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Manufacturing Companies Move Beyond GAAP Metrics

Manufacturing companies are typically run by backward-looking GAAP metrics, like Sales, Cost of Goods Sold, or Gross Profit.

However, with new innovative business models comes new forward-looking metrics that translate the predictability and recurring nature of the model.

Some of these key new subscription metrics include: ARR (Annual Recurring Revenue)ARR shows the money that comes in every year for the life of a contract, allowing for predictability of revenue. It’s a good measurement of the health of a business. Churn rate – Also called attrition rate, churn rate is a measurement of churn, i.e. subscriber turnover. Churn rate is the rate at which a business loses subscribers. CLV (Customer Lifetime Value) – Customer lifetime value estimates the total value of a customer over the course of its lifetime, calculating for both revenue and cost. CLV sees subscribers as assets and is a useful tool in managing and focusing on maintaining long-term subscriber relationships. ARPU (average revenue per user) – The ARPU measures the revenue of an individual subscriber as calculated by taking total revenue for a defined time period and dividing that revenue by the total number of subscribers during that time period. The ARPU is valuable for providing a per user view of revenue. Growth Efficiency Index – The cost of growth is measured by the growth efficiency index (GEI), which is the sales, marketing, and onboarding costs that are required to earn $1 in additional annual recurring revenue (ARR). These metrics are critical to steer, assess, and communicate the value of a recurring business. In a nutshell, businesses offering subscriptions live and die by these metrics! But producing those metrics from the traditional architecture that manufacturing companies are run on is a no go. While ERPs were sufficient to report GAAP metrics, they can’t automatically calculate these new dynamic metrics that continuously need to be recalculated. As a result, producing those metrics from legacy architecture will require manual calculations, Excel spreadsheets, and other insufficient, inefficient, and error-prone workarounds that lead to incorrect KPIs, uninformed decisions, additional costs, and a bottleneck to tackling scale.

Manufacturing businesses need the ability to calculate key subscription metrics in real-time for any transaction and the ability to surface these metrics across the order-to-revenue process.

These metrics enable businesses not only to monitor financial performance but to predict future outcomes, drive decisions, and uncover new revenue opportunities. Since, it is 6-7x more expensive to get a new customer than retain an existing one, the importance of proactively monitoring subscription metrics cannot be overstated. For manufacturers making the shift to dynamic subscription models, there are 3 core questions to ask to ensure that you have the right foundation to support this shift: 1. Do you know the future target operating model of your new business model? 2. Do you know what metrics should support decision making for this operating model, by measuring the real-time health of your new business model? 3. How would you currently pull those metrics?

When key data and KPIs are automatically generated, businesses can spend more time acting on the data, rather than struggling to compile it.

For example, with forward-looking insight into churn risk, a business can mitigate the risk through relevant campaigns, like introducing new pricing and packaging models, smart discounts, and more. When businesses have real-time access to monetization metrics, they are better positioned to steer, assess, and communicate the value of a recurring business. We see this in real-life use cases, like NCR Silver, where management is able to get accurate reporting on previously unknown keystone metrics, such as monthly recurring revenue (MRR) and average revenue per user (ARPU) — and then use this data to make smarter business decisions. Combining reporting on these new metrics with behavioral, usage, and demographics can generate insights that empower manufacturing businesses to incorporate feedback loops for continuous improvement.

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