Best Practices Will Only Take You So Far

Abstract

Yext Scout Index draws on more than 200 structured data points from 8.7 million Google search results to uncover the true drivers of Google Local Pack visibility. Across most industries and regions, active review management, reflected in high volumes of positive reviews and prompt owner responses, emerges as the most powerful signal. Profile completeness and curated photo assets further boost visibility — sometimes even more than reviews, depending on context. In hospitality, for example, a focused set of photos, a clear merchant description, and listed business hours correlate strongly with top‑ranked placements. Food and dining businesses, by contrast, draw relatively greater value from the recency and rating of individual reviews than other industries. Regional differences also emerged: the Northeast market exhibits lower sensitivity to traditional SEO signals, whereas the South and the West reward rapid engagement and timely responses. These findings make clear that effective local SEO requires strategies tailored to the unique strengths of each industry vertical and geographic market rather than a one‑size‑fits‑all approach.

Introduction

Every marketer knows that regionality has a strong influence on messaging and the voice of a campaign, so we had to ask ourselves, does it also have an impact on search and discovery behaviors? Using our new Scout Index research database, we've taken a novel approach to answering this question. What we've found is that search behaviors change based on industry vertical and regionality. For local businesses, appearance in the Google Local Pack can be a make-or-break moment in competitive markets. The following analysis examined more than 8.7 million search results across multiple verticals and regions in the United States to identify the key factors (signals) that differentiate between high- and low-ranking search results.

Key findings

• Review engagement dominates. Active engagement with reviews — both in volume and responsiveness — appears to be the most consistent driver of Local Pack visibility across all industries and regions. Businesses with high volumes of positive reviews and prompt responses to customers tend to outperform others.
• "Best practices" aren't universal. While profile completeness and timely replies generally help, their impact varies significantly across different industries and regions. For instance, hospitality businesses observed a negative correlation between the number of photos and higher rankings, suggesting that quality matters more than quantity.
• Regional quirks matter. The U.S. Northeast showed less sensitivity to most SEO signals, while the South and West were particularly penalized for slow response times. Reduced activity can lead to a decrease in visibility in these regions.
• Industry-specific oddities. Food and dining businesses benefit more from recent high-rated reviews than from overall volume or completeness. Meanwhile, hospitality's top performers leaned on curated visual assets and avoided cluttered profiles.

Methodology

A study of this scope demands a dataset with broad coverage across business types and geographic regions. The Yext Scout Index fulfills that need, offering a granular view of local search performance across diverse economic and regional landscapes. Unlike traditional rank trackers, the Scout Index captures not only what ranks locally but also what real users see when searching across 100 keywords and more than 3,000 Primary Categories. Crucially, it also retrieves the top 40 local competitors for each query and conducts secondary scans of their websites, reviews, and social media presence. This approach yields over 200 structured data points per business location (Abernathy and Ward, 2025). To uncover meaningful insights across six core verticals and four Census regions, we analyzed over 8.7 million Google search results. The focus was on identifying the signals that influence visibility within the Google Local Pack. The dataset included businesses operating in six major industry categories: Food & Dining, Healthcare, Retail, Financial Services (FINS), Hospitality, and Miscellaneous Services, spanning 2,500 of the most populous ZIP Codes in the United States (each with a minimum population of 36,500 per the 2023 U.S. Census). Each business record included detailed attributes such as review volume and velocity, business hours, contact information, media assets, social engagement, and Google Business Profile completeness. The analysis compared placements in Google's Local Pack versus standard Google Maps results, focusing on the distinction between high-visibility and baseline search presence.

Analytical approach

We applied several rigorous techniques to ensure the reliability and interpretability of the results:

• Data preprocessing: We applied informed imputation strategies, recognizing that missing values often carry business meaning. For instance, missing review response times indicate businesses that don't engage with customers, while missing ratings suggest limited customer feedback.
• Feature engineering: We enhanced raw data through mathematical transformations, including logarithmic scaling for count variables and power transformations for skewed distributions. Binary features were encoded as +1/-1 values to capture directional relationships more effectively.
• Optimal feature selection: Rather than analyzing all possible variations of each business attribute, we selected the most predictive version, reducing redundancy while maximizing analytical power.
• Regional analysis: Correlations were calculated separately for each region-industry combination, recognizing that local search dynamics vary significantly across geographic markets and business categories.

