In B2B setups less than 3 percent of leads convert to a meaningful conversation, leave aside being converted into an opportunity or a customer. This results in a lot of unwarranted effort expended by the field teams. Any sales or marketing leader, I have known, would part with her priciest possession, to get this right.
Unqualified leads not only lead to wasted effort from the field teams, they also give a false sense of demand in the market-leading to incremental and untimely investments towards capturing demand (e.g. expansion in sales teams) rather than creating demand (e.g. investments in content)
The most commonly deployed strategy to solve qualification matrix /issues has been lead scoring. While lead scoring models have existed and evolved over the past decade, these are still far from being effective in the B2B world. If we delve deeper into understanding why traditional lead scoring is not as effective in B2B space as it is in the B2C space, we realize that lead scoring has in fact evolved from the B2C space – where everything is centered around a buyer or a point of contact. In the B2B space, however, the buying process is not controlled by ‘a’ buyer, but by ‘multiple entities’ who are involved in the buying process that makes it multi-linear and complex. As a result, lead scoring has not been effective in B2B organizations.
B2B Sales involves several entities in making a buying decision. Depending on the type of product, various members from business teams, technology teams, procurement, compliance, operations, and others may get involved. Each plays its part in the buying process. Alongside, depending on the progression of the deal through the different sales stages –different people from your organization need to play their part well – where and when needed.
In Einstein’s words – “Everything should be made as simple as possible but not simpler”. Lead scoring techniques fall short in understanding and addressing the complexities of the B2B buying process.
With more and more digital interactions, it is increasingly becoming possible to decipher the B2B buying journeys. Account score, if modeled well, could be leveraged to map the journey stages and help organizations to drive account journeys towards the desired outcome. Many organizations, more so the ones which are adopting account-based selling methodologies are adopting account scoring. They want to leverage account scoring not only to determine the stage of an account in the buying journey but also the next-best action to accelerate pipeline-building.
Given the identified use-cases of account score and the number of independent variables which define the account journey, one needs to apply appropriate mathematical concepts to capture the multi-dimensional nature of the problem. One such concept is the concept of tensors.
Tensors are multi-dimensional matrices ranging from zero to N dimensions. An account stage is a complex object defined by multiple independent variables in different planes e.g., firmographic attributes, engagement attributes, relationship attributes, etc. Based on data availability, the account stage (score) can be represented by an nth dimension tensor. Given that most of the ML libraries work with tensors, leveraging tensors to represent account stage(s) comes in very handy while applying machine learning and deep-learning concepts in understanding and orchestrating account journeys.
Summing up, an account score is not just a number, but a multi-dimensional object where each dimension represents a particular facet of an account to accurately define the journey stage. With digital interactions taking center stage now, it’s time to reimagine account scoring and its use cases in B2B sales process.