The 3 Pillars of Truly Data-driven Companies

June 17, 2020 John Pestana

Driving your business forward with data is more important than ever.  

Customers expect efficient and high-quality experiences with your brand, and you know that meeting those expectations requires collecting information about their behaviors and preferences, personalizing content, and correctly attributing and allocating your marketing budget. 

All of these goals require data, but more importantly, they require accurate data. And, while data is always objective, the tools with which you measure and collect data are complex, prone to human management error, and therefore can provide you with a faulty view of your customers’ experiences and engagement with your brand. 

This blog post will detail the 3 foundational pillars we have found truly data-driven companies focus on in order to foster real growth from their customer data. Designed to build upon each other, the pillars these data-centric companies prioritize are:

1. Ensure data quality
2. Optimize customer experiences
3. Drive growth and ROI with accurate insights

1. Ensure Data Quality

Let’s start from the beginning, with the foundation of accurate insights and informed decisions: data.

Everybody wants to drive their business using data, yet many are still hesitant to put their full trust in the data they have. Given the complex nature of implementing and maintaining analytics solutions, this lack of faith is unsurprising. Often, companies struggle to address two common challenges that arise in the data collection process.

The Challenges:

  • Defining and managing data collection standards - Many companies still manage data standards and tracking code creation using a manual system—such as Excel spreadsheets—which makes the management process inefficient, time-intensive, and prone to human error. Issues that are likely to arise include: syntax and metadata input errors, inconsistent or duplicate tracking, misaligned measurement across departments, and a long data cleansing process to fix the resulting “dirty” data.
  • Maintaining data accuracy - Inaccurate data that confounds your marketing insights is all but guaranteed. This is because analytics data is only as good as the technology collecting that data, and, unfortunately, there are a number of issues that can arise in the data collection process which result in warped data. Potential complications include: 
    • Frequent website updates that may interrupt, relocate, or remove tags so they can’t find the data they need
    • Complex martech stacks that are difficult to manage and increase the probability of tracking failures going unnoticed
    • Internal miscommunications about implementations deployed by different teams that conflict and interrupt tags (and weigh down your site) 
    • Errors in page code that may prevent your tags from firing correctly and often lead to slow load times and other web performance issues

The result? Faulty data and low-quality customer experiences on your site.

The Solution: Standardize and Test Your Data Collection 

Fortunately, there is a way to both efficiently define/manage your standards and obtain/maintain accurate data. The solution? Standardize your data and then test, test, test.

Standardize Your Data

The following steps build upon each other to ensure data is managed and standardized in an organized and actionable way.

Establish and maintain a Tagging Plan: Also known as a solution design reference (SDR) or variable map, your tagging plan documents all analytics tagging information, including the name, expected value, and location of each tag, that helps your analytics architects know where and when to implement code. Your tagging plan should be monitored by a data governance leader and stored in an accessible location, such as Google Sheets or Box (or with an automated solution built for this purpose, see below), that allows for frequent changes and collaboration. 

Tip: The purpose of the tagging plan is not just to list technical details. It should also link each data point you’re tracking to overall business goals. Ask yourself:

  • What is most important for us to understand about our customers and their experiences with our brand? 
  • How does this metric help us to make the right decisions and reach our business goals? 

Every metric should be mapped to an end goal! 

Implement a Tag Management System: This keeps all the tags on your site inside a single master set of code. Using a TMS, you can set and update rules about when each tag will fire, as well as locate and correct tagging errors. Designed for use by non-IT professionals, a TMS also eases the burden on your IT team and minimizes miscommunication across departments. Check out our guide for choosing a TMS to see which system may be best for your business. 

Predefine data with an automated solution: In place of managing your data through a series of complex (and often convoluted!) Excel spreadsheets, you can create a standardized data taxonomy using an automated performance measurement solution. This solution will allow you to:

  • Create and manage tracking URLs, define and update metadata whenever necessary, and automate tracking code creation along with classification assignments wherever possible
  • Track dozens of attributes to move past the five standard UTM parameters and analyze more holistic and granular insights (more on this later).
  • Standardize your data before the collection process begins and take back the time you would have spent cleansing it later. 

With pre-standardized data, you can efficiently unify and analyze it for timely insights.

Test Your Data Collection

Your data is only as good as it is accurate, so frequently testing your collection technologies and processes is critical.

You may be familiar with the hard way of testing this. It includes hours of manual labor spent combing through lines of code, page by page, to investigate individual tags, implementation, and functionality on your site. This is an option, and how many organizations are still testing (if they're testing at all), but there is a better way available through automated analytics testing. 

Using a vendor-agnostic data governance solution, you can automatically monitor your data collection and ensure that implementations are deployed correctly by running regular and focused audits on your site.

Web Audit: A scan that crawls your site looking for missing, duplicate, or unauthorized tags and alerts your team to errors.

