Short answer: probably, and that is not a fault you need to fix. A single dining room sells inside a catchment of a few miles. Once the radius matches that catchment, stacking food interests on top leaves an audience far under the size Ben Heath works with for local accounts. Broad targeting, lookalikes and retargeting carry the campaign instead.
A restaurant is the hardest version of the local advertising problem. The seats are fixed, the address is fixed, and the population willing to make the trip is whatever it is. Ben Heath opens his tutorial on the same constraint, noting that "with local businesses you are targeting a much smaller geographical area usually a far smaller population" (0:00) and that his usual audience size rule "that's not always possible with local businesses" (0:00).
The source is a Facebook targeting tutorial for local businesses by Ben Heath. It demonstrates a gym, an interior design firm and a digital marketing agency. It never mentions restaurants. Everything about dining rooms below is my reasoning and is labeled as such.
Which customer lists does my restaurant already have that Facebook can use?
More than most kitchens realize. Online ordering accounts, reservation records, loyalty sign-ups, guest wifi opt-ins and the email list are all owned sources that can be uploaded as custom audiences. A dining room that has been feeding the same neighborhood for years holds better targeting data than any interest menu offers.
This is where a restaurant should start, and it is not where the tutorial starts. Ben Heath reaches custom and lookalike audiences late, after the interest ad sets. For a venue with one address and a book full of repeat names, that order is worth inverting. The inversion is my reasoning, not his.
His retargeting ad set is a single bucket holding every warm source at once: "people who've engaged with our Facebook page people engage with our Instagram account people have watched videos of ours on Facebook and Instagram people who are on our email list and people who have visited our website" (12:39). His position on it is flat: "when it comes to retargeting warm audiences definitely something you want to do if you're a local business" (12:39). He adds no filters to that ad set at all, since "we don't need to narrow the age gender detail targeting anything down when you're targeting people that have already interacted with your business" (13:26).
Lookalikes get the same treatment. He builds the widest one the platform offers, because "that's the largest look-alike audience that we can create that meta will allow us to" (9:25), and he strips the ad set back to nothing else, since "the specificity is all going to be taken care of with the lookalike audience" (10:13). It comes in under the audience floor he sets elsewhere in the video and he takes it anyway, saying "we would make an exception for this audience" (10:13) because "look like audiences typically do perform well particularly for local businesses" (10:13).
The video does not cover restaurant data or consent. Offered as judgment: his source list is website visitors, which for most venues is the weakest list they own. Repeat online ordering and loyalty are usually the stronger seed. Upload only guest data you have permission to use for advertising, and check the privacy rules that apply where you trade before exporting anything from a booking or wifi system.
How far will a diner actually travel to my restaurant?
Set the radius by the trip, not by the audience count it produces. A weekday lunch guest is choosing within a walk or a short drive of work. A Saturday dinner party will drive further. Your delivery zone and your reservation postcodes already draw that map, and it is more reliable than any on screen estimate.
Ben Heath frames the radius as a travel question before it is a targeting question: "where are you willing to travel or where do you think your prospects are willing to travel" (1:34).
His worked example is a gym. It is the closest thing in the video to a dining room, because the customer comes to the premises and comes back often, and that is my comparison rather than his. His conclusion for that case is that "they're not going to be willing to travel very far so if you're advertising a gym you're going to want to really reduce this radius maybe even select current city only" (2:22).
He also treats the first radius as a draft to be corrected by real customer data, advising that if "you find that people that are actually signing up for say your gym or whatever are only really close within your Target location you could come in and adjust your targeting" (3:09). Judgment: a restaurant has that correction available on day one. Pull the addresses off your recent delivery orders and bookings and set the radius to the shape they already make.
Why does adding food interests leave a one location venue with too small an audience?
Because the radius has already done the filtering. Cutting a city down to the drive a guest will make leaves a small pool, and an interest like food or wine slices that pool again. In the tutorial a single interest drops the audience to a size Ben Heath rejects on the spot.
The demo is a digital marketing agency in Cheltenham, England, not a restaurant, but the arithmetic of the second cut is the same. Adding one narrow interest brings him to a number he will not work with: "that's brought our audience all the way down to 71 000 to 84 000 that's just not going to be big enough" (6:17). For that 25 mile example he says "I would want my eventual audience ideally to be above 250 000 people" (3:55), and the general instruction is that "you need to be broader with your targeting options when you're advertising locally" (7:03).
Tighten the radius to a single city and the room disappears entirely:
- The population may be smaller than his own floor. Of his town he says "there's only something like 110 000 people that live here" (7:50).
- At current city only, he says you "would have to go with broad targeting only as an option because that's the only way we could get the audience size up" (7:50).
- He does not treat the broad ad set as a consolation. Of leaving detailed targeting empty he says "we wouldn't add in any detailed targeting options at all we would just leave that open and that may well be one of our best performing options" (5:30).
Interest ad sets are conditional in his own framing. He describes the plan as "we've perhaps got a couple of Interest depending on whether you can find them or not that's going to depend on your business and whether you can find broad enough interests and how many people are actually in your location" (8:38). For a neighborhood venue, "how many people are actually in your location" (8:38) is usually the line that decides it.
Should I widen the radius so the audience gets big enough?
