PRISM AI™ + SEO Dimensions Overview

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 (“write more content” or “get more backlinks”). We believed there were answers in the vast amounts of data if we could just analyze it at a deeper level. The statistical 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.

Share Of Voice

A measure of the market your brand owns compared to your competitors.

Share of Voice (SOV)

The first step in every analysis is to determine Share of Voice (SOV), which is a calculation of the percentage of total search traffic a brand can expect to capture based on keyword volume and average click through rates (CTRs). SOV is also a proxy for market share. For companies seeking to increase overall digital presence and brand awareness, SOV is best calculated as the share of organic traffic a brand receives for a certain industry topic relative to the whole.

Because SOV indicates the brand’s degree of reach to the addressable market, understanding a brand’s SOV in active and competitive markets is critical. The analysis provides a benchmark of the total addressable market, the competitive landscape, and provides insight into the effort required to increase organic visibility and traffic.

Search Ranking Factors (SRF)

Page-one of search results receive over 90% of organic search traffic; the advantage of knowing industry-specific tactics that positively influence page-one rankings cannot be understated.

Ranking factors have been historically obscured by search engines, and misleading information is provided based on anecdotal evidence which is often proliferated within the search engine optimization community. The result wastes human resources and marketing budgets, and ultimately does not produce intended results.

Arcalea’s proprietary PRISM AI™ SRF analysis solves for this challenge, as the analysis enables brand teams to direct the right resources to the right places, and ensures resources are not wasted by over allocation past the degree of positive returns. The goal of this analysis is to provide an innovative solution to the inefficiencies that are prominent in SEO.

Search Ranking Factors

Factors relating to a website’s content, technical implementation, user signals, backlink profile or any other features the search engine considers relevant.

Topic Clusters

A group of content that revolves around a central topic and use a pillar page to link to and from.

Topic Cluster Analysis

Search Ranking Factors are industry-dependent, but also Topic Cluster-dependent. Clustering has a goal of grouping or “clustering” contextual examples based on similar characteristics. By analyzing the number of shared search results between queries, the clustering method partitions keywords into distinct topic clusters, which enables brands to better understand target topics for content creation.

Arcalea’s proprietary PRISM AI™ SRF analysis provides brand teams with a comprehensive assessment of the search queries that are nested within an industry’s various topics. Through building extensive content hubs based on industry contextual demand, brands have the opportunity to increase their digital relevance and pageone probability around various topics. The aggregate search volume for each cluster provides information about the value a page within a topic cluster may have, which empowers a brand to fortify content where there is ample search opportunity.

Methodologies

Share of Voice

Share of Voice is determined for each brand’s website competitively, based on a list of aggregated, industryrelevant keywords within topic clusters. An industry list of keywords is determined by analyzing relevant terms with high search volume which are shared between industry competitors.

Following, the brand’s industry online search market share is calculated and analyzed; the voice share is defined by the total search volume, keyword ranking, and click-through rate for the given set of keywords.

Search Ranking Factors

Machine Learning algorithms are leveraged to deconstruct Google’s search ranking system to determine the most highly-correlated ranking factors with contribution to page-one search results within the overarching Cannabis industry. While the specific details of the methodology may be complex, the output is fairly straightforward. In essence, the contribution of each of the variables, in this case, ranking factors, is calculated by measuring the marginal contribution of each with respect to the predicted outcome (probability of ranking on the first page); depending on the value of each ranking factor, this marginal contribution can either be positive, negative, or neutral. The marginal nature of each of these ranking factors must be emphasized, as it highlights the concept that page-one probability is the cumulative outcome of a host of variables.

In any instance, the following analysis is Arcalea’s own analysis and provided as informational. Ultimately success is dependent on many factors including the ever changing search algorithms and competitor activity and success cannot be guaranteed.

Topic Cluster Analysis

Topic Cluster Analysis is used to identify, organize, and implement specific contextual content (i.e., “clusters”) in ways that help search engines understand a site’s structure and topic relevance. Because the output of this analysis reveals specific topics, off-page elements, and technical factors that contribute to industry relevance, the resulting clusters correlate to increased visibility and ranking for the domain’s presence in search engine results pages (SERPs).

The Topic Cluster Analysis is a multi-step analysis, measuring keywords, clusters, and link performance using statistical analysis, data mining, and machine learning algorithms. The output of topics, semantically related keywords, and site parameters create industry-specific clusters most correlated with page-one probability when developed and supported as recommended.