What we learned

While some factors appear to be universally important for local search visibility, the reality is more nuanced. Businesses that actively manage their online reviews, both in volume and responsiveness, tend to secure stronger Local Pack rankings. Likewise, a fully completed Google Business Profile consistently correlates with better visibility. However, these broad practices are just the starting point. The influence of individual ranking signals shifts meaningfully when segmented by industry and region. For example, hospitality businesses buck the trend, showing weaker or even negative correlations for attributes like photo quantity and profile completeness. In these cases, a smaller set of curated, high-quality photos has more impact than a large, unfocused collection. Similarly, food and dining businesses rely more on the recency and rating of reviews than on the sheer volume or completeness of their profiles, signaling that user expectations in those contexts differ sharply. Regionally, consumer search behavior varies just as much. Businesses in the Northeast, for instance, appear less sensitive to traditional SEO levers, suggesting regional market dynamics or higher baseline expectations. Meanwhile, operators in the South and Western regions suffer disproportionately from slow review response times, especially over weekends. This suggests that operational habits, such as waiting to reply, can actively hinder visibility. All told, the data shows that local SEO isn't just about checking boxes; it's about tuning your approach to fit who you are, where you are, and whom you serve.

Universal success factors

Certain business attributes consistently correlate with Local Pack visibility across all regions and industries.

• Review activity emerges as the strongest universal factor. Businesses that have a high volume of positive reviews and actively respond to customer reviews show significantly higher Local Pack placement rates.
• Business profile completeness represents another critical universal factor. Businesses with comprehensive Google Business Profiles, including complete contact information, business hours, and service descriptions, consistently outperform competitors with incomplete profiles.

Diversity in search behavior

Not every shoe fits, and the same can be said for how search works for each vertical. As evident from this feature analysis, the idea of best practices does get you a good distance towards your goal, but when we dig into industry-specific and regional factors, we observe a change in signal strength, which leads us to believe that user behaviors differ across markets and industries when it comes to search activities. ^{Figure 1 - Overall Feature Correlations with Local Pack Ranking: Bar‑style heatmap ranking the 20  most predictive features by their overall correlation with Local Pack placement. Review count, new reviews per month, and owner review responses lead the list, while average response time appears as the lone modestly negative factor.}

^{Figure 2 - Feature Correlations by Region and Industry: Composite heatmap breaking down feature—ranking correlations for each region‑industry combination. Bold vertical lines separate the Midwest, Northeast, South, and West, and within each region, the six industries are shown side by side. This nuanced view reveals, for example, that Food & Dining businesses in the South derive less benefit from review volume than the same vertical in the Northeast.}

Unique findings by vertical

• Hospitality businesses display distinct ranking patterns, being the only major industry where the presence of a location landing page and business attributes shows minimal impact on local pack visibility. Data suggests that rating, profile completeness, detailed merchant descriptions, and curated photos are more valuable.
  
• Food and dining establishments see fewer local pack benefits from completed profiles compared to other industries. These businesses also depend less on overall review and photo engagement, instead prioritizing recent, highly rated reviews.

^{Figure 3 - Industry Differences in Local SEO Ranking Factors: Heatmap illustrating how six industries (Financial Services, Food & Dining, Healthcare, Hospitality, Retail, and Other Services) differ in their reliance on ranking signals. Darker blue cells mark attributes with stronger positive correlations (such as business hours in Hospitality and review count in Healthcare), underscoring the need for industry‑specific SEO tactics.}

Unique findings by region

We also find minor changes in search behaviors at the regional level.

• While Local Pack visibility is generally affected by ranking factors across regions, the Northeast exhibits less sensitivity to these factors.
• Owner‑response lag hurts the South and Western regions the most. Midwestern businesses that "wait till Monday" risk losing weekend visibility. Automate or delegate your weekend replies — this small operational shift can preserve visibility during critical off-hours.

^{Figure 4 - Regional Differences in Local SEO Ranking Factors: A heatmap illustrating the average correlation of ten key business features with Google Local Pack rankings across the Midwest, Northeast, South, and West. This regional breakdown highlights how responsiveness, review activity, and profile completeness influence visibility differently depending on location.}

Conclusion

These findings suggest that local search is more signal-dependent than initially thought and that local search and local SEO appear to be more nuanced than originally thought. This would lead us to believe that Google's algorithms are more perceptive than initially assumed, as they seem to respond to unique user behaviors that vary based on both "what is being searched" and "where the searcher is". This wide stroke discovery personalization may require marketers to adjust their strategies and tactics as they refine and tune the dial for a particular region. The one-size-fits-all approach seems to be a relic of the past.