Case Study

Retailer Builds Omnichannel Customer View to Boost CX

Case Study Download PDF

Industry Retail

Initiative Analytics & Data Science Democratization

Results

Arm brick-and-mortar retail stores

with data about their best customers

Optimize and justify marketing spend

based on location and CLV data

Use RFM models to target lookalike audiences

in ad platforms that look like their top three quartiles

Improve supply chain

by leveraging data to understand what they should stock in certain locations

About the Company

Founded in 1995, this apparel and fashion retailer has thirteen store locations across the US, as well as an ecommerce storefront. Their business is evenly split between wholesale, ecommerce, and brick and mortar sales.

Challenges

An apparel retailer with e-commerce and brick and-mortar stores needed to find innovative ways to engage and influence customers across channels to compete with retail giants 100x their size. In order to win and retain customers, they wanted to build direct relationships in ways their competitors cannot. Accomplishing that goal meant unifying data across sources and marketing technologies.

With customers’ personal and transaction data split across online and offline sources, including Retail Pro, Shopify, and Klaviyo, the retailer needed a centralized view of the customer they could leverage to understand their behaviors and preferences across channels.

The retailer turned to BlueConic to unify their first-party data and fuel their omnichannel marketing strategy with bespoke customer experiences. The retailer’s first step was to make sure they could authenticate customer data from online and offline sources. After implementing a loyalty program to capture email address at point-of-sale in stores, the company started to reap the benefits of a unified, customer database in a CDP.

Solutions

Leveraging unified customer profiles in BlueConic, marketers at the company could now use normalized customer data from across their systems. Combining the power of online behavioral data, POS data, predictive CLV (lifetime value) modeling, and recency, frequency, monetary value modeling, the retailer can now:

Build new segments that combine CLV, RFM, and behavioral data to inform interactions & optimize spend

Using the profile data in BlueConic, and without relying on additional analytics or data science resources, the marketing team uses AI Workbench to calculate CLV and RFM scores at the individual level so they can create new segments such as ‘champions,’ ‘potential loyalists,’ and ‘needs attention’ based on realtime customer behavior. They use this data to advise store associates of high priority shoppers so they can provide VIP treatment when reaching out to customers over the phone or when they visit a store. Using RFM scores, they split their audience into four quartiles – the bottom quartile being their least valuable customer and their top quartile being their best. To optimize ad spend, they create and target lookalike audiences in Google and Facebook based on their top three quartiles and suppress ads for audiences that look like their bottom quartile knowing they are unlikely to convert.

BlueConic provides the ability to really simplify complicated datasets into actionable data."

SVP, Marketing & Digital Commerce, Fast-Fashion Retailer

Streamline and share insights dashboards with executives, in-store associates, and other stakeholders.

BlueConic’s dashboards enable the retailer to create CLV reports based on all their customer data in under three hours. Sharing this data helps quickly course-correct for any unexpected declines in CLV or RFM and easily summarize results for key stakeholders.

Gain operational efficiencies across the business

Scoring customers based on real-time, first-party data from across channels enables the retailer to make data-driven decisions across their business. Calculating the overall CLV of customers by store location can dictate marketing budget and paid search spend – those locations with the highest CLV get the most budget.

Additionally, as a retailer restocking inventory about 16x per year, the company can turn to CLV to understand which brands lead to higher CLV and which brands will be more popular in specific stores. Ultimately, they can even use that data to negotiate with their vendors.

Now we can use segments and the data contained in these profiles to create individual Channel experiences for our customers, so we can understand and treat people the same whether they're in our store or online on the web."