Typical Client Results
Clients who work with me experience a wide range of results that include but are not limited to the list below.
B2C Marketplace: Understand Product Usage Including Onboarding and Retention
The Situation
A private fast-growing marketplace company needed data to understand who their best users were across their web, iOS and Android apps. They needed to improve the onboarding experience for new users and to increase user retention for existing users.
The Intervention
Practico met with the founders of the company to prepare an analytics strategy that would provide their entire team with the data they need to measure the impact of their work. Practico then helped them properly implement Google Analytics and Mixpanel to track the customer journey from signup to retention.
The implementation included coaching on proper data collection practices to ensure data accuracy, something the client had struggled with in the past. Finally, Practico trained all major stakeholders on how to properly use the tools implemented and how to analyze the data for relevant insights.
The Results
The company has successfully adopted data by implementing a company-wide Slack channel where anyone can request a report or get their data questions answered. This makes data easily accessible to everyone even if they aren’t experts in Mixpanel or Google Analytics.
They are now able to dig into random spikes of activity and quickly diagnose what happened.
Finally, they were able to start segmenting their users by key attributes or behaviours to better understand retention.
B2B SaaS: Optimize Marketing Dollars, Campaigns & Channels
The Situation
A $10M Series A SaaS company wanted to know exactly what marketing campaigns were working and where they should focus their resources. They had access to high-level numbers such as total demos booked but didn’t have enough data to answer specific questions like “which of our blog posts is driving the most demo requests?” or “what pages do our users view before booking a demo?”.
The Intervention
Practico met with the marketing team to determine what questions they needed to answer and what existing data was available. Practico then helped them improve their implementation of Google Tag Manager and Google Analytics which weren’t being used to their full potential.
Finally, Practico provided company-wide training sessions to the entire marketing team and offered ad hoc analysis support around the creation and interpretation of their data over the following months.
The Results
The marketing team has successfully adopted the data and every team member is now able to generate the reports they need without having to go through a data analyst.
They are now able to see which blog posts were leading to conversions and what call to actions are most effective within those blog posts. They are also able to determine what channels should get more resources and which ones should get less.
Finally, they are now able to dig deeper into the specific user behaviors that drive conversions for their product.
Consumer Language Learning App: Data Strategy & Maximizing Engineering Resources
The Situation
A mobile consumer company was coming off a successful million-dollar crowdfunding campaign and was getting ready to release their mobile apps on iOS and Android. However, they didn’t have any data to understand how their customers were going to use these apps.
The Intervention
Ruben met with the growth and engineering to understand their technical stack and what data they will need over the next 12 months. He put together a strategy that would lay the foundation for the fast growth this company was expecting while still being able to handle their short term needs.
Ruben also helped their engineering teamwork through the technical implementation issues and coached them on how to properly maintain their data infrastructure.
The Results
The client was able to successfully launch their apps publicly and transition from their crowdfunding backers into public customers. They can easily understand what is going on with their product and how their key metrics are changing from month to month.
Their engineering team saved hundreds of hours by properly setting up a foundation that makes it easy to share data between tools. This also allows its growth team to get the answers they need without relying on engineering resources.
Financial App: Data Adoption & Building Symbolic Customer Segments
The Situation
An established payment provider had recently launched a new product as part of its growing suite of offerings. However, they weren’t seeing the kind of product adoption that they were hoping for and didn’t have the necessary data to solve or even understand this problem.
The Intervention
Ruben worked with the Product, Marketing and Engineering teams to understand what their initial data looked like, what were the current assumptions around how to improve the product and what reports do they need to start designing engagement campaigns.
He helped them solve technical issues in their data, train them on how to use their self-serve tools and coached them on how to properly maintain their data infrastructure.
The Results
The client was able to validate their assumptions which helped them to redesign their entire interface. They were also able to start quickly designing campaigns to engage their users and best of all, automate these campaigns so they could run without their input.
They were also able to tie their “Symbolic Customer Segments” to behavioral data to properly understand and communicate with their customers. These are incredibly helpful segments that describe the behaviors of an entire group in 1-2 words.
Finally, this team was able to prove the value of data to the rest of the company and is a critical first step in changing the overall culture of their company.
Government: COVID-19 Dashboard & KPIs
The Situation
In early 2020, the tourism industry had come to an astonishing standstill. Planes were grounded, luggage was collecting dust, and passports were stored away for another day. Then, in the middle of the fog of uncertainty, a governmental agency reached out for help. They had a fire hose of information that could help them understand the pandemic, but they needed help converting it to actionable insights—calmly drinking from it instead of being overwhelmed.
The Intervention
I helped them by starting at the end. In an ideal world, how would this information change their daily decision-making? Based on that, we worked on chiseling away the irrelevant, leaving only the most relevant metrics. We were able to narrow it down to 10 metrics from hundreds. In the dashboard itself, we built different levels of depth. People could spend one-minute reading summaries and move on, while others could spend 15 minutes working through the data. The goal was to help people discover insights, not give them more data.
Finally, we automated the data collection as much as possible and released our dashboard internally and externally to community stakeholders such as hotels and travel agents. Everyone was drowning in data, and the dashboard provided insights.
The Results
There was an instant change. The agency team went from being overwhelmed to knowing exactly where to focus their energy. Community partners felt supported. They couldn’t control the pandemic, but they could control their response to it. The condensed metrics allowed them to make difficult decisions without being paralyzed. Data helped them see through the fog.
The lesson from the story is that you don’t want more data in a crisis. It can paralyze you at a time when you need to be moving. For example, if a fire is heading towards your house, you don’t need to know the size and speed to immediately realize that you should leave your home.
Training: Building Trust into Data & Reports
The Situation
A training company had a wealth of data, but they weren’t able to trust the numbers. This prevented them from using these numbers to make decisions or even to find actionable insights.
The Intervention
Ruben helped them audit the data, track down discrepancies, and worked with them to understand why certain numbers looked off. In a few short weeks, the entire data set was accurate and working as expected.
The Results
The company was able to use its dashboards and reports in one of its upcoming launches. They knew that they had accurate numbers and were able to properly gauge the impact of this product launch.