Cannabis A.I. SEO Analysis and Recommendations Guide by Arcalea

A.I. Industry Analysis and Guide SUCCESS IS NOW A SCIENCE

N AT I O N A L A N A LY S I S

A.I. Industry Analysis and Guide FOR THE CANNABIS INDUSTRY

Table of Contents 01. INTRODUCTION

About PRISM A.I.™

Dimensions Overview

02. SHARE OF VOICE

National Findings

Regional Findings

Recommendations

03. SEARCH RANKING FACTORS

Introduction to Dependence Plots

National Findings

Recommendations

04. CLUSTER ANALYSIS

Analysis

Recommendations

05. CONCLUSION

06. APPENDIX

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Introduction

THE FULL SPECTRUM OF YOUR BUSINESS

All business leaders, including Cannabis brands, are continuously seeking to increase business value, while de-risking the organization and staying ahead of the competition. Cannabis brands are also faced with multifaceted challenges: startup costs, legal, regulation & compliance, increasing competition, and noise, not to mention the state & national implications. Ultimately most brands know they’ll be successful if they can just reach their target audience. And the test for success, especially in highly-regulated industries such as Cannabis, is organic traffic. This is why Arcalea created the PRISM AI™ platform, to leverage A.I. to determine the most important ranking factors in any industry. We have analyzed tens of millions of data points across hundreds of brands to deliver keys to success to our clients in Pharmaceutical, Manufacturing, Higher Education, and Healthcare…and now… Cannabis. For the first time, we’re publishing the findings to the entire Cannabis industry - so now you can access A.I. to discover the best SEO tools for your company or client - without the Data Scientist or SEO teams. Provide this report to your teams (or your agency) to reduce costs and increase revenue. And you can apply A.I. to increase traffic to your business today!

PRISM AI™ + SEO

PRISM AI™ + SEO Five years ago we conducted our initial analysis of the SEO industry using a statistical model to achieve better outcomes than the general SEO guidelines at the time

PRISM AI

(“write more content” or “get more backlinks”). We believed there were answers in the vast amounts of data

by Arcaléa

if we could just analyze it at a deeper level. The statistical

The first A.I.-based industry analysis for Cannabis.

models we used were far superior to general SEO best practices. But we knew it could get even better. Three years ago we began working on PRISM AI™ (Predictive Rank Intelligence in Search Marketing), an AI platform that could identify and weigh the precise search ranking factors that contribute to first-page rank. Now in its 11th generation, PRISM AI™ can identify the top-ranking factors in any industry, in order by weight, with discrete thresholds. PRISM AI™ can even distinguish between National, State, City, and County-level ranking factors. Having delivered breakthrough analysis to global brands in other industries, PRISM AI™ has now analyzed the Cannabis industry, enabling every grower, manufacturer, processor, and retailer to take advantage of its unique insights, to be able to deconstruct the precise ranking factors nationally, and empower their teams, partners, and employees to compete on new levels, to increase traffic and revenue.

01. INTRODUCTION

02. SHARE OF VOICE

03. SEARCH RANKING FACTORS

04. CLUSTER ANALYSIS

05. CONCLUSIONS

Dimensions Overview

SHARE OF VOICE (SOV)

SEARCH RANKING FACTORS (SRF)

TOPIC CLUSTER ANALYSIS

Screened image will be updated

Share of Voice

MEASURING MARKET SHARE VOLUME

Because brands are competing for the same positions and market share, a brand’s current SOV depends on how all other competing domains are positioned and ranking for the same topics. It is found that analyzed brands may rank highly for a subset of keywords, but do not utilize a wide enough spread of keywords that would contribute to a larger market share while still being relevant to users’ searches. The industry’s National SOV provides an overview into which domains have the most authority, and therefore are likely to rank the most competitively.

Search Ranking Factors

FINDING THE RIGHT VARIABLES

Brands compete for the same positions within search results as it’s a zero sum game; a limited number of brands can appear on each SERP, with positions 1-10 receiving the majority of search traffic. However, the PRISM AI™ SRF Analysis identifies:

The precise ranking factors which contribute to page-one rank

The discrete minimum and maximum values for each (how much is required of each factor, and when to stop investing)

The result is an A.I.-driven roadmap providing brand teams unparalleled intelligence, maximizing returns. The output from PRISM AI™ provides an itemized, ranked, and measured account of the top ranking factors driving page-one probability. All ranking factors are defined in the Appendix.

Dependence Plots

How to Read Dependence Plots A dependence plot depicts the relationship between variables and predictions. In the following analysis, the dependence plots visualize the relationship between ranking factor measurements and page-one probability. Each plot provides a visual understanding for when a specific ranking factor has a positive or negative contribution towards page-one probability, and the optimal range for implementation.

The y-axis represents the correlation on page-one probability.

Above the origin on the y-axis indicates a positive correlation on page-one rank.

The 0,0 origin is neutral, with neither positive nor negative contribution to pageone rank.

Below the origin, on the y-axis indicates negative correlation on page-one rank.

The x-axis labels the ranking factor.

In the example above, the Cumulative Layout Shift (CLS) is measured against page one probability. The majority of the observations are indicated by the cluster of points between 0 and 0.5 on the x-axis. However, because significant groupings are simultaneously below 0.5 CLS and below 0.0 page-one probability, the plot indicates to avoid CLS below 0.5. However, plotted points of 0.54-0.72 CLS are mostly above 0 on the y-axis, indicating a positive correlation to page-one probability and should be targeted.

01. INTRODUCTION

02. SHARE OF VOICE

03. SEARCH RANKING FACTORS

04. CLUSTER ANALYSIS

05. CONCLUSIONS

Dependence Plots

For a definition of all search ranking factor terms, visit the Appendix.

Target URL Rating Range

Positive impact on page-one rank.

Negative impact on page-one rank.

Negative Impact Range

In the example above, the URL rating (UR) is plotted against page-one probability. The plot shows perpendicular lines over each UR (e.g., 5, 6, 7, etc.). The longer lines from UR 0-11 include several points with negative page-one probabilities on the y-axis (-0.2 to -1). However, URs above 12 are indicated by lines of observations mostly above 0 on the y-axis. As a result, UR of 12 to 45 are more likely to have a positive correlation with page-one probability.

Cluster Analysis

A MULTIFACETED APPROACH

In order to increase organic awareness, visibility, and therefore online market share, contextual keyword relevance is coupled with the unique search ranking factors to increase the probability of ranking on page-one. This keyword relevance is identified in the “Cluster Analysis.” The following clusters represent the Cannabis industry’s most searched queries based on search volume. Also included are the unique ranking factors per cluster to increase the probability of ranking for each given cluster. These factors combine into a multifaceted approach to increase digital relevance by focusing on three dynamic elements: off-page, content, and technical recommendations.

Appendix 57 #

SUCCESS IS NOW A SCIENCE.

Interested in reaching out to us? Contact cannabis@arcalea.com for more information.

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