Shopping Campaigns

For eCommerce Shopping ad campaigns, conversion tracking setup is important because it allows for calculating the two most important metrics in paid search: return on ad spend, and advertising margin.

More importantly, it provides valuable information back to the automated bidding algorithms, which uses machine learning logic to maximize the performance of the campaign.

When a Shopping campaign sends accurate conversion tracking conversion values back to the automated bidding logic, automated bidding with machine learning can deliver significant performance improvements, and greatly reduce campaign management time.

A Shopping campaign launch depends on early serving momentum, accurate tracking, and careful monitoring of impressions, CPCs, search queries, and product feed signals. In the first few weeks, campaign data helps identify spend issues, refine negative keywords, and prepare for stronger optimization as conversions begin to build.

Shopping campaign structure helps organize product groups, ad groups, product types, bidding, reporting, and search query matching. A clear structure can make sizeable eCommerce feeds easier to manage and help search queries match more accurately to the right SKUs.

Negative keyword discovery helps Shopping campaigns become more selective by filtering out off-match searches and less relevant audience segments. Along with negative keywords, audience and demographic exclusions can help shape who ads serve to, improving campaign focus across product searches, in-market audiences, interests, age, gender, income level, and other targeting signals.

Shopping campaigns require more ongoing management for several reasons.  Most eCommerce stores are frequently updating their inventory, competitive products arrive at slightly lower prices, and as a feed changes automated bidding may require recalibration. 

The Shopping platforms are constantly monitoring eCommerce campaigns that utilize their ad platform.  Typically this is done by bots and it frequently results in Merchant Center exceptions, warnings, and errors arriving in the form of unwelcome emails. We monitor Shopping campaigns for these issues and handle them as they arrive. 

Reporting and performance management is a crucial need for eCommerce advertisers. Most of our eCommerce clients receive reports monthly, detailing performance in the period vs previous periods, and highlighting work on ongoing challenges/issues.

Shopping campaign serving parameters help guide launch bidding, serving schedules, and early campaign momentum. Manual bidding at the product group level can provide an initial baseline before automated bidding, while schedule settings help reveal when campaigns perform best across days, hours, and time zones.

A Shopping campaign competitive assessment helps identify direct and indirect competitors, auction pressure, product positioning, keyword overlap, and likely click costs. This research supports campaign structure, product feed review, budget planning, and keyword research before the campaign is launched.

Our shopping campaign performance management reports are designed to fit your eCommerce business objectives, product line(s), and campaigns. We include standard key performance indicators (KPI's), or we can implement custom KPI's most closely aligned to your business objectives. 

One difference with our shopping reporting is our segmented performance tables. Many shopping campaigns advertise thousands of SKU's so it can be quite helpful to view performance in tabular form, with sorting.

Our tabular reports can be segmented and summarize performance by product line, ad group, product type, product group, or custom label parameter - whichever is most useful to your business.

Shopping campaign optimization is driven by product data, campaign structure, and product-level performance. Stronger feeds, better product grouping, negative keyword management, and accurate conversion tracking help Shopping campaigns serve more efficiently and give automated bidding better data to work from.

Shopping campaigns' primary targeting mechanism is keyword matching between the search query and the product feed, with minimal advertiser control.  Audiences provide a mechanism for improving the relevancy of the auctions where the campaign bids for ad slots. There is sometimes significant benefit gained by excluding certain audiences from targeting. Negative audiences can help to shape campaign performance in the same fashion as positive audiences. Blastoff develops audience profiles that serve to maximize the performance of Shopping campaigns over time.