The 10 most important questions to ask your AI site selection agent

The model tells us that the strongest “pull” factors in this immediate 2-mile trade area are not general demographics or road traffic, but rather the presence (and density) of certain kinds of retail and entertainment destinations. In other words, shoppers and diners are being drawn here by the businesses clustered around the site. The top nearby drivers are:

1. Clothing Stores (NAICS 4481)
   Fashion-oriented retail brings foot traffic that can cross-shop into restaurants and service businesses.
2. Florists (NAICS 4531)
   Gift and specialty-shop traffic often pairs with lunch or coffee visits.
3. Building Materials & Supply Stores (NAICS 4441)
   These attract a steady stream of contractors, DIY homeowners and their crews throughout the day.
4. Automotive Parts & Tire Shops (NAICS 4413)
   Service-driven visits often generate ancillary trip purposes (e.g. grabbing lunch or a snack).
5. Educational Support Services (NAICS 6117)
   Tutoring centers, test-prep facilities and the like bring parents and students in mid-afternoon and early evening.

In addition to those hard-count categories, two “lifestyle” clusters stand out:

• “Whiskey Business” (craft bars/distilleries)
• “Trendy Eats” (higher-end fast-casual and counter-service concepts)

Both of those segments score highly in the model’s site importance ranking, suggesting that co-location with popular dining and nightlife spots will help drive incremental visits to your new location.

Compared to your existing portfolio of sites, this location’s 2-mile trade area really stands out along a few dimensions:

1. A disproportionately large share of moderate‐income homeowners
   Households with annual home values of $60–80K are roughly 6.7 standard deviations (σ) above your portfolio average, and the $40–60K and $80–100K buckets are each 3–3.5 σ above average.
2. An older housing stock
   Nearly all of the homes were built in 1939 or earlier—about 5 σ higher than your typical site—suggesting a mature, established neighborhood.
3. A strong Community & Social Services workforce
   Occupations in community/social services are over 3 σ above your portfolio norm. This often correlates with daytime stability (appointments, errands, meetings).

By contrast, this trade area has below‐average:

• “Fitness Fashion” lifestyles (–2 σ),
• Counter-service restaurant indices (–1.7 σ), and
• Weekend mid-day and afternoon traffic (–1.6 σ).

In plain terms, this location is uniquely anchored by an older, moderate-income homeowner base and a high concentration of community/social‐services workers—instead of the younger, on-the-go, counter-service–oriented consumers you see at many of your other sites. That profile suggests your new location here will draw more from stable, appointment-based and residential trip patterns rather than heavy weekend brunch or fitness–driven traffic.

Here are the top five high-performing stores in your portfolio whose trade-area profiles most closely mirror this location’s strengths (older/moderate-income homeowners + strong community‐services workforce):

1. Sandwich Shop (Location ID 13,285,601)
   Similarity score: 67.5
   Actual revenue: $812K
   Trade area: 75% homes built pre-1940, median home value $85K
2. Sandwich Shop (Location ID 13,285,526)
   Similarity score: 65.2
   Actual revenue: $798K
   Trade area: 72% homes built pre-1940, 4.8% community/social services workforce
3. Sandwich Shop (Location ID 13,285,351)
   Similarity score: 64.6
   Actual revenue: $825K
   Trade area: 70% pre-1940 housing, homeowner median age 1972
4. Sandwich Shop (Location ID 13,285,507)
   Similarity score: 63.6
   Actual revenue: $781K
   Trade area: 78% homes pre-1940, average household income $54K
5. Sandwich Shop (Location ID 13,285,408)
   Similarity score: 63.3
   Actual revenue: $845K
   Trade area: 80% pre-1940 housing, 5.2% in community/social services

All five generate actual revenues north of $780K and share very high proportions of older housing stock plus strong community/social-services employment—just like this site. Modeling this location’s Zeustimate against these peers suggests it’s pulling from a proven demographic base.

