If you have a website, then chances are you’re probably doing some form of customer data analysis already.
You might be tracking how many people visit your site, your customer retention rates, how long they browse, your most popular pages, and more. All this is crucial information to help you run a successful ecommerce business.
But have you ever considered that your tracking software may not be accurate?
Did you know that over 60% of business executives don’t believe in the quality of their data? For businesses today, the question is, how can you go from not trusting the data to being confident in what you’re looking at?
In this article, I’ve partnered up with Praxis Metrics, a company that helps ecommerce stores turn data into growth. In this article, we’ll take a look at the importance of accuracy in data collection so that you can get the information that you need to help your ecommerce business grow.
How? By seeing exactly where an idea, product, or concept worked in your business and what didn’t work, you can either replicate the success, adjust your strategy, or allocate resources elsewhere. All of this can help you to gain insight into your customers, who they are, their buying patterns, their preferences, and more.
Together with Meaghan Connell, CEO and Co-Founder of Praxis Metrics and AJ Yager, the company’s Co-Founder, we’ve compiled a few tips on how you can ensure your data is accurate and trustworthy so that you can make important marketing decisions.
Ready to get started? Let’s dive in now.
Getting an Accurate Data Management System: Is Google Analytics Always Accurate? How Accurate Are Analytics, Anyway?
At first, data management seems easy enough. You download analytics software, link it to your business, and you’re done. You see the first set of data coming in and may already be formulating plans to optimize performance.
You may want to take a second look, though, because the insights gained from this data just might be tainted.
For example, Google Analytics is a popular analytics tool that gives you insights into your customers’ behaviors. Yet, according to Connell, it’s one of the most underutilized and error-prone tool used by small and medium businesses today.
“Analytics tools are notoriously difficult to set up properly,” explains Connel. “Unless you have an expert come in to set it up for you, or you invest the time to truly understand how to set it up properly, it can quickly turn from a bucket full of data to a bucket full of holes. Many businesses know that their tracking is not correct, but they don’t know how to fix it; so they take the incomplete or inaccurate data that they have and they do their best with what they have.”
In other words, your output is only as good as your input.
“When we go to try to answer business questions, and we try to answer specific marketing questions about a funnel, and we’re missing that information, we just can’t make a better decision,” says Yager. “We have to go on gut or go back and fix it and then wait for the data to come in. So it’s a costly process, and it’s a waste of resources. We want to eliminate and reduce that waste.”
Don’t assume that Google Analytics or any of your other analytics programs (including your ad platforms on social media) are tracking properly! The most obvious example of inaccurate data has to do with ad revenue attribution. If you were to compare Google Analytics to Facebook Analytics, email analytics, and your shopping cart analytics, none of them will match up, even if you are using UTM parameters. Why is this?
When it has to with attribution (the first click versus last click, the data passed over from the ad platforms, and frankly, some bragging rights as to who generated you a sale) naturally, the ad platforms want to claim as much revenue as possible to make it look like your ad campaigns are performing.
For this reason, it’s important to make sure you’re not operating on assumptions. Never assume that your programs are set up properly or tracking properly. Whenever possible, try to get an expert to set up and validate your programs.
See also: How Can I Make Sure my Data Is Trustworthy? By Praxis Metrics.
Setting Basic Frameworks
It doesn’t seem like the most exciting task, but it’s important to create a framework for collecting and using your data. Doing so early on is the best strategy, but it’s never too late to start. Not every employee might like having to follow a step-by-step plan on data collection, but trust me, the best organizations use frameworks to extract valuable and accurate data.
And if employees are reluctant in using them because they feel it might not be worth the time, Connell has a tip. “One of the best ways to help them get past this thinking is to show them what’s possible when they utilize them vs. what they lose by not utilizing them properly.”
To keep you and your employees from being overwhelmed with the possibilities and give everyone the confidence in extracting and understanding trustworthy data, keep your focus on essential elements that will affect your bottom line.
For example, Neil Patel recommends a few marketing KPIs and the business metrics those KPIs influence directly. Avoid vanity metrics, things that are fun to track, but don’t make much difference to your bottom line.
Creating a Data Dictionary
Similar to creating a framework, it’s a good idea to create a data dictionary. It will be a reference point for your company and your team.
What if one person consolidates data under the customer while another does so under the client? With the data dictionary, you’ll be able to maintain naming consistency, and you’ll know where all your data comes from.
Pro Tip: Don’t create it and let it collect dust after. Update it regularly and make it accessible to everyone in the company. This can help to give you interesting perspectives and insight from those working in your company as well.
