CASE STUDY
THE HIGHER COST AND LACK OF AVAILABILITY OF BAKERY INGREDIENTS REQUIRED NEW SALES AND PRICING STRATEGIES TO MAINTAIN LONG-TERM CUSTOMER LOYALTY WHILE MEETING PROFIT GOALS .
customer could be a challenge , wasting time for the salesperson and the customer .
• Improved marketing conversion for new products or products that are about to expire
Zeelandia worked with the Infor Coleman AI team to implement Infor Product Recommender , a solution that generates the top five products to recommend to customers , ranked by the probability of success . Presented through a web or mobile dashboard using Infor Birst analytics , a salesperson simply selects a customer and product recommendations are then displayed . A salesperson can have up to five customer meetings in one day , so Product Recommender can save them almost a third of a day of prep work .
The next challenge Zeelandia addressed with Infor ’ s help was pricing . With the constant changes in cost and the unavailability of goods , the pricing of products had to be adjusted more frequently . This had been a challenge for Zeelandia as the spreadsheet-driven process for pricing thousands of products for thousands of customers was proving time-consuming and error-prone and was unable to keep pace with the frequency of required price adjustments .
With AI-driven product recommendations , Zeelandia has achieved these benefits :
• 83 % faster time to prepare product recommendations for a customer , from around 30 minutes to five minutes
• Better customer experience with intelligent , personalised product recommendations
• Increased revenue per transaction and share of wallet per customer
“ We have a very complex pricing setup that includes 10,000 individual prices ,” said Michal Rada , Transformation Leader and Group ICT Director for AI at Zeelandia . “ With the current market fluctuations and uncertainty , it was even more challenging to maintain , so pricing was the next obvious target to see what we could do with Infor using AI and data science to deliver intelligent price recommendations that would find the sweet spot between customer satisfaction and company profits .”
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