How Postcode Bidding Increased Local Inventory Ad Impressions by 300% and Local Sales by 40%

This article is for retailers and brands with physical shops that want to increase store visits and in-store sales while protecting and growing online revenue. The aim is not to choose between local and online performance, but to create a campaign structure that supports both.

Local Inventory Ads should be one of the strongest tools available to a retailer. They can show nearby shoppers that the product they want is available in a physical store, at the moment they are searching for it.

But many advertisers struggle to scale them.

The assumption is often that the ad format itself is the limitation. In our experience it is usually the campaign structure and the geography behind it.

When Local Inventory Ads sit inside broad national activity, local demand can become blurred. Google decides when those ads appear, while the advertiser has limited control over which store catchments deserve the most attention.

So we built a postcode bidding framework.

It combines postcode geography with real driving distance and drive time to identify which postcodes fall within a realistic store catchment. We then use those catchments to separate local and national activity, giving Local Inventory Ads a cleaner local context in which to work.

That helps local products appear more effectively around the stores that can actually serve the customer. The journey becomes simpler: search for a product, see that it is available nearby, visit the store and buy.

The postcode tool is the framework. Scaling Local Inventory Ads is one of the commercial results it delivers.

Why we built a postcode bidding tool

The original problem went wider than targeting. It was the connection between measurement, structure and activation.

Most omnichannel accounts are still judged mainly on what happens on the website. Google Ads sees an online order clearly. The revenue is captured, the conversion is attributed and Smart Bidding receives a strong value signal.

The store journey is less obvious.

A customer might click a Shopping ad, check whether a product is available nearby, look at opening hours, request directions and buy in store later that day. If that value never makes it back into Google Ads, the website purchase appears more important simply because it is easier to observe.

That creates three problems:

1. Budget moves away from campaigns that help drive store demand.

2. Local successes are averaged into wider national performance.

3. Bidding favours online purchases because they carry the strongest visible value.

Once we started widening the measurement model to include store visits and other local outcomes, two more problems became obvious.

First, the usual radius-based setup gave us very little control over where local demand actually came from.

Second, Local Inventory Ads were sitting within a structure that was not designed to prioritise the strongest store catchments. We had the product availability, but not a precise enough local framework for activating it.

That is what the tool was designed to solve.

Why radius targeting was not good enough

Radius targeting assumes that distance from a store is the best proxy for local intent.

Sometimes it is. Often it is not.

Imagine two postcodes:

• Postcode A is six miles from the store but sits across a congested city centre.

• Postcode B is nine miles away but connects to the store through a fast main road.

A conventional radius treats Postcode A as the stronger local opportunity. A customer may find Postcode B much easier.

The same issue appears around rivers, railway lines, bridges, retail parks and commuter routes. A radius creates a clean shape for the advertiser, but it does not model the customer's journey.

The better question is not, "How many miles around the store should we target?"

It is, "Which postcodes are genuinely accessible, commercially viable and worth investing in?"

What the postcode bidding tool actually does

The tool connects four types of information that are usually reviewed separately.

1. Store and postcode geography

We begin with the store location and build a broad universe of surrounding postcodes.

The starting list is deliberately wider than the area we expect to target. Filtering too early would allow our assumptions to decide which postcodes deserve attention before the data has a chance to do it.

2. Real driving distance

The tool connects with the Google Maps API and calculates the actual driving distance between each postcode and the store.

This immediately improves on a straight-line radius because it respects the real road network.

3. Expected drive time

Distance alone is still incomplete. Ten miles on a clear A-road is very different from ten miles through slow city traffic.

Drive time gives us a more realistic view of accessibility. It also makes it easier to identify postcodes that appear close on a map but are inconvenient in practice.

4. The drive-time sweet spot

We do not overlay Google Ads outcomes at postcode level. Instead, we use overall campaign performance to optimise the drive-time threshold over time. Around 30 minutes is often a strong starting point, but the sweet spot varies by business.

Depending on the account, that performance can include:

• Store visits

• Store visit value

• Store sales, where the account is eligible

• Local actions such as direction requests

This is the point where the threshold becomes an optimisation decision rather than a fixed assumption.

Geography tells us where the postcode is and drive time tells us whether the store is convenient to reach. Overall campaign performance helps us refine how far that catchment should extend.

The postcode workflow

The method has six practical steps.

