Data Science, Art of Analytics, Burning Leaf, Business Value Framework, RACE and other buzz words

Thursday April 13th, 2017

Optimise the End-to-End Customer Experience with Business Analytics Solutions

By now, almost all companies understand the importance of using data analytics to better understand the customer, process or product performance. However, many are still struggling with getting full value from the vast amounts of available data. Every company is in a different stage of analytical maturity, but to my experience most of the analytics are done in silos and usually one-off, domain or problem specific. Departments within an organisation do not share their Data Scientists/Analysts, in many cases would not share their data or know what the other department is doing. The missing part is the holistic approach to analytics, the clear view of capabilities, data and a production road map across the whole business. Many of them are also limited by their existing technologies and skill sets, which prevents the business from moving forward through analytics. I hear many variations of: “we build this really great customers cohort for targeted marketing and then we go and target everyone anyway because (don’t know how to put it in production, don’t have the skills, etc.)”.

There are many contributing factors to this disconnect from financial to politics. However, one major contributor to the analytical success is a team who is independent, technology agnostic and provides a full range of services and analytic solutions, including analytics business consulting and data science. I personally agree with authors from [1] that having a decentralised organisational model where analysts are scattered across the organisation in different functions and business units with little coordination would do little to propel a company towards analytical maturity.

Structured Engagement Accelerates Time to Value
How can a structured, well-coordinated and multidisciplinary team help a company to progress into the next stage of maturity and to leverage full benefits that the 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 that we use at Teradata is a technology-agnostic methodology called the Rapid Analytic Consulting Engagement (RACE) [2]. I have seen many similar variations of it: scrum, experiment, etc. RACE like 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 also gives flexibility. It allows for a pivot when 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 ask us to expand the scope of the current one. The success of such short engagements also lies in working closely with the stakeholders who understand their business and with the combined expertise in technology and analytics; it delivers the high value outcomes for the business.

Effective Customer Journey
What are the examples of the RACE like engagements? Most customer centric companies put a great emphasis on building and analysing “Customer Journeys”. One-off RACE engagements will never deliver the full benefits of it but having a clear purpose for creating the journey and methodical plan for building and analysing it will.
Great customer journey maps are rooted in data-driven research, and it represents the different life phases of customers, their experience and touch points. The full value of the journey is archived only if it 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 where users have come from and what they are trying to achieve. It will also help you to identify points in the process where they have given up.
Front-line staff: Speaking and recording of the front-line staff feedbacks that interact with customers daily, such as those in support and sales, is another useful way to understand customer needs.
Transactional data: Spending habits, budgets incoming and outgoing payments, etc.
• Social media: Sentiment while talking about your product/institution, connection that a customer has and their influence.
Customer history: Number of accounts a customer holds, tenure, relative income, NPS scores, etc.

Each dimension will require different techniques, analytical approaches and technology. Ultimately every customer journey is built upon life events and customer touch-points. The importance of a particular event or a touch-point varies between industries and from company to company.

There are many ways that we can look at the customer. For example, one way of identifying life events is to look at a customer’s behavioural patterns. ‘Burning Leaf Of Spending’ is one such example and looks at significant variations in customer-weekly spending patterns.

This Art of Analytics piece was born out of the RACE engagement with a global bank.

The Analytics
The ‘Burning Leaf’ was built across different technologies. Teradata Aster was used to integrate and process rich transactional accounts and credit card spending data. The Change Point Detection algorithm (CPD) was used to detect the change points in a time series and R was used to produce the visualisation.

Tatiana Bokareva - The Burning Leaf of Spending v1
Watch the ‘Art of Analytics: Burning Leaf of Spending’ video here.

The graph is read starting on the left and ending on the right, each line on the graph represents spending habits of an individual customer, you can think of it as a customer-spending time series. Each rise on the graph represents a significant deviation from customer’s average weekly spending. Such a rise represents a potential life event and the point in time when this event occurred: school fees; a new baby; a significant purchase (car, house deposit, expensive holiday, etc.). Once such a jump or fall is identified it triggers the event classification procedure.

Most customers have less than 8 major changes in a year, few have 10, hence the “tail” to the right of the graph, which reduces events’ search space. The system does not have to trace the entire yearly transaction history of a customer; it only needs to react when the change is detected.

Beyond Eye Candy
Art of Analytics goes beyond being a striking visual, the picture tells a story. Art can bring people together across traditional barriers such as age, income, education, race and religion. It acts as a powerful marketing tool and instrumental in helping to grow and attract businesses. It breaks the ice, ease and starts conversations with organizations about data and how it relates to their business problem. The reaction to an Art of Analytics picture ranges from “I never thought data could look like this”, “ I never saw my business through this angle” to “I want this for my business.”

Unlock Sales Opportunities
Understanding life events gives a more complete picture of who the customers are, where they are in their life journey, and what their financial needs are. Any major life event can trigger a sales opportunity. A marriage, kids enrolling in a private school or going to college, buying a house, having a child, or traveling overseas are all points along the customer lifecycle that are ripe for a targeted sale.

To identify events for up-sale or cross-sale offers, companies must have visibility across the entire customer lifecycle; from the first touch point until the present day. Moreover, the events library, the ability to expand, analyse and act upon it needs to be accessible to the business across an entire organisation.

An ability to act on these insights develops stronger business relationships with customers, encourages and rewards loyalty, improves NPS scores and pinpoints which particular financial products are most appropriate at any given point in time. In the end, all of these will lead to larger customer lifetime value and increased profits.

Repeatable Methodology Across Business and Industries
A RACE approach is a repeatable i.e. the same steps can be used to analyse 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 telecom, that journey can be very short and extending that journey and increasing customer value is of paramount importance. Another great opportunity for new sales is in the insurance industry. If an insurance company knows a customer is expecting a child or recently gave birth, then they know that 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 specific point in their live and based on their particular situation. Businesses with such a capability will retain customers and continue to be profitable.

“Do we really know what our customers want, and how are we meeting those needs?”

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Footnotes

[1] “Analytics at Work: Smarter Decisions, Better Results,” by Thomas H. Davenport, Jeanne G. Harris and Robert Morison
[2] http://in.teradata.com/News-Releases/2016/Teradata-Debuts-Agile-Analytics-Business-Consulting/?LangType=16393&LangSelect=true

Category: Tatiana Bokareva Tags: , , , ,
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About Tatiana Bokareva

Tatiana Bokareva is a Data Scientist for Teradata Aster, the market leader in big data analytics. Tatiana is a member of the Teradata Advanced Analytics team in Australia and New Zealand. At Teradata, Tatiana is responsible for data mining, analytics and ultra-fast analysis of unstructured, semi structured data using Teradata Aster advance analytics platform. Tatiana has a Bachelor degree with Honours and PhD in Computer Science from the University of New South Wales. Tatiana was runner up for the Dean Postgraduate Research Award in Information and Communication sector. She was awarded the prestigious Women in Engineering Scholarship. Tatiana publishes extensively and presents at many international conferences. During her PhD she was trained at NICTA (National ICT Australia). Tatiana’s PhD was in the area of the Internet of Things (IoT), namely her work evolves around building self-reliant, self-healing, fault-tolerant sensor networks. She also held part-time research appointments at the university.

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