<img src="https://ws.zoominfo.com/pixel/4z2e82sWyhmveGlCzuJa" width="1" height="1" style="display: none;">
Untitled presentation (1)

AI for Sales - AI/Machine Learning Primer for Sales

Authored by Ric Ratkowski on January 9, 2019


I had planned my blog “AI for Sales - In the Marketing Funnel” to be posted  before now.  It was a challenge writing it because I had to include terminology and background in Artificial Intelligence (AI), Machine Learning (ML), and Advanced Analytics, to set the right perspective.

This blog takes on the task of trying to level set around terms and characteristics of Artificial Intelligence(AI) and Machine Learning(ML).  My next blog, [in two weeks] will discuss the AI for the marketing funnel.


At best, I may be considered a citizen data scientist.  I merge my background in business, with a bunch of data and technology to help “figure things out”.  I do this by analyzing patterns in the data that supports different business situations to identify “ahhhs”.  My tool of choice is R because there is a lot of help and examples available via Google.

Artificial Intelligence vs Machine Learning

The only thing I find consistent about the definitions of Artificial Intelligence,  Machine Learning and Predictive Analytics is there are many different overlapping definitions.  I believe you can put a hundred experts on AI and ML in a room, ask them to define it and you will get 100 similar but different definitions. All will be willing to fight to the death that their definition is right.  

I think of AI and ML and a few other terms like predictive and prescriptive analytics in the broad category of “augmented intelligence”.   That is, independent of the category of technology or algorithms,  the business objective is the same, to help us better understand the business situation by using the data to “figure things out”.  Once it is determined that the algorithms are successful at "figuring things out" you can institutionalize them and automate the decision making or operational processes.

Augmented Intelligence Terminology and Examples

When applying “augmented intelligence” it is important to think about the questions you want it to answer.  This will dictate the approach.  I don’t want to over simplify the methods to answer the question but I think terminology is important.

  • If you’re looking for a numeric answer like a sales forecast you could start with a category of AI called “supervised augmented intelligence” like regression algorithms.  Regression is used when the output is a value.
  • If you're looking to drive categorization of a transaction or opportunity you could start with a category of AI called “supervised augmented intelligence” like a classification algorithms.  An example is having AI provide an unbiased suggestion of the sales stage of a sales opportunity, such as “Sales Accepted Lead”, “Qualification”, etc.
  • Maybe you don’t know the question to ask but would like to learn more about how the business reacts to different actions reflected in the data. These type of questions are categorized as “unsupervised augmented intelligence”. If you want to understand groupings of opportunities you would use “clustering” algorithms.  If you want to understand the rules that describe large portions of you data you would use “association” algorithms. This could be used to describe the activities that separate “OK” sales reps from superstars so I can understand the characteristics of superstars and get all my sales reps performing like superstars.  “Unsupervised augmented intelligence” is a tool to provide “intelligent insights” about my business.

The reason for calling out these categories of questions is:

  • To have a understanding that different algorithms are used to answer different types of questions*.
  • To provide the building blocks of AI, even though when you apply this to a business situation they all munge together.  From business perspective, you don’t care about the algorithms, from a design perspective, you do.
  • To understand AI is not a magic bullet where you throw in a bunch data and outcomes insights to run with.  Although software applications should insulate the user from all of these techniques.

In many cases the answer provides less utility than understanding the drivers of the answer.  As an example, we use regression analysis to provide a sales forecast** based on marketing and sales activity.  The forecast is just a number.  When I first start using it, I probably don't feel comfortable using the AI forecast as my target for next quarter.  What I would really like to know is what drives that number.  What marketing programs and sales activity really work so I can understand the levers I have to drive sales.

As an example, we are going to look at a regression formula that forecasts sales based on marketing and sales activities.  In the example, X1, X2, Xp represent the quantity of different marketing and sales activities the sales leads/opportunities have participated in. b1, b2, bp represent the impact each of those activities have on the sale process.


