In this age of Big Data, there is more information than ever before about your customers and prospects. Consider the data that your organization can harvest from every interaction a customer has with your business, including transaction details, loyalty card information, user device records, and customer surveys. There’s also a tremendous amount of external consumer demographic and segmentation data available from several sources. And, now there’s the ever growing array of social media content that your customers generate on sites such as Twitter, Facebook, Foursquare, and Yelp. Make one wrong move and a disgruntled customer can use the powerful forum of social media to air their grievances, quickly turning one unhappy customer into hundreds, or even thousands or millions, very quickly.
With the vast array of data that organizations have access to, Customer Analytics is becoming a top priority so you can predict how customers will behave when they receive a catalog, enter a store, research and buy online, or interact with your organization in any other way. The more you know about customer and prospect preferences, the more successful you will be at creating relevant offers that resonate with them positively.
While many organizations understand the potential value they have with data about their customers and prospects, they struggle with how to utilize it effectively. In many companies this data is spread across many disparate systems, with no ability to unify the data without resorting to long and complicated IT projects that must:
-
Pull in data from existing internal databases and data warehouses,
-
Add information gleaned through customer loyalty programs,
-
Integrate new types of data from social media sources, and
-
Correlate all of the above with location intelligence, predictive modeling, psychographic research, and other external data sources.
If an organization has to rely on a relatively small number of highly skilled and highly paid power users to deliver the analytics to address these challenges, its ability to react quickly to the demands of today’s connected customer is significantly diminished.
Lack of Data Insight Impedes Growth
Southern States Cooperative, founded in 1923, is one of the largest farmer-owned 
cooperatives in the United States. Owned by more than 300,000 farmer-members, it purchases, manufactures or processes feed, seed, fertilizer, farm supplies, and fuel. Thanks to strong customer loyalty and very high brand recognition among agricultural professionals, Southern States Cooperative now serves more than 1,200 retail locations in 23 states. Over time it has expanded beyond serving only farmers, and today sells products to rural American customers throughout its territory.
In order to maintain and extend its success, Southern States Cooperative recognized that it needed to continually optimize its marketing efforts, especially its very high-value direct marketing activities. However, the company faced some significant challenges to this goal. Not only did Southern States Cooperative lack a way to bring together customer and marketing data from multiple sources across the company for analysis, but also it had no consistent or sophisticated tools to drive marketing analytics, instead relying on generic productivity tools, such as Microsoft Excel.
The most difficult problem they faced was around how to better target, produce, and generate maximum revenue from the many direct catalog mailings the company produced during the course of a year. Employees had difficulties identifying the most relevant data, finding where the data was located, and accessing it. Because of this, employees often made requests to specialized data analysts to get information, slowing down all of the critical business processes.
While the company was successful in driving revenue for specific promoted products through its customer loyalty program, it was using a general ‘rule of thumb’ to determine to whom it mailed catalogs or promotions, essentially targeting customers who would have purchased products anyway. To really grow its business, Southern States needed to drive promotions to new prospects, but the company didn’t have an effective way to do this. Why? Because it could not easily blend internal customer data with market research, customer loyalty, and transactional information without long wait times and complex processes.
The Transformation to an Agile Organization
By putting strategic analytics software into the hands of line-of-business users within individual 
departments, Southern States Cooperative empowered these users with the ability to perform their own analytics without taxing IT resources. For the first time these users were able to combine all the data they needed from different departments and data warehouses, plus integrate psychographic research, point of sale data, and loyalty program information. With more information about their customers than ever before, employees could perform advanced analytics and make better (and faster) business decisions.
For example, the Marketing department at Southern States Cooperative performed an analysis of the performance of its direct mail catalogs, and found that it would be able to significantly reduce the cost of mailing and increase the response rate and revenue. Based on an analysis of the previous year’s catalog, they calculated that by better identifying the right customers for targeted mailings, they would have been able to reduce the number of catalogs by 63%, while improving the response rate by 34%, for an estimated incremental gross margin, less mailing cost, of $193,604, a 24% increase. When the current year’s catalog was included, the total gross margin grew to $206,812.
This type of value has even spread to other parts of their business. With better information about the performance of its retail stores, including information such as proximity to competitors and the concentration of certain types of customers around potential store locations, Southern States Cooperative is able to optimize their distribution network and focus their efforts on the most profitable locations.
Customer Analytics also helps them ensure that their farmers and retail stores receive the right products as buying preferences change with the seasons. They can now identify individual Stock Keeping Units (SKUs) and product groups that have strong seasonal buying patterns, set start/stop stocking dates at the wholesale and retail level, and determine the optimal inventory levels at each store. In doing so, Southern States Cooperative was able to reduce inventory carrying costs through the proactive identification of Slow Moving Inventory (SMI), and use this insight to modify processes and business rules to minimize SMI volume.
Overall, decision making has been sped up dramatically throughout the organization, in groups ranging from marketing to finance, retail, and buyers. Processes that used to require six hours to complete are now performed in three minutes. Highly skilled data analysts have been freed to concentrate on higher-level, mission-critical analytical tasks to better serve all of its customers – internal and external.
Greg Bucko, the company’s manager of customer insights summed up their results. “My number one responsibility is to make sure we understand our customers’ needs and wants. We’ve been able to answer marketing questions more quickly, more efficiently, and we have been able to increase the volume of insights we’ve gathered. We’re now able to make connections we were not able to make before.”
For more information on the transformation at Southern States Cooperative, check out their recent presentation at the 102nd Annual National Retail Federation (NRF) Convention & EXPO, “Closing the Gap Between the Customer and the Profitable Customer with Analytics”.
Conclusion
As Big Data moves into mass adoption, organizations have a mandate to extract real value and actionable insight from their data to remain competitive and meet customer demand. In 2013 that value is going to be focused on a deeper understanding of the customer from every facet or angle.
In order to transform an organization from one that merely possesses data to one that extracts real value from it, analytic capabilities must be placed into the hands of the business analysts and business decision makers who are closest to the problem. These people may not have PhDs in data science and statistics, but they know the business, know and understand the data that surrounds their business, and are smart about what they need to get done. This move toward Humanizing Big Data will make data more accessible, more usable and more experiential so people are empowered to make much more informed, strategic business decisions.
Author: Bob Laurent
Bob Laurent is Director of Industry Marketing at Alteryx, the leading provider of Strategic Analytics software. He leads the development of strategic marketing programs to expand the company’s presence in current accounts, penetrate new markets, and build a loyal customer base.