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AI For Sales - Business Examples

Authored by Ric Ratkowski on August 16, 2019

 

 

 

 

 

 

 

 

The last seven blogs provide a business-case approach to infusing AI through the entire sales process.  One of the purposes of this blog series was to demystify AI by using AI for Sales as an  example. 

Media tends to portray AI as a magic bullet.  Actual implementations show the less “black box” an AI solution is and the more “glass box” approach, the more utility it provides to the user.  The utility is created by uncovering the key drivers of an outcome and managing those drivers to increase results.

This blog summarizes the business cases by key sales challenges and and describe an approach to applying AI to solve these challenges across the following areas:

  • AI for Sales - in the Marketing Funnel
  • AI for Sales - Enhanced Data Collection
  • AI for Sales - Guided Selling
  • AI for Sales - Sales Pipeline Management and Coaching
  • AI for Sales - Numerically Calculating the Sales Forecast

AI for Sales - In the Marketing Funnel

AI for marketing covers many different areas.  The  following three use cases focus on applying AI in the marketing funnel for optimizing lead generation and understanding marketing attribution.

Challenge

AI Use Case

  • Managing a consistent view of the opportunity and all the related contacts/leads when companies have various names, email domains, email addresses.
  • Create methods for collecting sales interactions and apply algorithms to match multiple email domains and sources of information to specific leads and matching with multiple email addresses to leads to companies and opportunities.
  • Identifying the first substantive touch of a lead for the current buyer/seller journey.
  • Apply past sales patterns across opportunity segments (i.e. close-won, vs close-lost, industry, company size, etc.) to classify interactions as significant for the current buyer/seller journey across time.  
  • Understanding where marketing money is wasted on campaigns and activities that provide no benefit to the sales cycle for either attracting  leads or nurturing opportunities. 
  • Apply AI against all marketing and sales touches to understand which touches impact all close-won opportunities, which touches have never impacted close-won opportunities and understand the campaigns in context to the gray area in between. 

 

AI for Sales - Enhanced Data Collection

The following three use cases are designed to help the selling team better manage sales opportunities by having better information through automated and enhanced data collection into CRM systems for emails, meetings and other sales interactions. 

Challenge

AI Use Case

  • Managing a consistent view of the opportunity and all related contacts/leads when companies have various names, email domains, email addresses.
  • Apply AI when collecting sales interactions and apply algorithms to match multiple email domains and sources of information to specific leads.  Also apply AI to match leads having multiple email addresses to specific leads and then to companies and sales opportunities.
  • Understanding the typical buying team and insuring all team members are identified early in the sales cycles.
  • Use past sales patterns to identify the typical roles involved in close-won opportunities and alert the sales rep to missing members of the buying team early on in the sales cycle.
  • Consistent interpretation of the “tone” of emails, calendar items and meetings to accurately interpret opportunity health. 
  • Apply natural language processing[NLP] to rank the sentiment of an interaction.  Allow seasoned sales reps to also grade interactions as a method to refine the NLP lexicon for specific industry and company terminology. 

 

AI for Sales - Guided Selling

The following three use cases are designed to help the selling team better manage sales opportunities through guided selling.  

Challenge

AI Use Case

  • Understanding the appropriate sales process and steps for different sales scenarios (personas, products, pain points, industries, etc.).
  • 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.
  • Apply AI against past close-won sales patterns to 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”.
  • Apply AI against past close-won/close-lost sales patterns to Identify required sales stage changes, either forward or backward in the sales cycle based on an unbiased interpretation of the sales interactions.

 

AI for Sales - Sales Pipeline Management and Coaching

The ultimate challenge of sales pipeline management is to understand what is “real” and what should be removed from the pipeline.  It is easy to add opportunities to the sales pipeline, but it is a much harder decision to remove them. Many sales pipelines contain opportunities that have been forgotten, delayed, or became multi-push zombies. 

This problem can be resolved through the sales pipeline management and sales coaching use cases identified below:

Challenge

AI Use Case

  • Understanding the “real” opportunities in the sales pipeline and which ones have the highest likelihood of closing in this sales period.  It is simple math, if a sales rep has 50 opportunities working at one time, with an average of six decision makers on each opportunity, they have 300 data points/perspectives they need to manage.   
  • Identify the top opportunities with the highest likelihood of closing via an AI driven “Opportunity Health Score” that takes into consideration both positive and negative aspects of the sales interactions.
  • Understanding where deals are stuck in the pipeline so the appropriate activities can be taken and/or coaching on these opportunities can occur.  The more effective you scrutinize the opportunities – removing what doesn’t belong – the more predictive and confident you will be in your sales forecast.
  • Provide statistically derived opportunity insights to flag certain situations as “at risk” or “accelerators” to the opportunity.
  • Understanding the fluid buying cycle.  Knowing how to coach sales reps to develop their sales skills and learn what drives customers to move from one buying activity to the next.
  • Provide statistically derived sales rep insights to help sales management coach a sales rep to become better closers.

AI for Sales - Numerically Calculating the Sales Forecast

When you hear the phrase “AI for sales forecasting” it feels like the solution is an algorithm to predict “A sales number”.   The previous four areas of business cases build the foundation for applying AI to calculate the sales forecast.  The following three challenges and applications provide detail business examples.   

Challenge

AI Use Case

  • Understanding the impact of opportunities not yet created at the beginning of the sales cycle that will close by the end of the sales cycle.
  • Apply AI to understand opportunities generated and closed in the same sales period based on past sales patterns.  
  • Understanding sales rep biases vs actual track record for predicting what they are going to close in the period on a sales category/rep commit type forecast.

 

  • Use AI to assist in the generation of the sales rep forecast (category forecast) by providing benchmarks and patterns from past sales periods that suggest:  the percent to use to mark up or down the sales rep forecast based on historic sales rep biases; when an opportunity fits the profile of a “rep commit” but hasn’t been categorized by the sales rep that way.
  • Understanding how time degrades the weighted sales pipeline forecast.
  • Provide an AI based weighted [stage based] based on past sales, time patterns and identifies specific opportunities to close within sales opportunity clusters to provide a sales pipeline based sales forecast.

In Summary

Since this is the last blog in the series, I will be starting a new series next month looking at the complexities around Sales Applications that integrate on top of CRM solutions.  The next blog series will focus on how to balance the risks of data integration and provide specific examples of advanced application imbedding to mitigate and de-risk a complex sales application stack.

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