HomeNewsCan Machine Learning Fix the Prioritization Problem in B2B Tech Sales?

Can Machine Learning Fix the Prioritization Problem in B2B Tech Sales?

The rate that B2B technology sales teams successfully close deals and win customers hinges on how well they can target not just the right customers, but how quickly they can sift through the noise to pluck them out. That statement isn’t breaking news, but as many – if not most – B2B sales teams know, efficient and precise prospect targeting has been far easier said than done. Prioritizing unripe prospects that are still at the top of the sales funnel or that haven’t yet realized their own technology needs are only wasting resources. In contrast, highly qualified prospects that actively seek to make a purchase will do so with a vastly greater frequency.

This difference continues to be especially stark for B2B technology vendors. Their products might very well be as tremendously innovative and impactful as they believe, but perhaps hold less established mindshare among buyers. While building general awareness is important, identifying and targeting the customers that have a current reason to buy transforms sales (and marketing) efficiency, which becomes increasingly critical as the B2B technology vendor scales. Simply targeting companies with the appropriate revenue threshold or headcount too often means expending sales and marketing resources on businesses that aren’t anywhere near ready to write a check – and may never be.

Why ML, Why Now

As B2B tech sales teams likely come under more pressure to do more with less in 2023, automation will need to be part of the recipe. Machine learning (ML) is at a point where it can enable data-driven precision for approaching the right customer contact with the right pitch at the right moment. Teams wary of trusting ML to date may not have as much of a choice in the matter – but will be quick converts with the right strategy and workflow.

The right in-depth, ML-insights fed from the right data sources can identify accounts with the specific and current change agent factors that indicate just how hungry a prospect is for a technology shift. For example, a prospective customer might be preparing to introduce a revamped and modernized customer experience that requires a fresh frontend stack. Or, an enterprise might be playing catch-up in digital transformation initiatives, and making indications it’s about to take on a widespread cloud migration. A new leader joining the company or taking the reins in a key department may be the crucial breadcrumb pointing to a technology overhaul. Businesses that exhibit these change agents are far more likely to be in a buying phase, with the momentum, urgency, and allocated budgets to quickly adopt the right solution if presented to them.

B2B tech sales teams could (and should) similarly tap into ML insights to find and target businesses with legacy technology stacks showing clear opportunities for augmentation or rip-and-replace transformation. Businesses feeling the pain points of outdated technology that they could cleanly swap out for a vendor’s offering are often most ripe for conversation and conversion – and ML can take the guesswork out of this.

Alternatively, if a potential customer’s technology infrastructure allows a vendor solution to slide right into their stack and deliver tangible benefits, making the sale becomes a downhill proposition. For example, solutions where cloud adoption, or a certain degree of IT maturity, is requisite should be targeted at customers that meet that criteria. Mining the right data with ML can similarly allow vendors that can support cloud and data migrations to identify and focus on customers before those projects kick off. Prioritizing accounts that are actually ready and able to realize the advantages of what technology you’re selling will shorten the sales funnel and demonstrably boost efficiency.

Valuing Prospects

Lastly, diving into ML-powered insights can reveal the true potential value of a target customer, allowing sales teams to prioritize accounts by just how big of a fish they might have on the line. Metrics such as team size (not necessarily company size), current projects and goals, expansion-minded buyer personas, and more can indicate an account’s growth potential. Knowing the size and makeup of the internal team that will directly utilize a solution allows a vendor to gauge the immediate revenue opportunity. For example, vendors with data solutions that provide analytics, monitoring, security, or other functions can recognize a customer’s potential by the size of their data footprint.

These strategies for identifying and prioritizing customers with the most conversion and revenue opportunities are no secret: they’re practiced by sales teams across many successful businesses. That said, they are increasingly difficult (and increasingly time-consuming) to execute manually.

Several “intent” tools on the market claim to accomplish this type of prioritization using black-box machine learning methods and by looking at web searches and web traffic. Of course, this is not complete and needs to be supplemented with manual efforts and research into projects and pain points that ML can solve quite effectively.

As a result, many organizations have their teams expending tremendous manual effort and sorting through inadequate insights to try to identify the right customer targets. For example, a sales professional searching for the right decision-maker for a target organization’s software development team may waste significant time sifting through countless titles, from R&D to Engineering, Application Development, DevOps, Application Delivery and more. And they still may never find the individuals with the will and the wherewithal to make a favorable purchasing decision or influence it.

Meanwhile, a competing vendor 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 path toward conversion, efficiently. For B2B technology vendors, success means equipping their teams to be that competitor.

Author

  • Leena Joshi

    Leena Joshi is the CEO of CloseFactor, a company that automatically curates unstructured information about companies and extracts intelligence that go-to-market teams act on to drive qualified pipeline.

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Leena Joshi
Leena Joshihttps://closefactor.com/
Leena Joshi is the CEO of CloseFactor, a company that automatically curates unstructured information about companies and extracts intelligence that go-to-market teams act on to drive qualified pipeline.

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