By Malcolm Flanagan
I have been involved in franchising as a franchisee and franchisor since the 1980s and, until recently, the topic of franchising has not been the subject of anything but a passing comment during that time.
Franchising should be invisible, as it is simply a method of doing business, but due to recent revelations, everyone has a view on it. In fact, many of these comments are questioning the robustness of the franchise model.
To address these concerns, FRANdata has a unique perspective on the robustness of Franchising as a way of doing business. There are certain Critical Success Factors that can predict the likelihood of a franchise business to succeed.
Using a data-based approach and accessing FRANdata’s knowledge base of over 50,000 disclosure documents and data and research into over 3,500 franchise systems collated over nearly 30 years, FRANdata can predict the potential success or otherwise of franchise systems.
Critical Success Factors
What are the key differences between successful and unsuccessful franchise systems?
Based on our experience and research, there are 12 key lead indicators of a system’s success.
Let’s take a closer look at what is behind 6 of these, bearing in mind that it is actually the trend analysis of these 6 key factors that give the best predictor of franchise system success.
High scores in one, or more of the success factors will not guarantee success, it is the trend across all criteria that enable the underlying success in the business. Raw scores in their own right are valuable, but it is when they are compared to “best practice” in the sector, that the real value is uncovered.
The 6 Factors include:
1. System Sustainability
The System Sustainability rating is determined by a number of rating factors:
- Historical Unit Success Rate
- Resale Activity
- Average Unit Revenue Growth
- Unit Economics
If we look at these individually, we can tease out some of the measurement criteria, which are based on our insights gleaned from reviewing and analysing those 50,000 franchise documents and then overlaying that against actual performance;
Historical Sucess Rate
This is determined by looking at the performance of the system through economic cycles, the number of openings and closures (net), the trend of the brand in the market against other brands and the franchising sector in general.
This is somewhat self-explanatory, however, it is often the first sign of stress in a franchise system – consistently increasing transfers or closures can indicate lack of profitability or other issues for the franchisee.
We compare this to sector and industry averages to determine underlying trends.
Not all transfers are signs of system stress, some are bought about by retirements or opportunistic offers by parties that want to enter the system.
Average Unit Revenue Growth
The individual units (franchise locations) are investigated to determine underlying profitability and revenue.
System growth can be masked by net increases in numbers of units, whilst the unit revenue/profit growth may be in decline – a lead indicator of franchisee stress and failure.
Here, we model the economics of individual units and determine the break-even and stress levels for different circumstances and product mixes.
Poor performance of units against these models is often an early indicator of stress.
2. System Demand
This measures the demand for new franchises in terms of a pipeline of applicants, quality of applicants and the relative price paid compared to like franchises. It also looks at the sale of existing franchises using similar measures.
We also consider the number of units sold compared to like franchises, as well as the historical trends.
3. Value for Investment
Here we consider a number of factors to determine the value that the franchisee and franchisor receive from investing in new units, technology,
We examine the individual profitability of franchises in different markets and lifecycle stages to determine current and historical returns.
We compare this to like franchises.
4. Franchisor Support
We consider a complex matrix of support offered by the franchisor, including, but not limited to; field staff, support hotlines, access to senior management, marketing support, support of FACs, etc.
We consider the trends over time and again compare to like franchises.
5. Franchisor Stability
Here, our focus turns to the stability of the franchisor – this includes financial reporting, financial performance, including gearing ratios, bank or financier support, management stability and turnover, etc. over a period of time to determine underlying trends.
The financial and structural stability of the franchisor has been a lead indicator of future success or failure of systems in our research into hundreds of franchise systems.
6. Franchisee Health and Satisfaction
Franchisee health and satisfaction often go hand-in-hand – successful franchisees are typically the satisfied ones.
To determine franchisee satisfaction, we interview a cross-section of franchisees (chosen by us to represent various locations, performance levels, lifecycle phases, etc) to determine their satisfaction with their franchise, the franchisor support, the market conditions, their financial performance, etc.
We compare this to like franchises and overall sector satisfaction levels.
There are no simple or easy answers to what makes a successful franchise system, however, FRANdata is in a unique position to make data-based assessments of franchise systems to determine either “best practice standards” or “key success factors” in franchise systems.
The answers are found on a case-by-case basis by examining the performance of individual systems against “best practice” standards set by the best in that sector, or industry.
Franchising is a method of doing business, that can be applied and interpreted in a multitude of ways. By comparing individual businesses to the “best”, gaps can be identified and solutions to close the gaps developed on a case-by-case basis.
FRANdata has the data and information across franchise systems in Australia, the US and other countries to be able to assess the performance of a system against its peers in all 12 of the critical success factors, of which, I have briefly discussed 6 above.