As one of the fastest growing companies in the U.S., Kitu Super Coffee’s small but effective data team needed an analytics engine to quickly uncover trends in their e-commerce data and identify new ways to fuel growth.
What started as one college student’s energy remedy is now one of the fastest ready-to-drink beverage providers in North America. Kitu Super Coffee offers a protein-packed, caffeine-rich coffee without a sugary crash of other brands. Their unique flavor of fueling on the go is quickly gaining popularity with everyone from exhausted college students to professional athletes to health-conscious coffee-lovers.
But in this highly competitive market, Kitu Super Coffee must convert as many new customers to brand loyalists to stay ahead. While their small but effective analytics team was on top of their simpler in-store sales metrics, Kitu was unequipped to dig through their complicated, multi-channel e-commerce data. Kitu’s analytics team needed a powerful analytics engine to monitor and identify new ways to fuel online growth.
While a majority of Kitu Sales occur in-store, a sizable portion comes from online sources, such as Shopify, Amazon, and Walmart.com. But in a fast-moving market with an expanding retail footprint, VP of Business Insights Carl Ekman and Business Insights Manager Don Moore had to make a choice between understanding their in-store sales data and monitoring their e-commerce data.
Originally, they made the strategic decision to focus their time and budget, on analyzing syndicated point of sale and panel data. Unfortunately, these aggregated reports would never provide the granular understanding of their customer preferences that they desired.
“In-store retail data is just broad strokes. We can see data over a four week period in a few chain stores, but that’s basically as granular as we get.” — Carl Ekman, VP of Business Insights
The rich, granular e-commerce data they were collecting on customer preferences, promotional campaigns, and LTV lay untapped, meaning the e-commerce team was mostly left to make gut-feel decisions on how they could grow online sales.
“We had a massive amount of ecommerce data that is much more granular than our retail data and can give us more insights into our customers, but we weren’t able to analyze it very efficiently. We had little in the way of tools, and often found them to be more hassle than they were worth.” — Carl Ekman, VP of Business Insights
Carl and Don knew there had to be a better way to monitor their e-commerce data and augment their ability to find these critical insights without overextending their small team.
Before Carl and Don could begin tackling these tough diagnoses, they needed to build a cloud-native data architecture to enable real-time access, handle rapid scale, and quickly generate multi-channel insights.
Working with the Sisu team, Kitu streamlined their stack, combining disparate data sources into a smooth, end-to-end data pipeline. As orders are placed through Amazon, Shopify, and Walmart.com, data from their fulfillment channel, Whitebox, is piped into Snowflake using Matillion, an ETL tool. Sisu continuously monitors new order data as it arrives in the Snowflake warehouses, providing weekly diagnoses on Kitu’s key business questions, and helps Carl and Don carve out time for on-demand requests.
“Sisu helped us build our data stack, beyond just the technology. The Sisu team has been there for us on all fronts and is by far our best partner in terms of communication and customer support.” — Don Moore, Business Insights Manager
With a full view of the e-commerce data, the Kitu team began to understand their rapidly changing customer preferences in new ways.
“With Sisu monitoring our e-commerce data, we can diagnose opportunities at the transaction and customer level, allowing us to provide insights we wouldn’t otherwise be able to get to.” — Carl Ekman, VP of Business Insights
The Kitu e-commerce and executive teams wanted to understand LTV was impacted by a customer’s first purchase favor or product type. However, since Kitu’s product SKUs change frequently, the e-commerce team had given up hope that they’d ever be able to answer these questions. With the help from the Sisu team, they were able to enrich the data as the SKUs change to get a clear picture of customer preferences over time.
In one case, Sisu identified that not only did product mix impact LTV, purchase sequencing was also a huge factor in driving sales. From a product category view, they uncovered that customers who had repeat orders of the same variety pack had an LTV almost 15% higher than the average LTV.
Armed with these facts on a continuous basis, Don and Carl can now advise the e-commerce and marketing teams with proactive answers about changing customer behavior.
“In the past, we had no way of really analyzing purchasing and SKU-level trends with accuracy. But with Sisu, we’re able to quickly answer dynamic SKU-level data without adding much time to our already hectic data analysis schedules.” — Don Moore, Business Insights Manager
Sisu is now Kitu’s primary monitoring and analysis tool for their e-commerce data, watching changing retail metrics behind the scenes, and informing the e-commerce team of changing consumer preferences.
“With Sisu, we’ve been able to not only take the work off of the e-commerce team’s plate but not add that analysis work to our already stretched Business Insights team.” — Don Moore, Business Insights Manager
Going forward, the Kitu team is excited to analyze how efforts from marketing impact their metrics. From connecting ad clicks to purchasing patterns, promotions to AOV, and more. These facts will enable the marketing team to optimize their efforts to continue to expand the online business and create lifelong brand loyalists.
Online orders a year