Imagine the following: As you scroll through social media, you come across an ad. Not just any ad though, an ad for a product so specific to what you are looking for that you wonder how they were able to find you. How did they know that you were thinking about finally getting the motorbike you have always wanted or that you were talking with your spouse about adding a puppy to the family? It’s as if someone was eavesdropping on your conversations or reading your mind.
If you have ever had this experience—whether you know it or not, you have—then you have seen the work of predictive analytics and mass data in action. The ads you received were not random; it was likely the result of a targeted campaign using your data and advanced predictive analytics to reach you at the right time, now commonplace in B2C sales.
Over the last several years, there has been an immense rise in the utilization of artificial intelligence driven tools to help businesses target their customers more effectively with the right product at the right time. Though B2C businesses have been at the forefront of this development, the increasing availability of sales enablement tools and software has market-leading B2B companies catching up. Understanding the changing landscape of sales analytics and how these tools can help accelerate your company’s revenue growth is key to becoming a best-in-class sales organization and winning market share from your competitors.
Start by leveraging SBI’s Digital Maturity Evaluation Tool for a rapid diagnostic of your organization’s sales maturity across 8 major digital areas.
What Does the Next Generation of Sales Technology Look Like?
To develop a comprehensive plan to take your revenue growth engine into the next decade, you must understand the landscape. Artificial Intelligence, Machine Learning, and Predictive Analytics are at the forefront of many strategy conversations, but what do each of these terms mean, and how are they being implemented today?
Artificial Intelligence is the most referenced of these 3 terms and the overarching technology. Fundamentally, Artificial Intelligence is any usage of computer processes to replicate human intelligence. This can be the chatbot that answers common questions on your company’s website, a hiring software that pre-screens candidates, or a system that helps optimize which warehouse your company might ship from. The algorithms which drive these technologies are often built using Machine Learning algorithms.
Machine Learning encompasses the set of tools and computer processes that take large datasets and create insight and inferences without explicit instruction. Using machine learning, we can build predictive analytics systems to categorize the likelihood of certain future events based on historical data sets.
All in, we might say something along the lines of:
“A key initiative in 2021 is to grow revenues in our base by 7% using machine learning to build a predictive analytics model that identifies upsell and cross-sell opportunities.”
What Can the Next Generation of Sales Technology Do for Your Business?
Now that you understand the framework for AI in B2B sales, you can assess where building out an advanced process can help your business. Some of the common areas we see market-leading companies incorporate predictive analytics are in lead scoring, sales forecasting, upsell and cross-sell opportunity identification, and churn or risk assessment.
We typically see lead scoring conducted through a mixture of marketing and inside sales or lead development reps to move leads through the pipeline. In an advanced organization with well-defined MQL to SQL criteria, this can be effective, but market-leading organizations use predictive analytics to score their leads. Not only does this cut down on the cost of prospecting, whether through resource allocation or time cost, but it helps identify only the best leads with the highest likelihood to win large deals driving LTV up. In addition, advanced machine learning algorithms use years of pipeline data to determine which factors are key to closing deals faster and help drive your sales velocity up.
Even the best sales leaders cannot forecast nearly as well as machine learning algorithms can—and frankly, they should not have to. The sales forecasting process can be extremely time-consuming, typically involves multiple stakeholders, and is particularly susceptible to human error. Incorrectly predicting a few large deals or missing opportunities you felt were unlikely to close can significantly affect your ability to make your number.
Using the next generation of sales software and analytics, we can create and train a model to identify which deals are most likely to close and build forecasts that are significantly more accurate than humans can create. In so doing, we free up manager’s time and help provide them with context around the specific accounts and industries for their teams to call on.
Upsell and Cross-Sell Opportunity Identification:
Not only do predictive analytics allow you to identify the best prospects and customers to call on, but they can also help identify which are the most likely to buy additional or more expensive products. Machine learning algorithms are terrific at identifying buying patterns within your customer base and can identify which of your current customers are the most alike based on multiple inputs and recommend cross-sell opportunities based on the similarities between companies.
Churn or Risk Assessment:
Another way to use predictive analytics is to create a churn a risk assessment calculator that can determine which customers are likely to churn and need customer success intervention. Like an upsell or cross-sell model that identifies product fits, predictive analytics can determine which customers are likely to churn where there is a product mismatch or a drop in product usage. By identifying these problem areas, you can actively monitor and run campaigns against these customers to increase net revenue retention across the business.
The future of automating the sales process to find the right prospects, qualify leads, identify product fit, and help you make your number is already here. The next generation of sales technology is at our fingertips. The companies who adopt the fastest and conquer the data-driven processes landscape will grow revenue faster than their competitors. Will your team be leading the charge?
Want to know more about your company’s Go-To-Market maturity? The Revenue Growth Maturity Model is an assessment developed by SBI to help companies identify key risks to their revenue growth.
The 7-minute exercise will pinpoint your company’s strengths and gaps in these areas:
- Revenue Growth Strategy
- Revenue Marketing
- Pricing & Packaging
- Customer Experience
- Customer Success
Given your score, the RGMM will identify actionable insights that you can begin implementing immediately. As you climb the ranks of the maturity model over time, you will see lower customer acquisition cost, higher customer lifetime value, and more predictable growth attainment.
Where do you currently rank on the RGMM?