Daniel Newman is the Principal Analyst of Futurum Research and the CEO of Broadsuite Media Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise. From Big Data to IoT to Cloud Computing, Newman makes the connections between business, people and tech that are required for companies to benefit most from their technology projects, which leads to his ideas regularly being cited in CIO.Com, CIO Review and hundreds of other sites across the world. A 5x Best Selling Author including his most recent “Building Dragons: Digital Transformation in the Experience Economy,” Daniel is also a Forbes, Entrepreneur and Huffington Post Contributor. MBA and Graduate Adjunct Professor, Daniel Newman is a Chicago Native and his speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
One of the biggest problems for businesses today is sorting through data to find customer insights. In fact, most companies today collect mass amounts of data—probably too much, in my opinion. Mining through it all can be difficult and time-consuming for sales teams hoping to learn more about their prospects and improve their chance of a sale.
Thankfully, machine learning can help. Using artificial intelligence (AI) and as-a-Service (aaS) MarTech software, companies can better identify and interact with customers—without ever personally setting foot in the quagmire of dumped data. Even better: because insights are gained instantly, they have time to actually act on the data they’re given. (More on that in my piece for Forbes.com called “Customer Data Means Nothing Without an Action Plan.”)
If you’re not already using machine learning to harness the power of big data, you need to be. The following are just a few ways it can help.
Personalization at scale
That’s what Angel Morales, the founder of MarTech company ZIO, is calling machine learning’s role in sales and marketing. Using machine learning systems like Adobe Sensei , ZIO, SalesForce’s Einstein, and IBM’s Watson, businesses can get to know their customers even more intimately—learning when they shop, how much they usually spend, which channels they prefer, and what types of promotions work for them. Machine learning does more than take the guess work out of marketing. It virtually hand-delivers a personalized shopping opportunity to every customer—in the time, place, and manner that customer likes best. In short, it hugely improves your chance of a sale.
Customer Journey Mapping—and Marketing
It used to be that our relationships with customers were like book ends: we’d market to them before they bought our products, and sometimes we’d hear from them on the back end if the product was especially good—or bad. But nowadays, with social media, email, chat, and even smart beacons, companies can interact with their customers at practically any stage of the customer “journey.” But having the ability to communicate is just part of the equation. Being able to communicate intelligently is where machine learning comes in. Data pulled and analyzed can help you determine which special offer to send your customer—what they’ve been shopping for—what price range they’re looking to spend—and even when they happen to be within a certain vicinity of your store. It can alert you when they mention you on social media, and empower you to take a proactive stand if they aren’t happy. Talk about a winning strategy—customers today have barely any valid reason to say “no.”
Behaviors Beyond Bounce-backs
Remember the days when our marketing reports consisted merely of web hits, unique visitors, and Facebook followers? Today, those stats are almost useless—at least when it comes to understanding the type of products and services your customers want and need. But don’t panic. You don’t need to hire a new team of data analysts to mine more meaningful data. For instance, Angie, an AI-powered assistant from Conversica, can process thousands of inquiries—in the case of Epson America, up to 60,000—to gain insights that would be nearly impossible for a human to manage. Indeed, not only can Angie answer messages just like a person—she can make meaningful sense of the messages she receives, finding important trends about each customer or customer segment that turn into valuable insights for your company. Forget bounce backs and page counts—the numbers you need to do are about behaviors, patterns, and the predictions you can make from them.
In the end, what we’re talking about here isn’t just cool technology. It’s the ability to prepare the right deals and close them faster. This is especially true when you pair machine learning with other tools like Callidus Cloud contract making software and Adobe Sign—a system that allows you to prep contract and route them for e-signature automatically, ensure that the deal is always signed now—when the customer is still in the mood. In fact, contract automation is so easy that some businesses are using them to close deals right on the tradeshow floor—instantly. Literally all you need to do is sign. You can try it out for yourself with a 14-day trial of Adobe Sign.
Does machine learning mean your marketing team can take a hands-off approach to sales? Far from it. As with any new technology in digital transformation, you’ll want to take time to evaluate performance, tweak for improvement, and maximize your ROI. The difference is that with machine learning, you can be hands-on where it counts—not in your database or CRM, but with the customers themselves.
Additional Articles on This Topic:
Using Predictive Analytics to Improve Sales Leads
How Machine Learning Can Give Us Greater Customer Insights
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