Zilliant Expands Global Artificial Intelligence B2B Benchmark Report

Updated Global Report Breaks out Manufacturing, Distribution Segments

Zilliant, a leading AI-driven SaaS platform for maximizing the lifetime value of B2B customer relationships, announced an update and expansion of its Global AI B2B Benchmark Report. The company has published separate distribution and manufacturing industry benchmark reports for greater industry relevancy.

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The sample data analyzed includes 4.4 million products, 4.1 million customers, 13 currencies and $120 billion in transactions. Traditional business intelligence and analytics simply cannot compute at this scale. Created by analyzing over 1 billion B2B transactions, the report reveals consistent areas of uncaptured revenue and substantial opportunity available:

• ­The global B2B industry, overall, consistently fails to capture between 9 and 42 percent of available profit and revenue in four categories.
• B2B manufacturers consistently fail to capture between 10 and 50 percent of available profit and revenue; for a $1 billion company, this represents $100 to $500 million uncaptured annually.
• B2B distributors consistently fail to capture between 9 and 38 percent of available profit and revenue; for a $1 billion company, this represents $90 to $380 million uncaptured annually.

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“In November, we received an overwhelmingly positive response to the original report and quickly began work on phase two. Now, company leaders can understand which of the four categories is most prevalent in their industry. For example, manufacturers are disproportionately affected by cross-selling, meaning the data shows their customers would purchase more if given relevant product recommendations when buying – in person, on the phone, or online,” said Zilliant CEO Greg Peters.

The report, an anonymized and aggregated data analysis using Zilliant’s proprietary AI-driven techniques, reveals four problem areas: Cross-sell, customer churn, pricing inconsistencies and misaligned market pricing. Companies usually exhibit a unique mix of all four categories.

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