Series 6 – The Rise of Narrative Story Telling for Enhancing Sales Human Performance

Before we dive further into the merits of narrative storytelling, let’s review a few key points:

1.)  Sales Productivity of B2B Sales professionals is plummeting and some futurists are now warning that B2B Sales professionals will die out in less than 30 years;

2.)  Data patterns are everywhere – inside and outside the CRM systems that sales professionals use;

3.)  Storytelling approaches create a rapid foundation for cultural ingestion and learning and can go “viral” rapidly to inform cultural DNA about optimal pathways forward; and

4.)  Gen X’s and Gen Y’s trust technology and data interpretations more than the millennials to guide them to the desired “ask me” outcomes (i.e.: guide me to the best travel destination, get me to the fastest route avoiding traffic, pick the best songs or movies based on my tastes, find me the best mate based on my interests, find me the right car given my parameters, so why not find me the fastest route to make my sales plan using predictive analytics helps me see more to win more and make my quota, or simply advise me on what I should purchase based on my specific parameters, and place my order directly.”)

For over 15 years, I have heard that the impact of data required is not just good quantitative analysis, but also strong interpretation visually and orally. In other words, data without a story cannot be easily understood. In addition, not all learners learn the same way, some need visual + auditory + text and also repetition to get the full context, others may only need one method. Every adult learner is unique, but more important they learn from experience, role modeling, and 10,000 hours of practice, and a solid dose of positive reinforcement. In the rapidly changing world, learning with agile practices is key to rapid business growth and human performance acceleration to cope with the continual change waves.


Learning Dimensions of Stories 

Stories have different learning dimensions to them. Here are a few types:

Time based stories:  Stories can be about the past, present, or future. The most common type of analytical story is about discussing the past. Examples are annual reports which describe what happened last quarter or at year-end. They are like driving in the rear view mirror, and not the most valuable form of story.

OSlide1ther stories about current affairs are evident in market research surveys, which analyze current trends, or patterns to gain more insights on what people are thinking about, or creating context on future outcomes. For example, gallop polls on elections are a type of story predicting political candidate outcomes.

Future based stories – Stories about the future are based on predictions; and in the world of business predictive analytics is a primary method to predict futures. Predictive analytics take data from the past and generates based on valid historical outcomes, creates an informed statistical model, which is then used to predict the future. Examples in sales include: what customers are likely the best to pursue based on the quality of the leads sourced, history of the customer relationship, or in marketing what customers are likely to purchase or in customer service, what customers are likely to churn given signals visible in the data, based on similar transactions. What is key is the volume of history and underlying quality of data to be able to see the insights.

These types of predictive stories involve assumptions (notably that the future will be like the past in some key respects) based on statistical probability of a representative data sampling. The good news is that these methods are incredibly reliable, especially when an expert data scientist provides further validation and the model continues to learn and adapt as new data is entered further evolving the training model(s), with machine learning methods. If a vendor says everything is self-generating with not data scientist human judgment discovery on large global data sets, be alarmed, as our experience is that every company customizes their opportunity objects in SalesForce or Microsoft and many field attributes have no relevance to predicting a successful sale but the data may be filled in automatically or by a rep so the pattern is picked up as often being a statistically valid field, when in fact it is simply a variable of no use to predictive analytics. Some scrubbing is more often needed, but instead of months we are talking less than a couple of days.


Other key attributes in determining story patterns

Stories have a pattern as well that is important to architect into sales analytics. For example, are you trying to tell a ‘What’ story- which simply elaborates on what happened and the lack of insight. ‘Why’ stories go into the underlying factors that may have caused the predicted outcome. ‘How’ stories given more context on how to address the problem and explore various ways to improve the situation identified in the what and the why stories.

A complete story may have all of these focus elements. Designing analytic methods that engender all these elements are important in developing approaches to increasing sales performance using narrative story telling methods.

Finally, there are different types of stories based on the analytical methods that are used. Correlation stories examine the inter-relationships among variables, while causation stories will examine the impact one variable will have on another variable – what is important is to explain in narrative text what the visualizations are saying so clarity of the story can be interpreted due to the different adult learning styles.

This is an area, which is not mature in current predictive analytics methods provided by most vendors developing cognitive sciences and narrative selling methods.

A way to look at this is excellent narrative stories don’t get told without a level of skill in the art of storytelling and understanding the attributes of what makes up a good story and how to architect data insights into narrative stories to increase sales performance levels.

As discussed prior, before humans knew how to write, probably before they even had language, the means for passing wisdom from one person or one generation to the next was storytelling. Most likely our brains are wired to respond to and retain stories (though, oddly, not necessarily proficient at telling them). Nevertheless, it remains perhaps our most powerful tool of communication. Humans are generally more interested in a story than in the storyteller. In other words, if you want to get your point across, you need to learn how to condense the data into a good story.

Analytical tools offer no real assistance here. Storytelling is a craft that has to be learned, I’ve seen the word “storytelling” used a lot lately in analytical circles, but I’m afraid there is a lot of learning that is needed.

There is no doubt that a successful Data Scientist must be proficient in programming, modeling, and data modeling (extracting, cleaning, and feature engineering data).  However, there is another key skill that is often overlooked:  the ability to communicate findings clearly and effectively. If a Data Scientist cannot motivate the business buy-in to effect change, the powerful AI data sciences model will only collect dust on a shelf.  Stakeholders will only trust the model if they understand the value it adds, what has been done to create it, and why it works.  Business users should not be left to trust a data sciences “black box” blindly, without understanding the richness of the data model parameters. The journey is all about connecting the dots and moving business professionals to execute a successful action based on the learning insights.

