Outback Steakhouse’s decision to close 21 U.S. restaurants and allow another 22 leases to lapse over the next four years isn’t just a real estate move. It reflects a deeper operational recalibration built on data, digital engagement, and the economics of modernization. Bloomin’ Brands, Outback’s parent company, paired the closures with a third-quarter earnings report that included $33.2 million in impairments and closure costs and an additional $5 to 7 million in expected charges for the fourth quarter. The company also suspended its dividend in October as it redirects capital toward a multi-year turnaround plan that includes remodeling its remaining stores by 2028.
The move underscores a growing truth in the restaurant industry: technology, not intuition, now drives most strategic decisions about which locations to close, which to remodel, and where to invest next. Behind the scenes, restaurant chains are harnessing predictive analytics—AI-powered models that combine financial, operational, and market data—to determine which restaurants are best positioned to thrive in an increasingly digital, demand-driven marketplace.
Predictive analytics has become a core competency for multi-unit restaurant brands. Instead of relying solely on historical sales or subjective assessments, companies now synthesize dozens of live data inputs: POS transactions, delivery volumes, local demographics, labor costs, trade-area foot traffic, digital engagement metrics, and even customer sentiment on review platforms.
These models help operators forecast the long-term profitability of each location while accounting for modernization costs—everything from upgrading point-of-sale systems and mobile ordering platforms to introducing new kitchen automation and customer engagement tools. The calculus is no longer just about rent and revenue; it’s about technology readiness and the return on digital investment.
Outback’s closures, for example, can be read as the output of this predictive decision-making. Some stores simply don’t have the infrastructure or surrounding market demand to justify the expense of upgrading to today’s data-driven standards. Others, however, represent prime opportunities for digital reinvestment and redesign. Predictive modeling helps distinguish between the two.
For large restaurant groups, modernization is both essential and expensive. Outback’s refresh plan through 2028 involves new interior layouts, smaller kitchens, larger pickup zones, and expanded digital interfaces. But each upgrade triggers a cascade of technology integrations, including kitchen display systems (KDS), mobile ordering APIs, staff communication tools and data connectors between the restaurant’s front-end app and its back-end operations.
Legacy units built decades ago often lack the wiring, bandwidth, or spatial flexibility to support those integrations. Predictive analytics helps quantify these constraints by weighing retrofit costs against forecasted lift in guest frequency, throughput, and labor efficiency. In many cases, the data makes the decision clear: closing and redirecting resources to higher-potential locations is more cost-effective than retrofitting outdated ones.
For technology leaders, this is reshaping capital allocation. Instead of viewing technology as a discretionary expense, operators increasingly treat it as a determinant of viability. Stores unable to support unified POS, real-time analytics, and mobile-driven guest engagement risk being excluded from future investment cycles.
Outback’s digital transformation over the past few years illustrates this strategic shift. The brand’s mobile app now anchors its Dine Rewards loyalty program and supports mobile pay, digital waitlisting, and order tracking. The platform provides both convenience for guests and valuable behavioral data for the brand—insights into visit frequency, check size, and channel preference.
Ordering runs through Olo APIs, allowing seamless synchronization across dine-in, takeout, and delivery. That real-time visibility gives corporate and local managers better control over order flow, quote accuracy, and off-premise demand—data that feeds directly into predictive models measuring unit efficiency and guest satisfaction.
Behind the scenes, Outback has also simplified its menu to streamline operations and reduce kitchen complexity. Menu rationalization improves prep times, cuts waste, and allows for cleaner data capture through connected KDS platforms. This operational data, down to the seconds required for plating and dispatch, is increasingly critical for predicting table turns, staffing needs, and profitability per minute of dining-room occupancy.
Ten years ago, restaurant closures often reflected managerial intuition or end-of-year reviews. Today, they are the result of real-time machine learning models that continuously monitor store performance.
For example, AI-driven systems can now predict a restaurant’s future sales trajectory by correlating variables like traffic density, population mobility, digital order penetration, and loyalty engagement. When these predictive scores dip below a threshold (or when required capital expenditures exceed a defined ROI window) corporate teams are alerted to evaluate whether closure, relocation, or remodeling is the best option.
This analytical precision enables faster, less emotional decision-making. It also aligns finance, operations, and IT teams around a shared dataset rather than subjective debate. In practice, this means every closure decision has a quantitative rationale, backed by historical patterns and algorithmic forecasts.
Outback is hardly alone. Casual-dining peers like Chili’s, Applebee’s, Olive Garden, and Texas Roadhouse are all investing heavily in predictive analytics to refine their portfolios. What began as tools for sales forecasting and labor scheduling has evolved into enterprise-wide systems that govern capital investment and brand expansion.
For franchise-heavy systems, predictive models also help maintain consistency by providing data-driven recommendations to operators on staffing, hours, or digital activation levels. Brands with integrated data pipelines—connecting loyalty programs, POS systems, delivery aggregators, and review analytics are finding they can make smarter, faster decisions about where to focus their resources.
This shift mirrors broader trends across the hospitality sector. In hotels, predictive analytics now inform renovation schedules and pricing strategy. In quick-service restaurants, it powers menu engineering and dynamic pricing. For full-service dining, the same logic now applies to real estate and operations: analytics decide where technology can create advantage and where it can’t.
Outback’s portfolio changes are, in essence, the physical manifestation of a digital transformation. The company’s data infrastructure, built from years of investment in analytics, POS modernization and loyalty integration is now determining where its capital flows.
The next phase will depend on execution. Predictive models can flag underperforming stores, but operational discipline will decide whether remodeled locations deliver the promised efficiency and guest satisfaction gains. The key will be maintaining data quality, consistency, and cross-department adoption of insights, from kitchen scheduling to marketing personalization.
For the broader industry, the takeaway is clear: restaurant turnarounds no longer begin with new menus or rebranding campaigns. They begin with data pipelines, AI forecasting, and integrated technology systems capable of linking every transaction to a strategic decision.
Outback’s closures may seem like a contraction on paper, but in technological terms, they represent a recalibration—a realignment of capital toward locations that can sustain the next era of data-driven growth. The chain’s experience shows that the line between real estate strategy and restaurant technology has effectively disappeared. Today, the two are one and the same.
