I recently caught up with Steven McMullen, Senior Business Intelligence Manager at Stack Overflow, to discuss how his company tapped pre-IPO data analytics unicorn, Looker to modernize their marketing ops, acquire new leads and new customers. The business results were impressive, transforming Stack Overflow’s marketing strategy for the better.
insideBIGDATA: Who is Stack Overflow?
Steven McMullen: Founded in 2008, Stack Overflow is the largest, most trusted online community for anyone that codes to learn, share their knowledge, and build their careers. More than 50 million unique visitors come to Stack Overflow each month to help solve coding problems, develop new skills, and find job opportunities. Stack Overflow partners with businesses to help them understand, hire, engage, and enable the world’s developers. Its products and services are focused on developer marketing, advertising, technical recruiting, and enterprise knowledge sharing.
insideBIGDATA: What challenges was Stack Overflow facing in its data analytics strategy and processes?
Steven McMullen: Stack Overflow, the most popular developer knowledge-share in the world, faced a common challenge with cleaning and streamlining their data and workflow. Data scientists at the organization saw it as extremely frustrating, tedious and time-consuming, as data munging and cleaning zapped a massive amount of their time — time they and the org preferred they spend actually analyzing data to draw insights, answer questions and inform decisions. The data discovery, and cleaning and workflow process was not producing the results intended and reduced the time spent delivering value to the business.
insideBIGDATA: How did Stack Overflow approach finding a solution? What vendors did you consider?
Stack Overflow had too many homegrown tools and data sets in place that other members of the business did not know how to access. The process of trying to wrangle, understand and homogenize data that was being used by other teams was a major challenge that Stack Overflow invested a lot of hours trying to crack. Its end goal was to streamline and improve the data science workflow. At Stack Overflow, the data scientist is tasked with understanding where all that data is, accessing it, and integrating it so that it can be used for statistical analysis, modeling and machine learning to generate insight for key stakeholders to make decisions for the business. Their main tools are open source programming languages – the data scientists were looking for a solution to help them better interact with other teams in the organization. While struggling to streamline the process, they started looking for a solution that could integrate all types of sources to access data – that’s when they found Looker. Looker was a tried and true solution as various departments throughout Stack Overflow had been experiencing success , and actually that includes Steven himself before he transferred to marketing ops.
insideBIGDATA: How and why did you choose Looker?
In order to reduce the data-prep cycle and increase productivity, Stack Overflow selected the Looker data platform. The marketing ops team specifically uses Looker to generate leads and get new customers on board. LookML, Looker’s data modeling language, is a different take on accessing all the data as it allows the team to speak about data in a shared language and streamline the workflow. It allows collaboration with the finance, sales enablement and operations team because it exposes the logic they are using, metrics they are watching, the rules and parameters used for decision and it easily gives access to the data science team to integrate those into the kinds of statistical analysis of modeling and machine learning they do.
insideBIGDATA: Describe the results since implementing Looker.
Using Looker, Stack Overflow can access and manage all of its data quicker. But the capabilities unlocked were massive. Stack Overflow’s marketing ops team learned that there was a lot of flexibility in how they can visualize the marketing funnel in new ways. For example, Looker enables them to analyze conversation rates overtime as well as understand the overall health of the funnel. It allows Stack Overflow to create visualizations to show all campaigns happening at the same time and compare in real time.
In addition, Looker can query tables, in a matter of minutes – prior to Looker, it took hours. Users are able to access Looker data via API, and improve the value of that data because they now have additional insight.
While it used to take hours to answer a simple question like “How is this campaign performing?” Looker makes the information readily available for anyone to access. The team can make decisions faster and use the time savings to dig into other data projects.
Using these insights allows Stack Overflow to identify exact cost of generating new leads through marketing campaigns- Ex: if they want to bring in $1M, they’ll need 1,000 leads to get there. Those numbers allow Stack Overflow to identify the marketing dollars and type of campaign suited to generating a target number of new leads.
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