3 common difficulties for franchises and how Location Intelligence can help you solve them
In the first part of the franchises series we talked about some of the typical problems of franchising when it comes to growing and scaling. One of them is to design the expansion strategy and decide, both at a macro and a micro level, where they should open their next franchises. At the beginning of the expansion this decision is usually taken with the personal knowledge of an area or with intuition. However, when that area is already saturated and new locations have to be considered, continuing to rely on intuition can be dangerous.
We recommend 3 simple steps to improve the decision process and reduce the risk of not opening in the perfect location for your franchise:
- Understand your customers
- Prioritise expansion areas
- Select specific locations
- Understand your customers
The first step to knowing where to open is to know the profile of your customers. In many cases you may not have a clear idea of who the franchise is aimed at. What is the age range of my target audience? What is their purchasing power? What are their spending patterns? are some of the questions that can help you understand your customers' profile. Even if you have an intuition of the client typology, often these studies can give more depth and detail into the profiles or even discover new profiles.
We recommend 3 possible analysis:
- Surveys, either public or private
- Analysis of current clients
- Analysis of the profile of the surroundings of your stores
Before launching any survey, there is a lot of information already available in public surveys and reports, so using the latter can save you time and money. Although in some cases you need to pay for them, many times they are available for free. The advantage of public surveys is that they are usually carried out by agencies or companies with experience in the sector. Because of this it is likely that they will include several of the important points for your study and that are made with statistical rigor on a significant population base. In case it does not meet your needs, then it is advisable to launch a private survey.
Private surveys can be launched on your current customers, on a population that meets certain characteristics relevant to your franchise or on a random sample of the population. The first case will help you understand the specific needs of your customers, but may be less relevant if the franchise is recent and you do not have a significant customer base. It can also give equivocal results, such as in case the targeting is poorly done and the franchise is leaving a part of its potential market off. The other two options also have their pros and cons, being more relevant one or the other depending on the granularity and type of information you are looking for (e.g., trends in certain segments). For these reasons what we have seen that usually works is an in-depth survey of current customers, supported by a more general survey. Although as usual it will depend on the type of franchise and its situation.
Analysis of current customers
This analysis is complementary to the current customers survey. Both serve to obtain different types of information. For example, one can help you understand what types of products customers expect to find or how they value their point-of-sale experience; the other to obtain trends of purchase of a segment (e.g., online vs. offline); another to analyse purchasing patterns (e.g., shopping basket, periodicity).
Within the analysis of the current clients very advanced studies can be made, depending on the type of information stored. From the customer's zip code, to age, gender, occupation, number of transactions, shopping cart details, promotions applied, visits on the web, etc. An example of this type of analysis is the origin of customers, such as:
Also know how to reach your target customers with Location Intelligence here.
Analysis of the surroundings of an area
This analysis will help you understand what external factors are relevant when opening in a new location and in what types of areas your franchise can work well. The objective is to become an expert of the location and to understand exactly what the potential of a location is, according to the variables of the environment, such as the profile of the people who live or work there, the average expense or competition among others. To sum up, there are two major groups of analysis of an area:
- Analysis of an area's data
- Analysis of correlations of factors
In the first case it is advisable to perform this analysis both in the locations that work best and in the ones that work the worst, in order to draw conclusions about what to look for and / or avoid in a location. For the second case it is advisable to use the whole universe of your franchises, excluding outliers if necessary. In both cases you can study variables such as:
|VARIABLES TO STUDY||EXAMPLE OF DATA TYPE||TOPICS TO ANALYSE|
|Socio-demographic||Pyramid of population, floating population (workers, students), foreign population, population density, tourism, etc.||What is the profile of people who spend time around my franchises? What group of people (age, sex, educational level …) positively influence my franchise? What type of location (urban, rural) is the best for my franchise?|
|Socio-economic||Disposable income, unemployment, etc||What should be the wealth level of my locations? How should I adjust my pricing?|
|Commercial||Total expenditure by category, number of transactions by category, consumer origin, commercial area per inhabitant, etc.||What is the competitive index of my area? What is the average expense in the area in my category? What zip code do the people who consume in my area come from in my category?|
|Points of interest||Attractors and detractors||How does the presence of universities, metro stops, museums, etc. influence the performance of my franchise? How does the presence of competitors influence the performance of my franchise?|
|Traffic||Pedestrian traffic, road traffic||Do I need high pedestrian traffic in my area for the success of my franchise? What is the traffic around my location?|
Below you can see examples of the two types of analysis:
Analysis of an area's data
In this case we have done a very quick analysis, in which we have explored an area and its most important variables. Then we have investigated the population, to realise that our area is a young area, since it has the largest population below 35 years and the lowest population between 0-20 and 40-60 years compared to the municipality, province and country. Finally, we have compared the location with a location that is not working as well as we expected.
Analysis of correlations of factors
In this case we have correlated, for the data of all our stores, the sales per square meter according to the female population between 20-35 years, using a catchment area of 8 minutes walking. We can see a positive correlation between these two variables, which would serve to confirm a hypothesis that we had about our objective.
