Sparkling Society’s User Acquisition (UA) efforts, led by Richard Kos, are focused on rapidly scaling spend efficiently. The key metrics the team focus on whilst scaling spend are day 1, day 3 and day 7 ROAS and retention.
With two UA Managers and one BI expert, the fledgling team was struggling to get the reliable user acquisition insights needed to drive growth. Working across multiple games and OS’s there was a lot of data to keep on top of. Running campaigns across Google, Apple Search Ads, Facebook, Snapchat and multiple ad networks they had to stitch cost data from these sources with attribution data from Adjust and ad mediation data from Admob.
Stitching this data together in spreadsheets would happen weekly on Tuesday and took a full day. This process was slow, meaning reporting and optimisation would only happen weekly. This cadence hampered the scaling of spend within ROAS and retention constraints. Furthermore, Richard lacked trust in the manually stitched data and struggled to get the granularity required on cohorted ROAS and retention.
To hit growth targets effectively, Richard knew they needed a more scalable reporting solution for user acquisition.
Sparkling Society considered building it themselves, but quickly realised it needed a full-time resource just to maintain media partner API connections, let alone building it. They reviewed off-the-shelf solutions from major mobile ad measurement players. However, none could deliver the granularity of ROAS and retention cohorts or the flexible visualisation required for UA Managers to do their own reporting and visualisation.
A friend recommended Appsumer, and Richard quickly realised it could deliver the speed, reliable granular insights and visualisation flexibility required for less that the cost of an additional headcount. He rapidly adopted the solution, working with Appsumer’s dedicated implementation team.
Appsumer’s pre-built connectors automated the aggregation of cost data from all their media channels. The advanced mapping solution automated the normalisation of data to ensure naming conventions and inconsistencies from data sources didn’t create data inaccuracies. This data was then automatically unified with Adjust’s attribution data and internal sources to create a perfectly formed feed of user acquisition performance data.
Now user acquisition data ingestion was automated and QA’d by Appsumer’s team daily, the UA team could focus on daily analysis with Appsumer’s flexible visualisation tools. The UA team could quickly visualise day 1, day 3 and day 7 ROAS and retention down to a granular keyword, creative or sub-publisher level. They could then optimise daily and go much deeper given the time saved on aggregating reports.
New team members were also effective quickly with automated alerts based on constraints. Richard created ROAS and retention criteria and the team would get automated Appsumer reports emailed when those criteria were exceeded at, for example, a keyword level. He created bid multiplier prompts for the team, so that when ROAS and retention were under or over-performing they could adjust bids accordingly at a granular level. He now has an additional team member and has scaled spend at a faster rate than he has added team members.
As a result of this greater automation, Richard and his team have seen quantitative results, including:
A 2.5X increase in spend over 5 months, whilst maintaining ROAS and retention
A 50% reduction in time spent on reporting
Reporting and optimisation went from weekly to daily
25 hours less monthly time required from the BI team, so that they can focus on building predictive models
Qualitatively, their lives were also made easier through automated reporting.