In the third installment of our series focused on site selection best practices, industry experts Suzanne Beltran and Bill McKeogh discuss both the process associated with quantifying competitive impacts in predictive models and the challenges inherent in doing so.
Suzanne Beltran: The first aspect of our approach is to account for what we think of as locational convenience – specifically, the relative convenience for a consumer to visit your location versus visiting competing brand(s). When a competitor is in the same shopping center as you and/or on the same stretch of roadway, we classify that competitor as being “adjacent”, as there is no meaningful locational convenience for a given consumer that they enjoy over you. A critical component of our competitive analysis for clients is focused on uncovering how well they perform when sharing a shopping center or retail concentration with their competition.
The next element of our approach focuses on the larger trade area that our clients and their competitors serve. We first seek to define whether specific competitors are what we classify as “impacting”, an example being that both you and your competitor(s) are located 5 minutes away from the consumers that you both have the opportunity to serve, albeit in different parts of the trade area. There is no locational convenience for the consumer in an impacting scenario. We then work to determine whether any competitor(s) are considered to be “intercepting”. A great example of this is when the consumer has to drive past a competitor to reach your front door.
Bill McKeogh: When explaining the importance and relevance of various competitors to our clients, we explain that “we look at the world through the lens of the consumer, and in doing so work to account for the competitive influence experienced by consumers rather than focusing on the perceived impact to your stores.” It is quite common for real estate teams to lose sight of the fact that the competitive environment can vary somewhat significantly within a given trade area, depending in large part on where you are positioned directionally. That is a paramount concern that we have to control for in our modeling.
SB: Operators tend to think in terms of “who’s right next to me?” or “how many competitors are within 5 miles or 10 minutes”, when in reality your competition could be positioned to the east of your location and the consumer base is located to the west of you. Our approach is to take into account where customers are coming from relative to where the competition is. Locational convenience is key!
BM: One additional element worth calling out regarding competition – the more analogous a competitor’s offering is to yours, the more competitive they are likely to be with you. What clients often overlook is that there is perhaps no greater competitor than another one of their existing locations, what we refer to as “sister” locations.
BM: I find that most clients have a pretty good handle on who their primary competitors are, or at least have a good hypothesis of the brand(s) that they directly compete with. Our goal is to first align on the consideration set that will be evaluated from a competitive standpoint. The objective, outsider’s perspective that we bring to this process enables us to make recommendations regarding specific operators for inclusion in the analysis that our client might never have otherwise considered. We then work through our analyses to establish which of these competing brands ultimately impact their bottom line, proving or disproving their pre-existing hypothesis regarding perceived competitive operators.
SB: As with so many other aspects of our modeling efforts on behalf of clients, active collaboration with our clients is essential during this process. For some operators, identifying obvious direct competitors is a fairly straightforward approach – think Home Depot and Lowes, or Dollar General and Dollar Tree. In other situations, considerable thought must be given to what might constitute a competitive impact. We have clients that sell products through other retailers, in addition to selling direct to consumers and/or businesses themselves through their own brick-and-mortar locations. Those wholesale partners effectively serve as both a sales channel and a competing operator at the same time.
Once the competitive set has been defined, we then segment defined competitors into distinct groups – this can be limited to as few as two groupings (e.g., direct and indirect), and can also be a much greater number based on the nature of the industry in which the client operates, the breadth of products and services it provides, etc. We then work to determine which individual competitors and groups of competitors exhibit the strongest correlation to client performance, a process that results in the creation of competition scores.
BM: Ultimately we want to ensure that we are controlling for the competitors that we find to be most impactful to the performance of our client’s locations. Sometimes that requires a regional consideration, as a given competitor may have a stronger impact in their home market and/or in markets where our client is less established (i.e., the client recently entered the market and lacks brand awareness).
BM: A great example exists in the arts-and-crafts category amongst two operators with a national presence – Hobby Lobby is dominant in the Southeast, while Jo-Ann’s is strongest in the Great Lakes and Rustbelt regions. Generally speaking, retailers with a national presence have greater brand equity and thus are more competitive as a whole, but ignoring performance variances between both geographic regions and markets can lead to site forecasts that overstate or understate the potential of a proposed location.
SB: Another example of a regional competitor is Fuzzy’s Tacos, a DFW-based operator. They are a strong competitor throughout Texas in the fast casual sector, but are not as competitive in the Southeast.
BM: A significant challenge in many sectors is that the nature of the operator’s business/offering makes it nearly impossible to isolate the impact of an individual brand as a competitor. Using the quick service restaurant (QSR) space as an example – there is no way to know the real impact of McDonald’s to Chick-Fil-A on a national scale because there is such significant variance on a market-by-market basis, not to mention the interplay of all other QSRs within a given market (some of whom are national in scale while others are local/regional operators. Much like a snowflake, each competitive situation is unique. Therefore, a statistical correlation in the analysis is used to ensure that we are directionally accounting for the impact of competition. When looking across the entire network of a client’s locations, our goal is to help clients understand the degree to which any competitive scenario appears to be favorable or unfavorable to their performance.
SB: When it comes to competition, and this goes back to our earlier discussion regarding locational convenience, sometimes where the competition is located is more important than who the competitor is. It is also worth noting that when we are quantifying the factors that impact performance as part of our larger modeling effort, competition alone is not the sole factor that causes individual locations to perform poorly.
BM: Most operators come into a modeling exercise with the expectation that measuring competition is a straightforward, black and white proposition. While we provide our clients with meaningful insights through our work, an equally important outcome from our work is when clients gain an appreciation for the complexities involved in determining how best to account for competition during a site evaluation.
When clients realize why adjacent competition often aligns with higher store sales performance, we know they are in the proper analytical mindset for site forecasting.
SB: It’s the clustering effect, or what I like to refer to as “complementary competition”. For example, consumers often like to visit numerous retailers when shopping for apparel, and therefore might prefer to go to lifestyle centers and super-regional malls where concentrations of clothing stores exist. The gravitational pull for consumers is greater when numerous choices for a similar offering exist in proximate fashion to one another. This phenomenon exists in numerous sectors – from “restaurant rows” and competing car manufacturers that position dealerships near one another to healthcare providers that co-exist in medical office buildings (MOBs) and larger healthcare districts like Texas Medical Center in my native Houston, TX.
BM: An approach that we employ for operators in certain sectors, particularly retail and restaurant operators, is looking at market-level saturation. Simply put, market-level saturation consists of dividing total households by store count as a surrogate to gauge brand strength in a market. A great example of the power of market-level saturation is Whataburger – both in their home market of San Antonio and throughout their home state of TX. While McDonalds has ~16x the number of locations nationally, Whataburger’s market saturation exceeds that of McDonalds in the Corpus Christi market. When comparing Whataburger’s market saturation to the likes of Burger King (~8x greater locations than Whataburger nationally) and Wendy’s (~7x greater locations than Whataburger nationally), Whataburger physical footprint exceeds these much larger operators in nearly every “home” market. Market-level saturation serves as a great reference tool for comparison of your brand with your competitors, and can be helpful when weighting competitors on a regional basis.