Adaptability is a top benefit of programmatic advertising, when done right. Yet many advertisers are missing an opportunity when it comes to localizing their campaigns. Too many are still using a single overarching strategy nationwide – and by painting their campaigns in such broad strokes, they’re leaving potential customers in the dust.
We all know that relevance is the key to audience interest. That’s why advertisers need to implement local strategies with targeted execution. This is especially true if you’re a national brand with a diverse product line, dispersed local stores, franchises or channel partners. Instead of luring customers to a general brand site, where they might easily miss the product or service that would spark a direct transaction, you need to connect them to a solution at the exact moment you have their attention.
Think of it from the buyer’s point of view: would you react more strongly to a national ad about a new product that might not have anything to do with you – or an ad customized to your neighborhood and immediate need?
By connecting ads to audiences based on local preferences, you can elicit bigger returns with fewer impressions. Let’s say you’re running campaigns for a big box home improvement retailer chain. Instead of running one national campaign, you can use localization to fine-tune by area. Maybe Minnesota’s been hit by blizzards, spurring the need for shovels, snow blowers and ice scrapers. Oklahoma customers are looking for sandbags in response to flood warnings. By automatically optimizing and tuning ads at a granular level, you can show users the campaigns that matter most to their individual circumstances at that moment.
Of course, that requires the right kind of data.
Optimizing Local Audiences at Scale
Use a pre-packaged audience segment such as “Home Repair Intender” and you’re looking at a very broad audience. But by using unstructured data to optimize audience targeting, you can deliver customized local audiences at scale. At Simpli.fi, we optimize, on average, 10,000 data elements per user on each campaign, providing significantly more granularity than advertisers would get using pre-packaged segments.
By using unstructured data, you can find out which individual data elements are performing and which ones aren’t, giving you the power to sharpen your targeting focus. One important localization component: recency. Again, with the home improvement example – consider that a fixed audience for “plumbing intenders” may include users who searched on “clogged drain” both two hours ago and one month ago. You’ll obviously see much better performance from users who searched within the last few hours than from those who searched weeks ago and have probably already found a solution. (That said, optimal data recency varies according to what someone’s searching for; someone in the market for a car or house can stay in the market for weeks or even months.)
Ultimately this kind of insight helps you allocate budget where it makes the most sense: by product, by market, by audience or other elements. You’ll know which locations are popular with which buyers, or which products are getting the strongest response. For instance, our home improvement store might decide to promote a brick and mortar location in Oklahoma that needs help or a fresh batch of snow shovels that are on sale in Minnesota. Equipped with unprecedented visibility for every market you’re targeting, you’ll have the power to develop localized programmatic campaigns that are friendly to your budget – and compelling to your ideal audiences.