Marketing Attribution
Problem
- Company couldn’t tell which marketing channels and campaigns were converting the highest quality users on their platform.
- The data from the marketing platforms were disconnected from the data on their product platform
Solution
- Created marketing attribution data model that connects data from Google, Facebook, and LinkedIn top of funnel views to operational and paying customers to understand
- Gained insight into which marketing channel and campaign provided the highest ROI
Tools Used
- Looker, SQL, Snowflake, AWS, Fivetran, Stitch
Data Sources
- Google, Facebook, LinkedIn, Heroku Product Data
Sales Operations
Problem
- Company didn’t have insight into sales operational efficiencies and key sales metrics to understand performance or areas of improvement
Solution
- Built a new data model using the sales data and built dashboards that showed key sales performance metrics
- Gained insight into sales pipelines and efficiencies of sales team
Tools Used
- Looker, SQL, Snowflake, AWS, Fivetran, Stitch
Data Sources
- Salesforce, Hubspot, Heroku Product Data
Finance Analytics
Problem
- The company didn’t have easy to use dashboards to understand business financial metrics and performance in an easy to view way
Solution
- Built financial data models to easily view income statements by overall business, business units, and individual customers
Tools Used
- Looker, SQL, Snowflake, AWS, Fivetran, Stitch
Data Sources
- Netsuite, Quickbooks
Customer Retention
Problem
- Company did not have clear visibility into the retention rate of existing customers
- They didn’t know what factors led to higher retention rates
Solution
- Built a customer retention model
- Segmented the data by different factors, allowing the company to identify areas that mattered to the customers
- The company used these insights to improve their product and customer experience, increasing their customer retention rate for existing and future customers
Tools Used
- Excel, SQL
Data Sources
- Product Data, Quickbooks, Netsuite
Customer LTV/ CAC
Problem
- Company did not have understanding of their customer LTV (Lifetime Value) and CAC (Customer Acquisition Cost)
Solution
- Used customer data to create a LTV model by multiplying the average value of each customer by their average lifetime to calculate the LTV over time
- Using acquisition and marketing data, I created a CAC model by taking in the number of acquired paying customers and dividing that by the total marketing and acquisition costs
- This allowed the company to calculate their LTV/CAC ratio to understand the health of their business
Tools Used
- Excel, SQL
Data Sources
- Product Data, Netsuite, Quickbooks, Marketing Spend