Step 1: Build the postcode universe

Start with every postcode that could reasonably belong to the store's wider catchment.

Do not begin with a tiny radius or a list based only on the team's assumptions. The purpose of the first step is coverage.

Step 2: Calculate distance and drive time

Run each postcode through the mapping layer and record:

• Actual driving distance

• Expected drive time

• The most practical route to the store

This creates the accessibility layer.

Step 3: Apply a realistic travel threshold

Remove or deprioritise postcodes that are technically nearby but unrealistic for the customer.

The threshold needs to reflect the business. A 40-minute journey might be unacceptable for a routine purchase but completely normal for a specialist retailer or a high-value product.

Do not turn one drive-time limit into a universal rule. Treat it as a hypothesis and test it against performance.

Step 4: Activate the local geography

Use the cleaned postcode list to define the geography of the local campaign.

The wider national campaign can exclude the postcodes assigned to local activity. This prevents the two campaigns from competing for the same geography and gives each one a clearer job.

The local campaign can then use its own budget, conversion goals, inventory setup and reporting view.

This is also where the postcode framework begins to amplify Local Inventory Ads. The product feed is no longer relying on one broad national structure to serve both eCommerce demand and store-led demand. Local products have a campaign context built around the postcodes most likely to reach the store.

Step 5: Optimise the drive-time threshold

Once enough data has accumulated, review overall campaign performance as the drive-time threshold is tested and refined.

Ask:

• Does the current threshold generate efficient store visits?

• Is blended online and offline value strong enough at the current threshold?

• Does extending the catchment create enough additional commercial value?

• Would a tighter catchment improve efficiency without sacrificing too much volume?

• Is the starting threshold still right for this business?

This is how we find the drive-time sweet spot without overlaying performance at postcode level.

Step 6: Build and update the postcode catchment

The selected drive-time threshold determines which postcodes are included in the local catchment and which are ignored.

That catchment can guide:

• Which postcodes remain in the local campaign

• Which areas are excluded from national activity

• Where budget should be concentrated

• Where conversion values or bidding expectations need reviewing

• Whether the drive-time threshold needs more testing before a decision is made

The tool works best when it is run again and again. Treat it as an ongoing review of local demand rather than a postcode list you produce once.

Why Local Inventory Ads matter so much

For retailers, Local Inventory Ads connect nearby searches with products that are available in physical stores.

Google describes them as a way to show product and store information to local shoppers at the moment they search. They can also show options such as pickup today or pickup later. See Google's Local Inventory Ads overview.

This is what Local Inventory Ads look like in practice. The location labels beneath the product cards make the local element visible to the shopper.

Local Inventory Ads do more than add another Shopping format to the account. They are the point at which the postcode framework reaches the customer.

The framework identifies the catchment and the local campaign creates the right environment for it. Local Inventory Ads then show nearby shoppers what is actually available in store.

That can shorten the journey between intent and purchase:

1. A customer searches for a product.

2. The ad shows that the product is available at a nearby store.

3. The customer can check availability, opening hours, directions or pickup options.

4. The customer visits the store or completes the most convenient purchase journey.

The postcode framework improves geographic control on its own. Add Local Inventory Ads and it also improves what happens between a local product search and a sale.

The bonus: how to scale Local Inventory Ads

Many advertisers assume Local Inventory Ads are difficult to scale because Google controls when they appear and their practical reach can feel wider than the areas the retailer wants to prioritise.

The postcode framework gives us a more practical way to influence that environment.

1. Separate local and national activity

The national campaign continues to focus on broader eCommerce demand across the rest of the country. The local postcodes are excluded from that campaign and placed into local activity with a clearer store-led purpose.

None of this forces Google to show a Local Inventory Ad, but it does give Google a much cleaner signal about where the local product feed and store-related outcomes matter most.

2. Build local campaigns around real catchments

Use the postcode and drive-time model to define the areas that can realistically reach each store. Do not rely on one standard radius for every location.

This means the campaign is built around accessibility and observed value, rather than a circle on a map.

3. Strengthen the local retail foundation

The feed still needs accurate store codes, prices and availability. The Business Profile and Merchant Center setup also need to be linked correctly.

Verified locations, accurate opening hours and properly connected location assets support the same journey. If any of those details are wrong, better postcode targeting cannot rescue the customer experience.