Our client’s Storage Company is a leading provider of storage solutions, serving more than 100,000 corporate customers across 180 branches. The company’s primary objective is to ensure consistent revenue, maintain healthy profit margins, facilitate investments in other business verticals, and fund acquisitions. To achieve this goal, the leadership recognized the need to monitor factors that influence revenue retention.
The storage company faced several hurdles in maintaining consistent revenue. Fluctuations in billings and storage volumes, coupled with changing prices and market dynamics, made it challenging to predict revenue trends more accurately. The leadership was also in need to track the impact of departed and departing customers under the light of annual price revisions, bundled pricing, credits and service quality. Existing datasets from storage inventory and invoices were vast and complex, requiring comprehensive analysis to derive meaningful insights.
To address the revenue retention challenge, a multi-faceted solution was devised. The primary focus was on tracking individual key performance indicators (KPIs) to understand the company’s revenue health and the factors affecting it. For the study, several datasets were selected to gauge revenue retention effectively. These included:
- Billings Growth Year over Year (YoY)
- The proportion of revenue growth attributed to Price Revisions versus storage volume changes, and
- The magnitude of the impact caused by departed customers
Price differences, including price increases and credits, were analyzed in relation to customer retention. Additionally, monitoring customer care follow-ups for customers experiencing zero volume and consistent prices provided insights into potential pain points.
Data analysts played a vital role in identifying the scope of the problem. They conducted a thorough analysis of historical billing data, customer behavior, and pricing patterns. The analysis was segmented by corporate account level, industry level, markets, and branches to identify revenue trends at different granularities.
The data engineering team worked on processing and organizing the vast datasets from storage inventory and invoices and developed data pipelines to extract, transform, and load the data for analysis. This process ensured that the data analysts had access to clean and reliable data, enabling more accurate insights.
By examining existing customers and those who departed, the team measured the impact of price changes and the consumer price index (CPI) on billings. The analysis revealed that strategic price increases led to revenue growth, while fluctuations in hardcopy storage affected overall revenue.
Through a comprehensive analysis of customer departures, the leadership successfully identified critical areas for improvement in its retention strategies. A number of significant changes were implemented, including:
Annual Price Revisions
Our client has implemented an annual price revision policy to ensure that all customers, including those with a significant business portfolio who are currently inactive, are engaged and provided with updated pricing information.
After recognizing the importance of service quality and addressing invoice issues promptly, our client started prioritizing the issuance of credits to rectify any discrepancies.
Product Offering Revisions:
Based on a thorough analysis of customer needs, the organization realigned its product offerings and pricing to better suit the specific needs and preferences of its target audience.
Introduction of Bundled Prices:
A strategic move was made to introduce bundled prices, providing customers with increased value and flexibility in their purchases.
Implementation of Seasonal Discounts:
As an incentive for customers to engage with the brand repeatedly, the leadership introduced seasonal promotions and discounts to cultivate loyalty and encourage B2B engagement through destruction and on-boarding.
The adoption of this data-driven approach for revenue retention resulted in significant improvements. The company achieved better monitoring of budgeting and forecasting, leading to improved customer satisfaction and 85% revenue retention rate. By continuously analyzing competitors, the company stayed competitive in the market, further contributing to its revenue stability.
Moving forward, the Storage company plans to enhance its data analytics and capabilities to provide real-time revenue insights. By leveraging machine learning algorithms and advanced data analysis and insights, they aim to predict revenue trends more accurately and address potential challenges proactively. Additionally, the company plans to expand its data sources to include external market data to gain a deeper understanding of industry trends.