When Master B2B conducted executive roundtables with e-commerce businesses across the country, they expected to hear about generative AI management and personalisation strategies. Instead, the most frequent strategic priority was "managing our analytics and reporting."

This response reveals a critical shift occurring in B2B e-commerce. Companies don't lack data—they struggle with turning that data into actionable insights that improve profitability. While businesses have used e-commerce analytics for reporting for years, advances in the field of AI in e-commerce have spotlighted predictive analytics: using historical data to forecast future business outcomes.

The stakes couldn't be higher. In today's economic climate, every digital investment must demonstrate clear ROI, and every customer experience touchpoint needs to contribute to retention and growth. That's why leading B2B companies are embracing AI and predictive analytics not just as a reporting tool but as a strategic profit driver.

For part four of the Profitability Imperative series, Master B2B surveyed 86 global manufacturing and distribution executives to understand how they're approaching data and analytics investments. The results show that while 44% of companies increased their analytics spending in 2024, most are missing significant opportunities to drive profits through smarter data use.

Let's explore how forward-thinking companies are transforming their approach to data—and their bottom lines.

The power of predictive AI to transform business decisions

Predictive analytics represents a fundamental shift from descriptive reporting to strategic forecasting. While traditional e-commerce analytics tell you what happened—website traffic, average order value, conversion rates—AI and predictive analytics help you understand what will happen next.

This distinction matters enormously for profitability. Consider supply chain optimisation: instead of simply tracking inventory levels, predictive AI can analyse seasonal patterns, geographic trends, and demand signals to ensure optimal stock levels. Wendy's buying co-op exemplifies this approach, using AI and predictive analytics to manage inventory for their £1 Frosty promotion across 6,000 locations. As the head of purchasing for Wendy’s put it, companies that don’t embrace these tools will face "a distinct disadvantage" within years.

The survey reveals that B2B companies are most comfortable using AI and predictive analytics for customer-facing applications such as marketing campaigns (17.4% adoption) and product recommendations (12.8%). However, fewer than 10% are applying these tools to core operational functions such as demand forecasting or warehouse optimisation—representing massive untapped potential.

Use cases for e-commerce revenue growth

The most successful implementations of AI in e-commerce focus on three high-impact areas.

Supply chain and purchasing optimisation

Predictive analytics can parse vast quantities of purchase data to spot seasonal and geographic buying trends so companies can ensure they have the optimal amount of stock in the correct retail locations. This capability becomes crucial when inflation drives inventory costs higher, making it prohibitively expensive to maintain excess stock across multiple locations.

The survey data shows that only 8.1% of companies currently use generative AI for supply chain optimisation in their predictive analytics—representing another untapped opportunity for competitive advantage.

Dynamic pricing strategies

While dynamic pricing has been discussed for over 20 years, advances in AI are now making sophisticated pricing optimisation more automated and precise than ever before. Delta Air Lines recently announced it’s working with AI pricing technology to automate pricing decisions that previously required an analyst.

At a recent investor day, Delta president Glenn Hauenstein explained the power of these new pricing tools: "What we have today with AI is a super analyst. We have an analyst who's working 24 hours a day, seven days a week, and trying to simulate in real time—given the same inputs that an analyst sees today—what should the price points be?"

The financial impact is considerable. A study in The Journal of Professional Pricing found that pricing optimisation can provide a 2-5% improvement in EBIT—a meaningful margin enhancement that directly impacts profitability.

Turbo-charging the analyst role

Beyond replacing routine tasks, AI and predictive analytics are augmenting human analysts in powerful ways. Large companies are posting new roles seeking data scientists who can build tools that use large language models to ingest data and make inferences and predictions—essentially creating virtual analysts.

As the report describes, Walmart posted a Data Scientist role seeking someone who can build "a state-of-the-art SaaS platform that utilises LLMs and computer vision models to derive retail insights and automate decision-making processes, enhancing customer satisfaction and operational efficiency."

Meanwhile, existing data analysts can now run "what if" scenarios faster than ever before, enabling predictive analytics for e-commerce growth through better profit-maximising predictions. For example, analysts can quickly model questions such as: "What if we changed our supplier and moved all our manufacturing to a facility closer to that supplier?" or "What would the impact be on product margins if we eliminated one supplier but purchased more from a different supplier?"

