Google Ads has tightened the scope of its Performance Planner, a tool long used by advertisers to forecast spend and results. Beginning last month, the planner no longer supports Display and Video campaigns, and it has also dropped key impression‑share metrics such as impression share, top impression share, and absolute top impression share. The change signals a broader strategic shift toward conversion‑centric campaigns and automation, leaving upper‑funnel advertising to be planned with alternative methods.
What the Change Means for Advertisers
For marketers who rely on the Performance Planner to set budgets and predict outcomes for Display and Video campaigns, the update is a significant adjustment. The planner now focuses exclusively on campaign types that drive measurable actions—Search, Shopping, App, Demand Generation, Local, and Performance Max. Any existing plans that included Display or Video segments, or that used impression‑share metrics, are now inaccessible for editing or viewing.
In practice, this means that advertisers can no longer use the native tool to forecast impressions, reach, or share of impressions for their awareness campaigns. Instead, they must turn to other Google tools, such as the Reach Planner, or third‑party platforms that specialize in upper‑funnel metrics. The removal also underscores Google’s intent to prioritize performance outcomes over traditional reach metrics, encouraging advertisers to adopt conversion‑driven strategies and automated bidding.
Why Google Is Making the Shift
Google’s advertising ecosystem has been moving toward automation and data‑driven optimization for years. The company’s recent updates to Smart Bidding, automated creative testing, and performance‑maximizing campaigns all point to a future where the platform predicts and optimizes for conversions rather than impressions.
By eliminating impression‑share metrics from the Performance Planner, Google removes a key lever that historically allowed advertisers to plan for reach. This forces marketers to rethink how they measure success for awareness initiatives. Instead of relying on the number of impressions, they may need to focus on engagement metrics, viewability, or downstream conversion impact—often captured through attribution models or third‑party measurement services.
Adapting Your Planning Process
While the change may seem restrictive, there are several practical steps advertisers can take to continue effective planning for Display and Video campaigns:
- Use the Reach Planner. This tool is designed specifically for reach‑based planning and can help estimate impressions, frequency, and reach for Display and Video campaigns.
- Leverage third‑party forecasting platforms. Services such as Marin Software, Kenshoo, or Adobe Advertising Cloud offer advanced modeling for upper‑funnel channels.
- Shift focus to conversion metrics. Even for awareness campaigns, set micro‑conversion goals (e.g., video views, click‑throughs, or engagement) that can be tracked within Google Analytics or the Google Ads conversion tracking system.
- Integrate cross‑channel data. Combine Google data with other platforms (e.g., Facebook, TikTok) to build a holistic view of reach and impact.
- Experiment with automated bidding. Use Target CPA or Target ROAS for Display and Video campaigns to let Google optimize for conversions while still maintaining a level of reach.
By adopting these strategies, advertisers can maintain robust planning for upper‑funnel activities while aligning with Google’s performance‑driven framework.
Implications for Upper‑Funnel Campaigns
The removal of Display and Video planning from the Performance Planner may push advertisers to re‑evaluate the role of awareness in their overall marketing mix. While reach remains a critical component of brand building, the new focus on conversions encourages a more integrated approach where awareness is measured in terms of its contribution to downstream actions.
Marketers may also need to adjust their budgeting practices. Without native tools to forecast impressions, they might rely on historical spend data, industry benchmarks, or predictive modeling from third‑party services. This shift could lead to more data‑driven budgeting decisions, where spend is allocated based on expected conversion value rather than reach alone.
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