Ketto, a healthcare crowdsource site, helps people raise money for healthcare, surgery, and other medical expenses. It spends heavily on its branding efforts and relies on a multichannel marketing strategy to maximize the donations for the causes it supports.
Marketing performance data was diffused among multiple ad tech platforms making it difficult see which channels were truly performing and its true return on ad spend (ROAS). The two main challenges that were hampering their decision making were:
Unknown effectiveness of each marketing channel and their respective return on ad spend (ROAS).
Geographic and target segment saturation hampered spend allocation.
It is vital to acknowledge the role data plays in the complexity of Ketto’s issues. The marketing performance data was spread out among multiple marketing agencies and ad tech platforms. Further, these platforms provide performance data at an aggregated level using campaign taxonomy and definitions that they define. Ketto had to undertake the herculean task of maintaining an enterprise-grade, universal marketing taxonomy. Before getting started, taxonomy mapping and the subsequent data harmonization was required.
As part of solution development, Nabler first cleansed, standardized, and harmonized Ketto’s campaign taxonomy and performance data.
Once the performance of each channel was understood we could determine the impact of reach and frequency saturation on ROAS.
Channel performance insights were used to build a recommender model for an optimized marketing spending plan. These model recommendations are delivered to Ketto through Spend Optimizer, a web-based application hosted by Amazon Web Services. The Spend Optimizer use a machine learning layer that employs Market Mix Modeling (MMM) and Forecasting to quantify the impact of marketing inputs on sales or market share. Other key features of the Spend Optimizer include:
On AWS, Nabler deployed this solution using the following components:
For four weeks, Ketto tested the Spend Optimizer across multiple ad campaigns. After all test parameters passed, the solution was deployed across all the geographies and campaigns. During the testing, it was observed that for the scenarios, spend optimization lifted revenue by approximately 10%. Also, in those scenarios, by avoiding instances of low revenue, ROAS was 20% higher than the past observed averages.