How to use AI for sales: techniques and tools

The emergence of AI, particularly generative AI, holds significant potential across marketing and sales in three key areas: customer experience (CX), growth, and productivity. HubSpot’s State of AI report highlights that sales professionals currently deploy AI primarily for automating manual tasks, gaining data-driven insights, and crafting outreach emails. Another report by Salesforce indicates that sales teams allocate approximately 28% of their time directly engaging with customers. Tasks like data entry, meetings, prospecting, scheduling, and other administrative duties unrelated to the core sales process consume the remaining time.

AI improves sales effectiveness by automating these mundane tasks, allowing teams to allocate more time to engage with customers and prospects while reducing operational costs. AI can facilitate hyper-personalized content and offerings tailored to individual customer behavior, persona, and purchase history. Using AI accelerates growth by empowering sales teams with analytics and customer insights to seize demand opportunities. AI, combined with company-specific data, enables consumer insights at a granular level, facilitating targeted marketing and sales efforts.

Harnessing advanced data analysis and applications improves sales efficiency. According to Deloitte, the primary goal for most top-level AI users is increased efficiency, wherein 33% of AI users reported significant streamlining of organizational processes. Sales teams grapple with data overload from diverse sources. AI can be a filter that analyzes customer demographics, past interactions, and market trends, providing sales teams with a deeper understanding of customer needs and facilitating targeted outreach, lead scoring, and prioritization. McKinsey reports that sales professionals leveraging AI have seen a remarkable 50% surge in both leads and appointments, boosting the profitability of their business over time.

Conversational AI platforms analyze sales calls and meetings, transcribe conversations, identify key topics and objections, and assess sentiment, enabling sales reps to refine their communication strategies, perfect pitches, and address customer concerns more effectively.

According to Salesforce’s State of Sales report, the primary application of AI in sales is assisting representatives in understand customer needs. Your grasp of a customer’s requirements influences every aspect of your interactions, shaping your pitch, sales content, and overall outreach strategy.

Conversation intelligence uses natural language processing (NLP) to comprehend speech and text-based communication across various platforms like CRM, emails, and call logs. It serves multiple sales use cases:

• Call recording analysis: Sales managers can use conversational intelligence to identify coaching opportunities and learn from high performers’ techniques rather than manually listening to hours of call recordings.

• Virtual agent deployment: Conversation intelligence can function as a virtual agent, interacting with customers as the initial point of contact and directing them to the appropriate sales personnel. It can also follow up to track progress on opportunities.

• Automated note-taking: During client conversations, it alleviates sellers’ workload, allowing them to focus entirely on client interactions.

AI automates tasks like A/B testing techniques, data entry, meeting scheduling, and lead qualification, freeing time for sales reps to engage with qualified prospects. It also personalizes training materials and coaching recommendations, ensuring reps have the necessary skills when needed. Notably, a 2022 Deloitte survey found that 82% of respondents strongly believed AI would improve their job performance and satisfaction, indicating its potential to impact team morale positively.

AI analyzes historical sales data to predict future performance, empowering sales managers to optimize pipeline management, allocate resources effectively, and establish realistic sales OKRs. Additionally, AI identifies potential obstacles within the sales pipeline, facilitating proactive intervention to maintain deal momentum.

AI-driven predictive analytics predicts outcomes by identifying key variables and analyzing their impact, allowing businesses to make informed decisions based on data-driven insights. Unlike diagnostic analytics, which explains why something occurred, predictive analytics focuses on anticipating future events. For over half of salespeople, forecasts stand out as the most crucial data insight obtained from their sales analytics tool. Sales forecasting uses predictive models to inform pipeline planning, uncovering trends and seasonal patterns to predict forthcoming sales bookings.

AI personalizes the sales journey, ensuring every interaction resonates with the customer, facilitating stronger relationships, boosting engagement, and increasing conversion rates. By analyzing past behavior and preferences, AI recommends relevant content, tailors email outreach, and suggests the most appropriate communication channel to boost sales.

