Polaris Client Onboarding
Is Your Fast Track To Success
Our client onboarding is straightforward and can be completed within 7 days.
That leaves you more time to rule the world.
Polaris Ingests Your Historical Data
This important step is completed by the client by accessing their existing historical data and providing an export to MetricWorks. Their ongoing data integration with MetricWorks is also configured at this time. This step should take no longer than a day to complete. What makes it quick is that MetricWorks can receive the historical data via CSV exports, delivery to a shared S3 bucket, or sharing of clients’ API keys for their MMP. Usually, it is just the client’s Analytics team involved in this step.
Polaris Trains Models & Presents Results
Typically, within one day, MetricWorks generates and populates the econometric incrementality measurement data in the client’s Polaris web interface. Polaris will also expose a variety of incrementality info including the model error. During this step, the MetricWorks Customer Success team will train the client on econometric modelling and other features of Polaris. Potential teams involved in this step include UA and Data Science.
Analyze Your Incrementality Data
During this step, we want to make sure that the last touch data was properly imported. MetricWorks will train and guide the client in comparing the incrementality data with the last touch data within Polaris. The client analyzes the model info and provides feedback to MetricWorks. If available, the client will compare the data to any known incrementality ground truth. This step usually involves one to two days of collective effort on the part of the client’s Marketing, Analytics and Data Science teams.
Run An Experiment
This is where MetricWorks works with the client to coordinate and execute an experiment to deliver ground truth incrementality and prove causality. During this step, the MetricWorks will train the client on experiments and related features of Polaris. The client selects from a list of potential experiments generated by Polaris based on the desired balance of information gain versus risk. This step usually involves one to two days of collective effort on the part of the client’s Marketing, Analytics, Data Science and Product teams.
Close The Gaps In Your Measurement Stack
Polaris is the perfect complement to your existing last touch MMP. It is designed to deliver the MMP experience, but for incrementality, to make it easy to adopt, easy to use and easy to succeed in the new era of mobile marketing.
Easy To Use With Your MMP
Keep your last touch MMP and quickly integrate it with Polaris to get a single data hub for side-by-side comparisons of last touch and incrementality data.
Utilizes Historical Data
Get started in 24 hours. All we need is 2 months of your historical app events and marketing data. We can ingest it with a single API key for your MMP.
Preserves Your KPIs & Granularity
Keep all of your existing KPIs including retention, revenue, LTV, and ROAS. No painful surprises or process hiccups like those with SKAdNetwork.
No Migrations or SDKs
Saves you time and money by not requiring any migrations, SDKs or code changes. Easily integrates with your MMP, cost aggregator, or media partners.
Last Touch vs Incrementality
Interactive reports offer rich detail and comparisons between last touch and incrementality that give you full command of your marketing.
Data Science Expertise
Sophisticated data science expertise is captured in our fine-tuned regression algorithms to unleash the power of incrementality measurement.
Add robustness to the models by executing auto-generated experiments that measure ground truth incrementality while minimizing opportunity cost.
Transparency & Sharing
Understand our model internals like uncertainty and prediction error. Share model data with your data science team to boost confidence.
Ditch legacy last touch in favor of the ideal future: incrementality. Measure the true value of each granular traffic source in the context of your overall media mix.
"From having led a data science team, there is an awful lot of important insights that can come from having algorithms just analyze data at massive scale. That's something that people are not physically capable of doing."
– Anthony Cross, Former VP of Data Science, UA & Product Management,
Big Fish Games