How do you get clear and actionable insights to improve the performance of your digital marketing campaigns, increase customer engagement with content, or build revenue from your online shopping cart?
It’s likely that you have a set of reports, dashboards and analyses that you use to monitor how your digital initiatives are performing. You may even use predictive analytics and multi-channel attribution models to forecast and plan budgets. You may also be deploying personalization strategies based on online customer behavior.
Digital clickstream data-collected from platforms such as Adobe Analytics and Google Analytics- are the primary sources for all web site and mobile application reporting that drives digital content, marketing and product decisions. Digital data is the “big” in Big Data. It’s huge… it’s wild…it’s growing…and you have to collect it yourself
But, how much do you know about this digital clickstream data?
Is it accurate?
– Are all site visitor actions being counted?
Is it complete?
– Are all critical site and app functions being captured?
Is it dependable?
– Do you believe the numbers in your reports?
Accuracy, completeness, dependability – These characteristics are the foundation of digital data quality
The risk of poor digital data quality
If you can’t accurately collect and process digital data, you won’t be able to answer key questions about your investment in digital channels…marketing, retention, engagement, customer experience, and budget allocation. And you won’t be able to accurately determine how to improve digital customer experience.
This becomes a bigger risk as you plan to integrate digital clickstream data with larger data sets, such as CRM, sales, and call center records because the accuracy of the entire multi-source data set will be compromised. Inaccurate data will impact the more sophisticated and detailed reporting you want to drive from combining data sets to get a 360-degree view of your customers and could seriously skew segmentation models that are at the foundation of content personalization, targeted marketing, and customer lifetime value models.
How do you ensure digital data quality?
This checklist of activities will get you started on what you can do and what you should be thinking about:
– Validate that your digital data configuration and quality assurance methods are driven by your overall digital business goals such as marketing, communication, customer care, and ecommerce.
– Be clear on the end goal for the data capture, such as sourcing customized metrics, reports and dashboards for decision makers.
– Determine the customized data capture to address your organization’s particular use cases such as:
- Optimizing on-site and in-app customer journey
- Stitching customers across multiple devices
- Uncovering and diagnosing roadblocks to digital user experience
- Measuring effectiveness of digital advertising campaigns
- Creating and maintaining frictionless e-commerce shopping experiences
- Getting a second-by-second view of engagement with streaming video
- Implementing advanced visitor engagement methods, such as:
-Geofencing
-Scroll tracking
-Content engagement
- Preparing data that feeds personalized content and marketing initiatives in Adobe Target, Optimizely, CRM, as well as email marketing platforms and display advertising platforms
I recommend performing a data quality audit to establish a trustworthy baseline for maintaining digital data collection standards and performance.
This typically involves an examination of the state of current data collection based on business goals and requirements, conducting a gap analysis between the “as is” state and desired state, and prioritizing recommendations into an action plan and data collection specification that can be executed by your development team.
Fortunately, this isn’t complex, terribly expensive or time consuming. Think of it as scheduled maintenance for your car – change the oil, check major systems and make sure your tires are inflated – and you’ll avoid big headaches later.
For example, when we conduct audits for clients, we often these are the 3 most common issues:
1. The code that is being used is not up to date
2. The data isn’t formatted in a uniform fashion across the implementation
3. Data is not being collected at all on important site actions
When is a good time to conduct a data quality audit?
If you haven’t conducted a data quality audit, the right time is now! Of course, you may want to align the audit with other initiatives and milestone events. The following are all good use cases for initiating an audit:
– A basic digital analytics implementation done when the CMS was configured and there have been no updates since to the analytics implementation.
– Migrating from one analytics platform to another, such as moving from Google Analytics to Adobe Analytics.
– Undertaking a site redesign or major change to the web site.
– There has been a change in administration or management of the digital analytics platform.
– Adding new site sections or new apps to your organization’s digital properties.
– A change in strategy for your digital initiatives
What is the ROI on a data quality audit?
Each organization assesses return on investment differently. Based on our experience, we recommend that you consider these types of variables in your ROI calculations:
– Time and money saved or gained by correcting errors that cause inaccuracies or incomplete reporting
– Cost of risk associated with implementing tactics and strategies that fail due to poor data quality informing reports
– Time saved and value gained by setting up repeatable processes to maintain data quality
What happens after the audit?
After you’ve conducted the audit, corrected issues, and are confident in the data, you need to keep it going through scheduled quality assurance checks and data quality governance processes. We recommend you do this at least 2x/year; 4x/year based on overall site content volume and visitor activity. This maintenance incorporates the use of automated QA tools, such as ObservePoint, and the manual testing of identified use cases that mirror customer experience and data collection.
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