With multiple retail and online brands, it was imperative that J.Crew be able to take quick action on customer insights — but their legacy marketing service provider wasn’t allowing that to happen.
Murky data and unnecessarily time-consuming data analysis processes prevented J.Crew from tailoring its marketing strategies to meet the needs of contemporary shoppers.
Using customer insights and machine learning models from Acquia CDP has led to measurable performance improvements with J.Crew’s marketing campaigns, including one email campaign targeting a cashmere audience segment returning double-digit lifts over the “business-as-usual” audience in average order value (AOV), conversion rates, open rates and click rates.
Founded in 1983, J.Crew Group is an internationally recognized omnichannel retailer of women's, men's and children's apparel, shoes and accessories. The Company operates 152 J.Crew retail stores, 140 Madewell stores, jcrew.com, jcrewfactory.com, madewell.com and 170 factory stores, with 9,400 employees.
J.Crew needed to be able to quickly take action on customer insights — both in-store and online — but its previous marketing service provider (MSP) wasn’t able to deliver. This hindered J.Crew from being as agile as possible and impacted the company's ability to maximize their customers’ lifetime value across brands. J.Crew required a platform that could provide business insights without encumbering the company and slowing down the process.
Like many companies, J.Crew had worked with its marketing service provider (MSP) for many years. But over time, the solution that once fit the bill demonstrated obvious limitations. As the company continued to grow and evolve, J.Crew faced several specific challenges with their MSP:
- Synthesizing customer and transactional data from physical stores and online was a cumbersome process that impacted J.Crew’s ability to nimbly execute targeted marketing campaigns.
- Unactionable and messy data prevented J.Crew from capturing customer shopping insights, accurately calculating customer lifetime value, and delivering hyper-personalized customer experiences.
- This meant that the most essential data remained siloed, and the customer service team lacked valuable insights into customers’ journey, purchase history and lifetime value.
- J.Crew’s marketing team could not easily access dashboards or reports and needed support from business analysts who had the ability to pull lists and custom queries.
- The company couldn’t tailor discounts based on actual customer behavior, negatively impacting margins.
After considering numerous solutions, including development of a homegrown solution, J.Crew ultimately chose Acquia CDP. Since standardizing on Acquia CDP, J.Crew has realized many benefits, including:
- Improved data freshness: Daily, Acquia CDP updates all transactions, customers and engagement data (email, web), and all associated summary calculations and machine learning model scores, from the close of business the previous day, allowing J.Crew to quickly and effectively respond to their customers’ behaviors.
- Enhanced data quality: During the implementation, the teams realized that many of the existing responses and profiles were poorly deduped — specifically, their MSP introduced profile deduplication issues, which led to erroneous lifecycle classification of customers and incorrect marketing actions. J.Crew now uses Acquia CDP to feed customer data into their call center and has been delighted with the quality of the data provided, remarking that it is superior to data provided by their previous MSP. In fact, in every case in which J.Crew business groups have found discrepancies between Acquia CDP data and data sent by their MSP, J.Crew found Acquia CDP’s deduplication, aggregation and calculations were correct.
- Improved data accessibility: Now, J.Crew analysts have query access to all the Acquia processed/deduped/cleaned/connected/daily-refreshed data. Moreover, data is made available via Snowflake Data Sharing to use in conjunction with their other data sources and business intelligence/machine learning tools.
- Empowered data democratization: J.Crew’s marketing teams are now empowered to autonomously create complex audiences — including optional sub-segments and A/B tests — that they can deploy in various marketing execution systems. This means that J.Crew is infinitely agile; teams can have an idea and, within 30 minutes, can identify and pull the audience. Marketing is now self-reliant.
For example: In the event of a store closure, J.Crew wanted to retarget people who bought primarily in that store and redirect them to another store in their area. In the past, J.Crew’s marketers would’ve needed to approach their MSP for a custom query, but now they can segment the audience themselves — in minutes.
Additionally, J.Crew’s marketers can track campaign performance via Cohort Analysis. Specifically, the marketing team can save any audience they send to any marketing execution systems, then track each audience’s transactional performance in the days after the marketing action was taken (including tracking holdout test groups). Furthermore, they can see what products they are buying and in what brand/channel they are buying.
Another area where J.Crew leverages Acquia CDP is via feeds. J.Crew uses the customer data platform as a data hub to distribute feeds to their partners, including their digital agencies, machine learning vendor, data appending vendor, their call center, product review vendor, branded credit card vendor, and more. The CDP enables direct data to push into the brand’s email service provider, social media accounts, web personalization platform, and business intelligence tools, further enhancing their marketing team’s reliance and self-sufficiency. J.Crew also uses Acquia CDP in its direct mail activities, processing lists and tracking direct mail campaign performance.
Acquia CDP’s machine learning models also provide J.Crew with daily customer scores on numerous models, including “likelihood to buy” and “likelihood to pay full price.” Also, J.Crew uses Acquia CDP’s clean deduped data to feed and train their custom machine learning models, allowing their marketers to create targeted audiences and activate them in campaigns. And marketers can easily create and share monthly reports using Acquia CDP’s dashboards.
Using Acquia CDP has led to measurable performance improvements with J.Crew’s marketing campaigns. For example, J.Crew uses Acquia CDP to pull email segmentation lists based on customers’ site behavior to ensure relevant content is hitting their inboxes. This segmentation has led to an increase in open and click rates in emails.
In one campaign, J.Crew sent a personalized email to customers who have purchased or browsed cashmere in the last 365 days vs. its “business-as-usual” (BAU) audience. The cashmere audience saw double-digit lifts over the BAU audience in average order value (AOV), conversion rates, open rates and click rates. The cashmere audience was 10% of the full circulation and drove almost 50% of the total demand.
Below are some of the efficiencies and new capabilities that J.Crew gained upon migrating from the MSP to Acquia CDP:
|Before Acquia CDP
|With Acquia CDP
|Freshness of customer transactional and engagement data available for campaigns and reports
|Once a month, 3 weeks after end of previous month
|Daily refresh with yesterday COB data
|Call center agents have access to clean detailed customer data
|Suboptimal and inaccurate data exposed
|Improved accuracy of data available to call center
|Create ad hoc audience lists for targeted marketing actions via email and social
|Required days to weeks’ notice to the MSP or specialized SQL query development by J.Crew’s data analysts
|Independent list creation and push to email and social platforms by the marketing team members within minutes
|Create ad hoc reports and dashboards using fresh cleansed-deduped data
|Not democratized; all reporting was done through one team due to complexity of customer data model
|~1 hr using drag-and-drop reporting interface available with hundreds of attributes
|SQL query access for business analysts of cleansed-deduped data
|Query interface available with a two-week data lag for last month’s COB data
|Query interface available with yesterday’s COB data
|Track transactional behavior of any defined customer cohort
|Limited, manual process with heavy dependency on customer insights team and long delays in getting behavior trends due to bottleneck in data availability
Queries can be pulled by marketers within 20 minutes