behavior. We talked a lot about acquisition costs and advertising costs in the previous sections, but another important cost is the cost of goods sold (COGS).

In order to determine an acceptable CPA, you need to know what the costs of your business are.

Every business has their own view on how they calculate this metric. Some choose to include their operational costs in their COGS. Some only roll in the marketing costs, but not the salaries of the team. You need to determine the costs associated with the products that you sell in order to properly decide on acceptable margins.

Once you know the margins that your business needs in order to operate properly, then you can appropriately decide on your allowable CPA.

Tracking these metrics will allow you greater insight into your business and customers. Armed with this data, you can create exponential growth.

How to use data to create the lifestyle you want

How to use data to create the lifestyle you want.

We wanted to visit a topic that we haven’t really covered here on our blog before, so this week, we’re focusing on how to use data in your life. In this episode of the Stay Grounded Podcast, we’ll cover how to use data to improve your health, love, and overall wellness.

Check out the full podcast episode below as well as our insights.

How did the data-driven journey start for Meaghan and AJ?

Meaghan likes to joke that she was born with a spreadsheet in her hand. Her parents were computer nerds, so she grew up in a very analytical household. She used spreadsheets to map out her homework and assignments in school, and in college she created a weighted spreadsheet to decide what kind of TV she should buy for herself.

AJ on the other hand, focused on intuition and feelings. He tended to go more with his gut instincts, and then use data to examine the outcomes of those decisions. AJ’s grandfather taught him at an early age to always learn something new and develop himself, so he read voraciously and learned from other’s wisdom. AJ’s father was an engineer, so he learned how to be detail oriented from his dad, and then his mother was more of the social butterfly. AJ found a way to merge all of the best attributes from each of those influences in order to maximize his capabilities.

AJ’s dad helped him to see the importance of data early on, as they used data to track KPIs from across the farm. Meaghan wanted to teach calculus from an early age, so data came pretty naturally to her.

Their perspective on data

Despite what many people think, data is not something to be feared. Data is just another term for information. The more information that you have at your disposal, the more knowledge you have. The more knowledge you accumulate, the more likely that you can turn that knowledge into wisdom. Data doesn’t belong in some dark corner. Data affects and impacts every portion of our lives. By leveraging data, you can better understand your health and spirituality; it can help you plan out travels. Data helps you to spot patterns and trends. Once you see the patterns and trends, you can start to drill down into the why. Once you understand the principles that explain why and how things happen, you can begin to harness that to create the outcomes that you want.

The trick to becoming data driven is learning how to ask questions. Once you learn how to ask the right questions and find data to answer those questions, you can find new questions to deepen your understanding even further. Anyone can become data-driven, regardless of their background. People who failed math class can find truth in data. The secret to data is simply asking questions, seeking out the answers, and then taking action from it.

How do AJ and Meaghan balance data with intuition?

This is technically a trick question, because intuition is entirely based off data. While many view intuition as a gut feeling or a hunch, intuition actually relies on data, but your brain has processed the data in a manner that you didn’t notice, so you don’t recognize all of the data that went into that feeling. Intuition developed within us since the age of the cavemen.  If you think about it practically, the cavemen who recognized danger the fastest survived, while those who took too long to process that information probably got eaten. Those who survived passed this trait on to their offspring, and we carry that with us today.

How do you deal with outdated information that can feed your intuition?

The key to removing outdated information is exposing yourself to more information. The more information that you can get, the more likely that you can replace outdated or incomplete information with better, updated information. You can do this through travel, exposure to different ideas, cultures, norms, etc. The more data that you can gather, the more likely you are to have accurate data.

How do Meaghan and AJ find the data points that actually move the needle?

For businesses-

For businesses, we have a process that we go through called Metrics Mapping. This helps businesses find the metrics that will actually move the needle for them.

Praxis Metrics- Metrics Mapping Process

As shown in the image above, the process of Metrics Mapping starts with your business goals and objectives. In this example, the business wants to double their year-over-year revenue.

Once you know what you want to accomplish, you need to ask what questions you have about how you’ll reach that goal. In our example, they need to know what sources get them the best conversion rates on the site.

From there, you need to think through what metrics can help answer the questions that you came up with. In this case, the business needs to know their conversion rates for different funnels, their total traffic and where it came from, the results of their various split tests, and their ROI for each advertising medium.

