Shri Ganeshram is the VP of Growth at Eaze. He leads the analytics team, which provides cross-functional analytics across all teams within the company. Through their use of Heap, Eaze not only became more data-driven in their design decisions, but were able to build a more complete view of their users by combining different data sources across multiple devices to create singular user profiles.
Starting from square one
Eaze is revolutionizing medical marijuana delivery as the world’s first mobile marketplace connecting patients to nearby dispensaries. It’s the easiest, fastest, and most professional way to get medical marijuana delivered. Patients can get verified online in seconds and get a delivery to their doorstep in less than 20 minutes. Eaze’s success depends on understanding the needs of patients and being able to respond quickly to those needs. However, when Shri joined Eaze as VP of Engineering and the 4th team member, understanding what visitors were doing on the Eaze website wasn’t as easy.
When Shri came onboard, the team was using Google Analytics, and had very little set up in the way of analytics infrastructure. He was able to understand high-level metrics like total traffic and different sources of traffic, but he had no clarity around conversion rates, the user experience, or key metrics like registration, product selection, or orders placed. The only other source of data for his team came from their application’s database that provided information around order size and revenue. With no way to connect the two limited sources of data, real analysis proved difficult.
"When I started, the first thing I did was install Heap."
Shri knew he had to get to work on building analytics infrastructure that might help him get the insight he needed. He installed Heap and then started to build out Eaze’s data warehouse.
An influx of data leads to a big win
It took Shri just a few minutes to install Heap. After that, Heap started automatically collecting everything Eaze’s users were doing: every click, form submission, pageview, and more on their website. As Heap collected more and more data, Shri and his team were able to analyze how certain user interactions were affecting conversion and how users were engaging with different components of the product as they released it.
The Eaze team iterates on their design using a data-driven approach, using Heap as a way to inspire and validate incremental changes they make to their site. Heap enables them to iterate with measurement on their product and in their marketing efforts, tracking everything from new features to A/B tested email campaigns to make sure that final choices are always converting at a higher rate. In the case of their registration flow, insights found through Heap drove them to make a change that increased user registration by 20%.
Shri’s team also uses Heap to report on the performance of their website and its impact on the patient experience. By sending internal API request and response metadata with Heap events, they’re able to provide their engineering team with a daily report of their performance. This enables their team to associate API performance metrics with conversion rate, quantifying how response times affect conversion rate and revenue.
Combining data sources to power insights across the team
Heap has not only been the source of truth when it comes to design changes and what features they continue to invest in, it has also been indispensable to their view of each customer’s experience on their site. Their team is able to utilize both data-in and data-out features to better understand how factors outside of website behavior might affect a user’s experience and how likely they are to convert. The Eaze team is leveraging Heap’s powerful APIs to bring in valuable contextual information from Iterable. Because their team’s email marketing is powered by Iterable (including all their email A/B tests), having that information in Heap is crucial to understanding each user’s journey throughout the funnel and how each variation of email campaigns affects conversion.
Because the Eaze team is able to easily push their Heap data into their data warehouse, Heap provides a rich contextual layer on top of other sources of data and enables their developers to map together multiple data sources. A large challenge faced by most teams in this process is identity resolution--a user can access Eaze from multiple devices, browsers, computers, etc. Instead of having to worry about connecting user behavior across sessions and devices (something that can be very difficult and often inaccurate with other tools) Heap’s API makes this task easier by consolidating all user behavior under a single user profile, making it easier for teams like Eaze to join data across all sets and give them a rich understanding of their users behavior.
By melding Heap data with other data sources like Iterable, NPS data, and their application database, Eaze was able to answer questions like, “If someone had an order of this size, what’s the likelihood this email will trigger them to come back in the next X days?” or “For a user with an average order cadence of X, what will they do on the site when we send this email?”. Shri and his team are able to define an event in Heap and instantly have access to all the retroactive data for that event both in the Heap UI and in their data warehouse, powering internal dashboards used throughout the company. As a result, the entire team can track progress to company-wide goals, and no question goes unanswered.
The visibility into what users are doing on the site and what behaviors affect registrations helped Eaze greatly improve their sign-up flow and get more people using their product. The ability to bring Heap into their data warehouse and meld with other data sources provided the behavioral context behind transactional information. Installing Heap early in their company life cycle provided the visibility into user interactions that were missing with other tools, giving them the complete history of user interactions on their site and the peace of mind that they have a complete data set.