7 Sales AI Use Cases for the Salesforce to Accelerate Revenue Growth


  • Artificial Intelligence (AI) has achieved celebrity status in the CRM world – and for good reason. CRM systems such as Microsoft Dynamics and Salesforce are now infused with AI to greatly extend the value of CRM software.
  • Lead prioritization, account intelligence, opportunity insights, automated data entry, guided selling and intelligent forecasting are some of the sales AI use cases that can transform a sales organization.
  • When AI calculates which leads are mostly likely to close, which channels generate the most customer dialogue, what content is most effective in advancing a prospect through the sales cycle, what actions increase the likelihood of winning a deal, what price will maximize the close probability and company margins, CRM software shifts from an administrative tool to a sales advisor that directly aids salespeople and sales managers in their most important objectives.
Johnny Grow Revenue Growth Consulting

Sales technology brings process automation, information reporting and scale to the salesforce. Sales leaders understand technology is an essential enabler of successful selling, but struggle to identify which technologies offer the biggest payback. The leading CRM vendor, Salesforce, did a study to answer this question.

Sales AI Use Cases
Source: Salesforce State of Sales Report, Third Edition

The Salesforce study found that AI is the top technology to aid sales force automation (SFA) and sales performance. More anecdotally we know this is true as we've implemented CRM with AI and machine learning for a few dozen sales organizations. We also know that once a company green lights an AI project, their next question is where to start.

Implementing AI for the salesforce is an effort where the technology takes a back seat to the business objectives and use cases. The starting point for a CRM AI project is to prioritize the use cases that will drive the most important business outcomes. Below are 7 high impact sales AI use cases we have implemented with clients.


Predictive Prospecting

Prospecting is hard but knowing which companies to engage can make it easier.

Intent monitoring can alert salespeople of buyers that show interest in specific products or solutions based on their online research behaviors. These real-time notifications help sales reps prioritize their time and focus on prospects who are in-market right now.

AI can further calculate fit to pare down all potential buyers to those most qualified. AI fit analysis considers how the prospects firmographics, demographics, technographics, behaviors or customer lifetime value (CLV) match up to the company's Ideal Customer Profile (ICP). This is a type of prospect propensity modeling that prioritizes leads so salespeople avoid chasing deals they are not going to win and focus on the opportunities they will.

Another AI-based prospecting method is to analyze personal or business connections and relationships from your CRM system or social network (especially LinkedIn) and recommend new leads based on the highest fit target accounts.


Predictive Lead Scoring

There's no bigger priority than converting leads into customers.

However, most leads visiting the website, downloading content or educating themselves are not ready to buy and therefore should be deferred until they become sales-ready. Without a method to separate the lookers from the buyers, a tremendous amount of sales time and company money can be poured into prospects that are not going to make a purchase decision while ignoring prospects who are.

AI has elevated traditional lead scoring from a small number of rules based conditions that are manually or semi-manually calculated, and often subject to bias or emotion, to an objective calculation based on buyer firmographic, demographic and technographic data as well as buyer activity, interest, communication and online behaviors.

AI within your CRM application creates a more complete and accurate predictive lead score to prioritize accounts based on their likelihood of sales conversion.

Even better, the predictive lead scores generally include confidence levels and the insights that most influence the score. Our experience has shown that these insights are a valuable source of learning.

The most recent Sales Excellence research report found the Best-in-Class sales leaders (the top 15 percent) calculated multiple lead scores in order to double down on the best prospects. These included a traditional lead score based on the prospect's online behaviors and digital footprints. This score is typically calculated by the Marketing Automation System and is an implicit measure, meaning it measures behaviors to show how interested the buyer is in the vendor.

Another measure is the ICP (Ideal Customer Profile) score which is calculated in the CRM or Sales Force Automation (SFA) software based on how well the prospect's firmographic, technographic and demographic data align with the company's ICP. This is an explicit score, meaning it is calculated from relatively static data and shows how interested the vendor is in the customer.

CRM with AI can pinpoint the highest potential leads, aid salespeople in responding to the hottest leads first and save sales teams thousands of hours a year.


Guided Selling

AI can analyze the history of activities that resulted in won and lost sales and decipher the types, volume and timing of actions that led to or most influenced each sales outcome. AI then compares these findings to active prospect characteristics and activities as well as actions performed by the salesperson to make recommendations that will improve the likelihood of winning each deal.

AI guided selling recommendations may include next best play (if using a Sales Playbook), next best activity or action, or next best content, collateral or sales asset.

For sales managers, guided sales recommendations may include alerts with suggested activities when opportunities stall, regress or become at risk.

What makes these AI recommendations most actionable is that they are delivered to the salesperson in the context of their daily workflow. AI insights may be presented on a CRM dashboard, at the sale opportunity record or with a notification alert to the sales rep's email or phone.

