RESULTS
Paid Media
Finance
Increasing Return on Ad Spend to 5231% by Defining the Best Paid Media Channels for RewardPay
Increasing Return on Ad Spend to 5231% by Defining the Best Paid Media Channels
How In Marketing We Trust supported RewardPay in understanding their customers better
5231%
Return on ad spend
45.5%
Conversion to new customer from nurture campaign
42%
Revenue from Google Ads
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.
CHALLENGE
Gaining better insights into customer value and acquisition to make better business decisions.
RewardPay had two separate challenges that they needed solved:
Improve visibility around their best customers
RewardPay needed more data to determine which customers were most active, spent the most money, and how much business they were responsible for generating. 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.
Analytics to determine the best channels for lead generation
They also needed support tracking where their best leads came from. Which marketing platforms generated the best leads and how did they perform over time? Understanding their customers and how they were acquired would allow for more insightful decision-making when it came to future budget allocation, the best remarketing audiences, strategy development and execution.
SOLUTION
Access to a single database allows for evaluation of customer value and marketing channel choices.
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.
Building to create a better understanding of current customer value
In order to segment and identify the most valuable customers 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 RewardPay to drill through to see which customers fell within which quartile, the value of each quartile, and the related industry.
Based on the need to maintain constantly evolving segments we defined a simple flow for our bespoke ETL 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 (using the business industry information we’d gathered) and were able to use them to show which industries were primarily inhabiting each RFM segment.
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.
Results
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 such as this (anonymised for the case study) heat map which clearly indicates how much of the business’s income came from companies within the top quartile for both recency and frequency.
lifetime value by frequency and recency quartiles
The type of insights that we were able to share with the client by simply making use of their data was extremely revealing and valuable. Without the reports that we built the client would never have been able to know which marketing channel drove the most value and eventually the lifetime value of each channel.
5231%
Return on ad spend
45.5%
Conversion to new customer from nurture campaign
42%
Revenue from Google Ads
Decisive reporting about which marketing channels held the most value.
Using Google Analytics data we were able to build reports showing which marketing channels drove the most value, identify a customer’s first touchpoint, and attribute each individual transaction to the campaign that led to the acquisition of that customer; meaning we were able to show the lifetime value of each channel and identify which channels were the best at bringing in high-value customers.
This analytics 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 converted 45.5 per cent to customer status.
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