Decoding the dichotomy: demystifying subscription vs. consumption models in sales engineering

In the world of cloud computing, it is fair to say that the role of sales engineering professionals has undergone a significant transformation, in particular over the last 5 years as a large percentage of vendors have opted into delivering SaaS platforms with a consumption revenue model. In both the subscription and consumption models, sales engineering teams were pivotal in customer acquisition, in particular during the phases of discovery, architectural propositions, demonstrations, Proof-of-Concept and assisting with contract sizing.

However, with the shift towards consumption models, sales engineering teams still continue to have the traditional responsibilities as stated but with the addition (critically important) of the remaining customer lifecycle, emphasising continuous engagement, analytical understanding of product usage and cooperative ownership to drive customer success over the longer term.

For the remainder of this article, the term Sales Engineer will be referred to as SE as shorthand and I will be drawing from experience when expanding on this topic throughout the article.

In the subscription licensing model, customers paid a recurring fee for access to software or services. Traditional presales teams played a crucial role in this model by conducting product demonstrations, negotiating licence terms, and ensuring a smooth onboarding process. Once the customer was onboarded, the relationship with the presales team typically shrank as dedicated customer success teams took over. This created a disjointed experience for customers as after potentially months, quarters or even years of engagement, they were suddenly handed over to a new post-sales team to then build a new trusted relationship.

The primary focus for traditional presales has in the past been about establishing the initial customer engagement and then transitioning the customer to a separate team that would then focus on helping deliver production outcomes to the customer and in turn ensure revenue would be recurring and predictable.

The advent of consumption-based cloud services has structurally changed how traditional SEs interact with customers, particularly with direct engagements that span multiple years. In this model, customers pay for what they use, offering greater flexibility and scalability and in turn SaaS vendors in particular can quickly ramp up revenue across customers but with the tradeoff that in economic or corporate crises, customers are able to dial down spend very quickly.

Contrast this to yearly or multiple year agreements with the subscription model whereby customers are locked into a certain level of spend for longer, particularly if their employee headcount doesn’t change significantly and thus doesn’t impact the amount of subscription licences that are purchased from the vendor.

This shift has redefined the role of SEs, who must now navigate complex usage patterns and billing models (particularly for serverless compute) to provide value continuously over a customer’s tenure. The need to demonstrate value, in particular value to a customer’s business stakeholders places significant and constant pressure on the SEs of today to ensure the technology that they position is relevant and delivering value in a way that is visible and impactful to a customer’s key decision makers.

Furthermore, given that SaaS and consumption-based software evolves at a rapid pace, SEs must continually develop new skills and knowledge or be left behind. This is in stark contrast to traditional subscription and on-premise software where platform evolution was slower and thus SEs had far longer timelines to adapt to change. In addition, understanding customer usage patterns places a premium on an SE’s analytical ability to understand user behaviour on the platform and to predict how that usage is evolving into the future in order for them to provide the best possible recommendations to a customer.

Customer success as alluded to earlier is paramount in a consumption-based model and is intrinsic to the day to day life of the modern SE. In combination with an SE’s upskilling over time, they need to leverage these new skills and knowledge to focus on activities such as continuous customer education and to challenge a customer’s motivations and assumptions . Metrics such as customer usage rates, satisfaction, and retention become critical indicators of impact of SEs and customer success.

This is all the more important for larger/strategic accounts where it is effectively mandatory for an SE or team of SEs to be embedded across business units in addition to technical/platform teams. Today’s SE has thus evolved from the tradition of the ‘Trusted Advisor’ of the 2010s to the ‘Strategic Partner’ where customers integrate and trust SEs like their own staff in helping them to achieve business objectives.

The consumption-based model has led to the adoption of various tools and technologies to support SE efforts. The importance of understanding usage analytics platforms has already been stated, but if we take it one step further, SEs with Data Science skills have a particular advantage in not only being able to track how a customer used/uses the product in the past and present but more importantly how their usage will evolve over time using techniques such as time series forecasting.

This not only helps an SE plan strategic discussions and architectural proposals over the long term but also assists the wider account team to forecast usage/spend and thus being able to more appropriately right size contracts to minimise the risk under/overestimating deals. I personally have found my past experience with time series analytics in my studies and the real world to be of great help in aligning contract sizing with predicted usage.

The emergence of Generative AI in the last 18 months further creates an opportunity on top of typical technical upskilling to enable SEs to be more productive by harnessing the power of AI to not only simplify and automate manual day-to-day tasks but more importantly to enhance the reaction time to behavioural changes of the customer’s usage and provide better and timelier advice and recommendations.

For example, if the customer is projected to overspend over the next year due to inefficient use of the product, it is important for the SE to identify this trend quickly and articulate a series of solutions and if necessary coordinate engagements with their Professional Services/Education colleagues or external partners to correct the underlying issues and put the customer on a sustainable long-term and successful path.

Equally, if a customer is under-utilising the product relative to the contract size, an SE just as importantly needs to identify through analytics and their understanding of the customer’s internal roadmap. In this case, it may be the case the initial contract was oversized (which can be solved for) or the customer’s usage ramp up is being impeded due conflicting BAU commitments, a lack of personnel who have not been educated as SMEs with the product or even a lack of available staff to deliver projects.

In reality, not all SEs will have quantitative backgrounds in statistics, mathematics and so forth. To extend my prior statements on the need for SE’s to possess analytical skills, AI will become a significant enabler in closing the skill and knowledge gaps by providing all SEs with a powerful suite of tooling to understand the past, present and future of the customer’s product adoption lifecycle.

The shift from subscription licensing to consumption-based models in cloud computing has fundamentally changed the role of traditional presales professionals to one where presales as important as ever is but one core competency of the modern SE. No longer confined to initial sales cycle efforts, SEs and SE teams now play a critical role in the entire customer lifecycle, focusing on continuous engagement and cooperative ownership of outcomes in order to drive customer success ultimately through the achievement of business outcomes.