Running regular Web Audits allows you to catch errors before they create problems. Say for instance you are in the midst of running a new campaign. While frequent updates are being made to your site, tags may be accidentally relocated or even “fall off” your page, resulting in critical data being lost or misreported.

For this reason, it is important to test your data collection both before, during, and after release cycles to ensure everything is functioning as planned. Armed with an automated testing solution, you will be able to efficiently ensure accurate ongoing data collection across your digital properties. 

Case Study: RS Components

RS Components relied heavily on manual testing and needed a scalable way to test their analytics at each release. By taking advantage of the ObservePoint solution, RS Components was able to conduct Web Audits before each release cycle, allowing their team to quickly catch data errors and avoid data loss. Through automated testing, RS Components eliminated 7 hours of testing for every 3-week release cycle and boosted company-wide confidence in their analytics data. 

To see how ObservePoint can help you standardize and test your data collection, schedule a demo.

2. Optimize Customer Experiences

Web users today have high expectations of your site, and meeting them takes work. To optimize customer experiences, end-to-end journey tracking is necessary.

Customer Journey: The path of sequential interactions a person has as they navigate an experience with the brand.

The Challenges:

There are a few common challenges that can make journey tracking difficult: 

  • Tracking the customer journey across multiple channels - While it may be simple to track customer journeys within one channel, holistically tracking the entire customer journey across all channels, including both online and offline interactions, can be complex and time-consuming. 
  • Maximizing the accuracy of your customer journey tracking - Errors in tracking can cause information gaps about your customers’ behaviors and preferences. Errors may also result in slow page loads or broken links which interrupt your path-to-purchase flow and hurt sales.
  • Tracking the entire customer journey from end-to-end - It can be tempting to view this journey as beginning with the first interaction and ending with conversion, but just like you don’t want interactions to end after one sale, your understanding of the customer experience should span the lifetime of your relationship with them. 

Many businesses want to capture, unify, and understand each phase of the customer journey, but lack the tools necessary to do so. Fortunately, those tools do exist.

The Solution: Capture and Unify Every Touchpoint Across the Entire Customer Journey

You can capture the customer experience across every channel and phase with a robust combination of unified journey tracking, journey testing, and end-to-end attribution software.

Unified Journey Tracking

To capture a holistic view of customer journeys and obtain a comprehensive picture of your customers’ ongoing relationship with your brand, you need to compile data from all the tools within your organization’s technology stack and unify that data into a single set.

For organizations who do not build their measurement strategy on the foundation of a unified taxonomy, attempting to unify data requires dozens of hours spent cobbling together data from their data lake and other data repositories, where data is stored in its native format. Because this data is often structured in a variety of ways and measured at different levels of granularity, trying to cleanse, normalize, and unify data from a data lake is highly manual and nearly impossible.

By replacing this manual, error-prone process with an automated performance measurement solution that unifies data upfront, you can capture and unify data about customer journeys in real time to ensure that customer behaviors are tracked across:

  • Web and mobile 
  • Digital and offline 
  • Paid, earned, and owned media
  • Marketing, sales, product, and service departments

By replacing these data silos with a unified body of cohesive data, you will be able to analyze the entire customer experience from within an easy-to-use, automated system—allowing you to act on holistic insights in real time.

Case Study: Workfront

When Workfront was looking for a solution that could resolve inconsistencies and eliminate human error in their campaign launch processes, they used ObservePoint to standardize their data collection efforts. By integrating ObservePoint’s performance measurement solution with the rest of their martech tools, Workfront was able to unify their data, streamline workflow, and increase campaign velocity.

Journey Testing

In order to optimize customer experiences, it is important to validate that critical paths are functioning properly and important tracking data is being collected at every stage of the journey. 

It is possible to manually inspect customer journeys one at a time for functionality, but this method is slow, requiring tediously clicking through every path while checking the network developer console to make sure everything is functioning—and then doing that for every path after every website update.

With a data governance solution, you can set up Web Journeys to immediately alert you of tracking and experience errors to ensure that paths to key pages are operating correctly and customer data collection is uninterrupted. By addressing these errors before they create problems, your team can:

  • Use more complete data to develop stronger marketing strategies which drive sales
  • Ensure streamlined path-to-purchase flow 
  • Reduce advertising spend that is wasted on broken or misdirected journeys

Web Journey: An automated replication of a customer journey that ensures critical pathways are functioning properly and detect issues in path-to-purchase flow.

Case Study: Suncorp

When Suncorp needed to test critical user experiences on their site, they used ObservePoint to test their web journeys. Through automated testing, their team was able to locate errors and accurately measure the behaviors of their site visitors, which proved to be critical in improving the navigation and conversion paths on their site. 

