Big 5

Big 5 Sees Big Returns with Audience Segmentation Strategy in Google Ads
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With more than $1B in annual revenue, Big 5 is a long-standing sporting goods store with over 435 stores and an ecommerce website selling everything from team jerseys and camping equipment to fitness gear and sneakers.

With emphasis on driving both in-store as well as e-commerce sales, Big 5’s SEM & Shopping advertising efforts were limited in scope and complexity; they needed defined goals that would help the business both online and offline.

Services: Shopping & Paid Search


store visit revenue non-brand search yoy


store visit roas on-brand search holiday yoy


store visit traffic holiday post "last shipping day"


We started with an ambitious 9 ROAS goal during the Holiday season. To get there, we first identified opportunities to improve the efficiency of Big 5’s cross-channel media spend by improving on low-hanging fruit before launching a growth-focused account restructuring.

Through analysis of customer behavior and testing unique campaign structures, we designed a strategy where offline and online efforts would complement each other and efforts across channels marched steadily toward a singular goal.

Mobile SERP for “big 5 tents” query

Brand & Non-brand Search

We began by identifying the segmentation strategies that were most suitable to reach Big 5’s goals. For Branded keywords, the account was most effectively split by:

  • Match Type
  • Device
  • Five key groupings: Core, Sale, Location, Misspellings, Category

Because some segments worked better as online or offline sales drivers, we broke them out into their own campaigns to maintain tight control of our budgets and move media spend seamlessly to the most efficient areas.

Big 5’s efforts hinged largely on in-store sales, so it was important to have a strong mobile search strategy. Segmenting campaigns by device made sense because we saw that Store Visits and In-Store Coupon redemptions skewed mobile. By making these segmentation adjustments, Store Visit revenue grew 8% YOY, CPA decreased 19%, and ROAS increased 24%.

For Non-brand terms, we segmented by:

  • Match Type (as above)
  • Product Type—to pivot to seasonal products when needed and segment products more likely purchased in-store vs online.
  • Audience—breaking out remarketing/prospecting audiences into their own campaigns. We identified which audiences were driving online and offline sales, then tested how increasing budget for offline influenced sales online (and vice versa)
  • We also drove in-store sales by adjusting location settings to target only those within a 30-mile radius of stores. In doing this, we grew store visit revenue by 231% YoY while driving CPA down 50%. ROAS increased 116%.

Shopping ads

For Shopping, we started by auditing and cleaning up all existing product data to lay the foundation for our campaign structure. We also focused on the cumbersome process of setting up Local Inventory Ads (LIA), which required weeks of working through different technical requirements:

  • Reclaiming the client’s Google My Business account (which powers Google Maps and local storefront info)
  • Using our feed management platform to scrub, optimize, and connect the list of stores and local product feeds containing tens of millions of rows. This feed contained each store’s products, including pricing and availability.

A 4-week LIA test was recommended for one small market with separate campaigns targeting a 35-mile area around stores. This required breaking out areas without stores into their own dedicated campaigns. By segmenting this way, we could act on e-commerce goals vs. store-visit goals at scale. With this approach, Big 5 grabbed 4 out of 5 LIA spots on average, compared to 1 of 5 for traditional PLAs. LIA ads also produced CPCs 40% lower than PLAs. This test drove a ROAS of 13.5x!

Since the test was successful, we expanded LIAs to all markets where Big 5 has stores. From Thanksgiving to Christmas, LIAs were more cost effective than PLAs, producing an 11x ROAS.

Most advertisers ramp down online advertising after the “Last Day to Ship”, but we bucked the trend and increased budget to LIA ads after December 16th, serving ads early in the day to reach people making decisions on where to shop. During this time, LIA drove 25% more foot traffic to stores, and a 13x ROAS.


For our clients, we’re always working to identify the next best dollar, and Shopping allowed for the most growth while delivering on ROAS goals. Transferring Non-brand Search budget to Shopping ensured we spent Non-brand dollars efficiently as a supporting channel to dominate SERPs where it made sense.

Working closely with Big 5 to receive offline data and data from Paid Social & Display (managed internally at Big 5) was also key; keeping a uniform front allowed customers who were multi-touch converters to see a consistent message with increased frequency, allowing us to drive enormous revenue growth and surpass our goals. 

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