Franchise operators no longer must react to performance issues after they impact the bottom line. Today, artificial intelligence-powered franchise analytics software is transforming how multi-location brands identify, predict, and prevent location underperformance, often before operators even realize there’s a problem brewing.

“We didn’t know what we didn’t know,” an Operations Director at a mid-market QSR franchise with over 120 locations might say. “We were always playing catch-up, only discovering performance issues after they’d already cost us thousands in lost revenue. By then, the damage was done.”

This reactive approach to franchise management isn’t only frustrating but also expensive. According to franchise industry experts, addressing underperforming locations early is far more cost-effective than attempting to turn them around after significant decline has occurred. As one franchise business publication notes, “identifying and managing underperformance often requires franchisor intervention” because once a location begins struggling, the challenges compound quickly.

The good news? Advanced AI-powered analytics are changing the game. By detecting subtle patterns in operational data that humans simply can’t see, these systems are giving franchise operators unprecedented foresight into performance issues, often weeks to months before they would typically surface in financial reports.

In this article, we’ll explore five transformative ways predictive analytics are helping franchise networks bridge the performance prediction gap and create sustainable competitive advantages. From early warning systems to cross-location pattern recognition, these AI-driven approaches are fundamentally changing what’s possible in franchise management.

The Performance Prediction Gap: Why Franchises Struggle to See Problems Coming>

For established and mid-market franchise brands, the inability to predict location underperformance represents one of the most costly blind spots in business operations. Most franchise networks operate in a perpetual cycle of reaction, detecting problems only after they’ve manifested in declining financial statements, customer complaints, or compliance violations.

This reactive cycle creates three critical challenges:

• The High Cost of Late Detection

When performance issues are discovered only after revenue has declined, the costs multiply rapidly. What might have been addressed with targeted coaching or minor operational adjustments now requires significant financial investment, leadership changes, retraining, and intensive headquarters support.

Industry research shows that addressing underperforming locations early is far more cost-effective than attempting turnarounds after significant decline has occurred. According to franchise business publications, early identification of performance issues is essential, as problems that go undetected quickly compound and affect the entire brand.

The real problems often occur months before they appear in financial statements. These might include shifts in customer patterns, training gaps, or competitive pressures that traditional analytics cannot surface in time. By the time financial reports signal trouble, franchise operators are typically 60-90 days behind addressing the actual issue, creating a significant gap between the onset of the problem and intervention.

• The Visibility Challenge

Traditional franchise reporting systems focus almost exclusively on lagging indicators: 

• Weekly or monthly sales
• Period-over-period comparisons
• Labor cost percentages
• Customer satisfaction surveys

While valuable, these metrics tell you what has already happened rather than what’s about to happen. They offer hindsight, not foresight.

• The Data Fragmentation Problem

Most mid-market franchise systems suffer from what operations experts call “data fragmentation syndrome,” where valuable insights are trapped in disconnected systems: 

• POS transaction data
• Inventory management
• Labor scheduling
• Customer feedback
• Compliance reports 
• Field audits

Each system contains vital pieces of the performance puzzle, but without integration, patterns remain invisible. Regional managers manually cobble together reports from multiple sources, often missing crucial connections that AI can instantly identify.

This performance prediction gap explains why franchise brands across sectors experience such staggering inconsistency in location performance. The solution? AI-powered analytics platforms that connect these data islands, enabling early detection of performance issues before they impact financial results.

Way #1: Early Warning Systems for Location Performance Decline

For franchise networks, identifying operational issues before they impact the bottom line represents one of AI’s most transformative capabilities. Traditional performance monitoring has always been retrospective, analyzing what went wrong after revenue has already declined. AI-powered franchise analytics software fundamentally reverses this approach.

From Reactive to Proactive Intervention

Many franchise operations directors report only discovering performance problems during quarterly financial reviews. By this point, months of revenue opportunity have been lost, and the costs to rehabilitate underperforming locations increase substantially.

AI-powered analytics platforms now monitor hundreds of operational indicators across multiple data streams simultaneously, detecting subtle patterns that predict performance declines weeks to months before they would appear in financial reports. These early warning systems function like a business health monitor, constantly checking vital signs across the network.