Once you know what metrics you need, it’s time to find the source of truth for each of those metrics. The source of truth is the place where you can get the most accurate information on that metric. In the case of our example, traffic and traffic sources would come from Google Analytics (GA), Shopify could help us understand the funnel conversion rates and the ROI, etc.

The rest of the metrics mapping process can then be carried out from there to help you better visualize and interpret the data.

For individuals-

As an individual, you need to start this process by understanding your values. From there, it’s important to understand your personal data. You need to find the data points that matter for you as an individual.

If health is important to you, then you need to get as much information on that as you can. Get your blood and genetics tested for markers to see what things can drive the most impact for your body. Everyone has a different makeup, so it’s important to understand what does and doesn’t work for us personally, rather than following a trend or influencer.

The key to moving the needle is to create a feasibility quadrant. This graph (pictured below) allows you to map out the different options available to you in terms of difficulty and likely value. By creating this graph, you can prioritize your actions on things that provide the most benefit at the lowest cost to you. After that, we recommend focusing on the other things that were considered highly valuable, but difficult to achieve. As you have completed the low hanging fruit, hopefully, the other high-value prospects have shifted to become more feasible. From there, you can move on to the things that provide less results, but are still simple to do.

Praxis Metrics Feasibility Quadrant

How do you find the right questions to ask?

The easiest place to get started is to ask yourself where you are now. Start to analyze where you stand currently, and figure out the areas of your life where you want to do better, and where you want more. The main resources that we have in our lives are time, energy, and money. If you can find where you currently use those items, you can assess if those match your life priorities. If they don’t, then you have your questions lined up for you. “What do I want to change?” “How can I change it?” “Where to I want to get to?”. From there, you map out your steps on the feasibility quadrant and start working on it.

The trick to this whole process is attaching everything to a higher purpose of what you want in life. If you can tie your goals to a larger purpose, that will help you during the tough times. Once you have found the things that you want to accomplish, rate how happy you are with your current state. From there, you can find the things that you want to improve upon and then follow the paths laid out above.

Why is it so difficult to be honest with ourselves?

Meaghan and AJ’s theory on why it’s difficult to be honest with ourselves is because it’s painful. No one wants to admit to themselves that they have areas that they need to improve. That wounds the ego. Everyone has a version of themselves built up in their own head. Other people have other versions or variations of you built in their heads. Receiving feedback contrary to the image of yourself that you have built up in your head is difficult. Especially if that feedback is coming from yourself. But the only way to grow properly is to find the areas that you need to improve and then work towards a goal.

Meaghan has found that the best way for her to find the true version of herself is to look at her calendar and her bank account. Where she spends her time and her money tell her what she actually prioritizes.

Too often we create a vision of ourselves in our lives and in our business, and then only pay attention to things that fit that vision of ourselves or our business.

What areas of life to AJ and Meaghan recommend optimizing?

Love-

Many people don’t view love as something that you can quantify. Meaghan and AJ live to disprove that theory. They track multiple facets of their relationship in order to make sure that they each get the most out of it as possible.

The primary things that they decided to focus on were: growing apart, money problems, and sex.

In order to prevent growing apart, they studied each other’s love languages. This allows them to express affection and love in a way that the other will best receive it. Across their relationship, they correlations across the times when they fought. This correlation helped them realize that the reason that they were fighting was because AJ wasn’t getting enough of his love languages. Meaghan rectified this by adding in reminders on her phone to go and give AJ love according to his love languages every day.

Health-

Obviously, there are millions of metrics that you can use to measure your health. AJ and Meaghan track most of them. While we can’t go into too much detail on these things here, we do go into great detail on some of the most impactful tests that they have done here.

Business-

How you do one thing, is how you do everything. Life is messy, things run together. So the habits that you establish in your relationship and with your health can translate into the way that you run your business. The same issues that we talked about earlier with honesty can impact you in business. However, this isn’t always the case. People can be extremely successful in certain aspects of business where they fail in their normal lives.

How can you stay grounded in life?

AJ-

AJ uses his mornings to ground himself. He starts by writing in a gratitude journal, then meditates, and then drinks some water. If he dives right into the business side of things without taking this time in the morning, the day gets away from him.

Meaghan-

Meaghan uses travel to keep herself grounded. She and AJ plan out times during their year when they will be on the road. They’re not vacationing, just traveling and working remotely. That helps Meaghan to feel connected and new.

