Machine learning could solve the prioritization problem in B2B technology sales

The speed at which B2B technology sales teams successfully close deals and win customers depends on how well they can not only target the right customers, but how quickly they can sift through the noise to pick them out.

That statement is not news, but as many if not most B2B sales teams know, efficient And accurate prospect targeting is much easier said than done. Prioritizing immature prospects who are still at the top of the sales funnel or who have not yet realized their own technology needs is just a waste of resources. In contrast, highly qualified prospects who are actively looking to make a purchase are much more likely to do so.

This gap remains particularly large for B2B technology providers. Their products may be as wildly innovative and impactful as they think they are, but they may have a less established opinion among buyers.

While building general awareness is important, identifying and targeting the customers who have a current reason to buy changes sales (and marketing) efficiency, which becomes increasingly important as the B2B technology provider scales. Simply targeting companies with the right revenue threshold or workforce all too often means devoting sales and marketing resources to companies that are nowhere near ready to write a check — and may never be.

ML and automation: essential for B2B

As tech B2B sales teams are likely to face more pressure in 2023 to do more with less, automation will have to be part of the recipe. Machine learning (ML) is at a point where it can enable data-driven precision for targeting the right customer contact with the right pitch at the right time. Teams that have been wary of trusting ML so far may not have much choice, but will quickly convert with the right strategy and workflow.

The right deep ML insights fed from the right data sources can identify accounts with the specific and current change agent factors that indicate how hungry a prospect is for a technology shift.

For example, a potential customer is preparing to introduce a revamped and modernized customer experience that requires a new frontend stack. Or perhaps an enterprise is playing catch-up in digital transformation initiatives, giving indications that it is about to embark on a widespread cloud migration.

A new leader joining the company or taking charge of a key department can be the crucial breadcrumb that points to a technology overhaul. Companies that showcase these agents of change are much more likely to be in a buying phase, with the momentum, urgency, and allocated budgets to quickly adopt the right solution when presented to them.

Remove the guesswork

Similarly, B2B tech sales teams could (and should) leverage ML insights to find and target companies with legacy technology stacks; these present clear opportunities for augmentation or rip-and-replace transformation.

Companies that feel the pain points of outdated technology that they could gracefully trade in for a vendor’s offering are often the most ripe for conversation and conversion – and ML can take the guesswork out of this.

If a potential customer’s technology infrastructure allows a vendor’s solution to slip right into their stack and deliver tangible benefits, the sale becomes a downhill proposition. For example, solutions that require cloud adoption or a certain level of IT maturity should target customers who meet those criteria.

Collecting the right data with ML also helps vendors who can support cloud and data migrations identify and target customers for those projects are starting. Prioritizing accounts that are actually ready and capable of realizing the benefits of the technology you’re selling shortens the sales funnel and arguably increases efficiency.

Finally, diving into ML-powered insights can reveal the true potential value of a target customer, enabling sales teams to prioritize accounts based on how big of a fish they have at stake. Metrics such as team size (not necessarily company size), current projects and goals, expansionist buyer personas, and more factors can indicate an account’s growth potential.

By knowing the size and composition of the internal team that will directly use a solution, a supplier can estimate immediate revenue opportunities. For example, vendors with data solutions that provide analytics, monitoring, security or other features can identify a customer’s potential by the size of their data footprint.

Address pain points

These strategies for identifying and prioritizing customers with the most conversion and revenue opportunities are no secret — they’re used by sales teams in many successful companies. That said, it’s getting harder (and more time-consuming) to run them manually.

While some “intent” tools claim to achieve this kind of prioritization using black-box ML methods and looking at web searches and web traffic, it’s not exhaustive. It needs to be supplemented with manual effort (and research into projects and pain points that ML can solve quite effectively).

As a result, many organizations have left their teams with tremendous manual effort and sifting through insufficient insights to try and identify the right customer goals.

For example, a sales professional looking for the right decision maker for a target organization’s software development team may waste a lot of time sifting through countless titles, from R&D to engineering, app development, DevOps, app delivery, and more. And they may still never find the individuals with the will and resources to make or influence a favorable purchasing decision.

Meanwhile, a competing supplier – with sales teams informed by ML-powered data visibility into that same customer – can immediately contact the most likely buyer with the perfect pitch and be on the road to conversion – and efficiently.

For B2B technology providers, success means equipping their teams to be that competitor.

Leena Joshi is the CEO and co-founder of Close factor.