• Challenge: The effectiveness of AI models relies solely on the quality of the data they are trained on. Inaccurate or biased data can lead to unreliable results and potentially unethical sales practices.

• Solution: Implement data quality control measures to ensure the accuracy and integrity of the data used to train your AI sales tools. With the rise of generative AI for content creation, sales enablement teams must emphasize the responsible use of this technology to achieve positive sales outcomes.

• Challenge: Striking the right balance between AI automation and genuine human interaction is crucial. Over-reliance on AI can lead to impersonal interactions, ultimately damaging the customer experience.

• Solution: Empower your sales team to understand when human engagement is necessary to nurture leads and build relationships. AI should complement human interaction, not replace it.

• Challenge: Building trust and safeguarding data is paramount when using AI in sales, including trust between the user and the AI, as well as trust between the seller and the buyer.

• Solution: Transparency is vital. Educate your sales team and customers about the role of AI in sales. Ensure your AI tools have robust security measures to protect sensitive customer data and consider AI privacy best practices.

• Challenge: Sales teams may have concerns about AI replacing their jobs.

• Solution: Open communication is crucial. Communicate the types of tasks AI will handle and emphasize how AI can empower sales reps to focus on high-impact activities that require human expertise.

Gain a clear understanding of sales areas consuming unnecessary time and resources. For instance, many sales teams spend hours daily manually qualifying leads, diverting focus from high-value prospects. Conduct an audit to capture genuine challenges across the sales team. The objective is to document repetitive and time-consuming tasks ripe for optimization or automation with AI.

Define specific goals and objectives for AI integration in sales, such as improving lead generation or optimizing sales forecasting. Ensure these goals address pain points identified in the sales process audit and align with broader business objectives.

Goals should be measurable and time-bound, for example, aiming for a 17% reduction in lead qualification time, a 23% increase in sales team productivity, or a 13% rise in customer lifetime value within a specified timeframe. Quantifiable metrics are essential for tracking progress and achieving tangible results.

Assess your current tech infrastructure to optimize sales processes::

• Compile a comprehensive sales, marketing, CRM, and analytics tools list.

• Collaborate with sales teams to pinpoint AI integration opportunities.

• Determine system limitations, especially regarding AI capabilities and real-time analytics.

• Gather feedback from IT, sales, and other departments to identify pain points.

• Compare desired AI features with existing tool functionalities.

• Ensure data security protocols are robust enough to support AI implementation.

• Evaluate if current systems can meet AI requirements or if upgrades are necessary.

• Budget for potential upgrades like advanced CRMs and real-time analytics APIs.

If lacking in-house expertise, consider hiring external consultants for system audits. Efficient assessment ensures seamless integration and will improve overall sales performance.

Conduct a thorough cost-benefit analysis to justify AI investment:

• Quantify all potential costs, including software, hardware, training, and staffing.

• Estimate benefits like increased efficiency and revenue gains.

• Compare costs to benefits to project ROI annually.

Decide on AI tools and set implementation budgets accordingly. For instance, automation could reduce lead qualification costs by 25%, with a projected break-even period of 18–24 months. Careful analysis ensures informed decisions and long-term gains from AI integration.

Take the time to research AI sales tools on the market, comparing different solutions best-suited for your team. Here’s how to assess options:

• Attend vendor demos to witness tool functionalities firsthand.

• Ask targeted questions to evaluate capabilities against your needs.

• Read user reviews and case studies for practical insights.

• Reach out to current users for honest feedback.

• Assess integration ease with your existing tech stack based on audit results.

• Compare costs, including licensing fees and training expenses.

• Evaluate features like lead scoring, data analysis, and CRM compatibility.

• Shortlist tools that meet your requirements, garner positive reviews, and fit your budget.

