Optimizing Forecast Performance For Consumption Driven Business Models

As companies continue to grow and expand in an increasingly digital world, there has been a significant shift towards a consumption or usage based forecasting model.

According to recent reports, it is predicted that by the end of this year 2023, 61% of the general SaaS index will have adopted some form of usage-based pricing, with another 21% planning on testing UBP in the future. 

Recent examples of big tech giants who’ve shifted to usage-based pricing include Apigee, Google Cloud’s API management platform, and vertical software giant Autodesk.

Consumption Driven Business Model

This model is based on data-driven insights and predictive analytics, which allows companies to forecast demand and optimize production accordingly. Instead of simply relying on historical data or guesswork, the consumption forecasting model tracks real-time data and analyzes consumer behavior to make accurate forecasts.

One of the main benefits of a consumption forecasting model is that it helps companies become more agile and responsive to changing market conditions. By accurately predicting consumer demand, companies can adjust their production schedules, inventory levels, and pricing strategies to meet changing needs and maximize profits.

However, to be successful with a consumption forecasting model, companies must also ensure data integrity and performance. This means implementing robust data management processes, investing in the right technology and infrastructure to capture and analyze data, and ensuring that forecasts are accurate and up-to-date.

In particular, accurate forecasting is crucial for companies operating in highly competitive markets. Inaccurate forecasts can lead to excess inventory, production bottlenecks, and missed opportunities. On the other hand, companies that can reliably predict demand can optimize their production processes, reduce waste, and increase profitability.

Dealing with the costs of a bad forecasting tool can be a major headache for any business. Many organizations today rely heavily on customer data to make important decisions. With so much customer data available, it can be difficult to know which metrics to focus on, and how to use them to drive better business results. 

That's why businesses today are turning to a consumption driven forecasting model. This approach can help businesses get the most out of the customer data available to them, while also avoiding the costs of a bad forecasting tool. In this blog, we will be discussing tips and tricks to counteract the costs of a bad forecasting tool in your data performance.

With the rapid development of AI, we can expect to see more companies adopting this technology to gain a competitive edge and stay ahead of the curve.

Choosing the better forecasting model (Why the heck is it so important now?)

The consumption-driven forecasting model is a more effective way of forecasting sales because it is based on the actual consumption of products or services. This model uses data from various sources such as sales transactions, customer feedback, and social media to predict future demand. This makes it a much more accurate way to forecast sales as it takes into account real-time data and can identify trends and patterns that traditional forecasting models may miss.

This type of forecasting is also more compatible with data warehousing, as it is able to integrate and analyze large amounts of data from multiple sources. This makes it easier to identify potential problems or opportunities in the market and adjust business strategies accordingly.

In addition, consumption-driven forecasting allows companies to be more proactive because it provides insight into changing consumer preferences and behaviors. This allows companies to adjust their product offerings or marketing strategies in real-time, rather than waiting until after the fact and potentially missing out on sales opportunities.

Overall, the consumption-driven forecasting model is a better and more compatible way to forecast sales because it is based on real-time data and can provide more accurate insights into consumer behavior and market trends.

It is important to take the time to research and evaluate different forecasting tools before making a decision to ensure that you are making the right choice for your business.

General best practices for selecting the right forecasting tool

Choosing the right forecasting tool can have a significant impact on the accuracy of your predictions and ultimately, the success of your business. Here are some tips to help you select the right forecasting tool for your needs:

By keeping these tips in mind, you can select the right forecasting tool for your business needs and avoid the hidden costs of integrating a bad tool with your data warehouse or data lake.

Conclusion and Key Takeaways

In conclusion, integrating a bad forecasting tool with your data warehouse or data lake can have significant hidden costs that can impact your business. These costs can include inaccurate predictions, wasted time, and lost revenue causing hindrances to your business especially if it is based on consumption driven pricing and value realization.  

To avoid these costs, it's important to invest in a high-quality forecasting tool that is compatible with your data infrastructure and can provide accurate and reliable predictions. This may require some upfront investment, but it will ultimately save you time and money in the long run.

By taking these steps, you can ensure that your forecasting tool is an asset to your business rather than a liability, and that you are able to make accurate predictions that drive revenue and growth.

Check Aviso’s guide to consumption based models to know if you have the right set of tools for it and contact Aviso experts to get support on the same