Update: “Your Data Team Should Be Your Company's Conscience” — Forbes Technology Council
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Cross-Functional Data Enablement


Cross-Functional Data Enablement

Customer Since
Case Studies

Click here for details on Cover's process for evaluating analytics tools & choosing Periscope Data: Page 1

Cross-Functional Data Enablement

Stanley helps tackle analytics questions around every step of the customer flow for Cover. The company tracks how it acquires customers through paid marketing, organic traffic and social media, then collects data related to how well customers are navigating within its app. And with text messaging as its main communication medium, Cover then analyzes text from those conversations to make further predictions on conversion of customers.

On the product side, Cover does all its AB testing analytics within Periscope Data. They have event-based data piped in and run tests in Periscope to inform the business on whether they should fully roll out a new product feature or not.

“We’re always keeping a pulse on how engaged our customers are with our insurance agents. At the end of the day, everything is a conversion funnel, so we’re always just trying to optimize the conversion points through the Cover experience,” said Stanley. “We’re constantly pushing new features and always using Periscope Data to measure the impact that our changes are having on the conversion funnel.”

From there, Stanley and the Cover growth team use Periscope to email reports out across the company and externally on a daily basis, highlighting metrics on new customers and the amount of policies sold. Cover’s executive team is among those who often create some of their own dashboards in Periscope, and in the future Stanley hopes to create workspaces for those non-technical users to dig deeper and analyze data on their own.   

Stanley also owns the financial and operating models that are crucially important for the company to run the business every day and communicate to investors. By piping financial data into Periscope, he can instantly see how many customers came in, how many people were on the app, track conversion rates, analyze premium bookings, monitor ad spending and determine gross margin figures.

“Working with Periscope Data on our financial models is a really big time saver,” said Stanley. “I don’t have to go collect data from everywhere, I just have literally one canned query that I run on the first of the month that basically takes care of all our financial reporting right out of the box.”

To make all this data aggregation and analysis possible, Cover’s production databases are copied into an analytics database that Periscope Data points directly to – all Cover’s raw data tables are cached by Periscope and glued together through SQL Views.

“Periscope Data effectively plugs into all these data sources, and we’ve built ETLs and SQL views and everything to see all this data together and provide analytics for the entire life cycle,” said Stanley. “And because we have data sources all over, we sometimes have to upload CSVs directly and Periscope’s CSV upload feature is great too.”

Empowering Complex Data Analysis

As Periscope opens up more basic analysis tasks to be done by others, Stanley has advanced to work on more complex projects. He’s currently working on scaling a machine learning model to predict the likelihood of customer conversion.

“We have a lot of people on our app every day, we know there’s a lift on our sales conversion if we’re able to prioritize the people who are most likely to become customers,” said Stanley. “When someone comes on the app, we run them through our machine learning model via Python in Periscope Data and put a prediction score on them, and they go to the top of the queue for our salespeople if they are likely to convert.

With the integration of Python and R capabilities directly in Periscope, Stanley can easily insert Scikit-learn to run a Random Forest model on Cover’s customers, create predictions and then use that information downstream to optimize sales efforts.

“The integration of Python and R in Periscope Data is a huge win for us,” said Stanley. “We don't need another script running outside and talking to multiple different databases in order to do our machine learning work, we can just do it right in Periscope.”

Stanley says he’s also working on using Periscope to handle more advanced dashboarding and complex data transformations with SQL Views, and is excited about new features in Periscope Data that will allow him to better manage data recency and control when views run.  

“With regards to business reporting and analytics, I don't really see another tool that's going to be better for us than Periscope Data,” said Stanley. “It'll scale very well with the organization, we will just have to add a few extra seats over time. The fact that everything is through Periscope’s Amazon Redshift database means that we don't actually have to manage database scaling, so that’ll be pretty great for us moving forward.”