Building the team structure for analytics success

The survey revealed a worrying gap: 52% of companies lack dedicated data analysts for their e-commerce platform. This staffing shortfall directly correlates with low adoption of advanced analytics techniques. Without dedicated expertise, companies struggle to move beyond basic reporting to the implementation of predictive analytics for business growth.

Master B2B recommends a four-pillar approach to analytics team building:

• Data exploration and preparation
  A data analyst builds an understanding of available dataset accuracy and completeness and identifies outliers that could skew predictive AI models.
• Visualisation and storytelling
  Business analysts create compelling reports that translate complex e-commerce analytics into actionable insights for stakeholders across the organisation.
• Testing and validation
  Engineers and product managers collaborate with data scientists to build statistically sound testing frameworks, ensuring predictive analytics models deliver accurate forecasts.
• Scaling and implementation
  Data scientists build robust models based on testing results, working cross-functionally to implement new AI and predictive analytics capabilities across the business.

Companies successfully scaling AI in e-commerce report that this structured approach prevents the common pitfall of "analysis paralysis," ensuring insights translate into profitable action.

The data democratisation imperative

In each part of this research series, the importance of collecting and integrating data from across the organisation has been emphasised. One of the drivers of data silos is that teams are concerned their data will end up in places where they lack control over it. But this mindset is part of what contributes to the proliferation of data silos.

Breaking down data silos

In an era where customers purchase from multiple channels and generative AI tools require as much data as possible to create personalised experiences, data silos are no longer acceptable. This change—where all areas of the company have access to all company data—is called "the democratisation of data."

An executive from a global air conditioning parts manufacturer recently shared its mindset shift regarding data ownership: "We decided we should stop worrying so much about our proprietary data getting out, because anyone who really wants it can probably get it anyway. Instead, we decided to share our data more openly within the company. If the digital team and the sales teams are working off the same data set, then it is up to them to determine how to best serve customers."

Establishing data quality standards

Data democratisation doesn't mean abandoning oversight. As Gartner Senior Director Analyst Melody Chien recently noted, "Good quality data provides better leads, better understanding of customers, and better customer relationships. Data quality is a competitive advantage that data and analytics leaders need to improve upon continuously."

Successful data democratisation requires establishing a committee to provide oversight over how data is used and to ensure that data meets organisational standards. While data will never be perfectly clean, organisations should define what "good enough" looks like and set that as the standard for measurement.

The competitive advantage of integrated analytics

SAP CX addresses these challenges by providing integrated e-commerce analytics capabilities that connect seamlessly with ERP, CRM, and other business systems. This integration enables predictive analytics powered by comprehensive customer data—from purchase history and service interactions to financial metrics and operational data.

SAP's strength lies in data integration across the entire business ecosystem. This means every AI predictive analytics insight can incorporate profitability data, which helps to optimise decision-making for long-term business value.

In times of economic uncertainty, this integrated approach becomes even more valuable. Companies need e-commerce analytics that improve customer experience and demonstrably impact the bottom line.

Key takeaways for B2B leaders

As detailed in part four of the SAP and Master B2B’s four-part “Profitability Imperative” series, the opportunity to transform your commerce operations through AI and predictive analytics is available today. With many companies increasing their analytics investments, forward-thinking leaders are recognising the competitive advantage these capabilities provide.

Immediate action steps:

• Build your analytics foundation: 52% of companies lack dedicated e-commerce data analysts, making this your first competitive advantage
• Implement the four pillars: Focus on data exploration, visualisation, testing, and scaling
• Democratise your data: Break down silos that prevent teams from accessing critical customer insights
• Establish quality standards: Ensure data accuracy whilst enabling cross-departmental access
• Change your mindset: Move from asking "What happened?" to "What will happen?" and "How can we influence outcomes?"

These aren't theoretical concepts—they're practical solutions being implemented by leading companies today. Your data holds the key to enhanced profitability and sustainable growth, and the question isn't whether these capabilities will become essential, but whether your organisation will lead the transformation or follow others who act first.

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