In Dr. Bokareva’s previous blog, she described how the Art of Analytics allows viewers to understand customer spend through a work of art. Businesses can use these types of visualizations to unlock new sales opportunities.
By now, companies understand the importance of using data analytics to better understand the customer, process, or product performance. However, many still struggle to get the full value from the vast amounts of available data. Every company is in a different stage of analytic maturity, but in my experience, most analytics are done in silos and are usually one-offs or domain- or problem-specific.
Departments within organizations do not share their data scientists, analysts, or data, so they don’t know what other departments are doing. The missing piece to analytics is the holistic approach, which is the clear view of capabilities, data, and a production road map across the whole business. Many companies are also limited by their current technologies and skill sets that prevent the business from moving forward through analytics. I hear many variations of, “We built this really great customer solution for target marketing, and then we end up marketing to everyone anyway because we don’t know how to put it in production, we don’t have the skills to use it, etc.”
Many factors contribute to this disconnect, ranging from financial to politics. However, a driving force to analytic success is a team that is independent, technology agnostic, and provides a full range of services and analytic solutions, including analytics business consulting and data science.
Structured Engagement Accelerates Time to Value
How can a structured, well-coordinated, and multi-disciplinary team help a company progress to the next stage of maturity and leverage the full benefits that analytics and data can offer? One way is to use a Business Value Framework that prioritizes and aligns business analytic use cases with the company’s strategic objectives.
One example we use at Teradata is a technology-agnostic methodology called the Rapid Analytic Consulting Engagement (RACE). I’ve seen many variations of it: scrum, experimentation, etc., but the RACE methodology offers several unique advantages. It is a highly structured, strategic way to identify business processes with the highest ROI, data sources, and analytics to solve a problem. The entire engagement happens rapidly—unlike consultancies that require several months or more to review processes, make recommendations, and implement solutions—RACE delivers results in just six weeks.
RACE also gives companies the flexibility to pivot priorities if a more urgent problem comes up. In fact, in many cases we start with one problem and then the client requests another use case or asks us to expand the scope of the current one. The success of such short engagements also comes from working closely with stakeholders who understand their business as well as our expertise in technology and analytics.
Effective Customer Journey
What are examples of RACE engagements? Most customer-centric companies emphasize building and analyzing “Customer Journeys.” One-off RACE engagements will never deliver the full benefits of the customer journey, but having a clear purpose for creating the journey and a methodical plan for building and analyzing it will.
Great customer journey maps are rooted in data-driven research and represent the different life phases of customers, their experiences, and touch points. The full value of the journey is achieved only if it is based on a variety of dimensions such as sentiment, goals, touch points, and more. The sources of these dimensions come from a variety of data:
- Digital: The most obvious is website analytics, which provides a lot of information on users and what they are trying to achieve. This also helps companies identify points in the process where customers stopped shopping or otherwise abandoned their journey.
- Front-line staff: Recording and analyzing feedback from front-line staff who interact with customers daily, such as those in support and sales, helps companies understand customer needs.
- Transactional data: This can show spending habits, budgets, deposits, outgoing payments, etc.
- Social media: Businesses can identify sentiment from customers talking about products and brands, and determine customer connections and their influence in social networks.
- Customer history: Companies can find out the number of accounts a customer holds, tenure, relative income, Net Promoter Scores (NPS), etc.
Each dimension requires different techniques, analytic approaches, and technologies. Ultimately, every customer journey is built upon life events and customer touch points. The importance of a particular event or touch point varies between industries and from company to company.
Repeatable Methodology Across Businesses and Industries
A RACE approach is repeatable. The same steps can be used to analyze the entire end-to-end customer lifecycle to benefit any customer-centric business. It can be applied to telecom, retail, airline, and insurance companies that need a much better customer view.
For some businesses, like telecoms, that journey can be very short, and extending that journey and increasing customer value is of paramount importance. A great opportunity for new sales is in the insurance industry. If insurance companies know a customer is expecting a child or recently gave birth, then they know it is the best time to offer life insurance.
For others, such as banks and airlines, the journey can span decades. Because customers’ needs change—their buying habits at 20 years of age are not the same when they’re 50—companies must offer what a customer wants at a specific point in their live and based it on his or her particular situation. Businesses with such a capability will retain customers and continue to be profitable.
Learn more about Teradata’s Business Analytics Solutions and RACE.
Dr. Tatiana Bokareva is a senior data scientist for Teradata in Australia. She has more than 10 years of experience in research and analytics, and has been working in commercial data science and big data consulting for the last three years. She is responsible for the design of analytical solutions while leading and managing the delivery of analytic projects.