The intelligence behind Compare ERP
Artificial Intelligence is only as effective as the data it is trained on, and Compare ERP follows the same principle. The comparison engine has been extensively trained using publicly available data from leading sources, including global analyst firms, internationally recognized media outlets, and niche industry-standard sources known for quality and reliability.
Compare ERP’s intelligence is built on a foundation of over 500,000 verified peer reviews from ERP users. These reviews provide insight into real user experiences, enabling the detection of patterns and trends. The system is designed to flag anomalies for human supervisors, ensuring that inconsistencies and biases are removed from the analysis. Comparisons are based solely on trustworthy data.
This intelligence is further enriched by over 100,000 hours of human review of reports, articles, and blogs covering cloud ERP since 2007. Only sources regarded as industry standards for high-quality and reliable information are used.
The comparison engine evaluates all relevant data in the context of a buyer’s unique priorities, delivering highly personalized comparisons that identify the best options and potential challenges. By integrating Retrieval-Augmented Generation (RAG) models and Large Language Models (LLMs), Compare ERP contextualizes vast amounts of structured and unstructured data to provide accurate and coherent comparisons.
When selecting a new ERP solution, relying on a single opinion influenced by commercial considerations can lead to biased decisions. Compare ERP offers an intelligent, unbiased comparison based on comprehensive data analysis.
What is RAG?
A Retrieval-Augmented Generation (RAG) model enhances natural language processing (NLP) systems by combining retrieval mechanisms with sequence generation. This approach integrates two key components: a retrieval system and a generative language model.
The retrieval system sources relevant documents or data snippets from a large corpus based on input queries. These retrieved documents provide context for the generative model, which synthesizes the information into coherent, contextually enriched responses. By leveraging retrieved information, RAG models produce more accurate, informed, and contextually relevant outputs, improving performance in complex NLP tasks such as question answering and content generation.
Scoring Logic
Compare ERP utilizes advanced AI and machine learning to provide evidence-based intelligence for ERP buyers. The evaluation process is based on 14 foundational ERP Priorities, ensuring comprehensive and data-driven assessments of ERP vendors.
A ‘weighted double-score comparative logic’ is applied, considering both individual and comparative assessments across all 14 ERP Priorities. Each priority score is further weighted based on the specific circumstances of the ERP buyer.
To refine these scores, five statistical methodologies are employed:
- Non-Linear Scoring – Assigns exponentially increasing points based on performance levels, emphasizing differences between vendors while smoothing extreme variances.
- Z-Score Normalization – Standardizes scores across vendors, expanding the range to highlight even minor performance differences.
- Cluster-Based Scoring – Groups vendors into performance clusters, providing clear distinctions within and between clusters.
- Penalties and Rewards for Extremes – Adjusts scores to recognize vendors that either underperform in critical areas or exceed expectations.
- Round-Out – Ensures final scores are balanced by eliminating anomalies and data points that could skew results, creating a fair benchmark free from bias and commercial influence.
Understanding Comparative Scoring Logic
Comparative scoring evaluates each vendor’s strengths and weaknesses in relation to their closest competitors based on the selected ‘ERP Priorities’ and business criteria.
For example, a vendor may score well in a specific ‘ERP Priorities’ selection if there are few high-quality alternatives. However, if the selection criteria (industry, location, company size) introduce a broader set of strong competitors, the same vendor’s score may change.
There is no universal answer to the question, “Which is the best ERP vendor?” The best choice depends on the context of specific business needs and intelligence derived from all relevant data.
For buyers seeking a non-comparative analysis, Compare ERP provides a ‘Managed Introduction’ service, offering a direct evaluation of a vendor’s strengths and weaknesses without comparison to competitors.