Mastering Revenue Forecasting with Long Short-Term Memory (LSTM) Networks
Forecasting revenue in consumption-based business models can be complex due to varying customer usage patterns. In this blog post, we will explore how to address this challenge and develop an effective revenue forecasting model. One promising approach is leveraging Long Short-Term Memory (LSTM) networks, which excel at capturing short-term usage patterns while incorporating seasonal or cyclical components tied to the business calendar.
Understanding the Problem:
When dealing with a consumption-based customer base, revenue fluctuations occur based on individual usage patterns. Moreover, customer behavior can evolve as they progress through different stages of the customer journey, such as trying out the software (POV), onboarding in a production environment, or transitioning from a single cloud to multi-cloud environments. These factors further complicate revenue forecasting.
The Power of LSTM Networks:
To address these complexities, various time series models have been explored. Among them, LSTM networks have shown great promise. LSTM networks possess the ability to adapt and learn new usage patterns in the short term, while still capturing the consistent seasonal or cyclical components that strongly correlate with the business calendar.
Unveiling Customer Growth Forecasts through Cohort Analysis
Cohort analysis offers valuable insights that can greatly benefit businesses. One common question I often encounter is, “How can we forecast the growth of new customers when their current spend is expected to increase?” The challenge lies in determining the extent and timing of this growth. In this blog post, we will explore an effective approach to tackle this challenge using cohort analysis. By analyzing previous customer cohorts based on their sign-up dates, we can uncover average growth patterns for different time horizons, ranging from next month to even a year ahead.
Understanding the Challenge:
When forecasting the growth of new customers, it’s crucial to determine the magnitude and timing of their expected spend increase. “When” refers not only to the immediate future but can extend to longer-term projections. This uncertainty presents a significant obstacle in accurately predicting customer growth.
Leveraging Cohort Analysis:
One of the most effective ways to address this challenge is through cohort analysis. By grouping customers based on their sign-up dates, we can analyze the growth patterns of previous cohorts. This approach provides insights into the average growth rates for different time intervals, such as 1 month, 2 months, and beyond.
Approach to Cohort Analysis:
1. Data Preparation
- Gather historical customer data, including sign-up dates and corresponding spend information.
- Clean and organize the data, ensuring accuracy and consistency.
- Segment customers into cohorts based on their sign-up dates.
2. Average Growth Analysis
- Calculate the average spend growth for each cohort at specific intervals (e.g., 1 month, 2 months, etc.).
- Analyze the growth trends across cohorts to identify any patterns or variations.
- Consider additional factors, such as marketing initiatives or product enhancements, that might impact growth rates.
3. Forecasting New Customer Growth:
- Apply the average growth rates obtained from cohort analysis to new customer cohorts.
- Adjust the forecasts based on factors specific to each cohort, such as market conditions or customer behavior.
- Utilize the insights gained from cohort analysis to make informed growth projections.
By examining the growth patterns of previous cohorts, businesses can gain valuable insights into average growth rates at various time intervals. Incorporating cohort analysis into forecasting strategies enables businesses to make more informed decisions and plan for future growth effectively. I’ll follow up with some visuals that best show how to compare cohorts so you can replicate it for your company and be the data super star in your team.
Staying up-to-date on my coding