Despite being the backbone of revenue generation, sales teams are often overburdened by the monotony of their work. Be it reaching out to prospects, continually improving the definition of an ICP, or following up on conversations. At the same time, there is automation to make the process more efficient, much of it still requires hours of manual work.
But AI is now changing this narrative and preconceived notions about enterprise sales teams having to spend hours to close a deal.
Let’s explore how AI can impact B2B sales and provide a much-needed respite from grunt work.
Impact and use cases of AI in B2B sales
Below are eight unique use cases and trends of AI in B2B sales we’re observing:
1. Lead generation and qualification
You have an ideal customer profile and broad filters to define your target market. For example, your sales team would gather potential leads using platforms like LinkedIn for outreach and qualification.
However, you may unknowingly fail to recognize key lead generation signals within the target market, such as intent signals, behavior changes, and past interactions.
But with AI tools like Clay, this is no more a hurdle. Unlike humans, AI can analyze more significant amounts of data to pinpoint users that match your ICP.
For example, the tool can enrich your lead list using 10+ built-in sources, including HubSpot, Salesforce, Google, LinkedIn, and other channels, to help you identify prospects’ demographics, online behavior, interactions, and intent for better campaign personalization.
2. Sales forecasting
Several stats from CSO Insights and Gartner show how 50% of sales leaders lack confidence in their forecasting accuracy.
Sales forecast inaccuracies can lead to significant cash flow problems and client loss. Simply put, accurate and fact-checked sales data is a key differentiating factor in remaining competitive and, most importantly, in business.
AI algorithms are well-trained to analyze historical sales data and market trends and predict purchasing patterns necessary to generate accurate sales forecasts. These reports and insights can help sales leaders anticipate product demand, hire staff, allocate resources, and make well-informed decisions.
For example, AI in sales forecasting could generate reports on who your big buyers are so your sales and marketing teams can take care of nurturing them to ensure they retain.
Tools like Aviso AI, with their AI-led sales forecasting, make analyzing accurate data easy so you can confidently make important business decisions.
3. Sales Assistance
Client-facing teams like sales and support are often bombarded with questions. Sometimes they have answers ready, and sometimes they don’t.
These teams must look outside for answers, such as asking SMEs, sifting through documentation pages, and other knowledge transfer material. Doing so prolongs the already lengthy sales cycle and tests leads’ patience.
However, with the help of AI-powered sales assistants, they can address customer care inquiries and prospects’ questions 24/7 without having to wait for answers from other teams.
Docket, for example, is a proactive AI sales agent that’s perfect for sales, GTM, and support teams looking for accurate, instant answers.
Docket integrates with 100+ knowledge sources, such as Slack conversations, PRDs, CRM data, sales enablement docs, etc., and uses AI models to retrieve, qualify, and deliver verified answers.
Here’s how it works:
Your sales/support agent or marketer tags “@docket” followed by the question or concern in an integrated Slack channel. For example, “@docket, how does my SaaS handle data security?” Docket then pings you back a response so you or your team can provide answers just in time.
4. Sales automation
Sales teams perform endless tasks. They have much on their plate, from curating personalized outreach messages to data entry and meeting scheduling.
Most AI tools use predictive analysis, machine learning, and automation to streamline repetitive operational tasks.
AI for sales automation can significantly boost productivity. Your team will spend less time on menial tasks and more effort on strategic activities, such as building customer relationships and closing deals.
For example, your team could use an AI sales meeting automation tool like Avoma to generate transcripts post-meeting or AI outreach platforms like Postaga to carry out outreach and follow-up on auto-pilot.
5. Dynamic pricing
Uber, Amazon, and Google (Ads) are great examples of companies using dynamic pricing to increase profits and stay ahead of market competition.
However, forecasting demand, analyzing competitors’ pricing strategies, and understanding the target audience’s budget require in-depth, physically taxing research.
Therefore, investing in AI-based dynamic pricing software helps you improve your pricing strategies while eliminating the guesswork associated with setting prices manually. AI algorithms can analyze market fluctuations, changes in customer behavior, and competitor pricing so your business never falls behind.
For example, relevant AI solutions like Dynamicpricing.ai can offer insights into what your target market is willing to pay and suggest real-time, competitive price optimizations to maximize profitability.
6. Sales performance optimization
As a sales manager, tracking the team’s performance can be chaotic due to back-and-forth check-ins. Additionally, a lack of comprehensive visibility into certain performance aspects can impact decision-making.
That’s when an AI-powered sales performance optimization tool like People.ai can come in handy for monitoring sales progress and proactively identifying processes and employees needing improvement.
AI and machine learning algorithms can learn from large sales datasets and be used to:
Identify patterns and trends in potential failures
Develop and provide targeted training for teams based on their performance indicators
Identify top-performing team members based on scorecards
Visualize sales performance data to enable quicker decision-making
Also Read: Overcoming Sales Performance Gap: A Focus on Skills and Product Knowledge
7. Predictive lead scoring
AI in predictive lead scoring analyzes large amounts of data for you, considering lead behaviors, CRM data, interactions, and more. It can accurately predict which leads will most likely move to consecutive sales funnel stages.
Here’s how it works:
- AI removes human bias and can automatically analyze and evaluate leads based on various criteria, such as behavior patterns, responses, and online activities.
- AI determines shared lead traits that converted in the past and ones that didn’t.
- Tests and creates predictive scoring models to allow lead prioritization.
Almost 98% of surveyed sales teams by Salesforce think AI improves lead prioritization.
8. AI chatbots
Conversational AI chatbots provide human-like answers through deep learning of data, unlike regular rule-based chatbots that are pre-programmed with answers to specific commands.
Most AI chatbots can also analyze prospect intent, tone, and sentiment so the answers are delivered in a natural, relevant manner.
You could implement AI-powered chatbots like Google Bard or ChatSpot to assist prospects virtually and engage with them in real time. For example, an AI chatbot can be deployed on your home or product page for around-the-clock website visitor assistance, even during non-business hours.
Wrapping up
AI is still in its infancy and has a long way to go. But there’s no downside to adopting AI tools and technologies that align with your unique use case for a competitive edge.
The most in-trend generative AI tool, ChatGPT, proves that AI can handle human-like tasks and responses.
AI has allowed sales teams to focus on essential tasks rather than menial, repetitive activities, such as creating agreements, filling RFPs, editing terms, or answering prospect queries.
Speaking of queries, Docket is the closest you could get to a virtual sales engineer who integrates with 100+ of your knowledge sources to give you accurate answers so you don’t keep your potential buyers waiting.
Some Frequently Asked Questions Around AI in B2B Sales
1. What is generative AI for B2B sales?
Generative AI has opened up new avenues in B2B sales, from task automation to data-driven insights and personalizing customer experiences. For example, B2B sales teams can use Generative AI to create personalized outreach emails or messages for each prospect.
2. How is AI used in B2B Sales?
AI has plenty of use cases in B2B sales. Some examples are sales forecasting, purchase prediction, AI chatbots, AI assistants, lead generation, and sales automation.
3. How can AI help solve problems unique to B2B marketing?
AI is helping B2B marketers solve problems by:
- Improving consumer insights
- Analyzing large amounts of data
- Automating digital marketing processes, such as email marketing
- Improving lead scoring
- Analyzing competitors in-depth
4. How can AI help B2B customer success?
AI in B2B customer success can analyze a large amount of data to pinpoint patterns, behavior shifts, and trends. These insights can help customer success teams tailor their strategies to align with customers’ preferences and changing needs.