Although the formula produces a sales forecast[Y], just as important is b1, b2, because that determine how much impact a marketing or sales activity has on the sales forecast.  If b1, b2,...bp  is zero or close to zero it means it has little impact on Sales (Y).  You could have customers/prospects with 100’s of those activities and it doesn’t move the sales needle.  Likewise, if b1 is a large number, it says X1 has a big impact on the sale and you would want to maximize X1 to drive overall sales.

AI Should Not Be A Black Box

Marketing hype tends to portray AI as a magic bullet, plug it in, turn it on and let it do everything for you.  Actual implementations show the less black box it is, the more it is use for augmented intelligence, and the more utility it provides the customer. The utility is created by uncovering the key drivers and managing to them.  The following are two examples: 

Augmenting Decisions

This first example shows how AI can augment decisions.  In this example, the AI calculated sales forecast*** is set alongside manual and system aggregations for self reporting, manager reporting, quota, and pipeline to provide an unbiased perspective of the direction of sales and what the system thinks can be accomplished.     

AI is not a Black Box - sales forecast example

Business value of AI can be enhanced by providing drill down and reduce the Black Box nature of the forecast.  The above example shows the sales team under William Burris. Drill down allows me to interrogate the results at the sales rep and opportunity level and help “confirm” or “deny” the AI forecast and feel more (or less) comfortable in it.  If you don't feel comfortable with it, there are also levers to pull to change the AI forecast to "war game" key drivers.  This helps understand the pattern AI sees in the data and sensitivities to the forecast.  

Uncovering Drivers

The second example shows the sales opportunity health score.  It includes the score and the key activities and characteristics that drive the score.  Rather than just knowing that the score is high or low, you understand what the system see’s as important and the activities required to increase the health score.  The individual calculations that drive the health score are also used to create alerts and can be used as a basis for prescribing next steps for this opportunity. In this example all values add, but you can also have activities that are detractors of the health score (no customer interaction in the last two weeks could be a detractor).  Although not shown here, time is also a consideration in these calculations.  Activities three months ago may have little impact on the current score.

AI for Sales - Sales Opportunity Health Score Example


Key Ingredient for using AI Successfully

A key ingredient for using AI is having lots of observations.  In the sales process this means collecting as many interactions between the buyer and the seller as reasonably possible.  If sales reps provide little information about their interactions with the prospect it will be hard to apply AI in a meaningful way to provide accurate forecasts, next steps, judge pipeline health, etc.  In a subsequent blog we will review AI for helping collect interactions.

A second key ingredient is time.  Positive activity (emails, positive phone calls, web visits) that happened this week is a good thing (probably).  If that same activity was a month or two ago and nothing has happened since, it probably signals a problem with the account as it feels like they are less interested.  Algorithms have to have a time component.

This is the second blog post in a series of posts focusing on AI for Sales.  The first post is here.  My next blog will be about “AI For Sales -- in the Marketing Funnel”.  It will be out the week of 1/21.


*   For more information on algorithm categories check out this post “Which Machine Learning Algorithms Should I use?

**  This is a simple forecast for example purposes, actual AI driven sales forecasts use additional time based algorithms as well as levers to "war game" results to understand sensitivities. 

*** The AI calculated forecasts in this example are more complex than just regression analysis used in our example above.

Subscribe to our newsletter!

Leverage these best practices with automation and AI driven by TopOPPS

Learn how our customers are winning with artificial intelligence in their CRM:

  • Predictive Sales Forecasting
  • Automated Pipeline Management
  • Significantly More Updates from Reps

Watch Videos

More Recent Posts:

June 29, 2020

Best Practices for B2B Sales-Pipeline Monitoring & Closing Deals

This blog provides more details into the final two of the six tactical best practices outlined in the blog “Best Practices for B2B Sales - Sales Pipeline”.  It focuses on: Monitor the sales pipeline from different perspectives depending on the users role (sales rep, sales management, sales opperations, sales leadership) Focus on closing the best leads, nurturing the new leads and “close loss” opportunities as soon as possible Monitor the pipeline from different perspectives  The sales pipeline is the beating heart of the sales process and the revenue engine. ...

