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Blog #6 – Part 2: Narrative Story Telling enhances Sales Performance

B2B Sales productivity has hit an all time low in performance, and has dropped 15% over the last ten years, now running on 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 has radically changed.
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 and are ready to make a 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 do not want to speak to a generalist. This change in customer buying behavior is 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 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.

Stuck In The Past When You Can See the Future? B2B Sales Must Embrace AI Guided Selling Today or Be Left Behind!

“Are you going to hit your sales target this quarter?”

This question holds true for quota carrying B2B Sales Professionals. There is nothing more important than closing the right deals faster and being smarter.

Sales is hard and not getting any easier as sales leaders today find themselves strapped for time and are often focused on activities – not conducive to successful conversion or win rates. 30-60% of sales reps in the mid-tier do not meet their sales targets or forecast accurately, while customer facing productivity has declined from 50% to 36% over the past five years (Accenture, CSO Insights).

Adding to the noise in the sales process, the B2B sales cycle has been turned upside down and sales teams are scrambling to keep up with the changes. Sales and marketing leaders have not adapted rapidly enough, and hence, productivity has dropped dramatically, resulting in new questions like: Will robots be more efficient?

How did we get to this point and how can sales professionals leverage Artificial Intelligence, ‘AI’ guided selling to maximize sales performance?


The Game has Changed

Traditionally, sales teams would engage buyers early in the sales cycle to introduce features, competitive comparisons and negotiate price. Although buyers become aware of products and services through various marketing channels and do like to hear from vendors early, they do not look to sales for the bulk of their intel gathering as they are already through their research cycle, more often than not. Today, most information can be easily acquired on-line and buyers now rely on sales mostly to discuss solution fit and pricing terms before making purchasing decisions. The following stats aggregated by Spotio further demonstrate how buyers acquire their intel:

  • 84% of CEOs and VPs use social media to make purchasing decisions (IDC)
  • 80% of business decision-makers prefer to get company information from a series of articles versus an advertisement. (B2B PR Sense)
  • 77% of B2B purchasers said that they would not even speak to a salesperson until they had done their own research (The Corporate Executive Board)
  • 67% of the buyer’s journey is now done digitally (Sirius Decisions)

These changes have driven sales and marketing organizations to make significant investments in digital platforms to track and respond to buyers. Talk about pressure, what options are available? Its almost like you have to be superman or superwoman to hit your number.


Digital Age of Sales Enablement 

As we all know, sales professionals only have a certain number of hours in the day and certainly do not have the time combing through data. They are incentivised to sell, not conduct hours of research. Time is better spent on engaging buyers and building relationships necessary to either acquire new customers or maintain existing ones. It makes sense to arm sales professionals with quick, actionable intelligence to prioritize their sales cycle, help them forecast more accurately, and allocate scarce time more wisely. This guides them to successfully win outcomes and stop wasting time pursuing unproductive sales cycles.

Enter a plethora of sales apps designed to deliver information expediently to sales pros with the click of a button. No more wasting time acquiring necessary client background – there’s an app for that. No more asking your colleagues for internal documentation – there’s an app for that. No more losing time and money chasing after the wrong deals by forecasting inaccurately. The price of a miss is very high and impacts investor and employee confidence. Finding the right trusted and explainable AI solution is key to shifting tides in purchasing patterns and buying cycles.

It must be emphasized that purchasing an app is one thing, but having the company adopt these changes is a different matter altogether. Business leaders must drive digital transformation and work to evolve their organization into an analytically focused culture, strategically positioned to grow revenues predictably. So often CRM’s have poor data completeness that adoption is impacted. Estimates are that more than 60% of CRM investments are not offering a positive ROI, so to win, finding solutions that drive more value on the CRM stacks is key to execution success.


Moving from Big Data to Better Data

‘Big data’ was one of those catch phrases ubiquitous in the blogger sphere for a few years. Companies spent a great deal of time and resources gobbling up as much data as they could conceivably hold. Now the challenge becomes what to do with all of this Data. AI has been one of the most significant advances to sales enablement, even if it is not fully understood. Without having one’s eyes glaze over, think of AI as making sense of huge amounts of information, finding success patterns and predicting outcomes. The same way Amazon sends you product options based on your purchases or Netflix suggests movies to watch, AI can help guide sales professionals with predictive and powerful insights.

Customer Relationship Management tools ‘CRM,’ for example, offers mountains of juicy insights waiting for sales to uncover and pounce on. Data in most CRMs tends to be incomplete, misleading or lacking substantive guidance for making decisions. This is merely a consequence of a big data platform without proper validation of data. Understanding data historically has now shifted to further validating, organizing and leveraging intel to see not only what has happened but what will happen. AI can validate data quickly and efficiently, providing B2B sales with key predictive KPI insights into pipeline, feedback on accounts, opportunities and helps prioritize time to focus on activities necessary for closing the right deals.

Below are some of the benefits of using AI technology that we have learned from our proven client deployment, to improve sales performance:

  • Predictive Forecasting: Improve revenue forecasting and provide greater awareness on what is likely to happen using valuable data insights
  • Opportunity Scoring:Increase top line revenue growth by validating opportunities and increase insights on propensity to purchase
  • Account Scoring:Clean data sets in CRMs to identify the gaps in your data patterns and provide account intelligence on the most important accounts
  • Predictive Pricing:Track price and discount ranges to make assessments on pricing
  • Sales Enablement Training: Improve velocity of talent learning and improve productivity levels of sales

Just as Salesforce revolutionized customer relationship management, so too can AI guided-selling bring B2B sales performance to the next level. AI sales approaches yield on average an ROI of over 250%, compared to rear view BI approaches. No one today can afford to spend the time looking in the rear-view mirror.

More than an app it’s about creating a company culture around smarter analytics to support sales professionals, so they can beat their numbers quarter after quarter. This tighter focus and leveraging AI Guided Selling solutions is one of the modernized approaches that we have seen that works for our clients. It’s simply all about Seeing More to Win More.

Written by: 

Zoltan Lorantffy, Chief Growth Officer, SalesChoice


Edited by: 

Dr. Cindy Gordon, 

CEO and Founder, SalesChoice Inc.

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