There is an old fable about a frog submerged in boiling water. As told in the story, if a frog is placed in a pot of boiling water, it will immediately jump out due to the heat. But if the same frog is placed in the pot and the temperature is slowly increased over time, the frog will be unable to sense the minor temperature increases and will eventually die in the pot instead of jumping out. Like most fables, this is not true and has been disproved by scientists. But the frog story can be a useful metaphor to warn about how things that are changed quickly are easily noticed, but those things that change slowly over time can go unnoticed until it is too late.
The Aging Digital Analytics Tech Stack
As I speak to organizations about digital analytics platforms, I find myself thinking of the frog story often. Many organizations tell me that they know that their digital analytics product is not working nor adding value. When I ask them why it isn’t working, I often hear many of the common culprits:
- The digital analytics implementation isn’t answering the current business questions
- There are multiple data sources that have to be combined to answer some of the simplest questions
- End users are incapable of self-serving to answer their own business questions, putting strain on the core analytics team
- Data quality in the analytics implementation is not verified or trusted
As I have written in the past, many times, the problem in digital analytics implementations is not the platform being used by the organization. If you have poor processes or fail to correctly identify your business requirements, you can implement a new digital analytics platform and still end up with the same problems. But there are times when organizations are using the wrong platform for digital analytics. These organizations likely didn’t choose the wrong platform originally, but over time, their needs have changed so much that the platform they have now is incompatible with their current needs. Like the frog sitting in boiling water, their business changed slowly over time, but in tiny increments.
To illustrate this, let’s look at an example. Imagine that you work for a financial services organization that offers loans, debit cards and credit cards. When the internet was new, you built a website and shared information about your products. Most of the website was paper brochures that were moved online to educate prospects and customers about the products and services offered by the institution. At the time, the only platform was the website and most visitors were anonymous. It would have made sense to implement a digital analytics platform like Adobe Analytics or Google Analytics to track content, how each visitor was getting to the website and digital advertising performance. After a few years, the website would become more advanced. Authentication would be added, enabling customers to login and see their accounts. Prospects would start completing applications online instead of visiting branches. Customers would navigate online help resources and communities rather than phoning call centers. A few years later, customers would be able to complete transactions through the website and prospects could be approved for financial instruments in real-time.
Then smartphones arrived and much of the customer behavior would move to custom mobile phone applications. These applications would often be created by digital product teams instead of marketing teams and would allow customers to take pictures of checks for deposits and transfer money with the swipe of a finger. Both the authenticated portion of the website and the mobile phone app would continue to get more complex and new features would be tested and added to the application experiences using development methodologies like agile and the like. Pretty soon, a full digital transformation has taken place, as the mobile application receives the majority of customer interactions and has suddenly supplanted local branches and the website as the face of the financial institution.
Hopefully, the preceding scenario doesn’t sound too far-fetched and many of you have likely lived through some version of this digital transformation over the past decade. As you can see through this example, what started as tracking content on a marketing website transformed into a complex website and mobile application. As the needs of the digital customer experience became more complex, the marketing department was ill-equipped to handle full scale application development.
But all the while, for many organizations, one thing that didn’t change was the digital analytics platform that was used to measure and improve the new digital products that were being created. While the original digital analytics platform (e.g. Adobe Analytics or Google Analytics) was great at tracking content and which marketing campaigns were driving traffic, these platforms were pushed to track product-related scenarios, such as cross-platform user identification, customer retention, feature flags, experiments and so on. Even though these digital marketing analytics platforms were not intended for tracking complex applications or mobile apps, they were bent and contorted to meet the needs of this new application world.
In the case of Adobe Analytics, as the author of the book on Adobe Analytics, I was personally someone many turned to to try and fit a square peg into a round hole. In some cases, product teams realized that the digital marketing analytics platform being used wasn’t meeting their needs and went rogue and implemented a different digital analytics platform (like Amplitude) on the mobile application. This created different issues, since now product and marketing were using two different digital analytics platforms solely based upon whether it was a desktop or mobile app. It also meant that marketing and product teams weren’t collaborating and that the same data wasn’t being shared by marketing and product teams, which led to disjointed customer experiences.
So at the end of the day, organizations either created silos with two different digital analytics platforms or slowly adapted their digital marketing analytics product to their new application needs without taking a step back to realize that their digital marketing analytics platform was like the frog slowly dying in the pot of boiling water.
What Is Best for Your Organization Now?
If the preceding scenario sounds too familiar, it may be time for your team to reevaluate its digital analytics tech stack. This is something that I recommend doing every few years. I start this process with a simple question:
If I were building a new digital analytics tech stack, forgetting about what product(s) I have today, change management impacts, historical data, etc., which product(s) would I choose?
Many organizations never ask this question because they are afraid of the amount of work it would require to change digital analytics products. This fear holds them back from being honest with themselves about some of the possible shortcomings in their current digital analytics tech stack. Things like historical data, re-training users and re-implementing are certainly valid concerns, but before you worry about those, you should ask yourself the question above. If the answer is that you would continue to use the digital analytics platform(s) you have today, then you can feel good about the fact that you took the time to reassess. But taking the time to reevaluate your current needs and comparing those to the latest digital analytics platforms available is likely time well-spent.
At Amplitude, we have seen that many “digital-native” organizations—those without the baggage of legacy digital marketing analytics products, have chosen our product and leap-frogged (no pun intended!) many of their competitors. If, after doing an evaluation, you find that there are other platforms that are better suited for where your digital apps are today vs. a decade ago when you implemented your digital marketing analytics stack, then you owe it to yourself to consider whether the change management aspects outweigh the cost of continuing to use a digital analytics platform that may no longer meet your needs. In some cases, you may be facing a re-implementation even if you choose to remain with your existing digital analytics platform. Even if you decide that the change management impacts are too much to bear, at least you have made an informed decision and know the pros and cons of staying with your current digital analytics tech stack.
If you would like to discuss how your current digital analytics tech stack compares to the Amplitude Digital Optimization System, please contact us.