Sales Pipeline Management, AI for Sales, sales best practices, Sales Pipeline Visibility

June 17, 2020

Best Practices for B2B Sales - Proactive Follow Up & Content Delivery

This blog provides more details into the next two of the six tactical best practices outlined in the blog “Best Practices for B2B Sales - Sales Pipeline”.  It focuses on: Proactive follow up on opportunities in the sales pipeline Proactive delivery of the right sales content and insights to members of the buying team and guidance to the sales team It may feel like these two best practices could be consolidated into one called proactive selling. Both could be handled via alerting and prompting solutions or optimized with a process we call “Alerts Requiring Action”.    However, there are subtle differences between the two that make a big impact on sales.  ...

AI for Sales, Guided Selling, guide winning behaviors, sales best practices, Sales Pipeline Visibility

May 15, 2020

Best Practices For B2B Sales-Sales Pipeline Data & Process Improvement

This blog reviews best practices related to two of the six tactical best practices outlined in the previous blog “Best Practices for B2B Sales - Sales Pipeline”.  It focuses on: The ability to easily update the sales pipeline on a regular basis Sales process review and improvement An accurate sales forecast requires an accurate, timely and always up-to-data sales pipeline.  This data includes up-to-date: sales activity and buyer/seller interactions; buying team members and roles; opportunity descriptors and attributes; and company descriptors and attributes, in the CRM.  It also includes integrated reporting from the CRM to produce the sales pipeline, porting CRM information to Excel is not an option. ...

Pipeline Optimization, Sales Pipeline Management, AI for Sales, sales best practices

April 28, 2020

Best Practices for B2B Sales - Sales Pipeline

In my original blog in this series “Best Practices for B-2-B Sales,  I outlined five categories of best practices: The previous five blogs in this series, reviewed the best practices related to data.  The next three categories, Sales Process, Opportunity Management and Pipeline Analytics all are driven off of the sales pipeline. This blog introduces 6 different best practices related to the sales pipeline.  These best practices cut across all  three areas. ...

Sales Pipelines, Sales Process, Sales Tools, sales best practices, Sales Pipeline Visibility

April 14, 2020

Avoiding A Horrific June Sales Quarter: Sales Pipeline in Crisis

Our current economic climate feels all too similar to the great recession of 2008.  We knew the story back then, “Work harder and sell less.” That is all we could do, we didn’t have the technology to help mitigate the impact of the great recession by better sales pipeline visibility and better cost control by understanding what is “real” in our sales pipeline. This time around we will have to work just as hard, but if we work smarter with the help of technology we could even the playing field and even sell a bit more. ...

Sales Pipeline Management, AI for Sales, Sales Pipeline Visibility

March 28, 2020

New Times, New Ways to Run the Sales Team!

Jim Eberlin is founder of TopOPPS.  Jim posted this blog on Linkedin.  It was just a few weeks ago that I was preaching that in order to stay on top of things to give my team and customers what they needed, solve problems and make sure things don't fall through the cracks - I had to be in person.  Obviously that has changed - like it or not. ...

Sales Pipeline Management, sales best practices, Sales coaching, Sales One-On-Ones

March 23, 2020

Tactical Best Practices for B2B Sales-Company Attributes

This blog reviews the tactical best practices for collecting information about accounts [companies] where you have sales opportunities or are targeting for a sales opportunity.     This blog is the fifth blog in the series “Best Practices for B2B Sales”.  The first blog organized the best practices along five key areas. The second blog divided up the first area, data access, into four key areas.  This blog focuses on the last area of data access and collection. ...

Artificial Intelligence, AI for Sales, best practices

March 9, 2020

Best Practices for B2B Sales-Opportunity Attributes & Buyer Team Tracking

This blog reviews the tactical best practices for collecting information about a sales opportunity [opportunity attributes] and information about buying team members and roles.  Much of the information about the sales opportunity is not available electronically. The objective is to make it as easy as possible for the sales rep to enter it and provide benefit to the sales rep by presenting it in an intuitive manner to keep both sales reps and sales management up to date on the opportunity. ...

Artificial Intelligence, AI for Sales, best practices