End-to-end Attribution (Beyond Marketing) 

Understanding customer journeys entails tracking everything from how customers first interact with your brand to which experiences push them toward purchase and, eventually, customer loyalty. For this reason, it is important to collect data from across each phase of the customer experience. 

For many businesses, this can seem like a daunting task. It’s hard enough to unify your traditional marketing data (paid, owned, and earned interactions), let alone data from the entire customer experience. But holistic unification of the end-to-end customer journey is possible (and can greatly help you business) with the proper framework and technologies in place. 

By automating with an end-to-end attribution solution, you can efficiently track the customer experience from the marketing and sales phases, to the product experience, to service and support and beyond. 

With your data unified across each of these phases, your team can better understand how customers are interacting with your brand over time, allowing you to see what will most effectively help you to both gain new customers and help existing customers get the best value out of your service and become loyal to your brand. 

See how ObservePoint can help you optimize your customer experience by scheduling a demo.

3. Drive Growth & ROI with Accurate Insights

Every effort put into standardizing and collecting quality data, tracking end-to-end customer journeys, and optimizing customer experiences pays off once you get to this point: uncovering actionable insights to make the best decisions for your company.

The Challenges:

When data is inaccurate, when data analysis moves at a sluggish pace, or when the customer journey is misunderstood, you get:

  • Faulty decisions that funnel money into the wrong avenues
  • Insights that are difficult to trust and arrive too late to implement necessary changes
  • Sub-optimal customer experiences

Inaccurate or mistimed insights also make it difficult to demonstrate the success of marketing efforts to supervisors. And, even when you do get those insights in time, it can be difficult to visualize, report, and understand the complex results yielded by your data.

Armed with standardized and accurate data, you are ready to draw real insights and truly drive growth. 

The Solution: Act on Holistic and Accurate Customer Insights to Drive Engagement and Revenue

Once you have standardized your data, tested your data collection, and ensured you are collecting complete information about the end-to-end customer journey, you are ready to analyze your data and do the most important part of your job: deciding what your next move will be and where you will make necessary adjustments to drive growth. 

Accurate Attribution

This is where accurate attribution comes in. You need to know what content really deserves credit so that you can channel your money into the avenues where it’s working for you. 

The good news? If you've followed pillars one and two, you should have data that has already been standardized, unified, and tested. This is critical because the biggest obstacle to accurate attribution isn’t some faulty algorithm or insufficient single-touch model—it is incomplete or inaccurate data. 

With data complete and unified, the next step is to determine how you will use it to attribute credit. You are probably familiar with rules-based attribution models, including first-touch, last-touch, and multi-touch models. Each of these models has its pros and cons, but ultimately, each model results in some level of imbalance in how credit is distributed. 

An enterprise performance measurement solution solves this problem by offering a combination of heuristic and algorithmic attribution. An algorithmic attribution model attributes credit through a machine-led system that collects data from all your customers—including those who do not convert on your site—so that you can obtain a more complete and accurate understanding of which interactions lead to conversion and which do not. 

By employing multiple attribution models in concert with one another, you can accurately determine the ROI of your channel, publisher, and content types—but have you ever thought that you could look deeper?

Focus Your Analysis

The five standard UTM parameters (source, medium, campaign, term, content), were conceived over 20 years ago, and a lot has changed since then. Today's most data-driven companies are looking beyond the standard UTM parameters to delve deeper into which marketing endeavors are working and why. However, the number of additional attributes that can be tracked manually are limited, and each additional attribute means additional time spent on manual tasks such as entering tracking IDs into complex spreadsheets and attempting to unify data across channels later in the process. 

With an enterprise performance measurement solution, you can enhance your attribution strategy by automatically implementing dozens of channel and content attributes to measure the ROI of everything from your call to action, to your email subject line, to the color of font you use in a campaign. An effective performance measurement solution will standardize and unify this data upfront so you can act on real-time, actionable insights at both aggregate and granular levels.

To see how ObservePoint can help you drive growth and ROI with accurate insights, schedule a demo

Drive Growth & ROI with ObservePoint

So, to recap, you can drive your company forward using data. Using the correct processes and automated solutions for data governance and performance measurement you can:

1. Ensure accurate, quality data through standardization and testing.
2. Use end-to-end customer journey tracking to optimize the customer experience.
3. Act on accurate, holistic insights to drive growth.

With automated solutions from ObservePoint, you can give credit where credit is due and invest your time and resources where they are really working.

Take your attribution to the next level and maximize your ROI by scheduling a demo with us today.

About the Author

John Pestana

Prior to ObservePoint John Pestana co-founded Omniture, a web analytics software company based in Orem, Utah. John helped grow Omniture from a startup business to a large company with over 1,200 employees throughout the world. Omniture went public in 2006 and then sold to software giant Adobe for $1.8 billion dollars in November of 2009.

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