Most of the buzz around Big Data and advanced analytics centers on collecting and analyzing new and massively larger data sets in less time. Without a doubt, new tools and skills are proliferating.
The bigger development, though, concerns how Big Data is breaking down the walls dividing business units within companies. It’s not just that new data tools make it possible, but also that business people are changing how they think about and use analytics.
Recently, for instance, we helped a telecommunications provider develop a single, comprehensive view of its customers—an old idea only recently made practical. We digested and connected huge volumes of data previously housed in silos, including customer profiles, marketing and promotion activity, purchase and usage history, service experiences and payment history. That allowed the company to use the same reference point for multiple purposes—better calculation of customer value, a holistic segmentation, more executable churn models and a platform to increase upselling. Breaking down the walls between these legacy data sets unleashed value and changed the way the company managed its business.
Walls show up in the practice of analytics, too. One financial services company was supposed to use the same data sources in every business unit. In reality, the business units used different definitions of customer value and took a different point of view on how to best increase or manage that value. Bain team members worked with the company’s data and analytics teams to audit their data and algorithms, connect missing pieces, standardize the data and modeling, and build trust across the business units. Breaking down these walls enabled the firm to align on business priorities.
At another company, in the leisure sector, improvements in performance slowed down because the analytics team and customer service models were isolated from other business units and end users. Bain now is working with the analytics team to improve the transparency, scalability and credibility of the customer value models. We’re seeing opportunities to harmonize previously hidden analytics projects and launch initiatives that would have been impossible before closer collaboration with the business units.
Companies increasingly deploy Big Data in order to develop a better overall experience for customers. Our early work in customer analytics focused on gathering, analyzing and benchmarking customer feedback in order to find the factors that led to organic growth. Recent efforts often focus on linking customer feedback with operational data, discovering and prioritizing improvements in service that will translate to a better customer experience and greater loyalty.
The customer experience lens has a healthy effect of erasing, or at least blurring, boundaries between human resources, supply chain and marketing. Rather than managing inventory only for lowest cost and reduced stock-outs, leading companies are looking to optimize inventory in order to enhance service. Similarly, the goal of hiring, training and retaining employees at the lowest cost has shifted to improving employee engagement; motivated employees, in turn, create more loyal customers, who cost less to serve.
It’s no longer enough to be a good statistician or model builder. Data analytics now must directly and clearly serve the business, and the best analysts see how the pieces fit together to serve the whole.
Jason Lee is a principal in Bain & Company’s Advanced Analytics practice. He is based in Los Angeles.