Over the years, I have been amazed at how many organizations use multiple digital analytics platforms. Some organizations are so large that they may not even realize that multiple digital analytics platforms are being used! But many organizations end up using multiple digital analytics platforms for one reason or another. Here are the two most common reasons I have encountered:
One digital analytics platform is used on the website, and a different one is used within the mobile app
One of the main reasons organizations use multiple digital analytics platforms is technology platforms. It is very common for an organization to use one digital analytics platform on the website and another on its mobile application. My theory on why this happens is that one team (typically marketing) is responsible for the website, and another team (typically product) is responsible for the mobile app. I believe that this platform bifurcation stemmed from how websites and mobile apps evolved. Websites began as marketing collateral and as a destination for digital advertising, so marketing typically built and owned the website. Mobile apps started as more technical efforts to capitalize on smartphones, and we were initially created by IT teams which later evolved into digital product teams. Sometimes, when changes happen so slowly, like a , it is difficult for organizations to take a step back and realize what they have done. If a new business were to start today, it would be interesting to see if it would continue to have as much separation between marketing and product teams and use different digital analytics platforms on the website and the mobile app. I think that many organizations will look back on this practice and think it was odd years from now.
There hasn’t been one digital analytics platform that can adequately satisfy all use cases
Many organizations I speak to have one primary digital analytics platform but continue to use others because there are specific use cases or features that their primary analytics platform cannot satisfy. There is no perfect digital analytics platform that can do everything. Organizations have to identify what is most important to them and for their needs. Some organizations use Amplitude but feel they still need to use Google Analytics for its advertising integration. Other organizations use Google Analytics and want session replays, so they add an experience analytics product. Many organizations believe that one digital analytics platform cannot meet marketing and product needs without each team having to sacrifice features or functionality.
Issues Related to Using Multiple Digital Analytics Platforms
Regardless of the reason, using multiple digital analytics platforms is problematic for the following reasons:
Each visitor/customer has a different profile in each analytics platform
If your organization uses different digital analytics platforms, each visitor/customer will have a different profile in each analytics platform. Since most organizations want to understand each customer better, it is counter-intuitive to purposely segregate customer information into different profiles. Using multiple analytics platforms forces organizations to export customer data from each platform and find a way to unify it in another system. This type of unification takes time and money. It also prevents you from acting on customer profiles in real-time due to the inherent delays in unifying customer profiles. Splitting customers into multiple profiles simply due to the platform they use (web vs. app) or their activity () doesn’t make much sense.
Cannot easily personalize experiences across both platforms
Along similar lines, if you don’t have a unified customer profile with all relevant data points, it isn’t easy to personalize digital experiences. In today’s age of hyper-competition, personalization has become a key differentiator for brands. Customers expect brands to recognize them and provide relevant products, content and offers across all digital platforms. Personalization is most effective when done in real-time or as close to real-time. But if multiple digital analytics platforms force your organization to delay the unification of customer data, it can miss out on the key benefits of personalization.
Cannot build segments/cohorts using multi-platform activity
Another downside of using multiple digital analytics platforms is that it makes it more difficult to build segments/cohorts. Building segments/cohorts of users based upon behavioral data is a key component of digital analytics. Digital analytics platforms contain so much data that conducting analysis often requires breaking users into smaller segments/cohorts so they can be compared. Segments/cohorts are based upon data collected about customers and the events they perform. But you can only create segments/cohorts of users based upon data points known to the digital analytics platform. Suppose your customers are tracked in multiple analytics platforms. In that case, it is challenging to create segments/cohorts using data from both analytics platforms without exporting the data and creating them in another system (e.g., CDP). This severely limits your ability to build holistic segments/cohorts and do so in a timely manner.
For example, imagine that an organization uses Google Analytics for marketing analytics on its website and Amplitude for product analytics on its mobile app. A customer visits the website via an e-mail and places a product in the shopping cart but exits before purchasing. Next week, the same customer opens the mobile app and purchases the product in the shopping cart. Since two different digital analytics products were used in this scenario if the organization built a segment/cohort of users that came from an email and purchased, this customer would not qualify for the segment/cohort. Even if the same segment/cohort were created in both analytics platforms, the customer would not be eligible for either. Half of the segment/cohort criteria took place in one analytics platform and half in the other.
Cannot see the entire customer journey experience
Understanding and visualizing customer journeys through digital properties has become a must-have for most organizations. It is essential to know how often customers float between different platforms (e.g., web vs. app) and where within those journeys they drop off. This is another case where using multiple digital analytics platforms can cause problems. If customer journeys are split between different analytics platforms, it isn’t easy to re-create them after the fact. Customer journeys can be divided into thousands of different paths, and very few systems outside of digital analytics platforms can display these journeys. If your organization is capturing journeys on the website and app separately, you will never be able to understand the causes of drop-off and abandonment. This prevents your organization from learning how to improve customer journeys and digital conversion.
Requires digital analysts to learn multiple products
While not as crucial as some of the other items, using multiple digital analytics platforms can force stakeholders at your organization to learn multiple analytics product interfaces. Even if different teams use different analytics platforms, there will likely be a subset of people that need data from both platforms. As someone who has taught organizations how to use digital analytics platforms, I can tell you that it is very challenging to teach people how to use digital analytics platforms. Imagine trying to teach people how to use multiple!
Paying for multiple platforms
Most organizations would prefer not to pay for multiple digital analytics platforms, especially if there is overlap in data collection. Plus, when you pay for a digital analytics platform, the overall cost tends to go down proportionally as you collect more data. In other words, for many digital analytics platforms, you tend to pay the most for the first chunk of data collected and less for incremental data collected (e.g. CPMs scale down as more data is collected). Therefore, if you are paying for multiple digital analytics platforms, there is a chance that you are paying a premium on the initial data collected from each.
Reconciling data instead of leveraging data
Last but not least, one of the most frustrating aspects of having multiple digital analytics platforms is the constant comparison of data. If your organization uses multiple digital analytics platforms, there will inevitably be cases where there is overlap between data points. For example, both platforms might capture how often each product is viewed and added to the shopping cart. If this happens, it is only a matter of time before people at the organization ask why the same data point has different values in each analytics platform. I have seen many organizations become paralyzed by this type of data comparison. Even if the differences are inconsequential, the fact that they don’t match exactly causes concern that one of the analytics products is untrustworthy (but which one?). Organizations end up spending more time analyzing the differences between digital analytics platforms than they do leveraging the data they have collected to improve digital products and experiences.
Final Thoughts
As you can see, there are several downsides to using multiple digital analytics platforms. I believe that the issues outlined here will eventually cause organizations to standardize on one digital analytics platform. This is why I have predicted that there will be a over the next few years. It is also one of the reasons . Since Amplitude is already the , one of my goals is to help Amplitude increase its marketing analytics capabilities, so we can become the first digital analytics platform that can meet all of the needs of product and marketing and websites and mobile apps.
This is why I was so excited that at our recent Amplitude Amplify conference, we that will enable marketers to do the same things they have traditionally done with legacy marketing analytics products. While we still have a way to go, Amplitude is on a mission to help organizations avoid the preceding issues that result from requiring multiple digital analytics platforms.