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AI for Sales - Guided Selling

Authored by Ric Ratkowski on April 16, 2019

 

The next two blogs cover AI for the sales pipeline. 

This blog focuses on AI for guided selling.  Guided selling is the process of prescribing optimal sales execution steps to the sales reps to enforce an efficient sales process.  The tasks and process are based on sales patterns of past close-won, and close-lost opportunities.  Guided selling is key to maintaining a realistic sales pipeline.

The next blog will focus on sales opportunity management and sales rep coaching.  This can also be described as sales pipeline management and sales pipeline hygiene.  It includes tools to: understand deals with the highest likelihood of closing; detail buyer/seller interactions; and support the weekly 1:1 sales call. 

The ultimate objective of both of these areas is to understand sales pipeline reality - is the pipeline “real”.

Current State of Guided Selling

Selling is hard.  Being successful is even harder.  Sales reps have to keep track of the current state and next steps for many different sales opportunities. These opportunities have different objectives, could be focused on different products and need to solve different problems.  In addition, sales teams typically don’t have enough collective tribal experience from closed deals to know what works and what doesn’t in different sales scenarios.

The sales process is further clouded because sales team like to think they own the sales process.  They have a sales process that defines the progressive steps to bring a sales opportunity from initial lead through close-won.  However, in most cases there is very little similarity between the seller defined sales process and what really takes place.  The reality is the buying team purchases the product when they have completed all their necessary tasks to feel comfortable with their purchase decision.  The buyer controls the sales process.

Although the sales team doesn’t own the process, they can use AI to understand common traits and tasks of past closed deals and anticipate and manage the sales cycle to the typical processes of close-won opportunities.  They also use the standard sales process vs current buyer sales process to understand when the opportunity is in trouble.

Three AI Use Cases for Guided Selling

The following three use cases are designed to help the selling team better manage sales opportunities through guided selling.  The table below maps key challenges in the sales process to specific AI use cases.

Challenge

AI Use Case

  • Understanding the appropriate sales process and steps for different sales scenarios (personas, products, pain points, industries)
  • Identify the historic actual sales processes and cycles for the different sales scenarios, to optimize the process for the buyer requirements
  • With so many opportunities, and so much noise around the sales processes, it is hard for the sales reps to focus on critical next steps
  • Guide the sales rep and enforce sales rep behavior to focus on typical next steps in the buyer journey
  • With so many interactions around an opportunity it is difficult, and time consuming for the sales manager to develop their independent impression of the current sales stage and must rely on the sales reps “best guess”
  • Identify required sales stage changes, either forward or backward in the sales cycle based on an unbiased interpretation of on the sales interactions.


The first use case of AI for guided selling helps the sales reps apply the historic actual sales process to a current opportunity based on the particular sales situation.  It is accomplished by applying classification and regression algorithms against the various tasks and processes that were part of historic closed-won and closed-lost opportunities.  The objective is to understand the signals, triggers and time frames resulting in wins and losses.

Technically, the algorithms analyze close-won and close-loss opportunities across 20+ opportunity attributes and compare them to dynamic baselines of effectiveness measures.  This information can then be extended to current opportunities to understand when opportunities are on track, stalled or lost. 

This is communicated to the sales rep via a horizontal stacked bar graph with two bars.  The horizontal axis is time.  The stacked bars represent the different sales stages.  The top horizontal bar represents the historic sales cycle for previous close-won opportunities in the same sales scenario.  The bottom horizontal bar shows the current bar of sales cycle stages. The stacked bar within each stage represents the time in days for each sales stage.   This allows you to compare the historic sales cycle for this sales segment to the current sales cycle.

The second use case of AI is to provide process guidance and enforcement via alerting and allowing the rep to take action on the alert in the same interface.  The alerts prescribe the next and best action to take with an account and highlight red flags on the opportunity.  Alerts can be both predictive - generated by AI or prescriptive - generated by manual rule creation.  Manual rules help accelerate the learning process around infrequent by significant events in the sales process. 

The two screens below highlight the alerting process and how to take action on an alert.  The screen on the left is a series of predictive and prescriptive alerts on the opportunities.  This needs to be available at the sales rep, sales team or sales org levels and is shown based on the filters and security rights applied to the current pipeline view.  The screen on the right shows the results of clicking on the “eye” of an alert to take action to resolve the alert by making the stage change.

Screen Shot 2019-04-16 at 2.48.16 PM

Ideally, before the sales rep weekly 1:1 meeting with their sales manager all the alerts are resolved.  Alerts are key to the guided selling process.

The third use case of AI is to prescribe sales stage changes either forward or back in the sales cycle based on the type, frequency and content of buyer/seller interactions. These interactions are compared to the baseline of typical close-won opportunities for this sales scenario.  They provide an unbiased interpretation of sales activity.  If the current opportunity is achieving advanced milestones and having advanced interactions the AI engine may suggest the sales opportunity be move up to a higher sales stage.  If the current opportunity is not having the right interactions with the right personas/prospect roles/milestones it may suggest the sales opportunity should be moved back in the sales stage.    

These stage changes could be thought of as a higher-level alert and part of the second use case.  It is important to call out a key aspect of AI.  AI is not a singular application that sprinkles pixie dust on data and creates recommended next steps and alerts out of thin air.  AI is a series of different algorithms that perform specific tasks and answer specific questions.  Think of it as layers of algorithms that are applied when information changes.  Some of the algorithms look at dates and activities to determine if the close date is reasonable based on past history.  Other algorithms look at changes in milestones and sales interactions to determine if the opportunity is in the right stage. Still other algorithms may be designed to classify and segment historic close-won and close-lost to build statistical correlation models to be used the first two algorithms.

In Summary

AI to support guided selling is an important aspect of AI for Sales.  Alerting and resolving the alerts through action is a key component to drive a consistent guided selling process and maintain a realistic sales pipeline.

Gartner calls this out as one of the impacts in their research “Optimize Sales Execution With Artificial Intelligence for Guided Selling, 2019” by stating:

“AI functionality has permanently transformed the guided selling capabilities that sellers use to engage with prospects, manage deals and generate quotes”

Check out this link to see how these three use cases can be addressed with software. 

Fifth in a blog series on “AI for Sales”

This is the fifth blog in the series - AI for Sales Forecasting. 

The first blog: Artificial Intelligence [AI] For Sales Forecasting

The second blog: AI for Sales - AI/Machine Learning Primer for Sales

The third blog:  AI for Sales - In the Marketing Funnel

The fourth blog:  AI for Sales - Enhanced Data Collection

The next blog will be on: AI in the sales pipeline to provide insight to understand sales pipeline reality and to coach the sales rep.

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