Every lead that marketing passes to a sales team must be as qualified as possible to drive conversions and close more deals faster. After all, your most expensive salespeople should be spending efforts on qualified customers, not educating tire kickers who aren’t ready or are unable to make a decision.
It’s not enough to simply hand off prospects from marketing to sales to improve conversion rates. Marketers must bridge the gap between these separate departments by looking at data from a unified view. When you can see the big picture that emerges from data patterns found through machine learning, you can answer the big questions that ultimately drive conversions, like:
Who Are Our Ideal Buyers?
The best way to qualify raw inquiries before they are sent to sales is to first identify the key characteristics that make up an ideal customer, so your team will acquire more of the right kind of customers.
Machine learning helps sales and marketing teams quickly identify key characteristics of ideal target markets by uncovering purchasing patterns based on historical customer behavior and demographic data. In addition to historical data, machine learning analyzes incoming data from external sources, like web searches and social media, to provide a better understanding of who your most promising customers are so you can engage them most effectively.
Which Leads Are Most Likely to Convert?
Marketers have databases full of potential prospects, but not all leads are created equally. You have dead-ends, those that need nurturing, red hot opportunities and every degree in between. Machine learning takes into account both mainstream buying signals (click-throughs, purchase history) and more subtle cues like social media insight and industry trends to segment leads at more finite levels. In this way, teams can focus sales and marketing resources most effectively, giving each lead the right kind of attention, exactly when and where it’s needed.
By looking at data like pages viewed, videos watched, and links clicked, machine learning identifies patterns that can help predict a lead’s interest, sales readiness or even their priorities. It shows you not only how likely a prospect is to buy but also why and when that person will be ready to buy to save you time and money from playing the guessing game.
Which Sales Rep is the Best Fit for This Lead?
It’s not always easy to match the best rep to each lead. Sales managers can spend a lot of time manually creating rules that must be continually altered as staff change. Traditional territory-based and round-robin approaches are common, but conversions suffer when staff spend too much time working leads, which ultimately affects the company’s bottom line.
Machine learning can help you more closely pair leads with the right sales reps by analyzing historical lead closes and losses with predictive lead scoring. It can take seemingly unconnected lead attributes (i.e. geographic location, age of client, buying experience, education) and find relationships between these attributes and the individual DNA of your sales team.
How Should We Communicate with This Lead?
You are likely sitting on a goldmine of customer data that can target users with relevant, personalized messages. The key is to use this data to deliver the right message, at the right time, and in the right place.
Machine learning can define key characteristics quickly and to a level of personalization not achievable when customer segmentation lists are done by hand or by simple analytics filters. Two single males, from Manhattan, in their 30s may look alike on paper, but machine learning capabilities allow you to dig deeper to find nuances in behavior you would otherwise miss. Cue the message/product that shows you understand their unique differences and the probability of converting each skyrockets.
This “hyper-targeting” allows marketers to focus their efforts and money on the people who are most likely to convert into customers, as well as tailor messaging to create offerings in real time.
Where is the Cross Sell/Upsell Opportunity?
Of course, opportunity doesn’t end after that first sign-up for service or shopping cart purchase. However, data potential can’t be realized in absolute if it’s locked in different departmental systems and silos. Interdepartmental conferences and brainstorming sessions can only do so much to determine what product to suggest next or what offer to upsell—and when. It’s simply not humanly possible to see the combinations of behavior, engagement pattern, opportunity and environment all at once.
Machine learning works behind the scenes as your customers interact with product, support and account management resources—all the while leveraging integrated system data to identify behavioral and engagement patterns which indicate when a customer is likely to engage more deeply. These indications can then be used create sales opportunities that are better focused on a customer’s sweet spot and have a higher likelihood to convert to a purchase.
Learn more about how Wise.io is leveraging machine learning technology to drive highly-scalable applications that help sales and marketing teams improve their conversion strategies.