Improving B2B Sales with Emerging Data Technologies and Digital Tools

The B2B sales process is always evolving. The advent of Big Data presents new opportunities for B2B sales teams as they look to transition from labor-intensive manual processes to a more informed, automated approach.

“As data practices grow and mature, we can expect to see more data-backed tools expand and be leveraged by those in B2B sales,” described Forbes in February 2022. “This expansion is backed by investment, with major funding rounds… serving as proof points for the growing interest in advanced sales enablement solutions.”

Sales and marketing teams need to leverage every tool at their disposal if they hope to remain competitive. In this article, we explore some of the emerging strategies and digital tools that B2B companies are beginning to adopt to improve their sales processes.

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Why Common B2B Sales Methods Are Becoming Obsolete

Even in 2022, B2B sales teams continue to rely on manual interactions with data and contacts outside of a centralized digital environment. These interactions involved Excel spreadsheets or even sticky notes to track leads, set appointments, and follow up with prospects.

There have always been problems with this method. For one thing, it is incredibly time consuming. It also requires a lot of manpower, which can be difficult to scale. Additionally, it is not always accurate, as data can be misinterpreted, accidentally modified, or even lost altogether.

Efficiencies are critical in B2B sales as salespeople spend such little time interacting with potential customers in the first place. Gartner estimates sales reps have roughly 5% of a customer’s time during their B2B buying journey; “Lack of time with buyers coupled with rapidly shifting buying dynamics, fueled by digital buying behavior, is reshaping the strategic focus of sales organizations” as a result.

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5 Emerging Technologies in B2B Sales

Fortunately, “business leaders and data specialists are now working together to identify new opportunities, then building tailored models that can help optimize goal-oriented operations as prospects move into the funnel and along the customer journey,” as Forbes describes. Emerging technologies can help sales and marketing teams to drive sales. These include closely related tools that leverage big data, artificial intelligence (AI), machine learning, automation, and predictive analytics. Here is a closer look at how each of these technologies can be used to improve the efficiency and accuracy of the sales process.

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Big Data

Big Data is common to all modern industries; for B2B sales and marketing teams, Big Data can be used to track customer behaviour and purchasing patterns. This information can then be used to create targeted marketing campaigns and to identify potential leads.

“As business leaders look to apply new technology to their sales and marketing processes, we should see more collaboration between business units that have traditionally been siloed,” predicts Forbes. For example, accessing integrated Big Data resources helps sales teams collaborate with other departments whose operations are critical to sales success, such as IT and marketing teams.

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Artificial Intelligence (AI)

There are several applications of AI that can support B2B sales processes. In addition to supporting advanced analytics, leading sales enablement platforms use AI to make informed recommendations to sales teams. AI can support natural language processing (NLP), helping salespeople access critical resources or information within data environments without any technical background.

AI is especially useful for sentiment analysis as well—a critical and transformative tool for B2B sales teams. Sentiment analysis tools study quantitative data and customer behavior to make judgements about customer attitudes, frustrations, and sentiments about either an existing supplier or that of the salesperson. Understanding customers’ or prospects’ sentiments can help B2B salespeople shape their pitches and messaging before engaging these individuals.

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Machine Learning

Machine learning is related to AI; however, machine learning can be more directly applied to specific manual processes. Examples include administrative or search-related tasks that are tedious and time consuming, thereby reducing the amount of time salespeople can spend engaging potential customers. “B2B CIOs must introduce AI to the sales organization to free sales reps from administrative tasks and augment their decisions,” Gartner suggests in another article. “AI augments sales staff… [reducing] administrative sales work to give sellers more time to prospect, find new revenue, and upsell existing clients” as a result.

By its nature, machine learning improves its functionality over time to deliver better results. This is especially useful as salespeople’s “styles” often differ from one person to the next. Machine learning potentially optimizes the way each salesperson engages and responds to data resources, maximizing results in every individual case.

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Automation

Automation is often less advanced than machine learning and AI—it is often a function within those processes instead. But automation can help salespeople in their day-to-day tasks as well as improve their accountability.

For example, salespeople can set up automated reminders and notifications for themselves, such as recurring daily reminders or notifications about specific planned actions within individual sales cycles. Salespeople who wish to stay active on social media channels but struggle to keep up with those efforts may automate postings to an extent as well.

Increasingly, B2B salespeople also need to be more transparent and accountable in how they operate. Automation can assist with this, saving critical information or distributing data about their activities to the appropriate internal stakeholders without any effort required on the part of the salesperson.

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Predictive Analytics

Predictive analytics can help companies anticipate the likelihood of a lead converting into a sale. This allows them to focus their efforts on those leads that are most likely to convert, rather than wasting time and resources on low-quality leads. Predictive analytics can help determine where potential buyers are in their buyer journey, which enables salespeople to adjust their messaging on a case-by-case basis.

“Predictive analytics programs… identify the highest-propensity microsegments and inform the messaging to those microsegments,” as McKinsey describes. “Leads are then prioritized and allocated to sales channels based on both value potential and customers’ buying preferences.”

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Even with New Technologies, Improving Relationships is the Main Goal

Customers now expect companies to respond quickly and effectively when they have questions or concerns about products or services that they have purchased from them—in both B2C and B2B environments. The B2B sales process is growing longer and more complex as a result, driving the need for data technologies and resources.

Even so, the process continues to be highly dependent on interpersonal relationships. In 2022 and beyond, B2B sales will need to collaborate more closely with other departments in their organizations to deliver on those needs.

Far from replacing salespeople, successful data tools augment the efforts of B2B salespeople to improve how they use their time and deliver better results. They improve individual decision making and help salespeople automate key aspects of their workload as well. In this way, technology is trending towards enabling non-technical team members to make better decisions day-to-day—a huge advantage for the sales teams of the future.

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Partner with Uvation to Identify Technology Use Cases in Your B2B Sales

The consultants at Uvation can help you as your B2B sales team begins or continues its journey towards data technology adoption. Contact one of our B2B sales experts today to learn more.

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