Pedal to the Metal: Why AI Turbo Boosts Sales Enablement
Technology is playing an increasingly important role in sales. The pandemic pushed 90% of B2B sales online — and it doesn’t look like we’ll be going back to traditional sales anytime soon. It’s clear that both B2B buyers and sellers prefer the new digital reality.
What used to start as a casual conversation between two people, selling is now much more data-driven and strategic in approach. Technologies in sales enablement AI (artificial intelligence) present opportunities to knock sales performance out of the park — using data-based innovation to reach business goals faster and with more precision.
How exactly does artificial intelligence step up to the plate? Keep reading to find out.
The Marriage of Sales Enablement and AI
Sales enablement is a strategic cross-functional discipline designed to increase predictable sales results. Artificial intelligence achieves just that by using technological applications to automate tasks, tackle complex problems, and streamline the sales process.
Think about it like this. You have a destination in mind and two ways to get there: a standard bicycle and an electric bike. They’ll both take you to where you need to go, but at the end of the day, the electric bike gets you to your destination faster. Imagine sales enablement as the destination. You can probably accomplish a lot of your tasks manually, but using AI strategically will allow you to complete your tasks faster and with more accuracy.
If you want to come out on top, sales enablement AI needs to be part of your business strategy: Salesforce found that high-performing teams are 2.8x more likely to be using AI than underperforming ones — a 76% increase since 2018. As the second most valuable sales tool, it’s quickly catching on that AI adoption is making a mark in sales performance.
The top business benefits of integrating AI into your sales enablement programs are:
1. Additional sources of data input
Studies show that at least 50% of prospects aren’t a good fit for what you sell. Make every customer interaction you have count by acquiring intelligence from your market. Sales enablement AI empowers organizations to discover valuable insights from a wider range of data sources, like emails, phone calls, and chats, empowering your teams to allocate resources to opportunities that give the best return on investment.
2. Increased operational productivity
McKinsey reports that more than 30% of sales-related activities can be automated. By alleviating repetitive tasks that would otherwise have to be done by sales and marketing teams, organizations reduce costs and free up resources for more important tasks. Sales enablement automation has helped B2B marketers to improve their sales pipeline by an average of 10%.
3. Better and faster decision making
AI improves data integrity and reduces human error. With better data, teams can prioritize more effectively and make better business decisions. 66% of decision makers say applications of AI are helping them increase profits and reach their goals. Gartner analysts say AI is critical to helping businesses differentiate, gain competitive advantage, and survive industry disruption.
The Capabilities of Sales Enablement AI
Not all artificial intelligence capabilities are equal. This is because the field of AI technology is extremely broad, spanning countless industries with different applications — from autonomous vehicles to virtual surgeries to hyper-personalized classrooms. In sales and marketing, AI has transformed the way we do business through technologies like image and video processing, recommendation engines, and sentiment intelligence.
For sales enablement specifically, the most relevant AI applications are diagnostic and predictive analytics, natural language processing, and optimization analytics. Why? It comes down to collecting insights that help improve internal processes, understanding customer needs, and increasing predictable business results. Let’s go into more detail.
Diagnostic and Predictive Analytics
Diagnostic analytics examines correlations among variables in a database to find existing relationships between them. In sales enablement AI, these analytics are used to study problems and cluster data for market segmentation and personalization.
While diagnostic analytics try to explain why something happens, predictive analytics guess what will happen in the future by looking for patterns in the information they already have. Sales forecasting tools use predictive technologies to inform pipeline planning.
Natural Language Processing
Natural language processing (NLP) gives computers the ability to understand and respond to text and or voice data in much the same way human beings can. It’s designed to recognize, read, interpret, and generate what are called “natural languages” found in emails, call transcripts, website searches, etc.
Common NLP use cases are sales call analysis, chatbots, social media sentiment analysis, and automated notetaking — alleviating manual and repetitive work so teams can allocate more time to higher value tasks. Apps like Chorus use AI to quickly identify the behaviors of top-performing reps in sales calls so managers can replicate them across the entire team.
When a complex business decision involves trade-offs between business objectives and constraints, optimization models can prescribe the best course of action. This analytics model uses volumes of customer data to identify the next-best action with an account, whether that be a marketing pitch, a customized sales process, or opportunities for cross-selling and upselling.
Leveraging Artificial Intelligence in Sales
We’ve established that AI capabilities are a good match for sales enablement programs. Let’s flip it around and look at this relationship from a different perspective.
Drawing from our sales enablement framework, here we highlight key sales enablement processes and describe how AI and machine learning are evolving to complete these tasks at a higher velocity and greater accuracy.
