Optimizing the Customer Experience with Data and Analytics
Brands work hard so that their customers will know them. Customers expect their favored brands to know them, too.
Data and analytics inform product offerings, influence buying experiences across all channels, and improve fulfillment and service. Applying data and analytics across all of these management disciplines optimizes the customer experience. At 1-800-Flowers.com, Inc., we use data and analytics extensively to optimize the way customers experience our family of brands. Careful management of data assets and technology across a multi-brand enterprise enables us to maximize the value we deliver to our customers and ultimately, gift recipients. At every stage of the value chain, data and analytics play an important role, and that requires cross-discipline thinking.
Data and Analytics across the Dimensions of Customer Experience Product
Complex data and analytics inform the markets companies enter, how products are developed, and quality is managed for even the most commonplace of industries. This isn’t new. However, the industrialization of analytics has turbo-charged the role of data in these areas.
Within large corporations, the collection and analysis of granular data is faster and easier than it’s ever been before. Not too long ago data sat in organizational silos and was analyzed by business unit specialists. Those business units ‘owned’ their data and provided it to corporate layers in mediated forms. Data platforms and analytics are now routinely provided by centralized teams. This shared service model gives business units access to data capabilities that they may not be able to sustain on their own, but have become table stakes to stay competitive. With this centralization, data is treated (and managed!) as an enterprise asset.
Data and analytics inform product offerings, influence buying experiences across all channels, and improve fulfillment and service
At the same time, sophisticated analytic platforms and rich, detailed data to feed them are available in a dynamic, cloud-provisioned marketplace. The analytic playing field between large and small players has leveled out. As small players take advantage of this information and analytic availability, they find novel ways to partner and function competitively as larger entities.
The winner in this acceleration of how data informs product introduction and development is the customer: more choices, sooner, with a higher level of customization.
The 360-degree view of the customer. Omnichannel marketing. Distributed commerce. Data and analytic skills are the fuel that makes these marketing concepts possible.
The 360-degree customer view is the data marketer’s nirvana. It serves as a perfect North Star for guiding the B2C data roadmap. It’s important to know all the touchpoints customers have with the company and its brands. Each of these touch-points leaves a data fingerprint, and that data has a role informing customer interaction. This North Star may never be fully reached: ROI should drive the roadmap. Which data becomes part of a shared 360-degree view should be decided as part of the data governance process.
Omnichannel marketing is the activation mechanism for the 360-degree customer view. Analytics enables the brand marketer predict the optimal media mix by customer. Marketing this way isn’t just good for conversion rates: it improves the customer experience by bringing messages to the consumer where that buyer wants to hear them, without oversaturation.
Distributed commerce presents one of the more complex challenges to corporate data functions. This channel proliferation means that IT departments need to support transactions and data exchange across an ever increasing number of platforms. Data governance constituents and data analysts need to make smart choices regarding when automate the inclusion of data from new channels into existing analytic and automated platforms.
Supporting services is where complex analytics can improve the customer experience during the most human of moments: the delivery of a gift, the customer service transaction, the support center resolution. Enabling this to happen requires organizational engineering that matches data platform engineering. At scale, most organizations are structured by some matrix of ‘business units’ and ‘functions’. Data and analytics tend to be the kind of shared services platform that crosses business units (think ‘ERP of data’), but less often functions: they are business unit or brand agnostic and provide a functional capability. Internal consumers of the platform (e.g., the marketing department) know their customers, and the data and analytics capability supplies (or predicts) behavior. In supporting logistics or customer service call centers, which can be cross business unit themselves, the data and analytics capability will be pushed in a new way. Here, the internal function understands the framework of the interaction, but needs the data and analytics platform to supply both the customer knowledge and the analytics capability. In thinking through how to bring customer knowledge to these moments, a data and analytics team has the opportunity to show how well it knows not just the mechanics of its data but the meaning. Such a team can also use its relationships with other functions that serve as data sources to help bring additional insight to service capabilities.
Optimized Customer Experience in a Distributed Data World
There was a time when data was either acquired in one place and distributed, or collected from many sources and applied for a single clear use case. Occasionally an innovation would turn one of those assumptions on its head. New technologies, channels, and business models now somersault those assumptions continuously. By using data and analytics platforms as way to not only manage and enrich data but ensure that organizational boundaries are spanned, organizations can optimize the customer experience and enhance the value of their brands.