How B2B wholesalers are overcoming current crises with data-based customer management
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Declining sales, price pressure, supply chain problems: Why data-based customer management is the key.
Many wholesalers continue to face significant challenges due to rising purchase prices and uncertainties in supply chains. According to recent surveys by the IFO Institute, 58% of retailers still see the need to adjust their sales prices further.
Although some supply bottlenecks have eased, geopolitical developments, raw material shortages, and sustainability requirements continue to influence the availability of goods.
In addition, a drop in demand is weighing on the industry. Customers are reducing orders or switching to cheaper alternatives, leading to recession-related declines in sales and presenting wholesalers with new challenges.
The challenges remain – data-based solutions are more in demand than ever.
This new combination of rising prices, bottlenecks in the availability of goods, and now also a blatant weakness in demand is increasing the pressure to target and manage customers more precisely and in a more differentiated manner than before to secure sales, customer relationships, and margins.
However, as many wholesalers have been used to stable prices, growing sales, and complete product availability for years, they often lack experience with such dynamic markets. Urgent problems require an answer:
• Where can price adjustments be implemented without losing valuable customers and further sales?
• Which customer relationships are particularly at risk due to recession, price pressure, and delivery problems?
• How can lost sales be compensated, and what additional customer-specific sales potential exists or can it be exploited?
Systematic, data-driven customer evaluation and prioritization provide the best basis for solutions.
The combination of rising costs, uncertain product availability, and falling demand requires handling and managing customers in a more targeted and differentiated way than before to secure sales, customer relationships, and margins. It is no longer enough to apply blanket measures—companies need data-based strategies to secure their margins and stabilize customer relationships in the long term.
However, as many wholesalers have been used to stable prices, growing sales, and complete product availability for years, they often lack experience with such dynamic markets. Questions such as:
• How can price adjustments be implemented without losing customers?
• Which customer relationships are particularly at risk due to recession, price pressure, and delivery problems?
• What sales potential can be identified and exploited through data-based analysis?
The key: customer management and evaluation based on data and AI
Data-based customer analysis enables companies to make fact-based decisions and swiftly react to market changes.
Companies that analyze their customers based on potential and risk have clear advantages. Modern data analysis and AI-supported tools enable more precise decisions regarding sales, pricing, and delivery strategies.
Successful customer management with data-driven insights enables optimized pricing strategies through dynamic price sensitivity analysis. Wholesale companies can also carry out targeted churn management through predictive churn analysis, which strengthens customer satisfaction and loyalty. Your sales team can implement more efficient sales management and targeted sales measures through data-driven prioritization of customers.
Digitalization and AI as a success factor for efficient customer management
Traditional customer evaluation methods often fall short. Recently, technologies to support customer management have developed significantly. AI-supported predictive analytics software enables a more precise assessment of customer value, customer potential, and the extent of risk. This enables companies to develop individualized pricing strategies, identify and exploit sales potential, and minimize churn risk.
Modern tools offer:
• Automated customer classifications based on historical and current data
• Dynamic price analysis: AI recognizes the price acceptance of different customer segments and suggests prices per product and customer to your sales team.
• Forecasting ordering behavior: Predictive sales software can predict future orders and cross-selling potential from historical ERP data.
• Early detection of churn: Behavioral patterns can be used to identify risks early and take targeted countermeasures.
• Digital technologies have revolutionized customer management. AI-supported predictive analytics software enables a more precise assessment of customer potential and risks. Wholesale companies that use their data correctly gain a competitive advantage.
Practical strategies for data-based customer management
Data-driven customer management offers numerous approaches for increasing sales and securing profitability. Four proven strategies are:
• Dynamic price adjustments: AI-supported pricing systems continuously analyze customers’ willingness to pay and set prices according to customer behavior. This optimizes margins without losing customers.
• Efficient delivery management: Wholesalers use demand forecasts and strategic parameters to prioritize deliveries, prioritizing customers with the highest potential without jeopardizing market share.
• Individual customer approach: AI-supported systems identify customers who are receptive to personalized offers and optimize customer communication with targeted discount or service offers.
• Timely customer retention measures: Through precise churn forecasts, sales teams can take targeted customer retention measures early to lose fewer customers.
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Conclusion: Now is the time to adopt data-based customer management
Despite the significant and increasing challenges in the wholesale industry, data-based strategies offer a targeted approach to managing uncertainties. By integrating AI and analytics into customer management, companies can enhance profitability, protect existing customer relationships, and secure long-term competitive advantages.
Wholesale companies that use AI in the right places increase their profitability, use their resources more efficiently, protect valuable customer relationships, and increase their resilience. This enables companies to remain successful in the long term.
Now is the right time to implement data-based customer management and to optimally position yourself for the future with modern technology.