Praxis Metrics- Leveraging data to optimize ad spend

Praxis Metrics – Leveraging data to optimize ad spend

In this guest appearance on the Perpetual Traffic podcast, AJ and Meaghan talk about how to use your data to optimize your ad spend, and rapidly scale your business.

They cover everything from getting your tracking in order, all the way up to creating customized dashboards and leveraging complex machine learning and AI.

Enjoy the episode and our insights below.

The struggle today-

Many marketers feel that they aren’t getting the most accurate data inside of the ad platforms. Unfortunately, they are completely correct. Some marketers go so far as to purchase a cheap dashboarding tool in order to help them bring all of their metrics together into one platform in order to help them with this issue. Unfortunately, this will not solve the problem for them at all.

Why do we suddenly have this struggle with data? What drove us to this point?

In our opinion, the problem stems from an overabundance of data. Never in the history of the world has so much data been available to us. Even in the last 20-30 years, large-scale data projects were reserved exclusively for enterprise-level companies. But now, every company has access to “big data”; despite this, many still have the mentality that their business doesn’t have the same access to data, and therefore the same opportunities and responsibilities, as the larger organizations.

Because these smaller businesses fail to leverage the data available to them, they often find themselves utilizing incomplete or dirty data. If they utilized all of the tools and tracking options available to them, they would have a much more complete and accurate picture of what’s happening.

The opportunity today-

Similar to the dot-com boom of the late 90’s, we’re seeing a “data boom” today. Those that have embraced data and created strategic initiatives around data are already separating themselves from their competition. Taking action from data is the new competitive advantage.

Those who capitalize on data have the opportunity to outpace and out-scale their competitors. John Wanamaker said: “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”. Those who eliminate waste in their budget open themselves to amazing opportunities. By doubling or quadrupling down on the things that work, they can drive exponential growth.

How you can capitalize on this opportunity-

Ask the right questions-

We firmly believe in the Socratic method. Asking questions helps you find deeper truths. The trick is finding the right questions to ask that will propel your business forward.

We found that the best way to find these questions is through a process called “metrics mapping”. The diagram below walks through an example:

Praxis Metrics- Metrics Mapping

Metrics Mapping starts with the big goals of your organization. This could include doubling your revenue year over year, increasing sales of a certain product by 30%, etc. From there, we want to drill down to the questions that you need to answer in order to meet that goal. If you want to double your revenue, then why don’t you? What questions do you need to answer in order to hit your goal?

Once you have the questions that you need to answer, it’s time to figure out what numbers can help you answer that question. In the example above, we need to know how to increase conversions and revenue from the website. In order to figure out how to do that, we need to figure out conversion rates, LTV, CPA, and profitability.

Once we have asked the right questions and gathered the necessary data, we need to:

Get granular with it-

Averages are inherently evil. Averages by definition mash together your highs and your lows and give you one number to work with. In order to properly scale your business, you need to know what creates the highs and what creates the lows. Once you know that, you can scrap the things that bring in the lows and double down on the highs.

Going back to the previous example; once we’ve gathered the numbers, we can strategize our next move. Perhaps we need to update our nurture sequence to increase return purchases. We may have a funnel step that causes dramatic drop-off that we can eliminate.

By getting granular in our analysis, we can discover a myriad of opportunities.

What metrics should every business look at?

Every business suffers from “terminal uniqueness”. While every business has certain things that they specifically need to track, there are a host of metrics that every business should know.

The obvious metrics that fall into this category are ad spend, return on ad spend, etc. In addition to these, businesses should also look at cost of goods sold (COGS), shipping expenses, and overhead. Many businesses forget to factor these costs when they look at what their allowable cost per acquisition can be.

This allows you to look at your return on ad spend through the lens of profitability, rather than just revenue generated.

What is the biggest problem businesses have with reporting?

Over-attribution. We see this issue with almost every client that we work with. Facebook, Google, and every other ad platform utilizes different attribution models. Generally, the platform will leverage an attribution model that favors them, and makes them look the best.

So Facebook utilizes an attribution window, meaning that if someone clicks on your ad, and then returns to your site within 28 days, they will claim that they produced 100% of the revenue from that client. Google defaults to last-touch attribution modeling, meaning that wherever that user came from when they made the purchase receives 100% credit for the revenue of that client. Other platforms count view-through conversions combined with an attribution window, meaning that if they saw your ad and then purchased within a certain time frame, that platform claims credit for that sale.