2. Prioritise expansion areas
Once you know in which customers and types of areas you have to focus, you have to define the expansion area at the macro level. You may have found two locations that perfectly fit the standards defined in section one, but there are other factors that can cause this to be not enough. If for example they are very distant, the logistic costs would increase a lot your costs and you would have to either raise prices (potentially losing clients) or to lose margin. For these reasons, it is important to take a step back and decide before in which regions you want to expand.
There are several methodologies for prioritising regions. We are going to explain one based on an iterative and quantitative prioritisation, which can also be supported by a qualitative evaluation to refine the results. The method is similar to the one used to make our free index called Geoblindex. There are some differences, since it was done for an entire industry, not for a particular brand, so several variables did not come into play (e.g., logistic costs) and the qualitative valuation was not performed since there was no concrete target.
In the first place, it is necessary to establish the relevant variables, both internal and external:
- Internal: logistical costs (distance, time, money), brand image, strategic priorities, etc.
- External: potential market -calculated as the weighting of other variables, such as socio-demographic, commercial, etc.-, price of land in the area, etc.
As you can see, it is essential to have made the first point to understand which customers you are targeting. Thanks to the previous analysis you have obtained a list of variables that you have to enter in the weighting (positive or negative). Here each mark will have its own relevant variables, for example:
- Car dealer brand A: number of cars registered in your area, disposable income, number of families, transport costs / km ...
- Kindergarten Brand B: population 0-5 years old, areas with large families, educational level, organizational complexity, etc.
- Gym brand C: floating working population, educational level, number of competitors, proximity to headquarters, etc.
Once you have the variables, you have to define the weighting factors to arrive at the final score. For this, either algorithms or an iterative qualitative estimate can be used. Also it is recommended to normalize the variables so that each one contributes the same amount to the score (without taking into account the weighting). Later, complemented with the previous experience and intuition of the management and expansion team, the prioritisation of regions can be finalised.
3. Select specific locations
At this point you have already defined the target audience, the regions where to expand and now you have to decide where to open the next point of sale (you or your franchisees). Although, as in all sections, there are many models for deciding the optimal location (e.g., Huff model). However, there are other, simpler ways to get potential successful locations. Let's try two of the most direct ones:
- Search for areas according to the criteria established in the previous sections
- Look for similar areas to successful stores
Search for areas according to the criteria established in the previous sections
At this point the circle of the search for relevant variables, regions and specific location is closed. As you have seen, the analysis performed are useful throughout the whole decision process. Let's take a simple case: you have discovered that the areas with the greatest potential and probability of success are those that have a female audience of 25-30 years, with a disposable income of € 25,000 and with an average competitive intensity. You also know that your next location should be in Barcelona and that the typical catchment area for your shops is 8 minutes walk.
And now that? You can use all that data to find the best areas. Conceptually the steps to follow would be:
- Set search criteria: variables, catchment area and region
- Generate in your selected region all the areas catchment areas of the size defined
- Order from higher to lower these catchment areas according to the similarity between the variables established and those of the catchment areas
- Choose the catchment area depending on availability of commercial properties and preference, or give your franchisee a list to choose from
Visually, the process would be as follows:
Look for similar areas to successful stores
Another possible approach is based on the past performance of your other franchises. You can have successful franchises that you would like to have as a reference when choosing new locations. The approach in this case is very similar: look for areas in your priority region and sort them from largest to smallest by similarity according to your area of reference success. In this case you can also weigh with any of your variables identified in the previous steps to narrow the search. Another advantage of this type of approach is that you can perform the analysis with any of your stores as a reference, so you can elaborate several scenarios depending on whether your franchise is in a rural area, shopping center, etc.
Following the same structure as in the previous case, conceptually the steps to follow would be:
- Pick the store of reference of the study, its catchment area and one or several relevant variables to narrow the search
- Generate all catchment areas of the size defined in your selected region
- Sort these catchment areas from greater to lesser according to the similarity between the reference area and the catchment areas
- Choose the catchment area depending on availability of commercial properties and preference, or give a list to your franchisee
Visually, the process would be as follows:
Where has Location Intelligence entered this process?
As discussed in the previous post, there are several solutions to design a successful expansion strategy. However, thanks to Location Intelligence you can perform the analysis quickly and easily. Some of the benefits of using Location Intelligence can be, for example:
- Save time:
- Not having to collect multiple databases, homogenise them and bring them to the same level of granularity
- Not having to geolocate these databases
- Not having to visit multiple locations that do not meet the minimum potential needed
- Save money:
- Not having to buy multiple databases
- Not having to hire someone with the technical and statistical knowledge to treat the databases and make them actionable
- Not having to travel to visit unnecessary locations
- Not having to close a location for bad decision taking
In the next post we will talk about how to convince your potential franchisees to open a franchise with you. Performing all these analysis if it is not possible to show them in a convincing and actionable way is of little use.
This post is part of a series in which we will talk about 3 typical difficulties of franchises related to location. As we love Location Intelligence, we will focus on tackling location related issues. In the rest of articles we will focus on how to apply Location Intelligence to solve those difficulties, with real world examples.
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