Veronica Beard Slashes Acquisition Costs and Boosts Return on Ad Spend with Sigma
Started by sisters-in-law, Veronica Miele Beard and Veronica Swanson Beard, Veronica Beard is a fashion apparel brand for women that offers cool, classic American style to global distributors. With 15 freestanding stores and its own e-commerce site, Veronica Beard has built a collection that is inclusive of women everywhere and makes them feel confident every single day.
After several years of record growth from wholesale, fashion label and retailer Veronica Beard set out to build a direct-to-consumer presence, both online and in boutique shops, and build stronger relationships with their most passionate customers.
“The volume of first-party data grows exponentially once you start directly facing the customer,” says Max Lagresle, Ecommerce Analytics Manager at Veronica Beard. “It quickly became critical that we have the capacity to process and analyze data at scale so we can use that information to provide a better customer experience Our CMO, Karen Grajwer Usdin, has a very strong business and analytics background coming from ShopBop, an Amazon company. She has challenged all of us to use data to make better decisions, especially when it comes to improving the customer experience.”
With a clear mandate coming down from the CMO, Max began building out the data infrastructure needed to surface the third and fourth layer insights that accelerate business outcomes.
BigQuery was selected as their cloud data warehouse in 2019, Segment was utilized as their customer data platform, Fivetran was leveraged the company’s data pipeline, and Chartio was already in place as the BI and analytics solution. But with a very limited number of people in the organization able to read and write SQL, few were able to analyze, let alone access, the data they had at their fingertips. It was at this point that Max began evaluating other analytics and BI tools.
“I had used other BI tools in the past, but they lacked the agility and flexibility we were looking for,” explains Lagresle. “Many required knowledge of either SQL or their own proprietary coding languages. While powerful, these tools would have been a very heavy lift for us engineering-wise and would have prevented our business teams from finding the answers they need, the moment they need them. We really wanted to create an analytics environment where people could access the data they need to do their jobs and eliminate the BI bottleneck. Looker is too robust for a single user."
Veronica Beard’s data engineering agency, Data Culture, introduced the team to Sigma, and it became immediately clear to Max that Sigma was the perfect balance of flexibility, ease of use, and control that they were looking for.
With their full data stack in place, Max and the team at Veronica Beard began integrating data from their customer data platform, Segment, with other data sources via dbt to develop rich profiles of their customers. Raashi Hasija, Head of CRM at Veronica Beard, was a key partner in these analyses.
Sigma has been really helpful in collating all of our data together so we can create rich customer 360s.
—Raashi Hasija, Head of CRM at Veronica Beard
“We have our customer table and transaction-level data from our e-commerce store as well as our brick-and-mortar retail stores,” explains Hasija. “Sigma has been really helpful in collating all of our data together so we can create rich customer 360s. We're no longer isolated in our view of the customer from just the online shopper but have been able to add color from the brick-and-mortar shopper as well. This has helped us understand both the differences and overlaps between those two groups so we can make improvements online based on what's working in the stores and vice versa.”
One of the most impactful analytics has been customer decile analysis.
Sigma is much more agile compared to Looker.
—Max Lagresle, Ecommerce Analytics Manager
‘We basically looked at everyone who shopped in our stores and online in the last 24 months and divided them into 10 groups based on how much they spent,” Hasija explains. “Then we were able to separate the online and brick-and-mortar sales to determine what our top customers were doing. We asked questions like ‘What products are they shopping for? Are the online shoppers buying different things than our in-store shoppers? Which items are both audiences buying? Which items are unique to online purchases versus in-store purchases?’”
With a complete view into their customer segments, purchase history, and behaviors fueled by a combination of Segment, Shopify, and Sigma, the team began conducting a series of analyses aimed at increasing conversions and reducing costs.
Customer decile analysis uncovered that Veronica Beard’s most valuable customers are omnichannel customers that purchase both in stores and online. But the team was able to take this analysis one step further. They examined what the top customers’ typical purchases were, which helped them uncover exactly which item is likely to be purchased first, which is purchased second, and so on. Based on these insights, the marketing team can launch very targeted, high converting ad campaigns that surface the most attractive items to audiences when they are most likely to purchase them.
“Once we flag who our best customers are, we are able to give them early access to new arrivals through our email campaigns,” says Hasija. “We've seen an uptick of up to 25% in dollars per email sent when we target our best customers with these email campaigns.”
We've seen an uptick of up to 25% in dollars per email sent when we target our best customers with these email campaigns.
—Raashi Hasija, Head of CRM at Veronica Beard
Sigma has also helped Veronica Beard identify their least valuable customers so they can optimize their ad spend and reduce costs. Lagresle explains:
“Using data from our data warehouse and our analysis in Sigma, we realized that the bottom 9th and 10th decile of customers tend to be customers that make purchases but then return 100% of their items. We then send this lower value audience segment from Segment to Facebook and exclude them from seeing our ads. Based on the analysis started in Sigma, we’ve been able to increase our return on ad spend from Facebook ads by 11%.”
Being able to accurately understand and forecast how much money a customer will spend helps marketers better select target cohorts and reduce costs associated with acquiring them. One interesting insight the team discovered with Sigma was that customers that come in through markdowns are unlikely to purchase products that are sold at full price. Logically then, it makes less sense to spend the same amount on acquiring these customers. Using Sigma, Veronica Beard’s marketing team is able to conduct cohort analysis and dynamically adapt CAC targets for weeks when sales are running versus full price weeks.
I’ve built more reports by myself in Sigma in three months than I did in six months at my previous company where I had two full-time data engineers!
—Max Lagresle, Ecommerce Analytics Manager
“We have lowered our CAC by 20% since optimizing our Facebook ad spend based on the advanced cohort analysis we have been able to do with Sigma,” says Lagresle.
With a lean, but nimble team, Lagresle has been able to build a solid foundation for Veronica Beard’s data-driven direct-to-consumer motion and maximize his impact by empowering business users to leverage validated datasets and dashboards to answer their own data questions.
