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.

 

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.

 

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.

 

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.

 

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.

 

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.

 

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.

 

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.”

 

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.

 

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.