In the ultra-competitive world of self-storage, the best operators turn to data analytics to spot risks and capitalize on opportunities. In this industry spotlight, we examine how Pomerol’s self-storage clients have leveraged data in Qlik to fine-tune operations and drive growth through reduced churn, higher tenant lifetime value, and lower delinquency.

“Getting our data organized and flowing has allowed us to reinvent revenue intelligence. We now focus on leading indicators that support decision-making and have a real impact on our profitability.” – VP of Operations

60 years of sustained growth

With annual revenue north of $40 billion and more than 50,000 facilities nationwide, there’s now roughly 7 square feet of storage for every American. And while industries such as construction and hospitality can be extremely cyclical and turbulent, self-storage by comparison provides clean and predictable returns. Low-maintenance facilities built from concrete and steel, historical occupancy of around 95%, and predictable running costs provide a lucrative recipe for investors. With a flood of private equity looking for a home in recent years, self-storage has become a model investment vehicle.

At a crossroads

All that said, self-storage has its own unique set of challenges and is undergoing a transformation of its own, fueled by automation and unprecedented pressure on margins. The flood of investment in recent years has ramped up competition that some industry insiders compare to the airline industry – many feel a “race to the bottom” has already started. The barriers to competition between operators have come down – most units are now leased through an online transaction – and aggressive pricing is challenging brand loyalty. Add to that historically high interest rates and tariffs putting pressure on build costs, and poor operators are quickly exposed. As if competition and cost pressures weren’t enough, many operators feel overwhelmed by the pressure to adopt AI and automate operations – but kick that can down the road at your peril.

The future is already here

Mid-market and REIT-owned brands are leading the charge, with “smart units” enabling customers to monitor their storage units live from their mobile phones. And just as self-driving robotaxis will soon be the norm, we should expect self-storage operations to become increasingly autonomous. Onsite managers are rapidly being replaced by self-service kiosks. Augment that with AI, and customers will soon be able to negotiate a rate, sign a contract, and move in without any human intervention on the operator’s side.

Moving ERP and analytics to the cloud

A big step for many of our self-storage customers has been moving their ERP to the cloud-based Self Storage Manager (SSM). As one of the few remaining independent vendors specializing in this space, Self Storage Manager boasts a huge range of integrations, excellent scalability, and support for international markets.

Across our self-storage customer base, we’ve integrated Qlik Cloud with SSM to drive real-time analytics, machine learning models, and actionable operational alerting. And as the largest independent BI vendor in Gartner’s Magic Quadrant, Qlik makes the software combination a natural pairing.

Get your (data) act together!

In the new world of self-storage, operating efficiency drives competitive advantage, and data is the most valuable currency. Our clients recognized early on that data fuels automation and invested in data pipelines to channel data from their self-storage management system, financial ERP, and other platforms such as pricing engines and digital marketing. With data consolidated into one warehouse, it moves through stages where its quality is incrementally improved, and the final output is standardized, enriched, constantly flowing, and ready for downstream consumption by any product, service, or operation imaginable.

Put your data to work

Getting data organized allows you to tackle complex problems, like tailoring rate offers to individual customers. This requires pooling together competitive rate data, each customer’s transaction history, operational KPIs from nearby facilities, and even inbound marketing data. But the ability to optimize rates at this granular level has enormous potential to directly impact margins. Look no further than the airline industry to see how dynamic pricing has helped optimize revenues and maximize the value of inventory.

Focus on leading indicators

With your data in order, you can get to know your business intimately through your KPIs, with a focus on leading indicators such as website interactions, customer NPS scores, and revenue projections. These indicators focus on where you’re heading – they are your customers’ heartbeat and indicate what will happen next. Whether it’s portfolio analysis for investors, financial statements for the boardroom, or decision support for regional managers, different people within the organization need a different data story. Leverage report automation to get customized data stories into the hands of decision-makers.

Put the “intelligence” into revenue intelligence

As the data environment matures, prescriptive analytics is the next frontier for empowering decision-making, whether by a human or through autonomous action. Think revenue models based on current customers, predicted future customers, churn rates, and rate competition. Think prescriptive models that suggest rate plans for individual customers based on their predicted churn risk. Or machine learning models that predict delinquency before it happens.