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Now Is the Time for Sales Organizations to Embrace AI

By Kayleigh Halko CRM Buyer ECT News Network
Oct 11, 2019 11:18 AM PT
artificial intelligence can't replace sales reps but it can help them perform more effectively

Artificial intelligence is arguably the most disruptive technology to emerge over the last few decades. Consumers are producing data at record levels. It's estimated we'll produce 463 exabytes per day by 2025. Yet humans aren't equipped to process that complex information.

We're starting to rely more on AI to interpret massive amounts of consumer and third-party data in real time, and to make it relevant for our uses. As a result, businesses can create personalized and tailored interactions for their customers at every step of the buying journey, delivering unforgettable experiences.

These AI-powered moments aren't just for business-to-consumer engagement anymore. Business-to-business buyers now expect the same experiences and buying processes as consumers. They want a company to know them -- to engage with them seamlessly on their preferred channels in a way that adds value at their moment of need.

Just like consumers, B2B buyers do the majority of research on their own before contacting companies -- but when they need support from your company to make a decision, they will reach out. You'll need to be ready when they do.

This new reality seems like an obvious application for AI. Yet it can be overwhelming to figure out where to start. Smarter recommendations for buyers browsing your website, predictive social advertising, annual budgeting and business unit forecasting -- which should be the priority?

Ideal Proving Ground

Sales organizations are the perfect proving ground to implement AI and demonstrate immediate impact. You see, there are specific ways that with very little effort, AI can make a huge difference in the selling process. Plus, AI sales technology has become more accessible and intuitive, making it a technology sales reps actually want to use.

Yet many sales organizations are still hesitant to embrace it, focusing instead on empowering reps to be creative -- almost artful -- in how they respond to customer needs. This art of selling is essential, but it can be much more powerful when backed by AI-powered sales science.

Yes, great sellers can close deals without AI, but that narrow view doesn't ensure the entire sales organization operates at peak performance. If you don't infuse AI in the sales process expect the following:

  • Your pipeline may contain unnecessary risk;
  • Sales operations will spin their wheels deciding which accounts to prioritize and how to plan territory coverage;
  • Reps may not focus on the best opportunities in the optimal order;
  • Reps may miss an opportunity to progress an opportunity faster; and
  • Reps may struggle with bad account data and weak talking points, or waste valuable time researching these things on their own.

Start Small but Think Big

Let's start where it all begins, with data. AI relies on good data, and companies -- sales organizations in particular -- are sitting on vast amounts of customer relationship management data. That's the good news.

Unfortunately, we know from working with our customers that on average, 52 percent of company data in CRMs is inaccurate, incomplete, invalid or duplicated. That's more than half, which means that from the outset sales reps are at a significant disadvantage for making their number. AI can fix that.

There are services that help organizations enrich their CRM data with all the publicly available information from around the world. This information includes what software businesses own, who they've recently hired, if they go public, what major press announcements they've made, and even which events they plan on attending.

AI powers this enrichment, pairing publicly available knowledge with firmographic data and making it available in the CRM. As a result, your CRM is always updated, constantly refreshed, and working on behalf of sales.

Make a Plan

Sales planning is a historically complex and headache-inducing process that relies on forecast or pipeline data to optimize sales territories, quotas, and compensation plans with the goal of delivering on an overall revenue target. However, all too often that data is inaccurate, or located in multiple disconnected spreadsheets across the organization.

Have you ever used spreadsheets to manage your sales kickoff or quarterly business review budget? Think of the delays you incur and the time you spend gathering and inputting expenses alone. Then imagine doing that for thousands of sellers across multiple business units -- without delaying sales territory rollouts.

Enter machine learning. By applying best-practice planning methodologies to back-office historical financial actuals, sales leaders can create predictive models to help steer planning decisions.

AI works faster than humans and takes the guesswork out of planning to deliver right-sized quotas. Operations teams then can define territories fairly and roll them out faster.

Establishing clear sales assignments with right-sized quotas on day one means less sales turnover, less risk for high payouts when reps close much higher than their assigned number, and more deals that close earlier. It's a win for both finance teams and sales leaders.

Divide and Conquer

With an intelligent data foundation and a proper plan in place, AI can help sales teams further, to both broaden their nets and narrow in on what's important. By identifying where sales teams are having success -- in certain industries, business sizes or regions, for example -- AI can find lookalike companies that sales reps haven't yet considered. These accounts then can be nurtured from high-scoring prospects to qualified business opportunities.

It's important to note that having more fish in the pond doesn't always translate to a higher volume of sales. Reps need to know where to focus. They can do so by leveraging AI for account prioritization, making the best use of their time by narrowing in on those with the highest potential first.

Now What?

With priorities in place and sales conversations in motion, what possibly could be left for AI to do? If you're in sales, you've probably faced the question of how likely your deal is to close. If you're in sales management, you've certainly asked this question -- probably too many times to count. The answer always varies, but AI can help.

AI monitors how a current opportunity compares to similar account and deal attributes, sales activities, pipeline stages, and past sales outcomes. It can predict a more realistic win probability, and let sales reps and managers know when there is a big discrepancy that requires attention.

However, knowing there's a problem and knowing how to fix it are two different things. This is a perfect opportunity for AI to guide sellers to take the correct next best actions. They may need to pick up the phone and call the account, or offer a discount to secure a deal -- and those actions are identified from past sales efforts and outcomes.

When they do get their account's attention, AI can help sales reps get one step closer to closing with smart talking points -- relevant and topical information gleaned from third-party data.

For example, it's important for a rep to know if a company just opened a new subsidiary in a new market, hired a VP of finance, or reported headcount growth. It's equally important to know if a prospect announced major layoffs.

This type of relevant content lets reps know when to act or delay engaging. In the end, it helps build deep, long-term relationships that ultimately build pipeline.

The Art and Science of Sales

If you're reading this, you know AI isn't going to replace sales organizations. AI is a science, not a sales rep.

Still, by applying AI to a few key sales motions, companies can fine tune their plans, optimize work flow, and ultimately hit revenue and profitability goals.

AI can take care of the heavy lifting, so sales reps can focus on the art of sales to add value when buyers reach out -- exactly what they do best.

Kayleigh Halko is senior manager of CX product strategy at Oracle.

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