How Tirlán planned the Cream category for 2026

Competitors were growing in the Cream category, and Tirlán had a price increase planned for October 2025. The team needed to know three things before committing to it:

• where retail selling prices were heading, and how the increase would land against competitors
• which promotion mechanics were working, and which were only moving volume between Tirlán's own products
• where shoppers would switch if prices moved, and where availability gaps were costing sales

Answering those questions the usual way means pulling EPOS files from each retailer, recoding them, and building a separate Excel model for each question. That takes weeks, and by then the plan is already late.

• Retail selling prices: RSP trends by retailer and competitor, to set the pricing strategy on performance data rather than assumptions
• Promotion mechanics: which mechanics drove incremental sales across retailers, and where promotions were cannibalising Tirlán's own range
• Switching: switching patterns between brands and pack sizes, to see where share was at risk and how to protect it
• Availability: gaps in distribution across retailers, so lost sales could be found and recovered

Every analysis ran on the same coded, harmonised data, so the pricing, promotion and switching answers all agreed with each other.

• An RSP strategy informed by performance data, going into the October 2025 price increase
• A 2026 promotion calendar optimised to avoid cannibalisation across Tirlán's own range
• Market share protected with detailed switching models, so the team knew where shoppers would go and how to hold them

To produce that detail with third-party EPOS data, you'd be looking at around an hour. With Advise, it's literally a few clicks and you're there.

Category Manager, Tirlán

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