Read to learn how predictive sales analytics enables B2B sales leaders to accelerate sales with data science and grow accounts as effectively as possible.
B2B sales leaders are tasked with ensuring their teams can earn, retain and grow their accounts as effectively as possible. Even as their teams are faced with large books of business, competing and changing corporate objectives, and the rapid rise of eCommerce, revenue growth is critical — yet challenging. Keeping a sales team focused on the most important opportunities to predict, preserve and grow revenue has never been more difficult.
According to “The Future of Sales in 2025: A Gartner Trend Insight Report,” an exponential rise in digital interactions will break traditional sales models. The report states, “As customers migrate from in-person channels to digital alternatives, chief sales officers (CSOs) must engage in a fundamental mindset shift from leaders of sellers to leaders of selling; sales leaders belong where selling happens.”
As this shift happens, sales teams must undergo a skill set evolution. “Sellers’ decision making will be based on data, analytics and AI, not on intuition and experience.” The report also makes an eye-opening prediction. “By 2025, 60% of B2B sales organizations will transition from experience- and intuition-based selling to data-driven selling, merging their sales process, sales applications, sales data and sales analytics into a single operational practice.”
The imperative is clear. Sales leaders must equip their teams with data science-driven solutions to compete and win. Thankfully, sales software has grown more sophisticated to match the scale and complexity of B2B sales.
What is Predictive Sales Analytics?
Predictive sales analytics operationalizes data science-driven insights in a systematic closed-loop process, learning and consistently generating better guidance, delivered directly to sellers for every one of their customer accounts.
Predictive sales analytics can take the burden of manual sales data analysis off the sales team’s plate. Instead, predictive sales analytics gives them direct actions to take from day to day, including:
- What products should I be pitching to my current customer base? Which customers are most likely to buy which products?
- Which customers are slowly starting to buy from a competitor and will continue to defect if I don’t act now?
- Which products should I pitch to win back lost business?
- Which opportunities are most worthy of my time to pursue?
These cases and many others fit broadly under the scope of solutions known as predictive sales analytics. Though the term can mean slightly different things to different people, here’s a tidy definition from Hubspot:
“Predictive sales analytics is a type of analytics that uses predictive algorithms and patterns in historical data (typically gathered from a company’s CRM or ERP software) to create forecasts, anticipate prospects’ behavior and inform better campaign designs for both B2B and B2C companies.”
This blog post will focus on how B2B predictive sales analytics is used to translate customer, product and transaction data into actionable sales insights for sales teams.
Read: Top 10 Revenue Killing B2B Sales Myths and Getting Personal in B2B Sales
Why is Predictive Sales Analytics Vital to a B2B Sales Strategy?
B2B companies thrive on forging deep relationships with customers and nurturing those over time. The cost and risk associated with chasing prospects, while necessary, will rarely be as reliable or important to the bottom line as growing and retaining current customer business.
According to Forbes, “It can cost five times more to attract a new customer, that it does to retain an existing one.” Meanwhile, an increase in customer retention rates by 5% grows profits by 25-95%, per Bain & Company research.
Selling more to the customers you already have is thus less expensive, more effective and strategically important. So why do B2B companies struggle to consistently take advantage of these opportunities?
The answer lies in the growing complexity of the customer landscape. A field sales rep may have 50 or more accounts in his or her territory, if not more, and anywhere from thousands to tens of thousands of products to sell those customers.
Out of necessity, sales reps will follow the 80/20 rule. This means reps spend 80 percent of their time on the top 20 percent of accounts.
This means the top five or 10 accounts get heavy attention in the form of frequent sales calls and planning meetings. The rest of the account list does not receive the same attention. There are only so many hours in the day.
Being able to identify cross-sell opportunities across every customer in a sales patch is a massive data problem. It’s even more difficult to detect which customers are starting to buy from competitors.
Customer churn generally happens slowly, one or two product categories at a time. Catching the early signs of churn within a large, strategic account is a challenge. Especially in the wide expanse of non-core accounts. Further, sales reps are often tasked with executing a multitude of other corporate initiatives, making prioritization a constant challenge.
Modern sales teams need the kind of guidance only a predictive, science-driven solution can provide. Sales operations managers and sales VPs are increasingly turning to advanced applications that translate data into actionable insights for sales reps.
Read: Zilliant 2021 B2B Sales Ops Guide
