The results reveal a clear pattern: Sellers who spend more time actively engaged with their customers have higher quota attainment and drive better outcomes.
Driving Growth Through Engaged Selling Time
Go Back to the Basics
Improving engaged selling time starts with laying the groundwork to scale effectively. As sales organizations scale, they become increasingly complex. This complexity often includes nuanced product portfolios, more customer touchpoints and handoffs, disparate tools and data and expanded role ownership across the customer journey. All these different intricacies can cause friction in the sales process and lead to diminishing ROI.
Although advanced technology deployment can be helpful, automating a broken process results in a broken, automated process. Before introducing more technology and more complexity into the mix, organizations must address the root causes of pain points:
- Orient Sales Motions to Buyer Behavior: Understand customer preferences and patterns to ensure sellers take the right actions at the right time.
- Align Compensation Metrics to Strategic Goals: Design sales compensation plans around desired behaviors to keep sellers focused on engaged selling.
- Develop Strategic Sales Playbooks: Document specific plays and situational behaviors, outline clear expectations and equip sellers with proven techniques to close deals.
- Deploy an Effective Coverage Model: Identify the right roles, headcount and rules of engagement for success.
CASE STUDY: High-Tech Software Division Faces Limited New Logo Acquisition and Expansion Sales
PROBLEM: The head of sales had two primary concerns. First, the sales force had fallen behind the market in productivity per representative. Additionally, there were worries that the sales managers felt a lack of support and seller enablement.
APPROACH: Alexander Group benchmarked the company’s productivity and expenses against industry peers. From there, the team developed a future-state roadmap for increased support levels to offload non-net-new selling activities from core sellers and to drive separate emphases on new account acquisition and existing account expansion.
Leverage AI to Optimize the B2B Sales Process
If a customer-facing opportunity is a primary driver of sales success, then AI should be considered a best-in-class enabler for sellers to be more effective more often. When deployed thoughtfully, AI and ML models reduce administrative drag, improve seller preparedness and enable more timely, relevant interactions across the customer lifecycle.
Pre-Sales: Generate a strong, qualified pipeline for effective pre-sales time
CASE STUDY: Major Credit Card Provider Tech Stack Unable to Identify Incremental Sales within Existing Accounts
PROBLEM: The company was unable to keep up with rapidly transforming seller-customer interactions and sales oversight, driven partly by siloed data systems and tracking.
APPROACH: The credit card provider developed an in-house machine learning algorithm to dynamically route prospects from the marketing to sales pipeline at the most opportune points.
Sales: Increase internal time-to-knowledge to elevate engaged selling time efforts
CASE STUDY: Large Business Services Organization Uses Gen AI to Reduce Prep Time with Specialized Technical Product Knowledge Tool
PROBLEM: The organization faced a shortage of sales engineers relative to a large salesforce, which resulted in sellers lacking timely access to technical expertise, ultimately impacting deal velocity and customer experience.
APPROACH: The company developed an internal Gen AI tool deployed across products and business units that provided product expertise to increase win rates and deal size.
Sales: Improve customer health visibility to prioritize engaged selling time across at-risk accounts
CASE STUDY: Leading Telecom Provider Develops an ML Churn Model to Effectively Re-Engage with At-Risk Accounts
PROBLEM: The organization lacked visibility into service-related events that were driving churn and was unsure when to engage customers for renewals, leading to higher churn rates.
APPROACH: The company partnered with Alexander Group to develop an algorithmic scoring model using customer experience as well as customer/product lifecycle data and integrated this into their CRM to create a proactive view of churn and intent.
Post-Sales: Limit administrative burden and reduce time-to-resolution to improve the efficiency of sales facilitation and sales completion activities
CASE STUDY: Life Sciences Company Uses AI Monitoring Agents to Reduce Customer Downtime and Address Service Issues
PROBLEM: Manual diagnostic workflows were slow and labor-intensive, and service technicians had to engage in repeated back-and-forth with customers to collect equipment details and full reports. The company could not scale technician headcount to match sales growth, leading to service delays.
APPROACH: The company developed an AI-powered diagnostic to streamline diagnostic steps, reduce time-to-resolution and identify predictive service failure trends to improve overall customer satisfaction.
Alexander Group research demonstrates a clear, positive relationship between engaged selling time and quota attainment. As sales organizations expand their toolkits with AI and advanced analytics, leaders must remain grounded in the fundamentals that drive seller effectiveness while selectively adopting technologies that help maximize customer-facing time.
