Mobo Mexico was growing fast, but the larger footprint coupled with regional economic differences made the simple national pricing strategy increasingly inefficient. Could they further segment locally, using AI?
Mobo implemented a responsive pricing system to adjust prices and promotions weekly differentiating mindfully by store, using Evo Pricing and Evo Promo.
The result: increased revenues and margin. Careful clustering ensured comparable offerings at nearby stores serving similar customer bases, which meant better customer service and more punctual margin growth without hurting Mobo's strong brand heritage.
Responsive pricing: +6.3% revenues,
with better customer fit
Mobo Mexico Case Study
Introducing Mobo Mexico
Mobo Mexico: the leading electronics retailer in Mexico, with over 200 stores nationwide. The family-owned franchise had quickly become an industry leader, out-pacing even successful international competitors in the Mexican market.
Mobo sold both its own and 3rd party mobile phones and accessories. Its retail stores historically priced products using simple rules, initiating similar promotions and markdowns at the same time. Wholesale prices were similarly aligned to nationwide retail prices, with three simple price lists at a fixed discount to retail based on customer size.
Mobo struggled to find an optimal pricing strategy that would allow it to maintain its high-growth trajectory. Franchise owners in more expensive areas easily moved merchandise regardless of price, while stores in poorer areas struggled to move high-end items. Nationwide standard prices for products were not efficient.
The challenge: unpredictable customer response to pricing
Alberto Cohen, the CEO of Mobo Mexico, knew that there was the opportunity to grow revenues by using more dynamic, locally tailored pricing. He had installed e-tags in stores to lower the cost and effort to change prices and now needed to tailor those prices further.
I knew that our pricing strategy was leaving money on the table, but I worried that changing prices often or differentiating between stores geographically close to each other could damage our brand with customers.
Customer loyalty is hard to achieve in our industry, and our brand is our biggest competitive advantage. We needed a more dynamic pricing strategy but could not risk negatively impacting that brand.
-Alberto Cohen, Mobo CEO
Additional, critical problems stood in his way:
- Different customer segments at short distance: In large cities, two Mobo retail locations located only blocks away may serve drastically different customers, due to the local socioeconomic differences.
- Technology investment: Cohen was open to investing in new technology, but not willing to spend on a complex system if a simple Excel spreadsheet could achieve similar results. ROI was critical.
- Internal buy-in: Commercial Directors had been making these decisions for years. A new system would have to meet their approval to achieve widespread adoption.
The solution: responsive pricing based on hyper-local market data
Mobo partnered with Evo to implement Evo Pricing, and later Evo Promo, as a single holistic responsive pricing solution. Prescriptive Artificial Intelligence made recommendations based on granular local conditions, allowing per-store price differentiation.
Mobo is in an incredibly competitive market, so we couldn’t afford to lose momentum testing failing pricing strategies. We needed to find the right solution fast.
Evo’s AI is designed to quickly adapt to real-time results and achieve optimal outcomes in a short time. We decided to partner with them to accelerate the test and learn process to get positive ROI in weeks, not years.
Evo implemented responsive pricing: weekly adjustments to prices and promotional plans at a hyper-local, per-store level.
This approach relied on:
1. Tracking historical sales and market data
Evo combined historical sales data with extensive granular socioeconomic and competitor data to better understand hyper-local, per-store price elasticity. Impact is magnified by monitoring the consumer behaviour of 14% of the Mexican population.
2. Clustering with a size of one
The Evo system first calculates pricing ranges and promo intensity for clusters based not on traditional markers of similarity but rather a sophisticated calculation leveraging price elasticity and socio-demographic status. The AI then drills down to a more granular level to calculate price and promo recommendations for each store.
3. Adjusting prices weekly
The Evo platform measures the impact of prices systematically on a weekly basis. Evo tools can autonomously recommend new prices and promotions by combining this with external data, adjusting and reverting where appropriate.
To measure impact, Evo Pricing was initially deployed in 15 stores matched carefully against comparable controls during a rigorous six-week A/B pilot test.
Our Commercial Directors were sceptical of the approach. They worried that such variability would increase customer complaints and complicate their operations. I needed to show them and our franchise owners that this was a relatively low-effort, high-reward system as quickly as possible.
Pilot impact: +6.3% revenue growth
Pilot promotions created +7% margin within the limited scope of the test, and the pricing strategy delivered an overall +6.3% revenue.
The first weeks of the pilot made our team believers. KPI growth accelerated, and Evo’s autonomous system actually reduced operational complexity.
While a few customers initially experienced minor confusion that promotions were not proceeding according to traditional patterns, most noticed no change. Franchise owners reported no significant increase in negative feedback from customers.
Average per-piece product sales increased by +2% across the board. Even in stores with higher average prices, sales volume increased.
Evo’s unique data-driven approach to clustering and pricing allowed us to maximize revenue without the expected customer impact. The system made adjustments to pricing and promotions subtle enough to avoid backlash, yet they had an outsize impact on the bottom line.
Long-term results: $12 million top-line growth
After the successful pilot and increased growth throughout the first year, Cohen expanded Mobo’s use of both Evo Pricing and Evo Promo in retail and eventually wholesale planning.
This expansion resulted in an additional $12 million in top-line growth opportunities. Thanks to careful clustering, the changing prices and irregular promotions have not fazed customers: margin and revenue growth have continued to accelerate.
Despite weekly changes to prices and promotions, the automated system remained simple for all the franchise owners to use. Commercial Directors became enthusiastic adopters.
I recommend Evo to others in Mexico.
We worked together to achieve ambitious goals, and the results exceeded even my high expectations.
This has revolutionized our approach to pricing.
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