Table of Content
-
Product
-
Customer Education
-
Customer Success
-
Marketing
-
Professional Services
-
Data Science
-
-
Finance
-
Accounting
-
Credit Risk
-
Revenue Operations
-
Sales Operations
-
-
Healthcare
-
Insurance
Product Analytics - Customer Education
Project | 01
Time-to-Value (TTV) First Study/Test Launched
The customer education team's objective was to prove to senior leadership that customers were enrolling and completing UserTesting (UT) university courses prior to using our platform. The problem was that they didn't know how to analyze their data, and they didn't have any data stored in our data warehouse because data were stored in a third-party vendor.
As their Product Owner, I did the following to address business problems. I provided deep-dive analyses and methodologies with various well-defined product metrics to discuss the success of each metric and formulate strategies to improve customer experience on our customer education team.
1. Designed their product analytics roadmap and vision
2. Implemented the SDLC to build iterations of product requirement documents (PRDs) on measuring the ever-evolving product metrics success.
a) Developed time-to-value (TTV) product metrics creation and evaluated metrics' success (i.e. from the subscription start date, when the customer launched a first test/study)
b) Propose iterations of improved TTV product metrics to ask better questions
3. Build data pipelines via Fivetrans and DBT, including refactoring codes to the data warehouse (Snowflake). Continued to bring in more data and create more metrics for version upgrades using this framework.
4. Build versions (v2, v3, v3.1, v4) of customer metrics for three Tableau dashboards (First Study/Test Launched, First Results Consumed and First Observations Created).
5. Presented solutions based on analyses, recommendations and strategies for each version.
6. For the upgrades including enhanced product metrics (address better and in-depth questions because data continued to flow in and improved), dashboard designs, story-telling, and reporting to Customer Education's senior leadership and other C-suite executives.
Consistently trying to ask better and more in-depth questions and anticipated other business questions that were related to UT university by leveraging well-defined product metrics measurements.
Codes: here
Tool: SQL, Tableau, Snowflake, DBT, Github
Program Management: Jira, Confluence
Product Requirements Document (PRD): Customer Metrics
Project | 02
Skilljar and Docebo QAU/MAU
After the success of customer metrics v2 for customer education, they wanted to further look at UT university courses' monthly and quarterly active user (QAU/MAU), similar to subscription users at Netflix, trends as part of customer metrics v3. However, there was a university system change from Skilljar to Docebo. The objectives were to combine two systems - Skilljar and Docebo to look at QAU/MAU to see growth in both of the system.
I then have to identify two data sources and build ELT data pipelines from two systems to merge into one system and produced usage rate insights. This is a compliment dashboard to the First Study Launched, First Results Consumed and First Observation Created because we could look at if our university enrollments have increased over time so that we could see spikes in first study/test, results consumed and observations created.
Tool: SQL, Tableau, Snowflake, DBT, Github
Program Management: Jira, Confluence
Product Analytics - Customer Success / Marketing
Project | 03
Salesforce Campaign
The Program Management and Customer Success team hosted various Salesforce Marketing virtual campaign events for customers every month. The business problems were that they wanted to know the product usage rate on launching tests/studies and sessions prior to and post-marketing campaign events. Additionally, their data sat on salesforce but they weren't in our data warehouse.
To produce quantitative analyses to look at product usage rates, I built ELT data pipelines to massage the data and built product metrics to look at total campaign participants and their perspective account usage rates (monthly total and six months averages) before and after marketing campaign events.
I then presented insights and recommendations to Programming Management and Customer Success teams on why we saw some seasonality in data, outliers and spikes, and we could do in the next phase to boost the product usage rate by hosting various virtual campaign events throughout different slots in the month.
Codes: here
Tool: SQL, Tableau, Snowflake, DBT, Github
Program Management: Jira, Confluence
Product Analytics - Marketing
Project | 04
Marketing Top of the Funnel (ToFu) Data and Dashboard Migration to Snowflake
The Marketing team needed to migrate their data sources from Periscope (AWS Redshift) to Tableau (Snowflake) because we retired AWS Redshift and Periscope. Therefore, it posed a few challenges. First, it was data migration from Redshift to Snowflake. Second, it was to build an understanding of multiple marketing data sources. Third, it was to reduce the original codes (600+ lines) and to refactor to efficient codes (~400 lines) that kept the same data structure.
The solutions were I built a deck and presented it to senior management to address business problems, current stages, steps towards migration, advantages of refactoring, and timeline on completion.
By doing this migration, it created a backbone of writing efficient codes that would help speed up data ingestion and load times and reduce data storage.
Deck: here
Tool: SQL, Snowflake, DBT, Github
Program Management: Jira, Confluence, PowerPoint
Product Analytics - Professional Services
Project | 05
Time-to-Value (TTV) First Study Launched and Observation Created for Implementation Service (IS) Users and Non-IS Users' Behaviors
The Professional Services team saw the success of the customer metrics v2, v3 and v4, that I built iterations of product metrics enhancements and PRDs. Their team's offers implementation services (add-on services) from customers' subscriptions. Their objectives were to know and understand the product usage before implementation and after implementation for the first test/study launched and the first observations created (above section in customer education). They wanted to see the comparison between the two groups' usage. Eventually, they wanted to see that customers came to professional services and launched tests/studies as soon as possible as opposed to subscription customers that didn't subscribe to implementation services.
The challenges were that implementation services data weren't available since their data were standalone in other vendors. I had to work with the enterprise system and data warehouse team to bring the data into Snowflake. Second, they wanted additional filters where those data were scattered among the organization. Therefore, I had to massage the data until it was clean enough for metrics calculations and visualizations.
The results came just as professional services had hoped for, in which the majority of implementation services customers launched tests/studies and created observations within 30 days of their subscription after taking implementation services. Now, they firmly believe that this behavior will continue as long as they improve their curriculum.
Codes: here
Tool: SQL, Snowflake, DBT, Github
Program Management: Jira, Confluence