Artificial intelligence is the tech buzzword du jour right now. Companies all over are using AI in new and innovative ways. Some we’re oohing and aahing over and some that are creepy. There is no mistaking that AI is the next front in the digital revolution. With 3.6 billion people now using the internet as of 2016 (that’s 49.5% of the world population). AI is becoming the fast source of serving data to us that we want when we want it. If you use Amazon or watch Netflix, you know that your decision to buy is influenced by AI (through association algorithms). Ordered an Uber? AI (a location algorithm) was there to have a car in your vicinity quickly. If you’ve ever thought about a product or a place to vacation, and it seemed to magically pop up on your favorite web pages or in your email inbox, rest assured it was based on AI (classification algorithm) monitoring your online activity.
AI has now changed how companies make decisions; it’s affecting there power structure and even their prediction models. From research taken from across the web, we’ll look at exactly how you can leverage specific areas to make AI algorithms help your business and sales team grow and sell more effectively.
Lead Scoring: This is the one thing everyone gets all excited for. As a salesperson, as a sales manager, and even at the VP level. Everyone in sales loves lead scoring. (When it’s accurate). When a salesperson has a full pipeline of qualified leads or opportunities, they need to make decisions on a daily, weekly, and even hourly basis of where they need to put their focus. With a thousand things pulling salespeople every day, knowing where to put their time and focus on closing deals and hit their quotas is critical. Far to often this is left up to the salesperson’s intuition or gut feeling, and usually is done with the incomplete information. AI can compile historical information about a lead, even tapping into social media and the salesperson’s interaction history with that lead. (e.g., text messages, appointments, emails, voicemails, and phone calls, etc.) moreover, rank the opportunities or leads according to their chances of closing successfully.
Forecasting: Raise your hand if you like prediction… That’s what I thought. Every sales manager loathes this challenge of trying to predict what and where their team’s sales numbers will be every quarter. Again, AI takes the burden off of management by giving them more predictability and laser-like accuracy on revenue forecasting. This allows your company and each department, from operations to HR, to accounting to better manage their resources and inventory.
Optimizing Price: Discounts are always a sticky balance for companies. What discount do I give, when should I give one, and how do I not leave any money on the table and still make a fair margin. We all love the feeling of closing a deal, but at the same time, we hate to find out that we could have made more. This is where having an AI comes in; machine learning allows you to train your system to look at your previous deals, inform you of the ideal discount to give the client, and give you a rating on how likely you are to win the contract at various discount amounts. By looking at the mountain of sales data you already have in your system, of contracts you’ve won and ones, you’ve lost. You can create a pattern of repeatable steps that an AI can use to push your team forward. Some features that this AI should look at are the deal size (dollar amount, and product quantity), competitors, size of the company, area, and territory, company revenue, timing (Q1 vs. Q4), an existing relationship or new one, news about the company, and their industry. Something that would be a level beyond that is market indicators and even sentiment.
Performance Management: In management, we know that we need to review our sales teams’ progress on a monthly, or at least on a quarterly basis. By keeping the pulse on deals that are failing or trending the wrong direction, we can take corrective action quickly and effectively. This is often not the case, sometimes deals sour before we have the chance to take corrective action. With AI, managers can now use dashboards and BI to visually see which salespeople are hitting their quotas as well as which deals or leads are trending down, and are going to fall out of the cycle.
Upselling and Cross-Selling: What is the quickest and most cost-effective way to drive an increase in top-line revenue. Sell more to your existing client base. The big question is, who is more likely to buy more? You can market aimlessly to get a few customers to buy, or you can let AI help identify your current client base that is more apt to purchase upgrades or a new product offering. This way, you can have a net increase in revenue while reducing marketing costs.
We see that in these examples, the data gathered by the AI gives more accurate and actionable data that it can act upon and provide recommendations for. This will intern allow you to drive better action in your sales teams. The value of any prediction is based on how it can help to guide a salesperson’s or manager’s behavior, thus improving the company’s bottom line.
Where do you think you would begin if you were to start using AI in your sales teams?
One of the first steps is to determine what types of data already exists and what you’re going to use to or add to give a more holistic picture of your current leads and customer base. Take the sales department, for example, which needs to have both historical purchase data as well as pipeline data, or the marketing department which will need website analytics as well as integrated buyers personas and promotional responses. Through Microsoft’s Common Data Service, Azure Machine Learning and Microsoft Dynamics 365, you’re able to combine these data sets and train your AI to make better predictions about who is more likely to respond to an offer, take an upgrade, or close a new deal.
You should start considering where your data is stored. You will need to combine the data from your Customer Relationship Management (CRM) platform, as well as your Marketing Automation platform. This will serve as a repository for all your customer transactions, interactions, and sales. Microsoft Dynamics 365 already has begun rolling these features out in version 9.0 with Relationship Analytics, Relationship Health Scores as well as in the October release they are introducing the Azure Machine Learning side with Sales AI and their core focus on data sharing through Common Data Service. All of these now help you to take command of the examples above.
So In the long run, yes, it’s a good thing to add AI to your salesforce. It eases the pressure off your management, your sales staff, and various other departments, by allowing salespeople to focus on what makes them great, selling! We all remember what workflows did for our sales staff; this is just the next phase in salesforce automation. There will always be new challenges for companies to find new growth, expand their revenue streams, reduce costs, and increase market share, all while driving costs down. More and more companies are leveraging the existing data that they have and mining it for new business and growth using AI.
AI also takes time out of doing it; it would take teams of people weeks to crawl a database and give you the data; it takes computers only minutes. Data has become as valuable as gold, and companies who can capture the data, analyze it, and generate actionable insights will have salespeople can and will be able to close more deals, faster and more cost-effectively than ever before.
