PART 4 of 4: Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help
This post is the fourth in a four-part series. The third installment, “Existing Customers vs. New Customers — Exploring the Road Less Traveled,” discussed aspirational value and Big Data analytics’ role in attaining it. Here, we’ll discuss how all the elements described in this series fit together to drive revenue growth.
Businesses that overemphasize or exclusively focus on new business development to drive revenue growth are missing a substantial opportunity: their existing customers. Information about a business’ existing customers already resides in multiple areas of the overall corporate database, not just on a business development list. These customers’ profile information, consisting of demographic and psychographic details, preferences, and behaviors is there, offering a data picture that is far richer than the picture associated with prospective customers.
Applying Big Data analytics to this information allows the business to identify and validate not only customer needs, but also need states — the behaviors, motivations, trade-offs, and thresholds that guide purchasing behavior. By calculating aspirational value (AV), a target value for a customers’ spend, based on peak historical spend patterns, the business can quantify the hypothetical “total market opportunity” associated with particular customer segments and individual customers. Using AV in conjunction with Big Data analytics also allows the business to define a series of interactions that migrate the customer along the spend trajectory.
This approach closes the gap between customers’ current spend levels and a higher future-state target while potentially allowing the business to exceed that target. The key to maximizing AV is applying Big Data analytics to data on existing customers, since the business knows far more about them than it knows about prospective customers. Businesses use this rich knowledge base about existing customers to structure and execute more precise interactions than are generally possible with prospective customers. These companies can market to their existing customers with a high level of confidence that their interactions will yield the intended results, while also avoiding high customer acquisition costs. The result? Higher revenue, a higher probability of achieving that revenue, and faster time-to-revenue — all at a lower cost-of-revenue. This is profitable growth.
Not all approaches to Big Data analytics are equally adept at delivering this outcome. Many Big Data analytics solutions focus on discrete elements of the analytics workflow, forcing data science and analytics teams to utilize multiple solutions simultaneously and in sequence to generate insights. Solutions that don’t interface readily with the enterprise applications and execution systems that business decision makers use on a daily basis also create workflow inefficiencies. When extensive manual effort is required to deliver synthesized intelligence to points in the business that need to consume that intelligence, data scientists and analysts must spend valuable time enabling post-analysis data movement rather than focusing on conducting new analyses.
The more steps that are involved in extracting intelligence from Big Data and then putting that intelligence into action, the slower will be the time-to-results for the business. When revenue growth is an urgent priority, inefficient analytics workflows are an unacceptable impediment to value creation. Businesses can overcome many of the technical and workflow challenges associated with Big Data analytics by adopting a consolidated platform solution. Streamlining data ingestion and preparation, simplifying analytics activities — including the critical “test and learn” aspects of generating meaningful intelligence rather than mere analytical outputs — and systematically delivering this intelligence directly to business practitioners enables and accelerates the creation of significant value for the business. An integrated analytics platform helps a business make its approach to Big Data analytics both “smarter” and more effective.
In summary, Big Data analytics can help companies more effectively grow revenue when combined with…
An emphasis on existing customers as the primary growth driver
An analytical focus on identifying and validating customer needs, need states, and behaviors
Linkage to the concept of aspirational value
It is also critical to have an enterprise-grade analytics solution that…
Simplifies and accelerates Big Data analytics workflows (i.e. ingestion, enrichment, preparation, analysis, insight extraction, synthesis of intelligence, delivery of intelligence)
Maximizes agility by enabling rapid testing and learning of hypotheses and validation of analytical results
Enables business decision makers to readily utilize synthesized intelligence from Big Data even if these individuals have no data science background
Supports mass scalability across dozens of possible business use cases, spanning different business functions, market scenarios, and situational variables
Weaving together these elements enables truly smarter Big Data analytics, which can make a material difference in successfully addressing revenue growth challenges.
John Mack is Executive Vice President of Marketing for Opera Solutions.
Part 4 of a 4-part series: “Grow Revenue from Your Existing Customers: How Big Data Analytics Can Help”
To learn more, view a recorded webinar that explores this topic in more detail, examines new ways to apply Big Data analytics to the challenge, and reviews case studies from industry-leading companies.