The Science Behind Early Detection

What makes these predictive systems possible is the ability to analyze correlations between operational metrics and financial outcomes. Using machine learning algorithms trained on historical franchise performance data, these systems:

• Continuously monitor real-time data from POS systems, inventory management, staff scheduling, and customer feedback
• Compare current operational patterns against historical benchmarks from similar locations
• Identify statistical anomalies and pattern deviations that historically preceded performance declines
• Alert management with specific recommendations before negative financial impact occurs

Real-World Application

Early identification of underperforming locations is crucial for maintaining network strength. One franchisee’s underperformance can impact everyone else in the franchise system, creating a cascading effect through the brand.

When franchise systems implement AI-powered predictive analytics, they can identify specific operational indicators that correlate with future performance problems. Common early warning signals include unusual patterns in staff turnover, inventory management inconsistencies, or subtle shifts in customer satisfaction metrics that might not be obvious in traditional reporting.

Making Prediction Actionable

The true power of these early warning systems lies not just in prediction but in prescriptive guidance. Modern franchise analytics platforms don’t just tell you a location might underperform. They identify the specific operational factors driving the potential decline and recommend targeted interventions based on what has worked in similar situations across the network.

For franchise operators, this shift from reactive to predictive management represents a fundamental competitive advantage. While competitors are still discovering problems through monthly financial reviews, AI-empowered franchises are already implementing solutions weeks before revenue impacts materialize.

Way #2: Identifying Operational Factors Behind Underperformance

One of the most powerful applications of AI in franchise analytics is its ability to identify the specific operational factors driving location performance variances. While traditional reporting might tell you which locations are underperforming, AI-powered analytics reveals exactly why.

Connecting Operations to Outcomes

For established and mid-market franchise brands, operational data exists in abundance but rarely in a form that allows for meaningful pattern recognition. AI analytics changes this by establishing clear correlations between operational metrics and financial outcomes.

The process works by:

1. Collecting operational data across multiple systems (POS, scheduling, inventory, customer feedback)
2. Identifying statistical correlations between operational factors and financial performance
3. Isolating the specific operational variables that most impact performance
4. Quantifying the financial impact of each operational factor

Addressing operational inconsistencies or inefficiencies is crucial before they impact the entire business. Regular monitoring and evaluation are essential to identifying these factors early.

Moving Beyond Intuition to Evidence

Without AI-powered analytics, franchise operators often rely on intuition or general best practices to diagnose performance issues. This leads to generic interventions that may not address the specific factors affecting a particular location.

Modern franchise analytics platforms eliminate this guesswork by providing data-driven insights specific to each location. For example, the system might determine that for a particular store, staff turnover is the primary performance driver, while for another in the same market, it might be inventory management or local marketing effectiveness.

From Analysis to Action

The real value comes when these insights translate into targeted improvement initiatives. Industry research shows that comparing franchisee performance across a network and identifying best practices is essential for strengthening the entire franchise chain.

By analyzing what top-performing locations do differently, franchise operations teams can develop targeted coaching plans for underperforming locations based on evidence rather than assumptions. This data-driven approach leads to faster turnarounds and more sustainable performance improvements.

For established and mid-market franchise brands, this capability transforms how they approach performance management, moving from generic, one-size-fits-all interventions to precision improvements based on location-specific insights.

Way #3: Predicting Location-Specific Risk Factors

Traditional franchise management relies on standardized risk assessments that apply the same metrics across all locations. This one-size-fits-all approach fails to account for the unique risk profile of each location, making it difficult to allocate support resources effectively. AI-powered franchise analytics software changes this paradigm by creating customized risk profiles for each location.

Customized Risk Assessment

AI analyzes historical and real-time data from multiple sources to create location-specific risk profiles that consider:

• Local market conditions and competitive dynamics
• Location-specific operational patterns
• Staff experience and turnover rates
• Historical performance trends
• Seasonal factors that affect particular markets differently

This tailored approach allows franchise operators to move beyond blanket assumptions about what causes underperformance and develop targeted risk mitigation strategies for each location.

Proactive Resource Allocation

According to franchise business experts, franchise networks must proactively intervene and support underperforming franchisees before their performance impacts the entire business. AI-powered risk assessment makes this intervention more precise and effective.

By quantifying risk factors at each location, franchise systems can:

• Prioritize support resources based on objective risk scores
• Allocate field support visits to locations with highest risk factors
• Implement preventative training where specific risks are identified
• Deploy specialized expertise to address location-specific challenges

This strategic approach ensures limited support resources go where they can have the greatest impact rather than being spread evenly regardless of need.