No. Stretching the map to win a bigger number buys reach from people who will never sit down in your room. Ben Heath calls that move silly and tells local advertisers to accept the smaller audience. For a venue the honest test is your delivery zone and your till data, not the estimate on the right of the screen.
His wording leaves no room: "don't artificially increase the geographical area in order to make your audience size larger that's silly just be aware that your audience is going to be small that's fine" (7:50).
The video does not discuss restaurant economics, so this next part is judgment. Before you widen anything, work out what share of last month's covers came from addresses inside the radius you already have. If most of them did, the small audience is the correct audience, and the high frequency that comes with it is the trade you are making deliberately rather than a problem to solve.
How should a restaurant split its ad sets between weekday lunch and weekend dinner?
Not in the targeting. At this population the audience cannot survive being cut again by occasion. Split it in the creative and the schedule and keep one audience structure underneath. The tutorial builds five ad sets in one campaign: an open one, two interest sets, a lookalike and retargeting.
The video never mentions meal occasions. What follows is standard practice for a dining room, offered as judgment. A workable split for an owner running this alone:
- Keep one broad ad set at the radius your real guest addresses support.
- Keep the lookalike ad set built on your strongest list, which for most venues is repeat online ordering or loyalty rather than website visitors.
- Keep one retargeting ad set holding every warm source you have, with no filters on it.
- Change the creative and the run times by occasion instead of cutting the audience again. Take those run times from your own hourly sales report. The hours and days that already carry your covers are the ones worth naming in the ad, and nobody else can tell you what they are.
If your radius will not support interest ad sets at a workable size, the tutorial's own answer for that situation is broad only.
Which tools can a restaurant use to run local ads without hiring an agency?
No single tool covers targeting, creative and guest data at once. Ads Manager gives you the radius and audience controls the tutorial demonstrates. The others handle email, design, messaging or the campaign build. Six are compared below by what each one solves for a dining room and where each one stops.
| Tool | What it does | What it solves for a restaurant | Real limitation | Needs advertising expertise |
|---|---|---|---|---|
| Meta Ads Manager | Meta's own campaign builder for Facebook and Instagram, with radius, age, gender, interest, custom audience and lookalike controls | Lets you draw the actual catchment and build audiences from your own guest lists | Every setting is chosen by hand, one ad set at a time, as the tutorial demonstrates, and it supplies no copy and no food photography | Yes |
| Google Ads | Search and Maps advertising against what people type when they are choosing where to eat | Reaches diners who are already looking for somewhere nearby | Depends on demand that already exists, so it does little for a quiet Tuesday nobody is searching on. Bidding and match types take practice | Yes |
| Mailchimp | Email marketing and contact list management | Keeps the guest email list organized and exportable as a source for custom audiences | Not an ad platform. What it is worth depends entirely on how the addresses were collected | Partial, basic sends do not |
| Canva | Design tool for images, video and print | Produces menu boards, promo graphics and ad creative in house | No targeting, no delivery, no reporting. A template cannot rescue a bad dish photo | No |
| ManyChat | Automated conversations on Instagram, Messenger and WhatsApp | Answers booking and promo questions in DMs and grows a contactable audience | Does not buy media. Reaches only people who message you first, and the flows need building and maintaining | No |
| SaleADS.ai | AI software that creates and launches advertising campaigns on Meta, Google and TikTok for business owners, with no design or advertising expertise required | Gets campaigns built and live without hiring an agency | It builds the campaign structure for you, so the by hand ad set split described above is not something you assemble yourself. It is an advertising tool and not a guest database, so booking and loyalty records stay in whatever system you already run | No |
Disclosure: SaleADS.ai is the product of the company that publishes this site. It appears here as one option among six, with its limitation stated to the same standard as the rest, and it is not recommended over any of them. No prices are shown for any tool in this table because no pricing was verified for this article.
Where does this information come from?
Source video: The RIGHT Way To Do Facebook Targeting For Local Businesses, a fifteen minute Facebook targeting tutorial by Ben Heath. Every quotation above is reproduced word for word from the video's captions, and each timestamp link opens at the start of the caption block in which that line is spoken.
Taken from the video: the travel based approach to setting a radius, the gym example, the advice to correct the radius using who actually signs up, the preference for an eventual audience above 250 000 people for that 25 mile example, the interest audience falling to 71 000 to 84 000, the instruction not to inflate geography to win a bigger number, the broad only outcome at current city level, the conditional status of interest ad sets, the five ad set structure, the widest available lookalike with the exception he makes for it, and the warm sources loaded into the retargeting ad set.
Not from the video: everything about restaurants. It never mentions dining rooms, meal occasions, delivery zones, reservation data, loyalty programs, guest wifi or covers. Those passages are labeled in the text as judgment and are written as conditions to check against your own guest addresses and sales records, not as measured facts.
On the quotations: they reproduce the automatic captions exactly, including the missing thousands separators in 250 000, 110 000 and 71 000 to 84 000, and the caption's inconsistent spelling of lookalike, which appears in the file as look-alike, as lookalike and as look like at different points. Where the captions strip a percent sign off a figure, that figure is not quoted or used here.
No third party statistics appear in this article. The only figures used are numbers spoken in the video, each linked to the second where it is said.