A few real‐world factors could depress this location’s Zeustimate ($734K) and cause underperformance versus projections:

1. Lower “on-the-go” and weekend traffic than expected
   We saw the site is –1.6 σ below your portfolio average for weekend midday/afternoon traffic. If your concept leans on strong weekend brunch or impulse visits, the trade area simply won’t deliver enough volume.
2. A counter-service ceiling
   The trade area’s counter-service restaurant index is –1.7 σ below average. If that gap reflects less propensity to pick up quick-serve meals, pushing a grab-and-go sandwich menu here may hit a hard ceiling.
3. Overreliance on a single daytime cohort
   With Community & Social Services workers 3 σ over your norm, daytime demand is tied heavily to appointment-based trips. If local institutions consolidate or remote work reduces visit frequency, that core base could shrink quickly.
4. Demographic shifts in an aging neighborhood
   The housing stock here is very old (homes built pre-1940 are +5 σ). Neighborhoods with predominately senior homeowners sometimes see slower population turnover—and fewer new households moving in—so population growth (and revenue growth) may trail model expectations.
5. Competitive entry or changing retail mix
   A new fast-casual, coffee-oriented concept or a big convenience store opening nearby could siphon both your lunch and social-services‐worker traffic. Because this is a mature, built-out trade area, even small shifts in tenant mix can have outsized impact.
6. Site-level constraints
   If parking capacity, visibility, or signage are worse than the data suggest (pilot counts, field checks), you could undercapture even the modest weekend and counter-service demand the model assumes.

In short, this location’s profile depends heavily on weekday, appointment-based trips in an older, moderate-income enclave with below-average weekend and counter-service pull. Any further weakening of weekend traffic, shifts away from counter service, or loss of its core daytime cohort would pull actual revenue below the Zeustimate.

Here are the biggest drivers helping and hurting the Zeustimate for this location, per the model’s site-specific importance scores.

Strengths (top positive contributors)

1. Building Prototype (11.27)
   The particular building design/layout strongly boosts projected revenues.
2. Homes – $60K–$79,999 (9.18)
   A high concentration of households in this income band in the 2-mile trade area.
3. Square Feet (6.35)
   Larger store size correlates with higher predicted sales.
4. Wanderlust (4.11)
   A travel-oriented consumer segment in the area is associated with increased traffic/revenue.
5. 2010–2021 Housing Unit Growth (2.75)
   Rapid recent housing growth suggests a growing customer base.

Weaknesses (top negative contributors)

1. 4542 | Vending Machine Operators (−3.25)
   A higher density of these operators is linked to lower site revenues (competitive or cannibalizing effect).
2. Live & Local Music (−3.15)
   Areas with a strong “Live & Local Music” segment trend toward lower spending at this concept.
3. Neighborhood (East/West) (−3.03)
   The site’s east/west neighborhood designation historically underperforms versus north/south.
4. Industry: Management of Companies and Enterprises (−2.22)
   A bigger share of this employment sector in the trade area slightly detracts from projected sales.
5. Occupation: Healthcare Support (−1.57)
   Higher concentration of healthcare‐support workers is modestly associated with lower revenues here.

These “site importance” scores reveal where the site’s characteristics most strongly move the needle on its Zeustimate. Let me know if you’d like any deeper dive into any of these variables.

Demographic & Psychographic segments our model performs best with (in descending order of positive lift):

• Student Life
• Wanderlust
• Whiskey Business
• Dog Lovers

(And, to a much smaller negative degree: Yoga Advocates and Vending Machine Operators.)

Now, here’s how this location’s 2-mile trade area stacks up:

• Student Life: 34.1
  A healthy score, indicating a meaningful college-student population.
• Wanderlust: 63.4
  Well above average, signaling an adventurous/travel-inclined demographic.
• Whiskey Business: 40.0
  Strong affinity for craft-spirits in the area.
• Dog Lovers: 51.8
  Above-average pet-owner concentration.
• Yoga Advocates: 68.7
  Also well above average (this segment tends to exert a small downward pull in the model).
• Vending Machine Operators: 0.37
  Very low, which aligns with the small negative lift from this segment.

Bottom line: the site’s trade area is rich in the four top-performing psychographics (Student Life, Wanderlust, Whiskey Business, Dog Lovers) and has minimal presence of Vending Machine Operators. The only “misalignment” is a relatively high Yoga Advocates score, which our model associates with slightly lower performance. Overall, the area strongly matches the positive demographic/psychographic profile.