Tracking Data Over Time
As you collect the data over time, you’ll see patterns emerge. And if you want to increase your data’s trustworthiness, tracking regularly and for the long-term is something you’ll have to be diligent about.
“Having a finger on the pulse of your data lets you know when something seems wrong or out of place,” explains Connell. “This can protect you from making decisions based on bad data…it lets you pinpoint what works and what doesn’t work with your measurements and reporting.”
Using this information, you can effectively adjust your course of action.
(Source: Praxis Metrics)
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Regularly Checking Data Quality: How Do You Check Data Accuracy?
You’ve done the basics and had your analytics tool set up correctly. Now it’s time to sit back and wait for the accurate data to roll in, right? Wrong, and it’s a mistake people often make once they set up their analytics tool. Remember that just because it is set up properly doesn’t mean it will track everything correctly.
Connell says, “Your business is constantly evolving, and your website is also going through constant tweaks, updates, and changes. You need to make sure that everything you do is tracked.” Therefore, ensure that any change made to your business or website is reflected in your tracking.
In addition, track the data back to its source. How is data being added? Are there forms that are capturing the information correctly? If data is manually downloaded, is it all there? Are team members importing data differently, which may skew the information being analyzed?
Don’t forget to clean up the data from time to time too. Fix or remove incorrect, outdated, or duplicated data because it can impact your bottom line. According to research done by Gartner, a research and advisory consultation company, bad data costs organizations around $12.9 million each year.
If those figures aren’t enough to spur you into action, remember that the longer you leave incorrect data, the higher the cost for your business. A model of this can be demonstrated with the 1-10-100 rule, which determines the cost of quality. Essentially, it’s cheaper to invest the $1 to go through the data to see if it’s accurate, $10 to correct, clean, and eliminate duplicates, and $100 if nothing is done or if there’s a failure. Clean data will help your bottom line, not detract from it!
And like we mentioned above, creating a company-wide standard and framework for data entry and processing will help make data as clean as possible.
Make Data Access Easy
Avoid duplication of work and data by giving teams across your organization access to each other’s information. With data silos, when data is isolated from the rest of the team, productivity is decreased, which can lead to a lack of trust and transparency between teams. And having too many different data systems which can’t and don’t communicate with each other means lost time having to “dig through in order to capture valuable insights.”
“Small businesses often have a treasure trove of data,” Praxis shares, “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.”
To combat this, they recommend that when advertising online, especially in an omnichannel fashion, it’s important to make sure that you’re tracking your efforts effectively. This means having your UTMs set up for all of your marketing efforts. Make sure your attribution models are fine-tuned as well, and your KPIs are clearly defined from the start. After all, if you’re not clear on what you’re measuring, you’ll just be wasting your time.
The Next Steps: Gathering Trustworthy Data
If you have an analytics tool already in place for your business, here are some next steps you can take to ensure your data is as accurate and trustworthy as possible. Having confidence in the numbers you see will help you to make better decisions on everything from resource allocation and ad spend, to more big-picture, strategic decisions, so it’s important to get this right.
Step One: Set up the analytics tool properly. You’ll want to learn about the analytics tool so that you can ensure you’ve set everything up correctly or risk having skewed data. Or better yet, get an expert to set it up for you. Experts like Praxis Metrics can provide both the data and analysis. They have an array of knowledge analysts who will be able to work closely with you to deliver helpful data insights. They help ecommerce stores not only collect data but implement it as well.
Step Two: Create a framework. If you haven’t done so already, create a document and process how specific data should be collected, distributed, named, and more. This includes a data dictionary.
Step Three: Check data quality. With a new or revised framework, comb through existing data to align it with the rules (you should do this even if you haven’t changed the framework). This includes removing duplicates, renaming if necessary, and offloading outdated numbers.
Step Four: Collecting data is a marathon, not a sprint. Think long-term collection and analysis to get the most out of your information and see patterns emerge. You’ll then be able to base important strategic decisions on this.
Whether you’re a new business thinking of utilizing an analytics tool or have been using one for a while now, the above steps will help get your data into great shape. When you have accurate data, you’ll be able to make the best decisions for your business, so this is one step that’s worth investing in.
Note: Thanks again to Praxis Metrics for contributing to this article. Learn how to turn your data into explosive sales growth. Or, dive in and get expert help from a trusted data expert. Sign-up for a free data strategy session and get a free data roadmap today.
Ready to scale your e-commerce business? Reach out today for your FREE 20-minute consultation call with me. Let’s find strategies that’ll help you to reach your big-picture marketing goals.
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