Bloomingdale’s: Maximizing campaign efficiency and customer loyalty

Learn how Bloomingdale's was able to drive more customer-centric marketing, reduce churn, improve loyalty, and optimize their marketing spend.

Bloomingdale’s: Maximizing campaign efficiency and customer loyalty


Bloomingdale’s wanted to become more customer-centric with its strategic marketing planning. The luxury department store chain was looking for ways to provide customers with more relevant promotions, predict and reduce churn based on behavior, and optimize their marketing spend. 

Bloomingdale’s needed a solution that was custom-built around their relatively niche customer base. 

To improve marketing efforts, the team would need actionable customer insights and data to maximize campaign spend, target promotions, and grow their loyalty programs. 

Bloomingdale's solution using data and cloud


Bloomingdale’s partnered with Neal Analytics to create an advanced, cloud-based solution to produce more customer-centric marketing. 

The solution provided the company with estimated customer lifetime values per segment, predicted churn, and financial forecasts. These capabilities were paired with new reporting capabilities in Power BI to deliver dynamic segment profilesThose dashboard helped maintain an uptodate representation of their key market segments. 

The solution has four parts:

    1. Create new customer segments with machine learning 
    2. Create predictive models for churn and loyalty 
    3. Create a financial forecast model based on churn and customer lifetime value 
    4. Surface the results alongside the contextual demographic and socio-economic information for each segment 

First, Neal Analytics worked with Bloomingdale’s to re-segment their customers based on a range of characteristics, such as demographic and socio-economic information. 

Then, Neal Analytics created new customer segments by leveraging machine learning to analyze and compare characteristics from peer groups of Bloomingdale’s shoppers. 

These characteristics and segments also helped the model identify both Bloomingdale’s high-value, loyal customers and the customers at risk of churning. 

To help improve marketing spend efficiency and grow the company’s loyalty programs, Neal Analytics also created a churn model. It provided an estimated “churn point” of a customer based on their segment. Bloomingdale’s marketing teams could then identify customers who may be receptive to interventions, such as a targeted offer, and could extend those customers lifetime value. 

The churn model was then used to create a financial forecast based on the expected lifetime spend of a customer up to the predicted churn point. This information helped Bloomingdale’s better allocate marketing resources and optimize spending to engage high-value customers and improve ROI. 

By analyzing and identifying characteristics of each customer group, the machine learning models could estimate churn points, loyalty, and the predicted customer lifetime value for any given customer. 

Bloomingdale’s could use this information and leverage visual dashboards to gain insight into their customers and optimize marketing strategies to engage shoppers. 

two girls shop at Bloomingdale's


Bloomingdale’s worked with Neal Analytics to create new customer segments to understand their shoppers and how each group varied to reduce churn, improve loyalty and optimize spend. 

Understanding each customer segment’s needs and estimated lifetime value enabled the company to develop a more customer-centric approach to marketing. Visual Power BI dashboards and advanced analytics provided the marketing team with deeper insights into each customer segment to develop increasingly relevant promotions for shoppers. The goal is to eventually drive toward fully personalized marketing as the organization becomes accustomed to leveraging machine learning in its workflows. 

By integrating this cloud-based machine learning solution, Bloomingdale’s became better equipped to identify high-value customers and churn risks, analyze shopping behaviors, estimate lifetime spending to create new ways to reach and engage with their customers. 


“Our goal is to move away from promoting our brand and the brands we sell, towards promoting what’s relevant and of interest to each individual customer. We love the solution Neal Analytics developed and have integrated it into our operation process.”

— Padma Hari, VP of Customer Analytics at Bloomingdale’s