Sidecar’s implementation team set up fresh campaigns for Work ‘N Gear, and
Sidecar for Shopping went to work on the account.
Sidecar’s machine learning algorithms continuously digest and analyze channel data specific to Work ‘N Gear, as well as product attributes, and consumer search behavior to gain the most precise picture of trends that influence campaign and individual product performance.
As data changes each day, Sidecar for Google Shopping dynamically and immediately adjusts individual product bids to drive revenue.
Waterman was confident in this flexible, data-driven approach.
“It was a big weight off our shoulders to know that we had technology on our side. We didn't have to provide a whole list of things we thought might do well on Google Shopping. We could test a bunch of ideas, and the technology would start bidding up or down within that same day on the products that were converting well,” Waterman said.
By ingesting data from multiple sources over time, Sidecar’s technology and team pinpointed Work ‘N Gear’s top-performing products.
Seeing that a few brands, such as Carhartt and Timberland Pro, were strong performers, Sidecar’s customer strategy team isolated these brands into separate campaigns with higher bids to boost visibility and conversions on those popular products.
Waterman credits this strategy for the positive returns and greater efficiency she began to see. Within six months of launching Sidecar,
the branded campaigns boosted conversion rate 3% and improved ROAS by 20%.
The bidding strategy includes a hallmark feature of Sidecar for Shopping:
Search Query Manager. This capability unlocks the most valuable search queries to Work ‘N Gear’s business and allocates corresponding spend to bids on those queries, while pulling back spend on the least valuable terms. This helped Work ‘N Gear
maximize ROI in the channel, and cut wasted spend.
Sidecar’s customer strategy team helped connect Work ‘N Gear’s Google Shopping strategy to larger marketing efforts by aligning product bids to promotions and advertising campaigns.
This alignment was particularly effective during seasonal peaks and on occasions when Work ‘N Gear was able to promote products below MAP. For instance, when a work boots manufacturer allowed the retailer to offer discounted prices on select styles, Work ‘N Gear saw an uptick in positive returns.
Waterman added, “It was nice to be able to compare what kind of promotions and advertising we were doing, and see if our targeted advertising was pulling in the customers we wanted.”
With the customer strategy team keeping Google Shopping campaigns in tune with cross-channel marketing efforts, Work ‘N Gear’s products were better positioned as busy seasons approached, especially on the workwear side of the business, which experiences greater demand in the winter.
As machine learning algorithms continuously adjusted product bids and the customer strategy team directed the logic behind them, day-to-day management of Google Shopping became low-touch for Waterman and Connor.
“I don’t have to give Google Shopping a ton of thought or energy.
Day-to-day we let Sidecar handle the heavy lifting,” Waterman said.