This scenario can lead to multiple ad platforms claiming that they are responsible for the exact same sale.

How do we combat this issue?

If you’re interested in learning more on this subject, we have a separate blog post on ways that every business can work through the over-attribution problem here.

In addition to those tips, our biggest suggestion for fixing this issue is getting a multi-source business intelligence tool. By extracting data from the back end of each of the ad systems, you can piece together a client’s journey and create your own attribution models. This allows you to see your true customer journey, rather than just a simple metric provided to you by a biased platform.

Unfortunately, even that solution relies heavily on the tracking that you have in place. If your tracking hasn’t been set up properly, then you have to rely on the data reported by these platforms, rather than leveraging and creating your own.

Your output is only as good as your inputs-

It doesn’t matter how much you spend on your powerful tools, they still rely on the data that you give to them. If your tracking isn’t set up properly, it’s impossible for a dashboard to correct that for you.

Powerful insights require great data. And unfortunately, good data requires great tracking.

Good news though, if you can get your tracking nailed down properly, then everything else glides into place. The old adage of “measure twice, cut once” applies to data as much as carpentry.

Going back to the metrics mapping process, we want to help you find the “source of truth” for every metric that you measure.

The “source of truth”-

Every metric should have a place where the definitive answer lives. If you want to know how much revenue you’ve brought in over the last month, you can check your bank account, or Stripe, or Paypal. If you want to know how long visitors from Instagram stay on your website, Google Analytics could help you find that answer.

Each data platform specializes in different data points, and we want to get the best data from the best sources.

Where to begin?

We recommend that every business start with the projects that will move the needle for the business. This generally means starting with sales and marketing initiatives, as they generate revenue for the rest of the business.

We have been shocked at how many issues businesses solve by getting their data set up properly for sales and marketing. Also, by leading with these departments, we can generally start to uncover holes in other parts of the business. If we see a spike in cancellations that coincides with customer survey emails, we know that we clearly have something to fix there.

It’s important to remember with every data initiative that it’s a journey. As much as we wish that we could fix every data problem overnight, it takes time to solve these issues and answer these questions. From there, we need to take action from the insights that we gained, and then we can see the results.

Leverage your uniqueness into growth-

As we stated earlier, every business suffers from terminal uniqueness. While this can complicate projects, that is also the place where you can see the greatest results.

By leveraging your uniqueness in the things that you track, you can get extremely granular, and explode your business in ways that others can’t.

One of our clients, Fancy Sprinkles, found amazing insights by tracking what no one else bothered to track.

Fancy Sprinkles, which does exactly what their name sounds like, gets most of their leads from Instagram. They decided to go back through every post that they had ever done and manually put into a spreadsheet the variables of the post. They tracked whether the product was shot indoors or outdoors, close or at a distance, color palettes, everything.

When they overlaid this data with their social media engagement rates over time, they found amazing insights. During October, they assumed that they should post something orange and black to capitalize on the holiday. But after consulting their data, they quickly realized that those colors got the worst engagement in October. The data told them that they should use purple and green, outdoors, and close-up. Their engagement skyrocketed because of these insights.

Tracking may require an up-front investment. Fancy Sprinkles needed interns to work for hours to catalog all of that data, but once they had the historical data, it became much easier to simply input those data points on every post that they made.

When should people seek help with their data?

As soon as it gets annoying or frustrating.

This may seem simplistic, but it doesn’t make sense for you to abandon your superpowers only to beat your head against a wall.

Your company hired you because of your skillset, and if data doesn’t fall into that skillset, it’s better to outsource that than to take away time and energy from the things that you do best.

What should businesses have in place before consulting with Praxis?

We built Praxis to meet companies wherever they are on the data maturity spectrum.

If you need help with your tracking, we offer Google Analytics audits and implementations. We even have courses that can walk you through setting up your tracking at your own pace.

If you already have your tracking in order and want to move on to scaling your business and gleaning better insights, then we offer pre-built dashboards that can help you start leveraging your data into growth.

If you have issues unique to your market or business that you need specific help on, we offer custom dashboards and implementations that we can build from scratch to better suit your needs.

