B2B sales managers and managing directors of specialised wholesalers in Germany face many challenges in today’s competitive business environment.
The rise of e-commerce has made it necessary for wholesale distributors to leverage data mining in ERP to stay ahead of the competition.
By analysing wholesale customer data and implementing artificial intelligence in their ERP, B2B wholesale distributors can gain valuable insights into customer behaviour, identify the most profitable products, and find the optimal final price for each customer. They can also retain customers before they lose them.
This article will discuss the importance of sales data mining for wholesale distributors and why it is essential to start now. Let’s dig in!
The Importance of Wholesale Customer Data
Do not “talk around the hot porridge”. With 1.000 to 10.000 customers and 10.000 to 100.000 articles, only artificial intelligence can tell you which customers can buy more, will pay less or will leave altogether. That is why successful wholesale distributors leverage their ERP systems to analyse sales data and predict customer demand.
By collecting and analysing wholesale customer data, B2B sales managers can identify trends and patterns that take time to become apparent. For example, by feeding previous order positions to predictive sales software, distributors can identify the most popular products and categories and prioritise them in their marketing efforts. Additionally, data mining in ERP can help identify the most profitable customers and products, which can help sales managers focus on those products and customers with the highest purchase probability.
Furthermore, sales data mining can help identify customer behaviour patterns such as acceptable pricing ranges, average purchase amount, frequency of purchase, or preferred product bundles. These insights can help sales managers to tailor their sales strategies to the needs of each customer, improve customer satisfaction and boost sales.
The Benefits of B2B Wholesale Distribution Data Mining
How productive would you be if you could look into the future? Well, that is the main benefit of wholesale predictive sales software. Data mining in ERP for B2B wholesale distribution can provide several advantages for sales managers and managing directors. By analysing wholesale customer data, distributors can identify the customers at the highest risk of churning or leaving the company for a competitor. With this information, sales managers can proactively retain customers by providing incentives for repeat purchases, offering personalised discounts, and creating targeted marketing campaigns.
Additionally, data mining in ERP can help sales managers identify cross-selling and upselling opportunities by analysing customers’ purchase history and B2B e-commerce data. With thousands of customers of products, this information offers significant value. AI-based predictive sales software can automatise this process.
Moreover, analysing customer data can help identify customers likely to become valuable in the future. By prioritising them, sales managers can focus on retaining these customers by providing the best possible service and incentives. This way, they can create a loyal customer base that will increase sales and act as ambassadors for the brand, promoting it among their peers.
The Need for Predictive Standard Software for Sales
Do not try to reinvent the wheel. To fully leverage the power of data mining in ERP, B2B sales managers must use standard predictive software for sales. Predictive analytics can help distributors to forecast customer behaviour and identify the products that are most likely to sell.
Additionally, predictive software can help sales managers to determine the optimal price point for each customer, which can help to maximise profits while retaining customers. Using standard predictive software, sales managers can gain valuable insights into customer behaviour and make data-driven decisions to improve sales and profits.
Moreover, predictive software can help sales managers to anticipate demand, reduce waste and optimise inventory. By predicting what products will sell and when, they can avoid stockouts or overstocking, both of which can be costly for the business. Moreover, having the right products in stock and offering them at a suitable price can improve customer satisfaction, reduce lead times, and boost the company’s reputation.
Best Practices for Sales Data Mining in ERP
Sales data mining can be a complex process that requires a systematic approach to be effective. To make the most out of the wholesale customer data, B2B sales managers should follow these best practices:
Collect clean data: Ensure that your data is accurate, complete, and up-to-date. Use automated processes to collect and clean the data to minimise errors and inconsistencies. Start with the most reliable data, your ERP data.
Feed the data to standard software. This wholesale software employs statistical methods and data visualisation tools to analyse the data and identify patterns, trends, and correlations.
Create a data-driven strategy: Use the insights from the data analysis to create a data-driven sales strategy tailored to your customers’ needs.
Feed the newly discovered opportunities to your sales force, ensuring their customer segments and cases are correctly applied.
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Data Mining ERP in B2B Wholesale & Distribution – Conclusion
In today’s competitive business environment, wholesale distributors with 1.000 to 10.000 customers and 10.000 to 100.000 articles must recognise ERP data mining as their most critical investment.
The good news: there is standard software that can help them here. Predictive Sales Software automatically monitors and analyses B2B wholesale customer data. So sales managers can gain valuable insights into customer behaviour, identify the most profitable products, and find the optimal final price for each customer. They can also retain customers before they lose them.
To fully leverage the power of data mining in ERP, successful B2B sales managers use standard predictive software for wholesalers. By following best practices for sales data mining in ERP, sales managers can create a data-driven sales strategy tailored to their customer’s needs, improve customer satisfaction, and boost sales and profits.