Why mid-sized and small multi-location businesses should bet on reviews

The way consumers search for stores is starting to change little by little.

Until now, it was common to search for candidates on Google Search or Google Maps and choose a store by comparing them yourself. However, from now on,

“Which used car dealership is easy to consult with even for a first-timer?”
“Tell me a hair salon with good staff service.”
“Which restaurants are easy to use even with family?”

It is expected that the behavior of asking AI these questions and choosing a store from the suggested candidates will increase.

So, how should mid-sized and small multi-location businesses, which cannot match large corporations in brand awareness or advertising budgets, compete?

In conclusion, mid-sized and small multi-location businesses should not compete with large corporations on advertising budgets or brand awareness, but rather bet on the customer voices generated at their stores.

In this article, we will organize what kind of information AI uses to recommend stores, and then consider the differences in review acquisition power between individual stores, large corporations, and mid-sized/small multi-location businesses.

How does AI recommend stores?

When AI recommends a store, it does not decide on candidates by looking at just one piece of information.

It constructs answers that fit the question based on multiple pieces of information existing on the web, such as corporate official websites, store information, reviews, comparison sites, and social media.

For example,

“Tell me a nearby used car dealership that is easy to consult with even for a first-timer.”

If asked this, information about being nearby is not enough.

What kind of service does that store provide? What kind of information are they putting out regarding support for beginners? Are the same characteristics being mentioned by actual users?

These multiple materials are necessary.

I believe that three major perspectives are important for having your store found through AI search and included in recommendation candidates.

1. Unique information

This refers to the customer experiences that actually occurred at that store and specific episodes for each location.

"The representative explained things without using technical jargon."
"I wasn't rushed into a purchase and was able to compare things carefully."
"I brought my children, and the staff was very flexible and accommodating."
This kind of information cannot be found in general product descriptions or service introductions.
It is information unique to that specific store, born from actual points of contact with customers.

2. AI readability

Business hours, addresses, service details, and pricing are organized in a way that makes it easy for AI and search engines to understand what the information is about.
Accurate store information, clear page structure, Q&A, and structured data are all measures to establish this foundation.
While these are important measures, large companies with budgets and personnel can also implement them.
Moving forward, these will likely become prerequisites for any business rather than weapons for differentiation.

3. Objective proof

This refers to a state where the business is evaluated not only by the information it sends out itself, but also by third parties, such as through reviews, social media mentions, comparison sites, and industry media.
Of course, it is not as simple as saying that having many reviews will automatically lead to AI recommendations.
However, it is an important factor for comparing stores when the same value described by the business itself—such as "good customer service"—is also echoed by actual users.

It is difficult to compete head-on with large companies based on name recognition

When AI recommends stores or businesses, it is likely that famous companies with a large amount of information on the internet will be easily suggested as candidates.

Large companies have a vast amount of information accumulated over many years, not just on their official websites, but also in news, comparison articles, and social media. Their company and brand names themselves have high recognition and are mentioned in many places.

It is not easy for mid-sized and small businesses to compete with large companies in the same way by increasing advertising costs or content volume.

Furthermore, the measures to improve "AI readability" mentioned earlier can also be implemented by large companies with the funds and personnel.
So, is there no chance for mid-sized and small multi-location businesses to win?

That is certainly not the case.

Instead of competing head-on with name recognition or advertising budgets, you should take the battle to areas where large companies find it difficult to be thorough due to their sheer scale.

This strategy can be summarized into the following three points.

In AI search, voices from third parties, including customers, are just as important as information sent from the company itself.

Reviews can be accumulated through daily customer service without spending large amounts on advertising.

And because you can compete based on the specificity of the customer experience at each store rather than name recognition, you can adopt a different strategy from large companies.

That is precisely why mid-sized and small multi-location businesses should prioritize customer voices when planning their strategies.

Reviews create unique information and objective proof simultaneously

Reviews preserve the customer experience that actually occurred at that store.

"The representative's explanation was easy to understand."
"I wasn't rushed into a purchase and was able to compare things carefully."
"I was able to use the service with peace of mind even with children."
"They made suggestions based on local circumstances."

This type of information can only be shared by customers who have actually used the store.

Even when handling the same products or services, the staff, customer service, customer base, and relationship with the local community differ from store to store. The specific experiences born from these factors cannot be easily copied by competitors.

Furthermore, reviews are evaluations from third parties—the actual users—rather than from the company itself.

In other words, reviews are unique information specific to that store, and at the same time, objective proof provided by customers themselves.

Unlike advertising, they do not incur costs every time they are displayed.

With advertising, exposure generally stops once you stop the budget. On the other hand, reviews accumulate as information that conveys past customer experiences and serve as a basis for judgment for the next person looking for a store.

Of course, it is not completely cost-free.

Operations are necessary to provide a good customer experience, set up a smooth path for posting, and guide customers at the store.

