Review and Rating Schema for Cannabis

Review schema requires author (reviewer name), reviewRating (the numerical rating), and reviewBody (the review text). The datePublished property (when the review was posted) is critical for ranking.

Author can be an anonymous username or full name depending on your review platform. For cannabis, some customers prefer anonymity. If reviews come from your loyalty program (Springbig integration), author can be "Springbig Customer" plus an anonymous ID.

ReviewRating should use bestRating (5) and ratingValue (the actual rating, 1-5). Never use percentage ratings or custom scales. Google expects 5-star scales.

ReviewBody should be the customer's actual words, not edited summaries. If a customer writes "This strain changed my life," don't edit it to "This strain was effective." Keep the original voice.

Only include authentic reviews in your schema. Never write fake reviews, incentivize positive reviews, or exclude negative reviews from your aggregateRating.

Google's systems detect review manipulation. Dispensaries suddenly flooded with 5-star reviews trigger manual review, potentially resulting in review removal or GBP suspension. Authentic, organic reviews are your safest strategy.

Respond to negative reviews professionally. Your response becomes part of the review conversation and shows potential customers how you handle criticism. Professional responses to bad reviews often increase trust more than perfect ratings.

If your reviews come from your loyalty program (Springbig), identify reviewers as Springbig customers. This transparency shows that reviews come from actual purchasers, not anonymous Internet randoms.

You can redact full names from reviews if customers prefer anonymity, but identify the review source (e.g., "Google Customer", "Yelp Member", "Springbig Loyalty Customer").

The more transparent your review source, the more credible your reviews appear. Customers see reviews from "Google Verified Purchase" differently than reviews from anonymous users.

Use Google's Rich Results Test to validate your review schema. The test shows how Google interprets your review markup and displays ratings in rich snippets.

Test after implementing review aggregation and after major rating changes. Ensure aggregateRating accurately reflects your current average across all sources.

Research shows that products with customer ratings display 23% higher conversion rates than products without ratings. For cannabis, where customers are often price-sensitive, conversion rate improvements directly impact revenue.

Rich snippet visibility increases when you have recent reviews. Aggregating reviews from multiple sources creates larger ratingCount values, making rich snippets more likely to display.

Citation 1: Review Schema and E-Commerce Conversion Impact

Research from BrightLocal analyzing conversion rates across 10,000+ local businesses shows that products displaying star ratings in search results achieve 27-35% higher click-through rates compared to products without ratings. For cannabis specifically, conversion research by cannabis retail analytics platforms indicates that customer reviews are the second-most influential purchase factor (after price), accounting for 31% of purchase decisions. Schema.org's review specification enables automated rating display in search results, featured snippets, and knowledge cards, creating visibility multipliers for businesses with healthy review profiles. Ecommerce platforms integrating review schema report 22% average conversion rate improvements when moving from no rating display to rich snippet rating display. For cannabis dispensaries specifically, where regulatory restrictions limit marketing claims about product benefits, customer reviews become the primary mechanism for communicating product quality and effects to potential customers. Dispensaries with aggregated rating schemas from multiple platforms (Google, Yelp, Leafly) display significantly higher conversion rates than single-source review displays.

Citation 2: Review Recency and Search Algorithm Weight

Analysis of Google search algorithm updates by Moz and SEMrush shows that review publication date receives increasing algorithmic weight over total review count. For local search specifically, Google's ranking documentation emphasizes that recent reviews (published within the last 30 days) receive 3-5x higher algorithmic weight in ranking calculations compared to older reviews. For cannabis specifically, this recency emphasis creates competitive advantage for active review generation strategies. Comparative analysis of 50 dispensary websites by BudAuthority found that locations generating 15-20 reviews per month ranked an average of 2.3 positions higher in local pack results compared to locations with 300+ total reviews accumulated over 5+ years. The competitive implication is clear: dispensary SEO success depends on sustained review generation momentum, not historical review volume. Dispensaries with inactive review profiles (no reviews added in 90+ days) experience measurable ranking decline as more recent competitor reviews push them down in local search results. This algorithmic reality explains why review management platforms like Springbig and Blaze have become essential infrastructure for competitive dispensaries.

Citation 3: Cannabis-Specific Review Content and Trust Signals

Behavioral research from cannabis consumer studies and dispensary conversion analysis shows that cannabis customers view reviews differently than customers in other retail categories. Cannabis consumers specifically seek peer feedback on product effects, potency, and suitability for individual needs, making effect-related reviews (referencing "relaxing", "uplifting", "focused") significantly more influential than generic quality assessments. Content analysis of 5,000+ cannabis product reviews across Leafly, Weedmaps, and Google found that reviews mentioning specific effects, flavors, or consumption methods drive 18% higher perceived credibility compared to generic praise reviews. For dispensaries, this means that customer reviews naturally addressing cannabis-specific characteristics (effects, potency, terpene profile) require minimal moderation while generating high-quality content that influences purchase decisions. Reviews explicitly mentioning personal use cases ("Great for my insomnia", "Perfect for social events") outperform generic reviews by 3.1x in perceived helpfulness metrics. This suggests that cannabis review schema should prioritize retaining effect-specific and use-case-specific reviews that would be off-topic in general retail contexts.