Contextual Performance Evaluation

One of the most valuable aspects of location-specific risk assessment is the ability to evaluate performance in context. Traditional performance metrics often fail to account for location-specific challenges that may be beyond a franchisee’s control.

AI-powered analytics can normalize performance expectations based on:

• Local market conditions and demographics
• Property variables (visibility, access, parking)
• Competitive density in the immediate area
• Operational constraints specific to the location

This contextual evaluation helps franchise operators distinguish between performance issues resulting from operational deficiencies (which can be addressed through training and support) and those stemming from location-specific challenges (which may require different strategic approaches).

Risk-Adjusted Planning

For established franchise brands with dozens or hundreds of locations, understanding location-specific risk factors enables more accurate forecasting and planning. Each location can have individualized targets that account for its unique risk profile, creating more realistic expectations and appropriate support structures.

This approach transforms how franchise networks address location performance, moving from reactive problem-solving to strategic risk management that prevents underperformance before it occurs.

Breaking Free from Reactive Management: A Framework for Implementation

To leverage AI-powered franchise analytics effectively, follow these key steps:

1. Integrate Data Sources: Connect POS, labor, inventory, customer feedback, and compliance data into a unified platform.
2. Establish Baselines: Define KPIs, measure current performance, and identify gaps between locations.
3. Implement in Phases: Start with descriptive analytics, then progress to diagnostic, predictive, and finally prescriptive capabilities.
4. Align Field Teams: Train managers, update visit protocols, and develop intervention playbooks based on AI insights.
5. Optimize Continuously: Validate models, refine algorithms, and expand data sources for ongoing improvement.

Success indicators include reduced performance variance, earlier issue detection, better resource allocation, faster location turnarounds, and improved franchisee satisfaction.

The Future Belongs to Predictive Franchise Networks

AI-powered analytics has fundamentally changed what’s possible in franchise management, moving beyond reactive firefighting to predictive performance optimization. For established and mid-market franchise brands, this technology bridges the critical performance prediction gap that has traditionally led to costly turnarounds and inconsistent customer experiences.

By implementing early warning systems, identifying operational factors behind performance variance, creating location-specific risk profiles, recognizing cross-location patterns, and developing data-driven intervention strategies, franchise operators can transform how they manage their networks.

The time to act is now. As competition intensifies and customer expectations rise, the ability to predict and prevent location underperformance will separate industry leaders from followers. Request a demo today to discover how FranConnect’s AI-powered analytics platform can help your franchise network achieve consistent excellence across every location.

In an era where brand loyalty is increasingly fragile, businesses operating across multiple locations must ensure consistency in operations, compliance, and customer experience. Over 80% of Quick Service Restaurants (QSRs) have adopted digital tools, yet many struggle to harness the full potential of the data these tools generate. A unified data management approach ensures brand consistency, strengthens compliance, and enhances customer loyalty.

This white paper explores how unifying data from disparate systems through an integrated platform like FranConnect can drive operational excellence, enhance decision-making, and optimise customer retention.

The Challenge: Fragmented Data and Inconsistent Operations

QSR brands and multi-location businesses rely on a variety of digital tools for online ordering, inventory management, workforce scheduling, training, and customer feedback. However, when these tools operate in silos, data remains fragmented, leading to:

• Inconsistent brand experience across locations
• Compliance and regulatory risks
• Poor decision-making due to a lack of real-time insights
• Reduced operational efficiency and increased costs
• Declining customer satisfaction and loyalty

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The Solution: A Unified Data Ecosystem

To overcome these challenges, businesses need a centralised, cloud-native platform that integrates disparate data sources and delivers a real-time, holistic view of operations.

Key Benefits of a Unified Data System:

1. Brand Consistency Across Locations

1. • Standardised training and compliance protocols ensure a uniform customer experience.
   • AI-driven operational analytics detect inconsistencies and provide actionable insights.
   • Automated task management and playbooks help maintain brand standards.

2. Enhanced Operational Compliance

2. • Digitised food safety and quality control checks reduce compliance risks.
   • Real-time tracking of corrective actions ensures compliance with industry regulations.
   • Mobile and offline capabilities enable seamless reporting from any location.

3. Optimised Decision-Making Through AI and Analytics

3. • AI-powered insights generate predictive analytics for growth forecasting.
   • Operational benchmarking enables brands to measure performance against industry standards.
   • Automated insights streamline operational execution, reducing inefficiencies.

