Could IBM Watson, powered by artificial intelligence, beat James Holzhauer, whose legendary run on Jeopardy came close to shattering the show’s records? Watson has already taken on—and trounced—two legends in the game, tripling their scores in the process. That’s the power of machine learning.
Sales organizations know the benefits of adopting tools powered by machine learning: on average, they use 10 different sales tech tools, with plans to add four more to their repertoire in the next 12 months. Yet all that technology isn’t necessarily producing better sales outcomes. That’s especially true for tech that just collects—or, worse, requires sales teams to manually input—ever more raw data, contributing to information overload without helping your sales team win more deals.
Sellers today need to spend their time focusing on their customers, and they need to know that administrative tasks, such as entering data into a CRM, helps them do so. One way to do this is through sales technology powered by predictive analytics or AI. While these tools can’t predict the future with 100% accuracy, they can provide sellers insights into strengthening customer relationships and increase the likelihood of closing deals.
With so many sales technology tools in this space, how do you decide which tools will optimize your sales without bogging down your sales team? How do you invest wisely, freeing your sales team to spend time where it matters: on cultivating leads and taking steps to close more deals?
Here’s what you need to know about sales technology trends to make sure that at your organization, the future is now.
CRM platforms are the chief form of sales technology in many organizations. But often, CRM platforms serve as little more than electronic Rolodexes. Less than half of sales teams—46%—report widespread use of their CRM, according to the 2018 Sales Operations Optimization Study from CSO Insights.
Why is that? Because CRMs serve as an electronic database of customers, prospects and other contacts, organizations assume that these platforms should help their sales teams win more deals. But in reality, CRMs are designed primarily to give leadership teams insight into sales forecasts and transactions that have already occurred.
Instead of facilitating sales, CRMs require sellers to enter data about what has happened in the past without providing actionable insights for the future. In short, they’re time-consuming and backward-looking—which isn’t what deal-oriented salespeople need.
But that doesn’t mean you shouldn’t use a CRM. There’s an easy way to make any CRM perform better as a sales tool: add a single layer of technology with AI-informed predictive analytics.
Predictive Analytics and AI
In essence, AI allows technology to perform tasks that would previously have required human intelligence. For example, AI can expedite certain repetitive, administrative tasks that have historically taken time away from selling, leaving professionals more time to focus on client interactions. And that’s not all: AI can improve the quality of those client interactions as well.
How? In the past, a seller’s insights were based solely upon their own experiences and intuition. Today, AI can capture the experiences and insights of a broad universe of interactions, providing sellers with deeper, more precise insights that lead to better timed and targeted—and consequently more meaningful—communications with their customers.
When sellers analyze their individual actions and seller responses, they generally struggle to discern trends, even if their data is captured in a CRM platform. But when that CRM layers with a sales analytics tool like Scout, sellers can learn from previously invisible correlations between data sets. In layman’s terms, they can study their actions and learn which ones most likely yield success.
Thus, analytics tools can show which seller actions lead to wins—and which contribute to losses. Better yet, tools like Scout that are built on top of a proven sales methodology—like Strategic Selling with Perspective—can show sellers who they should approach within the buying organization, how they should make the approach and how they can model the impact of the solution for their business—and why.
In short, data-driven analytics allow sellers to accurately predict which of their actions are most likely to advance an individual opportunity down the sales funnel.
When you connect sales technology with sales methodology, you give sellers more than information. You give them actionable insights, couched in a repeatable, consistent framework that can help them manage their time, prioritize the right deals and close more sales.