Based in North Aurora, IL, Oberweis Dairy has 41 dairy stores in the Midwest and a seven-state home delivery network. The firm also sells wholesale through local and national grocery chains. Its dairy stores sell prepared ice cream treats along with milk, packaged ice cream and other fresh, perishable foods. Oberweis is a family-centric brand and all its milk comes from family farms.

When the firm wanted to expand operations it knew it needed a smarter way to target new customers, and it saw analytics as the key to that effort. Information Management spoke with Bruce Bedford, vice president of marketing analytics and consumer insights at Oberweis Dairy about the firm selected SAS as its partner, what the process was like, and how analytics has enabled the firm to capture those new markets.


Information Management: What business problem or challenge was Oberweis Dairy trying to solve?

Bedford: Farm-to-table delivery is a growing business and finding new families who want this customer experience requires modern analytics. We wanted to better understand our customer base so we could cross- and up-sell without cannibalizing from existing sales channels.

Customer acquisition is a very large investment each year. Specifically, through direct mail, we spend a great deal so it’s critical that we know the right customers to target, how to find them and understand their buying habits.

High income families are generally our targeted demographic and they are often the hardest group of people to reach with marketing materials, and quite frankly it’s very expensive. As a result, it’s important for us to focus on a segmented group of people.

SAS has been critical for us to identify who our best customers are within our existing clients and then take that information and apply it to the broader population. This helps us figure out who we can target that will be interested in our products.


IM: What was the firm’s experience with using data analytics, and why did it see analytics as a key factor in addressing the problem?

Bedford: Prior to working with SAS, the company did not pursue analytics to any significant extent, beyond what was possible using spreadsheets and fairly simply regression analyses. The company had also used consulting services for some analytics support, but decided to bring that resource completely in house after seeing what was possible with SAS, so we decided to deploy a business intelligence solution.


IM: What was Oberweis Dairy looking for in a partner/solutions provider, and what were the criteria in the selection process?

Bedford: We were looking for a partner that could help us build and grow our business. With our different businesses, we generate a huge amount of data in many different formats. We needed to pull all the data together and have the ability to analyze it in a way that would enable to better understand our consumers and SAS has allowed us to bring this information together in a way that had not previously been possible.

IM: Why was SAS selected and what separated SAS from the competition in the view of Oberweis project managers?

Bedford: After considering a variety of analytic tools, including full analytics software packages and more boutique type offerings, Oberweis selected the SAS analytics platform to provide analyses that would help develop more effective marketing strategies.

Beyond taking full advantage of the powerful analytics tools available in base SAS/STAT, one of the additional tools we use is SAS Text Miner to analyze notes taken by customer service representatives in our call center. We work to offer excellent customer service by exploring and analyzing the content in our customer relationship management (CRM) system.


IM: What was the project like (how did it unfold and what were the elements to it)?

Bedford: Oberweis first turned to the powerful data integration and data cleansing tools offered by SAS, as the company has data spread across multiple systems. Point of sale transaction data tied to dairy stores is in one data base and inventory and transaction data tied the home delivery and wholesale businesses resides in an entirely separate ERP system with its own database.

Some data are structured, whereas other data is unstructured and messy. Company management wanted to standardize the data so it could produce reports and analyses from a company set of structured clean data.

After implementing a data integration and cleansing process, a wide variety of management reports were developed and scheduled for automatic delivery to appropriate staff throughout the organization. Some reports, like daily sales reports include analytical results to aid in interpretation. For example, revenue in the dairy stores is substantially composed of immediate consumption ice cream sales.

Weather has a strong impact on consumer demand for ice cream, so daily sales are reported as both direct sales and weather-adjust sales, where temperature and precipitation are strong factors in adjusting daily sales figures. By showing weather-adjusted sales figures, store managers can more easily understand how local marketing activities impact sales.

Beyond reporting, Oberweis wanted to understand and respond to the factors responsible for home delivery customer account attrition. The company utilized SAS survival analysis tools to develop customer attrition models and found that certain promotional activities were actually leading causes of future attrition responses by a large segment of the customer base. As a result, Oberweis modified its promotional activities and saw a large improvement in customer retention.

More strategically, the company has used analytics to develop targeting models for expansion of its home delivery business. Most recently, analytics was used to define geographic boundaries of segments within the Richmond, Virginia and Raleigh, North Carolina markets to target for direct mail campaigns in support of growing its home delivery business in these markets where it had never had prior business.


IM: What capabilities did Oberweis Dairy gain?

Bedford: With SAS Business Analytics, we mine our data to better understand which customers shop through our different channels for more effective cross-selling and up-selling. The marketing efforts are critical as we expand in the coming years.


IM: What have been the results of the effort?

Bedford: Using analytics, we’ve improved customer retention by 30 percent and solved a host of other issues such as understanding how our customers shop across different channels, automated reports that once consumed 20 hours a week and learned that running specials on milk sold through grocery chains doesn't cannibalize from other sales channels. We also quickly found and fixed manufacturing glitches by mining customer complaint data and used SAS to create a program to root out bottle return fraud.

Advanced analytics can mean the difference between an economic model working, or not working. SAS has been absolutely integral into us understanding the right types of customers to target, and to understand the status of our best customers so that we can go and find more like them.

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