Digital Analytics Leaders: It's Time for Less Tracking Code and More Insights

By Tom Arundel posted 08-05-2021 06:18 PM

  

A product manager, an analyst and an engineer walk into a bar. 

The product manager says, “I need insights fast.”

The analyst says, “First, I need tracking requirements from the product manager so I can review them with the engineering and data QA teams.”

The engineer says, “First, I need a drink, cause this is gonna take a while.”

Does this scene ring a bell (maybe not the bar part)?

With new approaches like Continuous Product Design, new digital products and features are releasing faster than ever. But what happens when data capture doesn’t keep pace? Slow data capture leads to slow insights!

Let me share a couple of quick examples:

  • A large retailer has a complex digital experience and a multitude of options on each page, leading to what their team calls “micro-conversions.” Using traditional analytics tagging, they waited weeks for a release only to discover they still couldn’t get conversions.
  • A large bank was looking for insights into a critical drop-off point in their value funnel.  They spent weeks setting up traditional tagging on each page. Finally they released tracking only to discover they were still missing context about what was causing customers to drop off.

But what if you could minimize all the heavy tracking code and improve time to insight?

With today's best-in-class “auto-capture" solutions, key insights for your business (such as clicks, taps, form submits, friction, API errors and session replay) are included out of the box. The auto-tag then lets you easily configure additional insights remotely with an easy to use interface.

The woes of traditional manual tagging

Traditional tagging is resource intensive – and often still misses the context needed for product managers to make informed decisions. Here are 4 challenges to traditional manual tagging:

  1. Setting up new tracking code is time-consuming and technical.  Most traditional analytics tools demand detailed tracking code setup at every step of every page. This requires specialized analytics and dev teams to be in lockstep with product teams during each release, working to prioritize tagging for every particular product or feature. At a large enterprise, getting a new event (or tag) added usually starts with submitting a new ticket or change request. Your request is routed to a team of business analysts to gather requirements, and then to engineers and QA for development, testing and release. If all goes well (and depending on the backlog of existing requests) your one metric can take days, weeks or months. Inevitably, once tracking is deployed, new questions arise from analysis, leading to more tagging and further delays. These issues can be amplified on mobile apps, which rely on engineering-heavy SDKs. All of this process is intended to mitigate risk and prevent breakage.
  2. It's impossible to know everything to track in advance. With manual tagging, you have to identify which specific page elements (buttons, links, form fields, etc) to track in advance, involving detailed tracking requirements and considerable planning and foresight. As a product manager or marketer, it’s usually impossible to think of every tracking scenario in advance, much less every error or friction event that might occur. Data elements are frequently missed, sometimes resulting in a misaligned and fragmented view of customer behavior. You then must wait until days or weeks after new tags launch before analytics are ready.
  3. Your ever-changing digital ecosystem must be constantly regression tested or else tags break. As large digital ecosystems constantly grow and evolve, all of those tags need to be maintained to keep pace with ongoing changes to UX, products and platforms. Keeping tags in sync with an evolving customer experience involves massive coordination, especially data governance and QA teams. For example, when new products and features launch, tracking elements might be inadvertently removed, causing key analytics to break in both upstream and downstream data systems, resulting in permanent data loss and requiring re-configuration. More impactful, a large re-platforming project can wreak havoc, requiring a full overhaul and migration of tags across platforms.
  4. Performance and security risks grow over time. It’s not unlikely to see hundreds or even thousands of tags running on a typical enterprise application, often spanning dozens of domains. The complexity of managing multiple rules, triggers and events for tags grows as more tags are added, resulting in code conflicts, errors, inaccurate data or even broken pages. Too many tags can also lead to privacy and performance issues, slowing page load times and negatively impacting customer experience. To prevent privacy or security issues from tags, response and remediation processes must be implemented.

Why auto-capture?

Here are just a few key advantages of today's best-in-class auto-capture solutions:

  1. Automatic insights out of the box, with easy to config UI – A one time auto-capture installation (on both web and mobile apps) enables you to detect friction such as customer frustration and rage clicking and track the most relevant interactions, including customer experience viewing (session replay), out of the box. Instead of spending weeks working with developers to tag each KPI, you can quickly empathize with customers and see how they are experiencing your site. Once installed, you get segmentable, dimensional data in real time, without waiting for the next code deployment. To close any gaps in the implementation, you can easily configure new KPIs and events in minutes using the auto-tag.
  2. No detailed tech specs or “missed” requirements – With auto-capture, there’s no need to decide what to track in advance or worry about missing key context about customer behavior. This means you don’t spend weeks gathering business requirements and then hoping you haven’t missed a feature before a big launch. If something isn’t included in the auto-capture, you can easily configure using the auto-tag if necessary. And it won’t require time from internal engineering resources.
  3. Visualize customer experiences on web and mobile – Beyond standard journey analytics in dashboards, the best auto-capture solutions also include session replay to quickly visualize and empathize with customers in real-time, replaying any issues they might be experiencing, as well as quantifying the value and opportunity of those fixes. In addition, imagine every button tap, swipe and form field can be tracked automatically on mobile apps, with a single SDK install. This gives product and marketing teams the ability to better understand important mobile KPIs, like customer conversion, loyalty and “stickiness.”
  4. Capture all your errors, not just the ones you know – Traditional analytics software has relied on IT and operations teams to define what an error “looked” like and which errors to track. The problem with that is it’s almost impossible to know about every error until it happens. Instead, auto-capture can identify all application and system errors out of the box, regardless of whether they’ve happened before, ensuring you are aware of all errors, not just the ones you were looking for.
  5. Understand friction automatically – Who has the time to do their job and be a data scientist? Auto-capture can automatically surface potential revenue loss, customer frustration or engagement, and under- or over-performing segments. Look for customer struggle patterns and trends without the need for tagging, coding, or deep analysis. Quantify revenue opportunities and prioritize your backlog in real-time, without wondering whether the necessary data will be available to make critical decisions.
  6. Data privacy & security – Protecting customers’ data privacy has never been more critical. If done correctly, auto-capture is built with security and GDPR in mind. First, it never captures sensitive data by default. Second, an auto-capture tag comes with the ability to encrypt and decrypt data if needed for business reasons, e.g. identifying fraud.
  7. Highly performant – Slow performance kills conversions. As the digital ecosystem grows and evolves, so do interdependencies between tags and pages they reside on.  A change to a tag can break a page or cause slow load times. That’s why the best auto-capture solutions are hyper-focused on eliminating unnecessary code and reducing file size, minimizing performance impact on page load times and ultimately, customers.
  8. Complete autonomy over data definition and context – With auto-capture, events can be easily added, removed or managed and will always be available retroactively. Data elements can be seamlessly edited, renamed, grouped, and classified in whatever way makes sense for the business. And with advanced auto-capture solutions, artificial intelligence (AI) can easily interpret the meaning and context of every element on the page with minimal configuration. This allows for human-readable page elements (i.e. field name=“Country” instead of “field1234”) to surface in reports without human intervention. 

Spend less time tagging so you can get speed to insight

If you work in a big enterprise, you may have become accustomed to slow lead times for tagging and analytics, which leads to slow customer insights. But release cycles are improving, as more companies adopt Continuous Product Design, and this means insights are needed faster than ever.

With auto-capture, you reduce the time to release tags, and get access to insights in real-time. And with the right solution, this can all occur with minimal to no involvement from dev teams.

Now, there's something we can all raise a toast to.

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