Start with small-scale trials to assess selected AI tools’ impact on sales processes. Solicit feedback from the sales team to determine cost-effectiveness. Monitor metrics for performance enhancement.

• Validate tool capabilities and user-friendliness through real-world testing.

• Initiate a limited rollout for select 1-10 users over 2—3 months. Select participants across roles, regions, or product lines for diverse feedback.

• Develop measurable metrics to compare pre-and post-AI performance.

• Gather user feedback via surveys, focus groups, or interviews.

This approach uncovers potential pain points and ensures the tool delivers the expected value. Address UX or integration challenges before company-wide implementation.

Prepare employee training and change management programs for AI integration.

• Conduct thorough training sessions to empower staff to integrate the AI sales tool into their day-to-day tasks. Encourage questions for clarity.

• Opt for hands-on practical sessions rather than just presentations.

• Consider external AI trainers if in-house expertise needs to be improved.

Training builds confidence through practical experience, emphasizing productivity enhancement and skill development. Beyond training, address change management transparently via town halls and 1-on-1 meetings. Offer reassurance on job security and continuous support. The aim is to enable employees to adopt AI effectively, not just operate tools.

Establish performance metrics for ongoing assessment of AI integration in sales. Track key performance indicators (KPIs) like conversion rates and lead quality to gauge AI’s impact.

Compare pre- and post-implementation data to assess effectiveness by evaluating OKRsset. Ensure KPIs align with sales goals. Evaluate AI-generated insights against human expertise. Strike a balance between AI suggestions and the intuition of your sales team for the best results.

Drift, a conversational marketing platform, tackles the challenge of capturing qualified leads by deploying intelligent chatbots on your website. These chatbots engage website visitors in real time, answer their questions, and qualify them as potential customers.

Key features:

• Automated lead capture forms and meeting scheduling.

• Pre-programmed responses to frequently asked questions.

• Visitor behavior insights for personalized outreach.

Salesforce, a leading CRM platform, has integrated a robust suite of AI features called Einstein. Einstein tackles the challenge of maximizing sales efficiency and uncovering hidden sales opportunities.

Key features:

• Automates data entry and sales forecasting.

• Analyzes customer data to identify sales opportunities.

• Predicts customer behavior and suggests outreach strategies.

Extracting valuable insights from sales calls can be a challenge. Gong, a revenue intelligence platform, utilizes AI to bridge this gap.

Key features:

• Transcribes calls and identifies critical talking points.

• Analyzes sentiment during customer interactions.

• Provides coaching insights to improve communication strategies.

• Offers performance analytics for sales reps.

Streamlining the sales outreach process and improving communication effectiveness are constant challenges for sales teams. Troops, a sales engagement platform, utilizes AI to address these issues.

Key features:

• Automates email sequencing and tracks engagement.

• Suggests optimal sending times for emails.

• Personalizes email content based on prospect data.

Veloxy is a sales automation platform that leverages the power of AI to streamline workflows and personalize outreach efforts. It tackles the challenges of repetitive tasks and disconnected data, empowering sales teams to focus on building relationships and closing deals.

Key features:

• Automates data entry tasks like populating CRM fields.

• Utilizes AI to score leads based on various criteria.

• Personalizes email content and social media outreach messages.

Being well-prepared for sales meetings is crucial for success. Exceed.ai, an AI-powered sales meeting assistant, tackles the challenge of ensuring reps are fully prepared and can maximize the effectiveness of each interaction.

Key features:

• Analyzes customer data to suggest talking points and competitive intelligence.

• Transcribes calls and summarizes critical meeting decisions.

Finding a mutually agreeable time to schedule a meeting can be a time-consuming back-and-forth process. x.ai, an AI-powered meeting scheduling assistant**,** tackles this challenge by streamlining communication and automating scheduling tasks.

Key features:

• Integrates with calendars for suggesting availability.

• Uses AI to negotiate and schedule meetings based on preferences.