The two competitor “buckets” that look most likely to erode sales at this location are:

1. Counter-service concepts in general
   The trade area’s Counter Service Index is elevated (both the average and maximum indices are above 1.0), signaling a heavier-than-normal concentration of quick-serve brands.
2. Sub-sandwich shops specifically
   The site’s Trade-Area counts for Subway and Jersey Mike’s (and other sandwich-category competitors) are among the highest in your portfolio.

In practical terms, you’re up against a dense field of fast-casual and quick-serve sandwich operators here. Any promotional or menu strategy at the new site should anticipate strong local loyalties to those sub-shop brands.

You don’t have any of your own sites within a two-mile radius of this location – so there’s zero self-cannibalization risk. On the category side, however, you do see:

• Four Subway outlets inside the 2 mile ring
• One other sandwich shop location
• No Jersey Mike’s

Four Subways in a two-mile radius is somewhat above average for a suburban corridor, so you’re trading off a bit of “sandwich-shop saturation.” But with no overlapping in-brand locations and only one other non-Subway deli in the same ring, I’d characterize the area as moderately competitive — not a true oversaturation of your own footprint, and only a mild industry-level crowding.

The 3-mile ring around this location contains a fairly large, growing and economically active population—here are the highlights that tell us who your potential customers are:

1. Population & Growth

• Total residents: ~83,900 people
• Total families: ~20,300
• Daytime population: ~98,100 (about a 17% uplift over residential population, indicating a healthy influx of workers/shoppers during the day)
• Population growth (2010–2021): +0.97% (families grew even faster at +1.5%)

2. Household Income & Spending Power

• Median household income: ~$51,600
• Average household income: ~$63,200
• This suggests a predominantly middle-income trade area with reasonable discretionary spending.

3. Demographic Diversity

• Gender split: ~48% male / 52% female
• Age structure: a mix of young families, working-age adults, and established empty-nesters
• Ethnicity / Race:
  • Hispanic or Latino: ~17,300 people
  • White alone: ~37,300
  • Black/African American alone: ~29,300
  • Asian alone: ~4,550
  • Two+ races: ~5,060
• A diverse, multicultural customer base.

4. Household & Living Arrangements

• Over 20,000 family households, plus ~1,950 people in group-quarter settings (e.g., student housing, assisted living)
• Steady growth in families over the last decade

5. Daytime vs. Residential Dynamics

• The fact that daytime population exceeds residential suggests:
  • A significant office/industrial/employment presence
  • A strong retail/service draw for non-residents (e.g., commuters stopping for lunch, errands)

Putting it all together, your 3-mile trade area is:

• Large and growing, with over 80,000 area residents and almost 100,000 people coming in during the workday.
• Middle-income, but with enough higher-earning households to support discretionary categories.
• Demographically diverse, so product/messaging that resonates across multiple cultural segments will perform best.
• A blend of residential and employment uses—ideal for concepts that draw both local regulars and daytime commuters.

These characteristics suggest you have a stable core customer base supplemented by strong daytime demand—an attractive profile for most retail, quick-service, or service-oriented concepts.

Here are several targeted follow-up questions you can ask Zeus.ai Chat to peel back additional layers and sharpen your decision on this site:

• “Can you pull the Smart Comps for this proposed site and show me how those existing locations perform on the key trade-area metrics?”
• “Which site-level attributes (e.g. parking spots, drive-thru, square footage) are most important to the Zeustimate here?”
• “How sensitive is the Zeustimate to small changes in our top demographic/psychographic lifts—e.g., what happens if the Student Life score is 10% lower?”
• “What does a variable importance analysis tell us about the single biggest upside and the single biggest downside factor for this trade area?”
• “How does this site’s traffic counts (daily vehicle and pedestrian) compare to our best-performing sites and to the market average?”
• “Are there any high-value synergistic brands (coffee shops, gyms, retail anchors) nearby that could boost our foot traffic?”
• “What’s the risk of cannibalization from our own existing locations within a 5-mile radius?”
• “Can we see a competitor-saturation heatmap overlaid on the trade area so we can pinpoint potential micro-pockets of opportunity or overcrowding?”
• “How does household income and spending power in this 2-mile ring stack up against our top 10 performing sites?”
• “If we expand the trade-area radius to 3 or 5 miles, how do our key metrics and Zeustimate change?”

Asking these will give you a richer sense of not just “how well” the site should perform, but exactly why—and what levers you can pull to optimize its success.