The solution is data storytelling: using the power of narrative to communicate the findings in a way that resonates with your stakeholders.  Taking this approach by combining data science expertise with intuitive visualizations and most importantly, a story to connect the dots, the outcomes will be far more successful in end user adoption.

Data storytelling frequently employs data visualizations, but it involves much more than presenting a graph. Data visualization is often static: a chart may represent a single facet of the data, or layers of features for a more complex concept.  Or, it can be an interactive dashboard where the viewer is free to experiment with different scenarios and reach their own conclusions. At SalesChoice, we have integrated simulation capabilities into our software approaches and have experienced the increased management experimentation that these more agile methods enable. The augmentation of simulation with powerful graphical stories allows professionals to explore and shape their inquiries to solve specific problems they are seeking to learn from to optimize their sales results.

Data storytelling takes these ideas a step further.  It guides the viewer through the process of formulating a question and leads them towards the desired conclusion in a step-by-step fashion. In short, it takes the viewer on a journey through the data.  This difference between data visualization and data storytelling is captured in Moritz Stefaner’s analogy comparing data visualization to portraits:

“[Data] can reveal stories, help us tell stories, but they are neither the story itself nor the storyteller. Portraits have no story to them either.  Like a photo portrait of a person, a visualization portrait of a data set can allow you to capture many facets of a bigger whole, but there is not a single story there, either. Data storytelling marries data visualization with a guided narrative.  It pairs the data and the graphics with words, not only describing what can be seen in the image, but also telling a story to lead you through the analysis process.  A narrative “is the way we simplify and make sense of a complex world,” and data is certainly a complex world to understand.”


Predictive Narrative Story Telling Innovation for Sales Growth

Slide2When I founded SalesChoice, I had a big dream to help businesses, small to large do BIG things with their data to inform them of new possibilities. Over the past three years, we have now successfully developed three products, which do big things for our clients.

  1. Predictive Analytics – Predicts sales outcomes reliably at over 85% of the time based on quality data sets before the outcome happens, and prioritizes all client sales cycles into prioritized rankings in order to increases their odds of winning.
  2. Prescriptive Analytics – Provides insights on the reasons for the wins or losses – giving context on what changes can be made to alter or improve outcomes (i.e.: Alter a discount, or pricing amount, or augment a different product solution that can improve odds of winning)
  3. Propensity to Purchase Signal Detection – Provides insights on Surge Patterns on the world-wide web and identifies geographic locations relevant to a company’s products /solutions that are relevant to a buyer profile, increasing lead relevance for sales harvesting, and provides insights on micro-segments that historically have never been available to B2B Sales Professionals.


A Predictive Value Story: Macadamian Technologies

One of our B2B SalesChoice Professional Services’ customers, Macadamian Technologies, for example, statistically made the decision to only pursue sales opportunities that our SalesChoice Predictive Insight Engine™ prioritized in their SalesForce CRM opportunities either as an A, B, or a C.

An executive decision was made that any opportunities that SalesChoice classified as a C were not allocated out a B2B budget or to be pursued. The vision by management was clear – we want to go after high odds of closing based on winning patterns in the underlying data set, i.e.: the lead source, product/solution type channel or industry relevance are some of many custom factors that SalesChoice can mine to predict sales outcomes successfully.

The impact over 18 months resulted in a conversion rate increase from 35% win rates to over 65% win rates. The investment in predictive analytics, generated millions of incremental revenue to Macadamian Technologies, based on analyzing over 8 years of historical SalesForce data, and with strong focused leadership by their VP of Sales, Dinesh Kandachantha, the outcomes were incredibly powerful. See his video here for more information. (Source: Video :


Selly Says

 We have also been experimenting further with introducing a unisex Sales Learning Character, called Selly Says, which looks at sales data patterns and provides insights in unique ways to guide sales professionals to perform at higher levels. The coaching patterns are surfaced by our proprietary and unique AI and machine learning methods and the insights are also validated into loosely defined rule(s) that can further drive cognitive learning based on parameters of business value. We are starting to also explore having human feedback loops (i.e.: Acknowledging Selly when doing good work can also further inform the Algorithm).

For example, we advise a sales rep when his or her patterns are either positive or negative in informative email alert notifications with a language and visual illustration that is simple and easy to understand i.e.:

  • Hi Cindy, Selly here™, you have three opportunities that are progressing nicely, they are positive early bloomers, or
  • Hey, Glen, those three COMITs you have just submitted, Selly™ is not as confident as you are on closing, given similar patterns in the history. Check the reasons here (1. Amount is higher than any similar sales cycle in the past – the average range is $100,000 and you have proposed $1,000,000. Is this an entry error or something you want to reflect on?)



David Bjoe. Perspectives on impact of stories creating culture. (Source :

Thomas Davenport, Expert on Analytics and Big Data. (Source:

LeAnna Kent. Data StoryTelling: Bringing Life to your Data (Source:

Thaler Pecker. A Storyteller expert-discussing story attributes, (Source:

David Snowden, Expert in Cognitive Sciences and StoryTelling. (Source:

Berkeley Warburton, The Age of Distraction: Accenture White Paper, Fall 2016. (Source: