Episode 11: Fluency with Feedback — Exploring Reviews and Ratings with Wall Street English

Key Takeaways

• Review management at a global franchise requires top-down buy-in from owners and GMs
• All reviews should be responded to within 48 hours, with negative reviews as the absolute priority
• Never argue with a customer online — always move the conversation offline by inviting them to get in touch directly
• Uberall’s AI-powered review responses were described as the best feature release of the year
• Cleaning up duplicate and fake listings was a foundational step before review management could scale

Managing reviews for a single location is straightforward. Managing them across 400+ centers in 35 countries, operated by different franchise partners, in dozens of languages, with varying levels of digital maturity — that is a fundamentally different challenge. It requires not just the right tools, but the right organizational approach.

In this episode of the Local Marketing Beat podcast, host Christian Hustle sits down with Eirin Rushfeldt, Head of Digital Marketing at Wall Street English, to explore how the global English language academy operationalized review management across its franchise network, why roles and responsibilities matter more than response templates, how AI-powered review responses transformed the workflow, and what it takes to clean up and optimize listings at global scale.

Timestamps

00:00 Introduction to the Local Marketing Beat and guest Eirin Rushfeldt

00:32 Wall Street English: 400+ centers, 35 countries, franchise model

02:48 How important are reviews for an international language academy?

04:30 Collecting reviews: manual processes and the path to automation

06:57 Responding to reviews: 48-hour KPI, negative review priority, and taking it offline

09:42 Managing reviews across languages and franchise territories

13:00 How roles and responsibilities made the project succeed

14:39 AI-powered review responses: the best release of the year

17:00 Cleaning up duplicate listings and optimizing profiles to near 100%

19:00 Mobile app adoption and response times dropping to 15 minutes

Reviews Are Essential for Trust — Regardless of Industry or Country

“Nowadays, any product or service that you buy, you feel a bit uncomfortable if there are no reviews. And that goes across all markets. It’s trust. You need to build trust with the customer.” — Eirin Rushfeldt

Eirin explains that before Wall Street English could manage reviews, they first had to generate them.

Many of their 400+ centers across 35 countries had minimal review presence — not because the service was poor, but because review collection was not part of the operational process. For an English language academy competing in local markets against other schools, reviews are directly tied to both local search rankings and prospective student trust.

The team now targets approximately 15 Google reviews per month per center — a number that may sound modest but compounds significantly over a year. Collection methods include asking students at reception, manual outreach to current students, and a planned transition to automated review requests.

For any multi-location franchise, the takeaway is that review generation must be systematized, not left to chance.

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Respond to Every Review — But Never Argue Online

“The worst thing I’ve seen is when the company starts telling the customer that it’s their own fault. We always have the answering saying something like ‘please get in touch’ — you don’t always know who this customer is. We want them to reach out and have that conversation offline.” — Eirin Rushfeldt

Wall Street English’s review response policy is built around two KPIs tracked through Uberall’s analytics: response rate (ideally 100%) and response time (target: within 48 hours). The absolute priority is responding to negative reviews — leaving a negative review unanswered is the single biggest reputational risk.

Eirin’s guidance on negative reviews is practical and clear: Stay professional, never blame the customer, and always move the conversation offline. She also makes an important point about authenticity — a mix of positive and negative reviews is expected and even desirable.

A perfect 5.0 score can look artificial, and consumers are sophisticated enough to recognize that. What matters is how the business responds when something goes wrong. For brands managing reputation at scale, this balance between volume, authenticity, and response quality is the formula that builds lasting trust.

Roles and Responsibilities Are the Real Success Factor

“This is not just marketing — it goes beyond. It involves a service or possibly a sales team and we need everybody to be on board. It needs to come top down from the owner, the GM, to say yes, this is important.” — Eirin Rushfeldt

Eirin identifies what she considers the most important factor behind the project’s success: making review management an operational responsibility, not just a marketing task.

In a franchise model where the international headquarters cannot dictate how each territory operates, this required a deliberate change management process. A project manager was brought in to coordinate across functions — marketing, service, and sales.

Each territory was given the outcome targets (response rate, response time) but allowed to determine their own implementation — some assigned review responses to individual center staff, others centralized it at the national marketing level. The international team then monitored KPIs and provided feedback to keep motivation high. For any franchise brand trying to roll out review management across a distributed network, this model of defined outcomes with flexible execution is the approach most likely to succeed.

AI-Powered Review Responses Were a Game Changer

“That was a revelation to the network. They love it. It is probably the best thing that happened to them because there’s something satisfying about pushing that button that produces that answer. And it seems to also be great in all kinds of languages — Arabic, Korean, Vietnamese.” — Eirin Rushfeldt

Before Uberall’s AI Review Responses feature launched, Wall Street English relied on templates — pre-written responses for different review scenarios.

The feedback from the network was that templates were helpful but time-consuming to customize and resulted in repetitive-sounding responses. When AI-powered responses were rolled out, the reaction was immediate and enthusiastic.

Eirin highlights two critical advantages. First, the responses are generated using the business’s own information — location details, brand context, and the specific content of each review — producing responses that are relevant and on-brand rather than generic.

Second, the AI performs well across Wall Street English’s diverse language portfolio, including Latin languages (Italian, French, Spanish), as well as Arabic, Korean, and Vietnamese.

For global brands managing reviews across multiple languages, this eliminates one of the biggest operational bottlenecks: finding qualified reviewers in every language.

Cleaning Up Listings Was the Foundation for Everything Else

“One of our biggest issues was that we had so many locations that were duplicated. Nobody was managing them. There were fake ones out there as well. That cleanup of just getting everybody into the platform and having that ownership was definitely one of the bigger changes.” — Eirin Rushfeldt

Before review management could scale, Wall Street English had to solve a more fundamental problem: listing accuracy. Across 35 countries, many locations had duplicate profiles, unverified listings, fake entries created by third parties, and profiles that no one was actively managing. Staff turnover in franchise territories meant that access to profiles was frequently lost when employees left.

Centralizing all locations into the Uberall platform solved the ownership problem — new team members could be granted access without losing control of the profiles. The team then optimized every listing to near 100% completeness: accurate descriptions, photos, business hours, and all required attributes.

For any multi-location brand beginning its location data management journey, Eirin’s experience confirms that cleaning up and claiming listings is the unglamorous but essential first step before any visibility or reputation strategy can deliver results.