5 Key Takeaways From The Shift To Consumption-Based Revenue Models

As the business landscape continues to morph and diversify, an increasing number of organizations are transitioning from subscription-based models to consumption-based models. A prime example of this transition is New Relic, and their Revenue Operations leader, Ted Noble, has shared valuable insights gleaned from their first year of shifting gears.

Here are four key revelations from their journey.

Takeaway 1: Switching to consumption-based models impacts more than just your pricing and packaging strategies

The move to a consumption-based model isn't just a minor tweak—it's a seismic shift that resonates across every department and process within an organization. Beyond just pricing and billing, there are numerous other pivotal elements to be considered. These encompass buyer programs, contracts, the roles and duties of sales teams, technical account managers, product modifications, and user experiences. Furthermore, accurate data measurement and transaction computations, as well as revenue recognition and financial management, are paramount in a consumption-based model.

Takeaway 2: Conventional CRM tools may not be adequate for consumption forecasting

With the shift to a consumption-based model, the focus of forecasting transitions from contracts to actual usage. Traditional CRM tools, which are primarily designed around pipelines and contracts, find it challenging to adapt to this new focal point. Consequently, RevOps teams need to embrace new tools and platforms capable of absorbing billing data, conducting AI and ML-based analysis, and providing precise forecasting for both consmption and contracted business.

For more details, check out our post on  Consumption Forecasting.

Takeaway 3: AI and ML are indispensable for accurate forecasting in consumption-based models

Consumption-based models can be more unpredictable than subscription-based models, which makes accurate forecasting vital. For such scenarios, traditional forecasting tools may fall short.

AI and ML-powered solutions like Aviso can furnish superior predictions and insights, empowering sales and RevOps teams to make well-informed decisions. These platforms can also feed data back into the organization's enterprise data warehouse for further analysis. It's crucial to allow AI and ML models to operate uninterrupted, except when future projections don't align with historical data. In these instances, human intervention may be required to adjust the forecast.

Takeaway 4: Consumption-based models foster a more customer-centric business approach

One of the most pronounced advantages of moving to a consumption-based model is the heightened flexibility it affords customers. They can adjust usage based on their needs, ensuring no unused services or "shelfware" linger once the contract concludes. This model aligns seamlessly with products that involve data ingestion, as customer needs can fluctuate over time. Introducing rollover options and savings plans can boost flexibility even further, ensuring customers keep benefiting from the consumption-based model.

Takeaway 5: Consumption forecasting needs a unified tool to navigate CRR and ACR forecast

For consumption-based businesses, forecasting solutions must be capable of managing both CRR (Consumption Run Rate) and ACR (Annual Committed Revenue) for empowering managers to make confident forecasts and set clear, cascading targets. ACR and CRR are two sides of the same coin, and while some companies utilize these specific acronyms, different organizations might employ different terminologies or even develop custom metrics tailored to their specific business model and needs.

A unified tool should be able to bring CRR and ACR forecasts in the same instance on a very simple, intuitive user interface that allows businesses to combine all the various sources of data intelligence that they have, to create a more comprehensive, and accurate forecast. Such a tool not only needs to be capable of providing a holistic view of both CRR and ACR but also should allow for both top-down and bottom-up forecasting approaches.

Conclusion

Transitioning to a consumption-based model requires a significant shift in mindset and operations. It's essential to recognize the impact on every aspect of the business and adapt accordingly. Establishing processes and cadences similar to those in subscription models for the consumption model is crucial. This involves focusing on implementing new systems and structures for managing the consumption business and ensuring a smooth and efficient transition. By doing so, businesses can better adapt to the new model and improve their overall operations, customer experience, and ultimately, their bottom line.

Aviso helped New Relic make the leap to consumption-based models and drive customer-centric growth. At Aviso, we use powerful AI and forecasting tools to conduct consumption forecasting with high accuracy for successful consumption-based business models. An accurate forecast helps you with predictable growth, increased confidence, and zero lost opportunities. 

Curious about how AI can help you increase the accuracy of your consumption-based forecast? Sign up for a product walk-through of Aviso AI.