• Data Analysis
  • Data Science
  • Paid Media
  • Search Ads

Increasing Return on Ad Spend to 5231% by Defining the Best Paid Media Channels

About the client

RewardPay offers Australian businesses reward points when they pay superannuation, ATO, and business expenses on their American Express card via the RewardPay portal. 

Despite having a product that is simple to use and creates significant value for its customers, RewardPay was unable to determine which customers were the highest users of their platform, who was creating the most value for their business, and which digital marketing channels were providing the best quality leads. Understanding more about customers and their channels for acquisition was key to RewardPay’s future marketing success. 


Gaining better insights into customer value and acquisition to make better business decisions

RewardPay had two separate challenges that they needed In Marketing We Trust to solve so that they could better understand their customers and how they impacted their business: 

Improve visibility around their best customers

RewardPay had no visibility around who their best customers were. They needed more data so they could determine who ordered the most often, who spent the most money, and how much business these best customers were responsible for generating (as a percentage of total business). 

They also needed to understand which customers were at risk of leaving, so that more effort could be put into supporting them in staying and gaining more value from the RewardPay platform. 

Create analytics to determine the best channels for lead generation

They also needed support tracking where their best leads came from. Which marketing platforms generated leads that turned into high-value customers? How did these platforms perform over time – were there changes in efficacy? How did the lifetime value (LTV) of customers acquired match up to the cost of running the campaigns?

Understanding more about their customers and how they were acquired would allow for more insightful decision-making when it came to future budget allocation, best remarketing audiences (LALs, customer match), strategy development and execution.


Access to data in a single database allows for evaluation of customer value and marketing channel choices.

Creating a better understanding of current customer value

In order to segment RewardPay’s customers and identify the most valuable ones we:

  • Built an ETL process, using Java
  • Connected it to the sales database (Postgres DB, built on an RDS instance in AWS)
  • Extracted transaction data
  • Split customers into recency, frequency, and monetary (RFM) quartiles
  • Loaded this information into a data warehouse 

We then used QuickSight to connect to this database and build reports and dashboards which allowed them to drill through to see which customers fell within which quartile, what the value of these quartiles was, and which industry the businesses within each quartile belonged to. 

Based on the need to maintain constantly evolving segments we defined a simple flow for our bespoke ETL process to fit into the reporting process. From RewardPay’s side, every time a transaction was performed a record would be written into the data warehouse. We built a scheduled process to:

  • Pull data from the data warehouse
  • Pull data from Google Analytics
  • Pull data from a service supplying company information (industry, etc.)
  • Join the Google Analytics data and company information data to the transaction data
  • Perform RFM segmentation
  • Write back to the data warehouse

We also built drill-down reports which, using the business industry information we gathered as part of the ETL process, we were able to use to show which industries were the primary ones inhabiting each RFM segment, allowing RewardPay to understand which sectors were the most valuable. 

Measuring marketing channel performance

To provide better understanding around which marketing channels were the most performant we built a second ETL process to load data from Google Analytics into the data warehouse, essentially consolidating the Google Analytics data and the commercial data. 

We could then identify which channel led to a user’s first visit and write time series reports to show how much money individual marketing campaigns had generated over time.

Creating some simple ETL (extract, transform, load) processes allowed us to copy data from multiple sources and place it into a database, which could then be presented in a way that gave RewardPay insights into the value of their customers and the marketing channels that attracted them.






Easy to access to robust reporting empowers RewardPay’s marketing activity.

Transparent reporting on customer value

Using the data in the warehouse we were able to build reports which clearly indicate how much of the business’s income came from companies within the top quartile for both recency and frequency.


return on ad spend


conversion to new customer from nurture campaign


of revenue from Google Ads

Decisive reporting about which marketing channels held the most value

Using the data we gathered from Google Analytics we were able to build reports showing which marketing channels drove the most value. We were able to identify what a customer’s first touchpoint was and then attribute each individual transaction through the software platform to the campaign which led to the acquisition of that customer. 

This allowed us to go beyond showing how different channels performed in the short term – we were able to show the lifetime value of each channel and identify which channels were the best in terms of bringing in high-value customers in the long term.

The analytic set-up resulted in us being able to provide evidence of return on advertising spend (ROAS) of 5231 per cent, with 42 per cent of revenue directly attributed to Google CPC visitors, and additionally being able to show that nurturing campaigns to site visitors from all platforms converted 45.5 per cent to customer status – all of which was valuable information that will continue to inform marketing decisions into the future.