Praxis Metrics- How to create a data-driven culture

How to build a data-driven culture in your company

Creating a data-driven culture within an organization is a monumental task; especially if the organization is well established. In this blog post, we hope to outline the benefits of creating a democratized data-driven culture and some steps that you can take to achieve it.

What is selective data culture?

Most companies have a selective data culture. In this culture most employees don’t deal with data. Data resides in the C-suite and with the data team (if one exists). General employees receive nuggets of information, but they never see the numbers behind it. This often leads to something called the “Atlas effect”.

The “Atlas effect” occurs when an organization relies on one individual to keep all of the data and insights in their head. A system like this results in the individual becoming invaluable to the organization and causes major disruption when they leave.

In order to create a true data-driven organization, you need to democratize your data. This means sharing as much information as you can with your team. This creates a culture of transparency as well as serving as inspiration for your teams.

Our client, Organifi, has created a culture around their data. They democratize their data by having their dashboards displayed on TVs in their office that anyone can look at. And they have daily huddles around their data to make sure that they meet their goals every day.

This has created what they call the “lift effect” for their business. The “lift effect” has resulted from everyone seeing each other’s metrics, causing them create friendly competitions between departments.

You can see more about the effect that this type of culture has had on Organifi here:

Data democratization allows you to engage your entire team in the business data. By doing this, you can leverage the collective strength of your organization. This protects you from relying on individuals, and the “Atlas effect”.

What are the benefits of a democratized data-driven culture?

Employee engagement

“You can’t manage what you don’t measure” -Peter Drucker

In a data-driven culture, employees with less technical skills still work with, and benefit from data. Data allows employees to track their performance and impact on the organization over time, keeping them more engaged in their work. Employee engagement massively helps organizational growth, as engaged employees measure 17% more productive than their peers. Additionally, engaged employees report 20% higher sales than disengaged employees on average.

When employees know the criteria that they are measured against, it helps them remain focused and engaged in their work. Allowing them to track their performance over time helps to remind them of their improvement over time, or serves as a motivator in times of stagnation.

Better ideas

In addition to allowing employees to track their own performance, data-driven organizations allow employees to contribute their specific understanding and knowledge to an analysis. This diversity of viewpoints allows organizations to benefit from a wide variety of ideas. These ideas help them experiment with a number of solutions, and discover new opportunities.

Having someone from marketing look over finance data may seem counter-intuitive, but they may provide critical context to a trend that the finance department didn’t have. Having an operations expert look over sales data can help them understand needs of the team and update or implement new processes to streamline their performance.

Consistent value

In data-driven cultures, employees can discover, reuse, and adapt data to their situation. For example, investing to know the lifetime value of your clients pays off massively over time, as this information provides contextual for your finance, marketing, and operations teams.

As employees gain exposure to data, their data literacy will naturally improve. As data literacy improves, the insights that they bring to the table will get better and better. This cycle increases the potential output of every employee, lifting the entire organization to new heights. This is known as the ‘lift effect’ and we’ll talk about that more later in this post.

Financial

As touched on in the previous benefits, data-driven cultures experience several major financial benefits. One study found that data-driven companies had a 20%-30% higher EBITDA than similar companies.

In 2006, only one of the top-10 companies by market capitalization was data-driven. By 2017, data-driven companies held 6 spots on the list.

Data recently surpassed oil as the most valuable commodity in the world. Is your business sitting on an untapped oil field?

How to start democratizing data

The easiest way to democratize data is to share it. Organifi decided to display their data so that any and all of their employees could see it. Other companies may choose to do weekly meetings where they announce important business KPIs to the entire team. No matter how you go about it, the goal here is to get everyone excited and involved with company data.

Next, it’s imperative that the data be connected to a goal. Data is like gasoline, the goal gives you a destination, and your actions are the vehicle used to reach the destination. Data should fuel the decisions that you make to get to your destination.

From there, the process simply repeats itself. Create new goals, gather new data, share with the team, gather their insights, and hit your goals again.

As you complete this process over and over, it will become the norm and part of your organizational culture.

If you find yourself struggling to create a data-driven culture in your organization, we can help you achieve your goals. Schedule a call with a Praxis Metrics data expert to see what’s possible for your organization.

Praxis Metrics- Data Predictions

Predicting data trends (2018-2020)

For this blog post, we wanted to revisit a guest episode that we did with the Vision Tech Team. This episode was filmed at the end of 2018, and focused on the changes that we saw in the data landscape in 2018, as well as predictions for 2019.