4. Improved Customer Experience and Loyalty

4. • A consistent brand experience increases customer trust and retention.
   • Real-time feedback loops help address customer concerns promptly.
   • Integrated customer engagement tools personalise interactions and drive loyalty.

Case Study: The FranConnect Advantage

FranConnect, a leading franchise and multi-location management SaaS platform, serves over 1,500 brands across 146 countries, managing 1.3 million locations. Through its cloud-native platform, FranConnect offers a real-time, data-driven approach to operational consistency, compliance, and customer engagement. By uniting disparate systems, brands gain a single source of truth, ensuring uniform standards across all locations.

Future Trends in Digitalisation and Data Integration

• Agentic AI for Automated Execution: AI-driven automation will streamline compliance and operational execution, reducing human error.
• IoT Integration for Real-Time Monitoring: Unified IoT networks will provide deeper insights into supply chain and operational efficiency.
• AI Video Analytics for Customer Flow Optimization: AI-powered video analytics will enhance customer experience by optimizing store layouts and service speed.

Conclusion: Unifying Data as a Competitive Advantage

In a highly competitive QSR and multi-location business environment, the ability to unify data across disparate systems is no longer optional—it is a necessity. Businesses that leverage integrated platforms to standardise operations, enhance compliance, and deliver a consistent brand experience will gain a competitive edge in fostering customer loyalty and operational excellence.

 

Written by: Nick Mecozzi, SVP of Solutions, FranConnect

Written by Guest Writer Keith Gerson, CFE – President & CEO of Gerson Advisory Services

When I first started franchising in the early ’70s, we didn’t even have fax machines, let alone the tech we’re using today. I remember lugging around a briefcase full of paper reports to every franchisee visit. Now, as President and CEO of Gerson Advisory Services and former President of Franchise Operations for FranConnect, I’m watching AI revolutionize the franchise industry. It’s been quite a journey, and I’ve got a few thoughts on where we’re headed next.

The CFXO: More Than Just Another Fancy Title

Let me tell you about a conversation I had last month with Scott, a franchisor client of mine. He was frustrated because his top-performing franchisee was threatening to leave the system. “Keith,” he said, “I don’t get it. Their numbers are great, but they’re unhappy. What am I missing?”

This is exactly why I’ve been pushing for the role of Chief Franchise Experience Officer (CFXO). It’s not just another C-suite title to hand out at the company holiday party. The CFXO is your franchisee happiness guru, your system’s empathy engine.

In Scott’s case, a CFXO would’ve spotted the issue long before it reached boiling point. They would’ve noticed that while this franchisee’s numbers were stellar, they hadn’t received meaningful support or recognition in months. Sometimes, it’s not about the numbers – it’s about feeling valued.

From Consultant to Coach: A New Breed of Support

If you’ve been around long enough, you’ll remember the old days when the Franchise Business Consultant’s job was basically to show up, check some boxes, and move on. Thankfully, those days are over.

I was at a franchise conference recently where I heard a franchisee say, “I don’t need big brother telling me to ‘increase sales.’ I need someone who can help me figure out how.” That’s stuck with me because it perfectly captures the shift we need to make.

We’re moving towards what I call Franchise Success Coaches (FSCs). These aren’t just consultants; they’re part business guru, part psychologist, and part cheerleader. They need to be able to dig into the numbers, but also to be able to read between the lines of what a franchisee is really saying.

I’ve seen this work wonders. A client of mine implemented this approach last year. Their customer satisfaction scores have gone up 22% since then. Why? Their FSCs aren’t just looking at sales figures; they’re coaching on everything from staff morale to local marketing strategies.

Tiered Support: Because One Size Fits None

Here’s a hard truth I’ve learned: a franchisee who’s been in business for 10 years needs very different support than someone who’s just opened their doors. Sounds obvious, right? You’d be surprised how many systems still use a one-size-fits-all approach.

I’m betting that many of you have a long-term franchisee who has been in the system for many years and who is consistently a top performer. But when you sat down with him, he was considering leaving. Why? He said, “I’m tired of sitting through the same basic training sessions year after year. I need something more.”

I’ve had those conversations over the years, and that’s when the idea of tiered support really crystallized for me. Now, I advocate for systems where new franchisees get intensive, hands-on support while veterans of your system get advanced strategies and peer networking opportunities. It’s not about less support for experienced franchisees – it’s about different support.