Even so, the ability to accumulate unique information through daily customer service without spending large amounts on advertising is a major appeal for mid-sized and small companies.

Please also take a look at this article regarding the importance of reviews for gaining AI recommendations.

Individual stores find it easier to incorporate review acquisition into customer service

Many individual stores, such as hair salons and restaurants, are skilled at acquiring reviews.

The owner personally asks for a post at checkout.
They naturally guide customers during conversations with regulars.
They reply to every single review posted.
If a staff member's name is mentioned, they share it with them immediately.

These actions are possible because the distance between management, customer service, and promotion is short.
In individual stores, the owner is the manager, the on-site supervisor, and the primary driver of promotion.
It is easy to realize that reviews lead to customer traffic and sales, and because the distance to staff is short, policies can be put into action immediately.
Precisely because the scale is small, it is easier to incorporate review acquisition into daily customer service.

Large companies find it harder to ensure thoroughness across all stores

On the other hand, for large companies with hundreds or thousands of stores, the difficulty of permeating review acquisition policies to all stores and all staff increases.

There are differences in the level of understanding and implementation of measures depending on the store.
Who guides the customer and at what timing becomes ambiguous.
Even if reviews are collected, the results do not return to the front lines.
If it were advertising or website renovation, it could be executed collectively under headquarters' leadership.

However, reviews are born from the one-on-one contact between store staff and customers. They do not increase just by throwing budget at them; they require the understanding and action of each individual.
Large companies have an advantage in name recognition, but they are not always at an advantage when it comes to acquiring reviews.

Leveraging the Strengths of Mid-Sized and Small Multi-Location Businesses

Mid-sized and small multi-location businesses do not have the same level of closeness between management and every customer as individual shops do.
On the other hand, they are not as disconnected between headquarters and stores as large corporations are.
This intermediate scale is a strength when it comes to review strategies.
Headquarters can establish common policies and posting workflows while operations are tailored to the customer base and service style of each store.

Methods from stores that achieve results can be rolled out to other locations, and staff members who are highly rated by customers can be identified and praised.

You can achieve both the on-site strength of an individual shop and the systematization and horizontal expansion of a multi-location enterprise.

You can act with more detail than a large corporation and more organizationally than an individual shop.

That is precisely why mid-sized and small multi-location businesses have reason to bet on reviews.

However, even for mid-sized and small businesses, moving the front line is difficult

That said, being the right size for a review strategy and easily establishing that strategy are two different things.

Headquarters may have enthusiastically issued a directive to 'start collecting reviews at all stores,' but the front line perceived it as 'another task added to our already busy schedule,' and six months later, it had become completely hollow.

Many companies have likely had such an experience.
For store staff, customer service, sales, reservation management, and cleaning are the core daily tasks.
If you add review guidance without the purpose being sufficiently shared, it will look like 'work increased by headquarters.'
Setting a target number of reviews and simply telling staff to 'collect more' will make it difficult for them to continue acting as if it were their own responsibility.

What is needed is to design the process so that it includes the value that collecting reviews brings to the store and the staff.

'Staff Value': Turning Customer Feedback into Staff Value

Reviews contain not only evaluations of the store but also customer reactions to the work of each individual staff member.

'The staff member's explanation was easy to understand'
'They listened to my concerns with genuine care'
'I was happy that they remembered my name'

Such feedback conveys the value of their own work, which staff members themselves often do not notice in their daily routines.

Deliver reviews that mention names to the individuals themselves.
Share actions that were praised by customers within the store.
Praise and recognize staff who provided excellent service.
Utilize those examples for training at other stores.

If customer feedback can be returned to the front line, collecting reviews will no longer be just a customer acquisition strategy for headquarters.

From 'we must collect reviews' to,
'we use customer evaluations to improve our own work.'

The 'Staff Value' we are working on is a service based on the concept of visualizing the contributions of stores and staff from customer feedback, including reviews, and connecting that to praise, training, and store improvement.'Staff Value'

Customer feedback increases.
Staff value becomes visible.
Praised actions spread throughout the organization.
As a result, even better customer experiences are created.

By creating this cycle, reviews become more than just customer acquisition data; they become an asset that enhances the value of the store and the people who work there.

Mid-sized and small multi-location enterprises should bet on reviews now

In the era of AI search, what is needed is not just making your site easy to read.

What value does that store offer, and what do actual customers appreciate? It is important to continuously accumulate that specific information.

You cannot surpass the brand awareness of large corporations in a short period.

However, you can certainly compete in terms of the specificity of customer experience, the freshness of information, and the density of implementing measures on the ground.

Operate the front lines like an individual store, and systematize it as a multi-location enterprise. Then, feed the gathered customer feedback back into staff recognition and growth.

Mid-sized and small multi-location enterprises are in the best position to choose this way of competing.

Become a company chosen for its customer feedback, not its brand awareness.

Now that AI search is spreading, isn't it the perfect time to steer toward a review-first approach?