We found this episode particularly relevant given the changes in the data landscape that we detailed in last week’s post (which you can find here).

Enjoy this short podcast episode, and our insights below.

Insights from 2018:

  1. The data landscape changed rapidly, making it very difficult to predict what different platforms would do.
    1. Infusionsoft became much more open with their data in 2018, while Facebook began to clamp down on what data they would allow access to.
  2. Businesses began to understand the value of data, and started to understand it’s importance.
    1. Similar to the dot-com boom, businesses that fail to take advantage of their data started falling behind in the market.
  3. Data overload started coming to a head. Businesses started needing to figure out what data actually drives results.
    1. Trust in data became an issue. Data validation became a necessary part of data projects.
  4. Data visualization became the new buzzword.
    1. Everyone became obsessed with visualizing their data.
    2. This often came at the expense of actually driving new insights, as they would simply slap graphs on the same data.
  5. Automation started to spread
    1. In the beginning, nobody tracked anything.
    2. Then, businesses started tracking, but the data was stuck in silos.
    3. From there, data nerds started creating complex pivot tables and Excel sheets to bring the data together.
    4. Now, automation became feasible, and started taking over. This removed the need for manual reporting, and made the visualizations better.
  6. Going back to data overload, tracking exploded in 2018.
    1. You started being able to track anything that you wanted, and this lead many companies to overload on data.
    2. For those that actually leveraged this data, it caused explosive growth.
  7. Chatbots exploded onto the scene.
    1. Chatbots began to expand their footprint, with very promising results.
  8. Businesses began realizing the value of data democratization.
    1. Rather than having just one data nerd knowing everything about your data, businesses began to share data with all employees.
    2. By opening up data to the entire organization, you can gain insights that you previously would have missed out on.
    3. This allows organizations to leverage the combined brain-power of their entire team, rather than just the select few.

Predictions for 2019:

  1. Increased competition for data.
    1. As more companies realize the value of data, more and more companies will start competing for it.
    2. As data becomes more ubiquitous, companies will get better and better at targeting and marketing. This will drive inefficient organizations into the ground, leaving only the best to survive.
  2. Data overload to the max.
    1. As more data becomes available, businesses will need to start deciding what metrics actually matter to them.
    2. Most businesses will need to hone in on the handful of metrics that actually drive results (80/20).
      1. Those that capitalize on this principle will thrive in 2019
  3. Increased transparency and accountability.
    1. As more companies move towards data democratization, we will see a shift towards increased accountability across organizations. This will create cultures that thrive on extreme accountability.
    2. Since everyone has access to the data, it will become much more difficult for low performers to hide behind the work of others.

Looking back on 2018, things seem so much simpler. They only worried about compliance with GDPR. In 2019, we have a host of regulations on the horizon to worry about, decreased access to data and information, and platforms throttling data.

Looking forward to 2020, we’re likely to see an acceleration of the changes from 2019. We expect more data regulations to come into effect, and as a result of this, we expect a decrease in marketing efficiency.

One of the things that every business should start doing is gathering data into a data warehouse. This protects you from the whims of the larger platforms, and gives you complete ownership over your data. By gathering your knowledge and wisdom into your own database, you insulate yourself from the storm that is brewing on the horizon.

Praxis specializes in data, so we can help you take ownership over your data, create back-ups, and help you understand your data better than ever before. You can learn more about our products and offerings here. If you have immediate questions, you can contact a Praxis Metrics data expert directly.

Praxis Metrics- Major data privacy changes- What you need to know

Major data privacy changes- What you need to know

The data landscape rapidly changes and shifts, but a flurry of recent announcements will shaken the core of how we measure and track customers.

What is happening?

Basically, until now we’ve been living in the wild west of data, but after a wave of data scandals a new sheriff has come to town. And this sheriff is changing all of the rules. The new priority for data is privacy first, marketers second. These new rules are coming through legislation, and the gods of the internet. We’ll explore what’s happening in both groups, and what happens next.

Legislation

It all started with GDPR, but now consumer data legislation is popping up around the globe. In the US, the California Consumer Privacy Act just officially passed (and will go into effect in 2020); meanwhile, similar regulations are developing in Brazil and India as well.

What do these laws entail?

