For more details on implementing JDA pricing solutions or how we solve the challenges discussed above, contact Ryan Kinzy

For more details on implementing JDA pricing solutions or how we solve the challenges discussed above, contact Ryan Kinzy

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DoubleBlaze Implements JDA Pricer at Guess to Boost Profits via Flexibility, Speed, and Accuracy

  • Guess gains higher profits with smarter, easier pricing solutions. [what can we quantify?] hyperpersonalized — instead of the whole store base as a single entity, they can do each store.
  • Consolidated pricing system puts everything in one place for better decision-making and faster deployment.

The Guess Challenge

Guess (North America) needed a better system for managing prices. Competitive threats were driving the need for more speed and flexibility. Coordination between host merchandising, eCommerce, and point-of-sale systems had become too cumbersome.

After DoubleBlaze implemented the JDA Pricer solution, Guess saw an immediate boost in profitability for both online and store-based sales, with a pricing management system that was more powerful and easier to use than before.

Key Results

Guess Project Manager, Jen Lange, describes several wins that Guess gained through this implementation

  • MODEL TEXT WE’D LIKE GUESS TO SAY “[Because of our increased speed and flexibility] we routinely adjust individual store prices based on store inventory, promotions, [local trends, local weather, competitor moves, etc.? We can do this for a week or a weekend, tuning our prices to maximize profitability.]
  • MODEL TEXT “[With faster price corrections], we have reduced lost profits (from lost margin if prices are too low, or lost sales volume if prices are too high) and errors in consumer behavior analysis (i.e., purchases based on incorrect prices).?] Changes that took 2-½ hours now need just 20 minutes.
  • MODEL TEXT “We are much smarter about pricing outcomes, thanks to the JDA Pricer tool. Now, we can readily determine effective prices regardless of overlapping hard marks, promotions, coupons, etc. Our pricing models are more accurate, and easier to create across our product lines.
  • MODEL TEXT “We’re spending more time thinking and less time manipulating data. Previous systems were based on multiple spreadsheets, databases, and other tools. We spent much of our effort just getting the data to line up so that we could read it or send it. Now we spend much less time handling data which gives us much more time to ask good questions and design smart pricing.”

“We are so pleased with DoubleBlaze because… [insert testimonial words from Guess into the middle of the case study here]”

Matching Solutions to Needs and Constraints

Here are just a few of the effective and innovative approaches that DoubleBlaze implemented for Guess:


Needs and Constraints


Need 1  Consolidation?

Solution 1 how bad was their previous system? How does JDA pricer help consolidation?

Need 2 Visibility

Solution 2 what does JDA pricer do to improve visibility?

Need 3 Communications/Uniformity?

Solution 3 were there challenges around communicating with multiple POS systems at different stores? Or any other non-uniform systems?

Need 4 Understanding Guess business processes? And pricing opportunities?

Solution 3 did you bring any special skills to deciphering the Guess business processes so that you could put together a good pricing system? Did your skills allow you to identify opportunities that you could seize with JDA pricer that didn’t exist for them beforehand?

Constraint 1 Conversion from old system to JDA pricer? Challenges at host merchandising system?

Solution 4 how did you address challenges to convert Guess from its old system to JDA pricer?

Constraint n eCommerce?

Solution 5 what smart things did you do to make eCommerce go better? What technical challenges did you surmount?

“What made the Guess challenge unique was…[insert descriptive  words from DoubleBlaze here]. We were glad to create an integrated solution that [insert words that show how smart you are] …”