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Like for like (LFL) is a term that is often used in retail circles to identify the rate of growth in stores that have essentially the same circumstances and traits to determine if the growth in revenues have been more or less the same in each of those stores. In order to accomplish this, a chain will exclude any stores that do not comply with the criteria used for the analysis, such as stores that have been expanded, recently closed, or opened within a specified time frame. Stripping out or excluding the stores that do not fit the criteria used for the comparison makes it easier to determine if all the remaining stores are experiencing a similar growth pattern, sometimes as the result of specific marketing or sales strategies implemented since the last comparison took place.
While like for like growth assessment is a common tool used by retailers, the same basic strategy can also be used with other types of businesses that operate multiple locations. For example, a restaurant chain can make this type of comparison, usually by identifying all restaurants that are established within communities with a certain population range, use the same menus, and are all of the same layout and design. As with the retail model, the chain may choose to exclude any locations that have been opened over the last year or two.
Hotel chains can also sometimes use this model as a means of gauging how certain changes in polices or enhancements are affecting business volumes at hotels that serve the same basic demographics and have been in business for more than a certain number of years. This approach can often provide a good idea of whether those changes are having an overall positive impact on business, or if there are indications that the changes are failing to attract or even retain customers who have routinely stayed at the hotels from time to time.
With any application, the idea behind a like for like assessment is to understand in greater detail just how well those locations are doing when it comes to revenue generation. The process often calls for comparing the results of the most recently completed period with those of past periods, making it easier to determine if profits are relatively flat over time, or have moved upward or downward. The data generated from a like for like analysis can often provide important clues in how to proceed in the future, both in terms of improving the performance of the stores of locations involved, and also planning the establishment and operation of newer locations so they also have a better chance of success.