In the digital arena, organizations seek to attain lead conversions from web navigations. These conversions simply entail an interaction when potential consumers ask for more information, or download sample documents or free trials of products and services. The limitation of this metric is that it doesn’t provide any qualification of the lead as to whether the person is a potential long term customer or just a short term curious visitor.
In SaaS initiatives, management can overcome this drawback by proactively applying the power of data science to digital resources. But the process can be complex.
Digital transformation involves the increased utilization of electronic/technology platforms in conducting activities or processes. These technology based platforms facilitate the creation of insightful data resources as they record user interactions. Software as a Service (SaaS) has grown significantly in this evolving digital era and provides a classic example of the process of creating data resources through user touch points or interactions. It’s just the normal way business is conducted.
However, not every prospect that interacts with e-based products and services are true customers. The trial period involves a testing phase as potential users investigate functionality and attributes. This mere “conversion” in the digital marketing space leaves a level of uncertainty as to probability of acquiring a prospect as an eventual consumer.
Applying the tools of data science can yield valuable results for SaaS providers, but the analytic process is far from cookie cutter. In order to achieve results in more effectively identifying qualified leads, executives need to engage stakeholders of the data and business process arena, in other words, apply both the art and science of analytics to develop a decision support model. The SaaS platform has the strategic advantage of naturally digitizing user interactions, thus facilitating a valuable data resource, and analytic techniques can yield actionable information describing behavioral attributes of qualified leads from these data based metrics.
However, the complexity of better identifying lead qualifications, isn’t dominated by the analytics in data science as one may expect, but largely entails the "art” of data science -- the data identification, gathering and management activities that are required to more fully address the issue. Management can better estimate qualified leads by conducting a knowledge-intensive brainstorming process that identifies enough of the essential data variables that underpin the issue. Once this more structured data resource has been delineated, strategists can unleash the power of the “science” of data science (e.g. implement AI or machine learning techniques) to uncover the actionable patterns that make up the model that ultimately can more effectively identify a qualified lead.
Some potential patterns may include:
- Daily usage of the product or service as measured by days and minutes
- Level of use of the service (depth of use)
- User contact issues with provider
In the SaaS case, the digital touch points of users interacting with software services only provide a portion of the story behind qualified leads. Additional data resources that address descriptive attributes of the user play an important role as well. Stakeholders in the analytic process need to identify and extract data that exists in digital platforms external to the SaaS entity.
Data that describe the business users in a B2B case can include type of job or job title, amount of time in the role, skill base, type of company etc. which can yield valuable insights as to the full picture of a qualified business lead. What the entire solution entails is a behavioral and descriptive data resource of users.
The end result is that SaaS providers that effectively implement data science (e.g. create a fully balanced model with AI) can more effectively target market additional users and, depending on the patterns uncovered, can direct sales personnel with actionable information on how to manage current users interacting with the product or service (e.g. involving a technician early on in the process).
As the digital era continues to evolve, with new technology based platforms that facilitate processes across industry sectors, more valuable insights regarding descriptive and behavioral attributes along with expected outcomes could be achieved with the application of the art and science of data science to uncover these golden nuggets of information to solve problems.
Stephan Kudyba is a professor of Business Analytics and MIS at the Martin Tuchman School of Management of New Jersey Institute of Technology. He has published numerous books, and research articles addressing analytics, information technologies, and management. He is a member of the International Institute of Analytics and the Society for Information Management and has provided data science based solutions for organizations across industries.