A private equity platform running 10 to 20 operating brands almost always has 10 to 20 site-selection processes, because each one arrived with its portfolio company and nobody ever replaced it. The fix is not one set of criteria for every brand. It is one method, one data spine, and one report shape, with each brand keeping the variables that actually drive its sales.
That distinction is the whole job. Get it wrong in one direction and every brand keeps its own black box, so the platform cannot compare a coffee site to a pet-services site. Get it wrong in the other direction and you force a fitness concept to score on drive-thru logic, which produces comparable numbers that are all wrong.
Why a platform ends up with a dozen site-selection processes
Buy-and-build is the dominant shape of private equity right now. PitchBook counted 8,473 add-on acquisitions among 11,167 US private equity deals in 2024, close to 76 percent of all deal activity (PitchBook, 2024 Annual US PE Breakdown). Each of those add-ons brings an operating team, and multi-site operators bring a real estate practice with them.
Holding periods have stretched at the same time. Bain & Company's Global Private Equity Report 2026 puts buyout hold periods at exit at around seven years, up from an average of five to six years between 2010 and 2021 (Bain & Company, February 2026). Seven years is long enough for a brand to open a lot of units on criteria the platform never examined.
Nobody chose this. It is what happens when you buy going concerns and leave them running. The vendor contracts, the spreadsheets, the broker lists, and the unwritten rules about what a good corner looks like all came with the deal. Because none of it is broken in an obvious way, none of it gets touched.
The cost shows up at the portfolio level, not the brand level. Each brand can defend its own next site. The platform cannot rank one brand's next site against another's.
What actually breaks when brands do not share a standard
Three things, in the order operating partners tend to notice them.
The growth cases are not comparable. Every brand submits a capital request with a story attached. Without a common method, the platform is grading prose. The brand with the most confident presenter gets funded, and confidence is not correlated with site quality.
Cannibalization goes unpriced across brands. A single brand watches its own stores. A platform with two food concepts in the same region has overlap that neither brand's model sees, because neither one has the other's customer data in view. That is a portfolio-level question by construction, and it needs cannibalization analysis run on a shared footprint rather than inside each brand's own silo.
Duplicate spend hides in plain sight. Five brands, five data subscriptions, often covering the same geographies. This one is hard to size honestly: we could not find a published benchmark for duplicated site-selection data spend across portfolio companies. Most teams we work with only discover the total when someone pulls every portfolio company's vendor contracts into one spreadsheet.
What a platform standardizes, and what it must not
The useful split is between the method and the criteria.
Standardize the method. That means the same data sources and the same refresh cadence, the same trade area definitions, the same scoring mechanics, and the same report format that comes out the other end. Anyone in the platform should be able to open a site report for any brand and know where to look.
Do not standardize the criteria. A drive-thru coffee brand lives on morning commute patterns and turn-in geometry. A pet-services brand lives on household composition and drive time from home. A fitness concept is a destination, so walk-by traffic is nearly noise for it. Forcing all three onto one weighting produces numbers that agree with each other and disagree with reality.
This is the difference between a shared standard and a flattened one. The site selection criteria belong to the brand. The method belongs to the platform. When a platform sets criteria per brand from that brand's own store performance, the scores stay honest and stay comparable at the same time, because what is being compared is how far above its own bar a site sits, not raw numbers from different worlds.
Ranking the next growth dollar between brands
The reason to do any of this is capital allocation. A platform has one pool of growth capex and several brands asking for it.
Once outputs are comparable, the question changes shape. Instead of asking which brand deserves more capital, the platform asks which sites deserve capital, and the brand ranking falls out of the answer. A brand with 40 candidate sites above its bar and open whitespace is a different investment than a brand with four candidates and saturated core markets, even if both are growing.
That ranking is also the thing a fund needs at exit. Buyers pay for a credible unit-growth story, and a credible story is one where the remaining whitespace has been counted rather than asserted. This overlaps with the broader discipline of real estate portfolio management, except the platform version has to work across brands that share nothing except an owner.
The dollars behind each row are not small. Arcos Dorados, the largest independent McDonald's franchisee, guided to 105 to 115 restaurant openings in 2026 against total capital spending of $275 million to $325 million, a figure that also covers modernizations, optimizations, maintenance, and IT (Arcos Dorados, January 2026). Most platform brands run smaller unit economics than that. The point is the same either way: new units are usually the largest discretionary line in the plan.