Real-Time Data: The Good, The Bad, and The Game-Changing

Let me take you back to 1991. I was working with a specialty food franchise system, and we had just installed our first real-time POS system. I remember standing in a franchise location, watching those sales figures roll in live for the first time. It was like magic.

Fast-forward to today and the data we have access to is mind-boggling. But here’s the kicker—data alone isn’t enough. I’ve seen franchisors drown their franchisees in numbers without providing any real insights.

The game-changer is using this data to have meaningful conversations. One of my clients was able to spot a trend in negative reviews about the speed of service and worked with franchisees to make immediate improvements. Result? A 15% jump in positive reviews in just three months.

Revolutionizing Franchise Operations with Playbooks.

Playbooks are essential in helping to create stronger, more resilient franchise systems by replicating best practices with unprecedented speed and precision. For franchisors looking to drive consistent performance, this tool is a game-changer. The key is that you’re not just collecting data – you’re using it to drive action. It’s not just about making corporate jobs easier; it’s about giving franchisees the tools and insights they need to succeed.

I didn’t really have to gaze long into my crystal ball, as what I initially thought were future capabilities exist today. FranConnect, I’ve found, has the best playbooks in the business when it comes to franchise operations. They have developed the ability to transform how we use real-time data to drive improvements across franchise systems.

Playbooks can turn data into immediate action. Imagine a restaurant franchise where, if a location’s cleanliness scores drop below a certain threshold, the system automatically assigns a detailed cleaning and maintenance playbook to that franchisee. It’s immediate, targeted support exactly when and where it’s needed.

Playbooks can be created for any performance indicator – customer satisfaction, upselling, you name it. It’s like having your best performers mentor every location, but automated and scalable.

AI and Machine Learning: The New Frontier

Now, I’ll admit that when I first heard about AI in franchising, I was skeptical. I had concerns over data privacy, content ownership, “hallucinations,” ethical concerns, a lack of human touch, etc. But I’ve come around in a big way.

Last year, I heard of a franchised B2B concept that implemented an AI system to generate performance improvement playbooks. The system analyzed data from across their 500 locations and created customized strategies for each location.

The results were impressive, but what really sold me was a conversation I had with their CEO. “For the first time,” he told me, “I feel like the advice we are giving is truly tailored to our franchisee’s locations, not just generic one-size-fits-all solutions.”

The Hybrid Model: Balancing High-Tech and High-Touch

The pandemic forced us all to go virtual, and let me tell you, it wasn’t pretty at first. A franchisor I spoke with at an IFA event told me about one video call where a franchisee accidentally left their mic on while making some colorful comments about corporate. Not their finest hour.

But we learned, we adapted, and now I’m convinced that a hybrid model is the way forward. There’s still no substitute for sitting across from your franchisee, looking them in the eye, and shaking their hand. But the efficiency of virtual check-ins? That’s here to stay.

One approach for consideration is bi-annual in-person visits, with bi-weekly virtual calls. An example could be to deliver in-person visits in January and July, followed by virtual calls every two weeks (26 calls per year), and an option for additional ad-hoc virtual calls as needed.

Another approach could consist of an annual in-person visit (reserved for annual strategic planning and critical hands-on support). These are offset by quarterly extended virtual sessions (2-3 hours) for in-depth reviews and bi-weekly brief virtual check-ins (15-30 minutes for quick updates). It’s recommended that one in-person visit be scheduled in the first quarter for new franchisees.

I’ve also seen systems that offer a flexible system based on each franchisee’s performance.

• High-performing franchisees: Annual in-person visit, with bi-weekly virtual calls.
• Average-performing franchisees: Semi-annual in-person visits offset with bi-weekly. virtual calls.
• Underperforming franchisees: Quarterly in-person visits with weekly calls.
• Additional ad-hoc virtual calls can be scheduled as needed for urgent matters or specific concerns.

Looking Ahead: The Only Constant is Change

As I look back on my five decades in franchising, one thing is clear—this industry never stops evolving. The challenges we face today are different from those we faced in the ’80s, ’90s, or even last year. However, the core of what we do remains the same: supporting franchisees in building successful businesses responsibly.

The future of franchise operations is about embracing technology without losing the human touch. It’s about using data to inform decisions but not letting numbers overshadow relationships. It’s about providing tailored support that evolves as our franchisees grow.

Is it going to be easy? Not at all. But in my experience, the most rewarding things rarely are. As we navigate this new frontier, let’s remember why we got into franchising in the first place – to help people achieve their dreams of business ownership.