Praxis Metrics- GDPR

General Data Protection Regulation (GDPR)-

GDPR is a law passed by the EU in 2016, and began enforcement in 2018. The stated goals of the law are to: harmonize data privacy laws across Europe, protect and empower all EU citizens data privacy, and reshape the way organizations across the region approach data privacy. It does this by levying heavy fines against any business that is found in violation of the regulations. This applies to all companies processing the personal data of data subjects residing in the Union, regardless of the company’s location.

California Consumer Privacy Act (CCPA)-

The CCPA will allow consumers to force companies to tell them what personal information they have collected. It also lets consumers force companies to delete that data or to forbid them from sharing it with third parties. This law aims to target larger businesses, and only applies to businesses that earn more than $25 million in gross revenue, businesses with data on more than 50,000 consumers, or firms that make more than 50% of their revenue selling consumer data (I.E. data brokers).

While this law only applies to customers who live in the state of California, 17 other states are currently exploring similar legislation. It’s likely that most companies will just adopt these practices across the board.

Corporate regulation

Apple

Praxis Metrics- Safari Privacy Update

Apple has changed how it handles personal data, with it’s ITP (Intelligent Tracking Prevention) framework in Safari. Third-party JavaScript cookie lifespans are now capped at seven days on all Safari browsers. This new, limited lifespan breaks traditional remarketing efforts and attribution models.

Both Google Analytics and Adobe Analytics use a default 30-day conversion window, allowing you to see the impact of every touch that impacted a conversion in that time frame. Those attribution models on Safari browsers will now only collect data on the last seven days prior to conversion, deleting any data collected before that point.

For remarketing, marketers now only have seven days to programmatically target Safari visitors. After that, their data will be deleted, along with the ability to retarget them.

Other effects from this change include: cross-device visitor tracking becoming unreliable, and a dramatic uptick in unique visitor counts. Visitors who span multiple devices and have a buying journey more than seven days will look like new visitors when they finally return, skewing the data. Additionally, since they now look like new visitors every seven days, new visitor counts will skyrocket.

Praxis Metrics- Firefox Privacy Regulations

Mozilla

Mozilla rolled out similar features to its popular internet browser, Firefox, earlier this year. They recently rolled out an “Enhanced Tracking Protection” feature, which blocks all third-party cookies by default. They also began blocking over 2,500 tracking domains, many of which control multiple cookies, and plan to “update and improve this list over time”.

Praxis Metrics- Chrome Privacy Update

Google

Chrome will add a browser extension that will showcase the names of the AdTech providers on each page and the personalization factors associated with each cookie. They also plan to provide user-level cookie control for third-party cookies.

What can we do?

First party cookies

Moving from third-party tracking cookies to first-party cookies will help protect against these updates and changes.

Most of the changes implemented by the tech companies target third-party cookies, but none of them target first-party cookies yet. This allows you to continue tracking your customer journey without interference.

This change also provides a number of fringe benefits, including: ownership of the data, reduced likelihood of blocking, and better storage and utilization opportunities.

Owning your data insulates you from changes or updates to any future terms and conditions. It also allows you to store the data indefinitely.

In order to implement this, you’ll need to develop the cookies and have a data-warehouse to store the information collected.

It should be noted with this solution that since you own the data, you assume 100% responsibility for it. This includes compliance with the privacy laws previously discussed, as well as the protection of the data.

Pixels

Tracking pixels have managed to avoid much scrutiny yet, and therefore they have escaped the proverbial regulatory hammer so far.

Pixels transmit their data directly to a server, rather than storing data in the browser. This makes the pixel extremely useful, as the user cannot delete the data by clearing their cache.

As regulation ramps up, we predict that most tracking will transition from cookies to pixels, and the data produced by these pixels will move to large data-warehouses for storage. Similar to a first-party cookie, the data gathered from pixels will become the responsibility of the pixel owner.

What comes next?

It is clear that the old way of collecting data is officially dead. Privacy and consumer protections are here to stay.

The solutions that we presented here only serve to fix the issues created by these updates to browsers, they will not help avoid any of the new legal regulations. The internet is entering a new age, and every company will have to grow and adapt to this new ecosystem.

If you’re freaked out by all of the changes hitting the data landscape, we can help. Book your complimentary data strategy session with a Praxis Metrics data expert who can walk you through these changes.