Generate Lookalike Leads More Intelligently
AI-powered tools can help you automatically generate leads, directly increasing your pipeline. Chatbots and virtual assistants have conversations with potential customers on your website automatically, collecting contact information and qualifying them before handing them to a salesperson to close.
What about prospecting new leads? Sales enablement AI can do this too — and at scale. It finds patterns and extracts insights from your customer data, essentially building ICPs for sales and buyer personas. These are indispensable cross-functional tools that help define the market problems you’re solving for, inform and map out future product releases and launches, and describe the people you’ll encounter along the buying journey.
Artificial intelligence in sales can even use the connections it learns to predict which leads your teams should pursue next, and you might not need to have the headcount to pursue them. Instead of needing a human sales rep to start every conversation, AI can automate initial lead outreach making it easier to scale lead generation activities.
Narrow Down Best-Fit Leads with Laser Focus
In today’s digital world, salespeople can be overwhelmed with information from contacts that are actually a poor fit for their business. This is why qualifying leads is a valuable sales enablement process.
Marketing, sales, and dedicated sales enablement professionals implement lead scoring best practices that assign positive or negative weighting numbers to accounts. The total “lead score” indicates how good a fit a given lead is. Learning from lead conversion data, sales enablement AI can help you build smarter leads scoring systems, making it easier to generate best-fit leads and convert them at higher rates.
For example, say a potential prospect wants to see a demo but more information is needed to see if it’s worth the rep’s time. Instead of the sales rep asking a slew of questions to qualify them, AI-assisted software can do it. Virtual sales reps can analyze the conversation and determine whether the lead should continue down the pipeline or be sent to marketing for nurturing.
Sales outreach is arguably the most time-consuming task in the sales cycle. Tools like Salesloft, Hubspot Sales Hub, Outreach minimize most of the manual, repetitive work associated with sales outreach so salespeople can spend more time learning about and connecting with their customers.
Turn Data into a True Asset for Your Teams
Modern sales are driven by data. And thanks to powerful, AI-based solutions, we now have more sales data than ever at our fingertips. But too much data can be counter-productive – especially if you don’t have the skills and tools to turn it into real insights.
Sales teams often have a high-level understanding of what reports are to their business, but they might lack the technical ability or time to create and manage these reports. Sales enablement professionals can bridge this gap, creating systems to turn data into a true asset for their sales teams.
AI and machine learning are reshaping the way business KPIs are chosen and applied. In sales enablement automation, you’ll likely be generating reports that help salespeople and managers gain holistic and granular views of their progress such as win/loss analysis, activities logged by salespeople, and leads generated.
As you increasingly leverage sales enablement AI to resolve issues and automate processes without manual intervention, you might start reporting on other metrics like automated-versus-manual resolutions, time savings, and associated cost savings. KPIs provide anchor points in AI and machine learning projects by defining what outcomes to expect.
Build a Clear Vision of Your Top Accounts
Account-based marketing (ABM) looks at key accounts as opposed to individual leads. The key with ABM is that it focuses on engaging a small number of the right accounts, but it comes with the benefit of a larger deal size. With 70% of marketers using ABM in 2021, up 15% from 2020, it’s a quickly growing marketing strategy.
Sales enablement AI and machine learning can help organizations identify those right accounts by painting a detailed picture of your organization’s ideal customer. Drawing on concepts discussed above in lead generation, scoring, and qualification, diagnostic analytical models cluster data in a way that builds a refined vision of your ideal customer — one that becomes increasingly refined as the technology learns over time.
The more you understand your ideal customer, the more you can target specific accounts and craft personalized content that speaks to your buyer with where they are in the buyer journey. Effective sales enablement content helps salespeople attract and engage buyers, address their concerns, handle objections, and ultimately close sales.
With AI and machine learning, you can increase the impact of sales content. Have you ever received an email and you can’t tell whether the sender is satisfied, annoyed, or being sarcastic? Sentiment analysis, a branch of NLP, has the capability to scan text and analyze views to understand and gauge your customers’ reactions to content. Teams in sales, marketing, product, and customer success can use this to inform future strategies and content creation.
Integrating AI into Your Sales Enablement Strategies
Today’s B2B buyers expect a seamless and customized buying experience. Sales enablement AI has the potential to change sales forever, offering organizations the ability to build strategic sales enablement processes that can be personalized to enrich your customer’s buying experience.
At TPM, we work with B2B technology-focused businesses to streamline the entire buyer journey — from marketing consulting, strategy, content development, to sales enablement services. Being in this business for the past 10 years, we understand the need to adapt to an ever-changing digital world and we’re prepared to meet the challenge. See how we can help set your marketing to automation.