Set the standard at diligence, not a year after close
The cheapest moment to apply a platform standard to a new brand is before you own it.
In diligence, the platform's own method is the sharpest tool available for testing a growth story. The seller's deck says there is room for 300 more units. Scoring the existing footprint on your method tells you which stores are carrying the average, and running the whitespace tells you whether 300 is a count or a wish. This is the same work as commercial real estate due diligence, pointed at the expansion plan instead of the lease file.
Then the diligence model becomes the operating model. Nothing gets rebuilt after close, the underwriting the deal team used is the same underwriting the brand runs against, and the sites already under LOI can be re-scored before anyone signs.
The alternative is familiar. The standard arrives 12 to 18 months after close, once someone notices the reporting does not add up. By then the brand has approved a year of openings on the old criteria, and those units cannot be compared to anything. You either live with the gap or go back and re-score them, which is the same work done twice.
What "defensible" means to an operating partner
Operating partners and CFOs are not asking for a better score. They are asking to see the reasoning.
That is a harder bar than internal credibility. A brand president can approve a site on judgment. An operating partner has to explain the same decision to an investment committee, and later to a buyer's diligence team, both of whom will ask why this site and not the other one. A score with no visible reasoning is worth very little in that room. Defending a site forecast to a committee is hard enough for one brand; a platform repeats it for every brand in the portfolio.
Getting there takes two things. Every variable and weight has to be visible, so the answer to "why did this site score where it did" is a list, not a shrug. And projections have to be checked against what the store actually did, which is the only way anyone finds out whether the method works. In a January 2026 survey of our customers, teams reported forecast error roughly half the industry norm and around 80 percent fewer underperforming locations once the workflow is in place.
The unit-growth effect is what a platform is buying. Cavender's went from nine new stores in 2024 to 27 in 2025 with every new location performing at or better than expected (customer story). That is a single-brand result, but it is the pattern a platform wants running in parallel across a dozen brands, with one place to see all of it.
None of this makes the decision for you. The platform team still knows things the model does not: which landlord will actually deal, which market has a manager ready to run a new unit, which brand president has capacity this year. The standard exists so those judgment calls are made on top of comparable evidence rather than instead of it.
Frequently Asked Questions about site selection for private equity platforms
How do private equity platform teams standardize site selection across multiple portfolio brands?
They separate the method from the criteria. One scoring method, one data spine, and one report shape apply across every brand, so outputs are comparable. Each brand keeps its own variables, weights, and thresholds, set from its own store performance, because a gym and a drive-thru are not driven by the same things.
Why does each portfolio company arrive with its own site-selection process?
Because it was bought as a going concern. Whatever the operator used before the deal came with the deal: its data vendor, its spreadsheet, its broker relationships, and its unwritten rules about what a good site looks like. Nobody at the platform ever chose that process, and nothing forces a change after close, so it usually survives until someone deliberately replaces it.
How should a platform rank growth capital between portfolio brands?
Rank candidate sites, not brands. Once every brand scores the same way, the platform can put this quarter's real candidates in one ordered list and fund from the top, weighing how many sites clear the bar, how much whitespace is left, and what the cannibalization exposure looks like. The alternative is comparing memos written to different standards.
When should a platform apply its site-selection standard to a newly acquired brand?
During diligence, before close. If the same model that tested the growth story becomes the operating model afterward, nothing gets rebuilt and the underwriting carries forward. If the standard arrives a year after close, a full year of openings has already been approved on the old criteria and cannot be compared to anything else in the portfolio.
How does GrowthFactor compare to Placer.ai for a private equity platform?
Placer.ai is a foot-traffic data provider, strongest as a source of visitation data that several portfolio brands can share. GrowthFactor is a site selection and market planning platform, so it takes data like that as one input and produces a scored, explainable site decision per brand with the variables and weights visible. A platform team often wants both: a shared data layer underneath, and a decision layer that makes brand-level outputs comparable at the portfolio level.
Where to start
If you are staring at a portfolio where every brand does this differently, the first move is not a rollout. It is picking the two brands with the most capital at stake next year and running both on one method, then putting their candidate sites in a single ordered list. That list is the artifact that makes the case for everything else, and choosing the platform to run it on is a smaller decision than selecting portfolio management software for the whole organization.