Editor’s note: Wunderkind, a leading performance marketing software, specializes in delivering tailored experiences to individuals at scale. Today, we learn how BigQuery’s high performance drives real-time, actionable decision-making that lets Wunderkind bring large brands closer to their customers.
At Wunderkind, we believe in the power of one. Behind every website visit is a living, breathing person, with unique wants and needs that can be (and should be) met by the brands they trust. When our customers and our customers’ customers get the experience they deserve, it has the potential to transform what’s possible — and deliver impactful revenue results.
Our solutions integrate hyper-personalized content into the customer experiences on retailer websites to help them understand and respond accordingly to each individual shopper. In addition, we provide these shoppers with personalized emails and text messages based on their interactions onsite. For example, we’ll alert a shopper with a ‘price drop’ message for an item they browsed, an item they left in their shopping cart, or about new products that we think they’ll love. Ultimately, our best-in-class tech and insight help deliver experiences that fit individual customers, and conversions at off-the-chart rates.
With the billions of one-to-one messages we send monthly, it effectively means we track a lot of data – in the trillions of events. Because of this, we want a deep understanding of this data so we can tailor our content specifically to each unique user to ensure it’s as enjoyable and engaging as possible.
Wunderkind’s data journey to BigQuery: how we got here
Back in its start-up days, all of Wunderkind’s analytics relied on a MySQL database. This worked well for our reporting platform, but any sort of ad-hoc inquiry or aggregate insight was a challenge. As an analyst, I had to beg engineers to create new database indexes and tables just to support new types of reporting. As one can imagine, this consumed a lot of time and energy – figuring out how to get complicated queries to run, using SQL tricks to fake indexes, creating temporary tables, and whatever else was necessary to improve performance and execute specific queries. After all, this is a company built on data and insights – so it had to be done right.
To get the most value out of our data, we invested early in the BI platform , Looker. Our prior business intelligence efforts for the broader business were also hooked up to a single relational database. This approach was very troubling for a lot of reasons, that included but were not limited to:
We could only put so much data in a relational database.
We couldn’t index every query pattern that we wanted.
Certain queries would never finish.
We were querying off a replicated database and had no means to create any additional aggregate or derived tables.
Along with our new Business Intelligence approach, we decided to move to BigQuery. BigQuery is not just a data warehouse. It’s an analytics system that seems to scale infinitely. It gave us a data playground where we could create our own aggregate tables, mine for new insights and KPIs, and successfully run any type of data inquiry we could think up. It simply was a dream. As we were testing, we loaded one single day of event logs into BigQuery, and for a month, it fueled dozens of eye-opening insights about how our products actually work and the precise influence they have on user behavior.
BigQuery’s serverless architecture provides an incredibly consistent performance profile regardless of the complexity of the queries we threw at it. With relational databases, you can run one query and get a sub-second, exceptionally low-latency response, while another will never finish. I sometimes joke that every single query run against BigQuery takes 30 seconds — no matter how big or small. It’s a beautiful thing knowing that virtually any question you think up can be answered in a very reasonable amount of time.
BigQuery allows our Analytics team to think more about the value of the data for the business and less about the mechanics of how particular queries should run. By combining BigQuery and Looker, I can give teams across our company the flexibility to work with their data in a way that previously only analysts could.
I’ve also found that BigQuery is one of the easiest and best places to learn SQL. It’s well suited to learn for so many reasons, including:
It’s very accessible and in-browser, so there’s no complicated setup or install process. It’s free up to a terabyte per month. Its public datasets are vast and relatable, making your first queries more interesting. Real-time query validation lets us know quickly if something is wrong with our query.It’s a no-ops environment. No indexes are required. You just query.
BigQuery + Looker = Data Love
Our Analytics team has three key groups of stakeholders: our customers and the teams that serve them, our research and development (R&D) team, and our business/operations team.
We recognize that every customer is a bit different and take pride in being able to answer their unique questions in the dimensions that make the most sense for their business. Customers may want more detail on the performance of our service for different cohorts of users or for certain types of web pages in ways that require more raw data than we provide in our standard product. BigQuery’s performance lets us respond to customers and offer them greater confidence around our approach. Thanks to Looker, we can roll out new internal insights very quickly that help inform and drive new strategies. Plus, with dashboards and alerts we can uncover cohorts and segments where our product performs exceptionally, and areas where our strategies need work.
Our R&D team is another important stakeholder group. As we plan new products and features, we work with BigQuery to forecast and simulate the expected performance and incrementality. As our product develops, we use BigQuery and Looker to prototype new KPIs and reporting. It’s helpful to easily stage live data and KPIs to ensure they’re valuable to the customer ahead of productizing in our reporting platform. BigQuery’s speed means that we can aggregate billions of rows of raw data on the fly as we perfect our stats. Additionally, we’re able to save significant engineering time by using Looker as a product development sandbox for reporting and insights.
Our final key stakeholder is our internal business operations team. Business operations typically ask more thought-provoking and challenging ‘what-if’ questions geared toward driving true incremental revenue for our customers and serving them optimally. For example, they may challenge the accuracy of the industry’s standard “attribution” methods and whether we can leverage our data to better understand return on spend and “cannibalization” for our customers. Because these tougher questions tend to involve data spanning product lines and more complicated data relationships, BigQuery’s high performance is essential to making rapid iteration with this team possible.
Unlocking the insights we need to truly ‘get’ our customers
Across these stakeholders, we truly empower Wunderkind with actionable data. BigQuery’s performance is key to enabling real-time, iterative decision-making within our organization and in tandem with our customers. Looker is a powerful front-end to securely share data in a way that’s meaningful, actionable, and accurate. As much as I love writing SQL, I believe it’s best reserved for new ad-hoc insights and not standardized reporting. Looker is how we can enforce consistency and accuracy across our internal reporting. We’ve found the most powerful insights come out of conversations with our stakeholders. From there, we can use our data expertise and product knowledge to build flexible dashboards that scale across the organization. While it can seem a bit restrictive for some stakeholders, this approach ensures the data they’re getting is always intuitive, consistent, clean, and actionable. We’re not in the business of vanity metrics, we’re in the business of driving impact.
BigQuery is the foundational element that drives our goal of identifying not just our customers’ needs, but those that drive their customers to purchase. As a result, we can deliver better outcomes for customers, more rapid evolution of our products, and continuous validation and improvement of our business operations. We aim to maximize performance, experience, and returns for our customers – BigQuery is instrumental in helping to derive these insights. Even as Wunderkind has grown, we’ve been able to operate with a proportionally leaner team because BigQuery allows our Analytics team to perform most data tasks without needing engineering resources.
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