How Machine Learning Enabled Latitude Financial Services to Increase New Product Uptake by 10x
Who are Latitude and what problems were they facing?
Latitude Financial Services is a leading provider of interest-free shopping and consumer finance in Australia and New Zealand.
Sandhya Iyer, explained their vision – “Our vision is to become partners in money for our customers and to increase their financial inclusion and access. Our data strategy has always been aligned to deliver that.” By analysing their customers’ preferences, Latitude worked to predict their financial needs and offer the most relevant product at the perfect time—what they call “Your Next Best Product.”
Latitude’s old models would take days to develop a prediction. Then, the data team would have to manually lift and transfer the results to another system before the marketing team could deploy them in campaigns.
That latency made it impossible to capitalise on the customer’s moment of readiness.
“We needed machine learning expertise to improve the way we build propensity models and help us put customised offers in front of customers for a quick and timely decision.”
Although Latitude had their own team of data professionals, they knew that partnering with machine learning specialists would be a faster and more productive approach.
“We started to look for someone to augment our team and bring in the latest skill sets and tools so we could leverage the full value of the data we were collecting.”
By the time the data project was approved and funded, the clock was ticking for Sandhya and her team to find the right partner and start delivering results.
After exploring a few options within their partner network, they discovered eliiza.
“eliiza stood out with their proactive and collaborative approach in aligning with their clients’ needs and working to find a solution together.”
Knowing it was critical for their partner to bring advanced machine learning expertise and transmit it to the in-house team for long-term maintenance, Sandhya was confident she’d found the perfect fit.
“When I explained our business problem to the eliiza team, they said, ‘We have people who understand that problem. Here are their profiles.’ It gave me peace of mind knowing my partners already knew what I wanted.”
She signed with eliiza enthusiastically for a seven-month project.
eliiza quickly proved to be precisely what Sandhya was looking for.
As the only Google Cloud Partner and AWS certified machine learning partner in Australia and New Zealand, they brought a deep level of specialisation that other data consultants couldn’t match.
“The Big Four are great, but eliiza brings a niche service. They came in with deep expertise in machine learning and data engineering, along with the ability to problem-solve.”
With a practical approach to helping clients better understand and leverage their data, the team was an ideal partner for the project.
“They worked closely with our teams and delivered exactly what we needed—a machine learning model that could predict customer needs and put the right product in front of them, through whatever channel they chose.”
Making timely and accurate recommendations
eliiza’s team analysed Latitude’s needs and implemented data modelling techniques that could make sense of their data quickly.
“Our data strategy revolves around connecting deeply with our customers and assisting them proactively with their financial goals. To help them save for their next holiday or home renovation, we needed to be able to act on those predictions in real time. eliiza brought us that capability.”
That accelerated processing was one key to enabling effectively personalised offers.
Delighting customers with omnichannel flexibility
The other critical component of the project was allowing Latitude’s Next Best Product to show up on whatever channel customers chose.
“This machine learning not only shortened the time from data collection to activation, but it allowed us to respond to audiences right where they were, whether it was by SMS, email, or mobile app. That omnipresence made a huge impact.”
Eliminating latency skyrocketed the effectiveness of the personalised offers.
Unleashing efficiency with automation
Best of all, the new models made the process 100% automated, freeing the data and marketing teams from repetitive data entry and low-yield labour.
“The automation simplifies both the customer’s life and the lives of the people trying to roll out these campaigns. Our customers get recommendations right when they need them, and we can refocus our time on things that add more value and serve them better.”
The eliiza team also developed an end-to-end self-monitoring solution, allowing the team to receive instant notifications via Slack if performance degraded.
This improvement eliminated time-consuming manual checks the team used to perform semi-annually and prevented lost opportunities from the system running sub-optimally.
Upskilling the in-house data team
Another essential win that came from the process was that Latitude’s in-house data team walked away with improved skills and capabilities.
“Our team raved about the eliiza people. They were super professional and quick learners, able to quickly gel with our people and start delivering on the project.”
Sandhya was blown away by their mix of technical expertise, cutting-edge knowledge of modern platforms, and business acumen.
In only seven months, Latitude and eliiza built a new propensity model that delivered personalised offers to customers with virtually no latency, driving a 10x increase in uptake of graduated products.
“The machine learning model delivered omnichannel campaign activation in real time. As a result, we saw our customers taking up newer products and graduating into other Latitude products at a rate that was 10 times higher than before.”
The new machine learning model not only peaked the effectiveness of digital campaigns, but it made the creation process 66% more efficient.
“Our three-member team used to be constantly involved in pulling data together and passing it to marketing. With our new automations, it only takes one person to govern those processes and the rest are able to focus on adding value.”
With continued optimisation of marketing performance and unleashed productivity for the data team, the collaboration was a resounding success.