Series 6 – The Rise of Narrative Story Telling for Enhancing Sales Human Performance
B2B Sales productivity has hit an all time low in performance, and has dropped by 15% over the last ten years, now running on an average of in field productive face-to-face customer time at roughly 30% (Accenture, 2016).
In other words, 70% of B2B Sales professional time is spent in administrative overhead, (responding to emails, in putting data into administrative systems, contact management, customer relationship systems, billing and inventory management systems, internal meetings, answering /returning mobile calls, responding to text messages, etc.). Something is very wrong with this picture, impacting trillions of revenue realization globally due to the noise overload in B2B selling processes and practices.
As attention spans shifts from how to survive with the deluge of information to how to thrive productively, new approaches are reshaping the B2B world. We have already experienced the way businesses buy from and sell to each other. This has radically changed over the years.
Customers today are far more knowledgeable, and more demanding in demonstrating value. Today, customers are more comfortable getting information online and doing their own research which allows them to make decision more rapidly. With the amount of product knowledge online, through web or video conferences, customers come to the sales professional informed with specific questions and prefer not to speak with a generalist. These changes in customer buying behavior are increasingly a major challenge for suppliers with rigid, “siloed” sales structures and inflexible sales operating models, which are challenging vendor’s core delivery capabilities.
Companies servicing larger named or managed tier one accounts are rapidly striving to generate sales growth and find pathways to increase customer satisfaction. In addition, organizations of all sizes are following the lead of business-to-consumer (B2C) retailers such as Amazon.com by making smarter use of data analytics to predict customer purchasing or churn, increase sales, and deepen relationships. In other words, customers today, simply want it all – or everything.
Customer needs today are far more diverse and are changing daily, adding tremendous impacts to sales organizations capabilities. For small businesses, they are primarily relying on low-cost sales channels, telemarking, online engagement etc., and for larger accounts or higher value channels, direct or face to face sales for key or named accounts continue to dominate channel approaches. Irrespective customers are demanding simple, fast, and inexpensive transactions, on the one hand, and still demanding complex solutions designed by experienced and global delivery teams. The impact is that B2B companies are over investing and more often are under delivering against customer’s high expectations.
As a result, B2B sales organizations are struggling on multiple fronts. First, they need to develop flexible Omni-channel models that can seamlessly manage diverse needs simultaneously. High value transactions are increasingly complex, and require more gain sharing, risk sharing or service level agreements, asking vendors to partner to put more “skin in the game,” to ensure value is being achieved. With this complex customer landscape, sales professionals are required to sell more product portfolios, yet the buyer profile is outpacing the knowledge base of sales professionals competencies, as customers want world-class expertise at every step of customer engagement. As a result, B2B company’s costs are skyrocketing, as they have to make investment decisions to add in layers of sales specialists who can support customer needs on the front line.
Value of Narrative Story Telling in Making the Most out of Data
The use of customer data and predictive analytics is on the rapid rise, growing CAGR over 40% according to experts (Gartner, 2016; IDC, 2016) and is no longer solely being leveraged by B2C sellers such as Amazon.com.
Predictive Analytics in sales can be used in a number of ways. B2B sales teams report that the rapid adoption of prediction techniques has increased the volume and quality of sales leads and improved their customer conversion rates. Predictive analytics are becoming widespread both in markets serving smaller customers (larger data sets facilitate predictive modeling) and in those with large customers (companies can examine statistical patterns in performance across accounts, opportunity types to identify the most positive pathways to win more rapidly).
These newer forms of advanced predictive analytics are prompting sales and marketing teams to develop new data science centric strategic and operational roles. They are also driving more frontline sales managers and sales professionals to become more sophisticated data users, reducing the influence of old-fashioned and raw, and very limiting gut instinct in driving the decisions of sales teams.
To survive, vendors must now retrain staff, retool sales processes, and allocate time in new ways to ensure that data is recognized as a powerful asset and competency for recognizing sales performance.
How do narrative storytelling approaches augment predictive analytics methods?
Businesses today are dealing with more data than in the history of mankind, and analyzing it to create value for leaders and decision makers is an ongoing challenge. We are at the stage where big data using advanced AI, machine learning and predictive analytics methods can have a very positive impact on the success of businesses.
Machine Learning delivers an assisted way for users to gain new insights or advanced insight into why events occurred, or what is expected to happen in the future (Probabilities) and can give guidance into insights to alter future outcomes. Traditional BI analytical methods, standardized reports are not able to handle advanced analytics causing many businesses to leave out vital pieces to the story that they are able to tell with their data. Analytics’ teams are being tasked with transforming data into specific business directives, and current BI Infrastructures leave value on the table in the form of unforeseen insights.
Gartner Predicts that by 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products developed from a decade earlier. The role of machine learning will be to automate the data discovery process. Implementing machine learning approaches will further assist in mining big data and enable richer story telling capabilities. Leveraging predictive analytics and narrative story-telling methods will change the way that organizations’ execute their analytics initiatives by leaning on the computer’s power to continuously learn and adapt overtime, continually mine and find hidden connections in massive data sets, and deliver more high valued insights for decision makers.
The International Institute for Analytics (IIA) predicts that in 2016, organizations will recognize how critical it is to communicate analytics and develop stories behind the data. In order to make use of the abundant business data, businesses need to have an effective system to make use of the abundant business data, business needs to have an effective system in place to help organizations solve problems and be as successful as possible.
As data grows, human driven investigation of the data becomes less and less effective, causing errors to become more prevalent. In the case of data storytelling, reliance on human interpretation to identify the focus of the story may translate to telling the wrong story or one that does not take into account the entire story.
Through the use of predictive analytics via machine learning, decision-makers gain the ability to look at data like never before. Combing human expertise with unbiased machine intelligence delivers a powerful combination of human and machine interaction of which almost every business can benefit from. Companies with a clear strategy in place that also adopt machine learning will be able to extract deeper insights from their data. Today, traditional BI and manual reporting methods leave out too much value, and in 2017, business leaders will need to consider a machine-learning component to drive their storytelling and decision–making processes.
Narrative story telling uses data storytelling to construct an impactful learning story for businesses to highlight what is important to look at versus other variables to create a guided learning discovery to find the best route or destination.
Story telling is critically important to a business’ decision-making process and through data, machine learning is able to help decision makers discover not so obvious patterns in data and derive predictive intelligence. This advanced approach to analytics will ultimately create a more holistic and proactive story for decision makers to provide value and understanding as the market shifts intensify.
Data Storytelling or Narrative Story telling is poised to be one of the next big waves in analytics; and it’s an exiting concept to improve human performance against. However, as its stands today, the process still relies on humans to identify interesting points on which to build and tell the story.
David Bjoe. Perspectives on impact of stories creating culture. (Source : http://www.jstor.org/stable/2393432?seq=1#page_scan_tab_contents)
Thomas Davenport, Expert on Analytics and Big Data. (Source: http://www.tomdavenport.com/about/)
LeAnna Kent. Data StoryTelling: Bringing Life to your Data (Source: http://www.predictiveanalyticsworld.com/patimes/data-story-telling-bringing-life-data/8278/).
Thaler Pecker. A Storyteller expert-discussing story attributes, (Source: http://thalerpekar.com/sharing/).
David Snowden, Expert in Cognitive Sciences and StoryTelling. (Source: http://cognitive-edge.com/).
Berkeley Warburton, The Age of Distraction: Accenture White Paper, Fall 2016. (Source: https://www.accenture.com/us-en/insight-selling-high-tech-age-distraction)