Mike Johnston is the Director of Business Analytics at fintech company Spreedly, a payments orchestration platform. In this interview, Mike shares how he came to work in data, the challenges he’s encountered at Spreedly, and how he’s driven business impact by pulling data into Salesforce that triggers personalized marketing messages based on product usage.
This interview is based on our webinar How Spreedly saves 1 hour a day with Looker and Census.
What is your role at Spreedly?
I'm the Director of Business Analytics and also I'm in charge of Business Intelligence at Spreedly. That means I'm in charge of Looker, all the data that gets poured into it, the visualizations that we create on top of it, as well as a lot of the ad hoc and project-based analysis that we run in our company – think things like churn analysis, pipeline analysis across different departments.
How did you wind up working in data?
About 10 years ago I worked as a math teacher, which I loved, but I wanted to get into business. It sounds silly, but I Googled what kind of career could get me into a startup. I started in financial planning and analysis (FP&A) then moved to finance operations, and that’s when I started to work closely with data. I saw that it was really hard to get data out of our back end, so I started learning SQL as I wanted to be able to pull in data and understand what was happening.
An executive told me not to waste my time learning SQL, but that didn’t feel right to me.
Eventually, I joined another company as a Director of FP&A. In that role, I worked directly with the business intelligence team and saw how they had the pulse of all the data – they understood the data sources, the source of truth, and had access to everything so they could build systems on top of it. I realized I wanted to be part of it.
That brings me to my current job, when I joined Spreedly as Director of Business Analytics.
What was your first project at Spreedly?
I had the good fortune of being in charge of our Salesforce migration. We implemented a more modern data tech stack which included Snowflake and Snowpipe.
Our goal was to get our users’ behavioral data populated into Salesforce from Snowflake. We did pull data into Snowflake but it was totally unstructured, so we thought we should bring in a Business Intelligence tool to add a semantic layer where the business rules would live. For us, it made sense to use Looker, as we didn’t have dbt fully set up.
Today, we leverage Looker for our entire semantic layer to build out our business rules. To get there, we invested a ton of time and energy pulling in these operational tools, pulling in the right tables, joining them the correct way, and building out a lot of measures with derived tables and pre-aggregates.
Show & Tell: Pulling product usage data into Salesforce and Pardot to trigger personalized messages based on the customer journey
One of our most important use cases is communicating with customers based on where they are in the customer journey. The more personalized the communication, the better.
Before using Census, we used a simple time-based cadence. Based on how long ago someone had shared their email address, they got certain emails. For example, they’d get an email on day 7 of their trial. These emails shared pertinent information, but they weren’t influenced by user behavior or specific challenges.
To make these messages more personalized and relevant, we created different types of emails that are based on user behavior. For example, if a user connects their account to Stripe — one of our payment gateways — we now know they’re further in their journey than someone who has only added a test payment method. Based on this behavior, we can then send messages congratulating them on connecting to Stripe, then make suggestions on what they should do next.
To create these trigger-based campaigns, we had to pull product data into Salesforce, then into Pardot. Census made this process relatively easy and we have used it for a number of other use cases, as well. Now, based on customer behavior, we are also able to alert our SDRs when prospects are ready for one-on-one conversations.
Can you share more about the impact this has on the business?
At a high level, return on analytics (ROA) means tying prioritization or operationalization of data to business impact. In our particular use case, we're helping customers through their customer journey: We can look at how many customers are converting through a new nurture campaign as compared to a prior one. Ultimately, we’re using data to fuel and support pipeline revenue.
What advice would you give to others in the data space?
For anyone working in data, it’s important that new projects start with understanding the business impact. Business impact is measured with company KPIs that are different across organizations and departments, but they’re likely to be metrics like revenue growth and customer retention. When you’re going to use data to fuel a new project, consider how you’ll impact specific KPIs.
It’s essential to talk to the people in operations, those who are selling your product, and of course, work directly with customers. Ask them if there are ways that you could provide more information or help to streamline their workflows. There are often really simple solutions that will help your teams operate better with data and use data in their daily decisions.
👉 Learn more about how Spreedly uses Census here.
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