When AI and machine learning-based guided selling delivers account-specific recommendations with targeted and predictable outcomes the CRM software shifts from an administrative tool to a sales advisor role.


Intelligent Product Recommendations

Every business development manager knows the fastest route to the highest margin sales is selling more products or services to existing customers. What they often don't know is which products will best sell to which customers. AI product recommendations can answer this question.

AI algorithms can identify the target audiences, customer segments or individual customers who are most likely to purchase additional products. Based on firmographics, purchase history, spending patterns, product utilization, life cycle maturation patterns and online behaviors these product recommendation algorithms show which customers are most likely to purchase additional items or a better version of what they currently own. Even the simplest of recommendation engines propose up-sell items, cross-sell items and bundles with confidence levels.

Intelligent product recommendations deliver a two-fold benefit. First, you don't spend a bunch of time pitching products to customers who aren't going to buy them. This saves the company money and avoids annoying the customer. Second, you increase customer share which contributes to a host of essential revenue factors such as customer lifetime value (CLV) and customer retention.


Configure Price Quote (CPQ) Intelligence

Timely quotes, intelligent pricing and sales agility are essential to winning more deals. They are also the hallmarks of Configure Price Quote (CPQ) software infused with AI.

CPQ software is typically part of the CRM stack and when embedded with AI provides sales teams guidance on which deals to quote with what products and at what prices.

CPQ software with AI delivers:

  • Product guidance, such as identifying the best product or product substitutions, upsell or cross-sell items
  • Price guidance on quotes, to identify optimal pricing based on price agreements, purchase contracts, rebate and incentive options, and customer segmentation-based pricing models
  • Deal structuring scenarios such as product trade-offs and margin modeling
  • Automation and insights for Special Pricing Requests (SPRs), a process that when automated can save salespeople thousands of hours annually
  • Price optimization and deal intelligence at the CRM opportunity record, including identifying the price to maximize sales win rates, or dynamic pricing which restructures the deal to improve buyer receptivity and seller margins
  • Price discounting intelligence to provide guardrails for margin protection and quality of service
  • Margin insights during the routing and approval process
  • Integration with downstream business applications such as commission calculations, contract and document management systems and tax processing apps that can share additional data to build greater intelligence
  • CPQ with AI can also synchronize multi-channel and omni-channel sales and thereby bring consistency to sales configuration and pricing regardless of channel

CPQ with AI delivers the right price for the right products in the first quote. This improves quoting efficiency and minimizes quote revisions and sales cycle duration.


Intelligent Sales Forecasting

Predicting revenue for new business, upsell of existing business, and renewals is a best done as a science.

Sales forecasts built on AI replace emotions and wishful thinking with data-driven calculations built on variables that correlate to sale wins and losses. AI sifts through sale opportunity history to weight factors such as lead scores, opportunity scores, and engagement signals from email, calls, meetings and other activities in the CRM system which measure sentiment, volume and recency. These intelligent forecasts also consider each sales rep, the type of customer, the product or solution, the competitor and other factors that correlate with past results.

Perhaps more importantly they include data-driven rationale which explain their predictions and analyze their own results to adjust and get better over time.

More accurate predictions don't just improve forecast accuracy, they provide advance notice of deals that will be lost so sales managers can swiftly inject coaching, tactics or otherwise mix things up in a way to turn the deals around. Converting sale opportunities from lost to won is the upside of intelligent sales forecasting.


Labor Improvements

AI with machine learning is resolving some age-old sales and CRM challenges.

A top frustration among salespeople is entering data into the CRM system. Fortunately, new intelligent tools are automating much of that process.

Automated account data capture apps gather customer and contact information from multiple sources such as websites, social media and identity graphs and insert company and contact data into the CRM system automatically.

There are also third-party apps such as D&B, ZoomInfo, Inside View and many others which also upload company and contact information, as well as firmographics, demographics, technographics and account intelligence, into CRM without manual effort.

Sales activity updates can be automated with Natural Language Processing (NLP). Sale activities may be phoned in to a CRM system that applies a voice to text upload and automatically creates the CRM activity record. Another option is to sync emails to create CRM activity records. CRM vendors such as Microsoft Dynamics and Salesforce with Einstein offer integration from Outlook so that any or all emails from a contact in the CRM application are automatically inserted to the CRM account record.

Another AI-based performance improvement we've implemented several times is targeted deal coaching. Each month sales managers review the sales pipeline with an eye to which deals will close. What they are really looking for are deals that are not forecasted but could be and forecasted deals that unbeknown to the salesperson are in trouble.

AI can identify both types of sale opportunities and prioritize them on a CRM dashboard or escalate them to sales managers for real-time focus or course correction. This single AI algorithm drives prioritized coaching opportunities which delivers a double digit improvement to the sales win rate.

See the 7 high impact sales use cases that show how #CRM software is using #AI to transform sales results and revenue growth.

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