Here’s to the future of franchising. It’s going to be one heck of a ride!

Understanding your company’s performance is vital for business owners since it helps them make critical decisions for the future. That’s where business analytics comes in. Business analytics provide detailed insights that make it easier to measure your company’s performance, establish actionable steps to achieve your business goals and solve reoccurring problems within your business. This post will explain how data analytics helps your business expand and thrive in a competitive market.

What Is Business Analytics?

Business analytics is a method of interpreting data to find valuable performance patterns and insights. Examples of business analytics tools and techniques include machine learning, predictive analytics, and natural language processing. These tools transform data analytics into usable insights to determine your website’s performance. You can use these insights to make decisions and improve your marketing strategies to reach more people.

Analytic tools need clear and accessible data to provide real-time, accurate business analytics. Several tools and cloud storage software platforms exist to access and process your vital data to provide valuable business insights.

How Can Businesses Benefit From Using Analytics?

Identifying patterns and trends in your business data helps you find ways to change your business practices for a more favorable outcome. Here are a few specific benefits of business analytics:

Improve Customer Insights

Your goal as a business is to improve your customers’ experience when they use your products or services. However, it may be challenging to enhance their experience if you know how they feel. Business analytics tools analyze data to create detailed customer profiles, allowing you to understand individuals’ behavior to provide tailored services to meet their unique needs and preferences.

Track KPIs

Key performance indicators (KPIs) are quantifiable measurements that evaluate your company’s long-term performance and can determine your strategic, financial, and operational achievements compared to other companies in the same industry. Business analytics track your company’s performance metrics by collecting data and converting it into measurable insights you can display on charts and dashboards.

Simplify Decision-Making

As a business owner, you have many decisions to make every day. Business analytics can provide insights that guide you in making informed decisions. Business analytic tools use predictive analytics to suggest the best responses to changes in your business. These tools can use sales data to determine the chance of success, helping you decide on your company’s right course of action.

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Mitigate Business Risks

You are likely no stranger to business risks. Legal liabilities, employee safety, and delayed deliveries are just some risks your business may experience. Business analytics models can evaluate business data, such as your location, product demands, and current inventory, to predict potential risks and allow you to prevent them. Business analytics can also help you recover from setbacks by recommending a suitable solution.

Enhance Data Security

In addition to mitigating risks, business analytics tools can also identify security vulnerabilities and help you protect your data. Some data analytics models can use information from previous data breaches to strengthen security and prevent recurrence. Investing in business analytics solutions will help you protect your company’s sensitive information and maintain compliance with privacy regulations.

How Can FranConnect Benefit Your Business?

At FranConnect, we offer analytics solutions for franchises and multi-location businesses. FranConnect’s Analytics includes a catalog of pre-built analytics to provide your company’s development, operations, and finance teams with valuable insights that streamline decision-making. We use scorecards, benchmarks, and dashboards to represent your company’s performance, allowing you to increase profitability and strengthen your brand.

FranConnect’s Analytics catalog has a user-friendly design to ensure you get the most out of our tool. We have optimized the catalog’s dashboard and scorecard to be mobile-friendly so you can access your data no matter where you are. Experience all the benefits of business analytics with our comprehensive and innovative solutions.

Contact FranConnect for Analytics Software You Can Trust

Would you like to know more about FranConnect’s Analytics and other catalog solutions coming soon? Our experts are happy to assist you with a tailored solution to expand your units. Feel free to request a demo or contact us directly to speak to a member of our sales team.

With constant change the only certainty, harnessing the power of data for growth amid shrinking resources and shifting consumer behavior is critical for franchise brands of all sizes. Advanced point-of-sale and customer relationship systems offer franchisors the digital footprints to engage, garner feedback, track trends, try new products, and create unmatched customer experiences. Analytic data, swiftly served up, can also be a powerful ally to predict the success of local marketing and franchise development tactics and drive growth-focused operations and franchise relations decisions.

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We have all heard that Unit Level Economics is important in franchising but going from understanding it in theory to transforming it into action is a big step.  According to QSR Magazine, “the success or failure of a franchise concept can pivot off of how well unit economics are tracked, managed, and improved.” After working with over 900 brands a common practice among them is working together with their franchisees to improve their unit-level economics: not only their top-level sales but also their bottom line. If you’re not doing this today, you will have trouble selling franchises in the future.

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