AI PersonalizationBoosting an Electronics E‑Commerce’s Revenue by One-Third with AI-Modeled Conversions
AI Personalization

Boosting an Electronics E‑Commerce’s Revenue by One-Third with AI-Modeled Conversions

Working with digital ad campaigns is a constant balancing act between traffic volume, lead quality, and the effectiveness of algorithmic learning. Our client – an online electronics retailer – faced a challenge common to niche product categories: how to optimize low-traffic campaigns while preserving a high conversion rate. This case study explains how we tackled that challenge by integrating Google Ads with an AI-powered solution, resulting in a nearly 28% uplift in revenue (about one-third), higher conversions, and a sharply lower CPA.

Client Background

Our client is a U.S.-based e-commerce company in the electronics sector (the brand must remain confidential under NDA). The specifics of their products aren’t crucial – what matters is that they sell niche, high-margin electronic goods. The client came to us struggling to scale their advertising results without sacrificing efficiency. They had seen some success with broad automated campaigns, but those were hitting limitations. We knew there was an opportunity for improvement by using a more customized, data-driven approach.

The Challenge

The main challenge was to improve the performance of ad campaigns for niche, high-margin products with limited search demand. Initially, the retailer had been relying on broad Shopping campaigns (product listing ads) and some retargeting, which delivered stable results for high-intent traffic. However, more specific keyword segments (like searches for “[category] + [brand]”) were underperforming significantly. In order to grow revenue and profit, we needed to rethink the strategy.
We set out to achieve three key objectives:

  • Reduce dependence on a single, broad Shopping campaign.
  • Segregate the major product categories to enable more precise budget allocation and bidding for each category.
  • Ensure the Google Ads algorithms could continue learning effectively even with the smaller, niche campaigns (i.e. overcome the limited data problem).

Why We Pivoted Away from Broad Shopping Campaigns

Product Listing campaigns (e.g. Google Shopping or Performance Max) are a powerful tool for e-commerce, especially for quick wins. A broad campaign can aggregate multiple networks and formats: ads appear on Search and the Display Network, you can include images and videos, and it even covers retargeting. This all-in-one approach provides wide reach and lots of data for Google’s algorithms to optimize on. It’s an easy, efficient way to start – but it comes with drawbacks:

  • Limited control or granularity. You cannot create separate ad groups for different query themes or specific product categories within a single Shopping campaign.
  • No demographic bid adjustments. Options to adjust bids by user attributes (gender, age) or use observation audiences are very restricted in these automated formats.
  • Hard to course-correct. If something goes wrong, the advertiser has virtually no levers to pull – you’re relying entirely on the algorithm’s “black box.” Tweaking or troubleshooting a broad auto-campaign is difficult.

Our initial Shopping campaign performed well at the start – it gave the client a great seasonal sales boost. But when it came time to scale up, we ran into problems. Simply increasing the budget on the broad campaign caused efficiency to worsen: cost per acquisition started rising and the campaign began shifting spend toward lower-intent placements (more Display network spend vs. Search), which dragged down the conversion rate. This was unacceptable to us, as it would erode profitability.
Moreover, the one-size-fits-all campaign made it impossible to prioritize spend by product category. In our case, the client had three standout product categories contributing about 80% of revenue. With a single campaign, we couldn’t easily funnel more budget specifically into those top categories while spending less on others. The lack of structure was holding us back.
Finally, we noticed the broad Shopping campaign was cannibalizing traffic from the client’s other marketing efforts. We had separate search, dynamic ad, and retargeting campaigns running in parallel, and the all-in-one campaign often overlapped with them, competing for the same “hot” high-intent users. In short, the traditional approach, while initially successful, was starting to limit growth and efficiency.

What We Did

Realizing the limitations of the status quo, we decided to restructure the account for more control. We paused the broad Shopping campaign and shifted to new, more manageable campaign formats:

  • Dynamic Search Ads (DSA) campaigns targeting the top categories.
  • Image and Gallery ads showcasing product visuals for key categories (to capture attention on relevant searches where visuals matter).
  • Smart retargeting banners focused on re-engaging past visitors with the exact products/categories they viewed, to capitalize on warm leads.
  • Classic text ad campaigns for branded and category+brand keywords, allowing manual optimizations where needed.

This segmentation gave us the granular budget control we wanted – we could now allocate and optimize spend for each major category separately. However, there was a trade-off: each individual campaign now received a smaller slice of the overall traffic pie. With lower volume per campaign, Google’s Smart Bidding algorithms had less conversion data to learn from in each campaign, and we indeed observed performance initially dip. Conversion rates and volume started to decline as the campaigns struggled to exit the “learning phase.” After a few weeks, it became clear that simply segmenting the campaigns wasn’t enough; we needed to find a way to feed the algorithms more data so they could optimize effectively.
Over the course of about two months, we fine-tuned settings and tried to push through the learning period, but the efficiency remained below expectations. Rather than give up on our more structured approach (which we knew in principle was right), we looked for a solution to the data scarcity problem in those campaigns.

Enter AI-Modeled Conversions (FunnelFlex.ai)

To overcome the limited data issue, we tested an AI-driven strategy: using modeled conversions via the FunnelFlex.ai platform. FunnelFlex.ai is an AI marketing solution that acts like an extra layer of optimization on top of Google Ads. It evaluates each session’s probability of converting in real time, based on a machine learning model trained on the client’s first-party data (user behavior, historical conversions, etc.).
When a user shows strong intent, FunnelFlex.ai generates a custom conversion event via Google Tag Manager, embedding a dynamically assigned value. This event is passed into Google Ads as a standard conversion.
Note: We do not pass fractional or probabilistic values. Each event includes a full conversion trigger with a value selected by the model.
This enables Smart Bidding to:

  • Train on more frequent, higher-quality conversion signals.
  • Optimize more quickly in low-volume scenarios.
  • Increase efficiency across segmented campaigns.

A/B Test Results

We tested FunnelFlex.ai in a split-run against one of the top-category dynamic campaigns:

  • Conversion rate increased by 68%.
  • CPA dropped to one-third of the original.

We then scaled this approach across all major product campaigns.

Final Results

  • Conversion rate increased by 21.85%.
  • Average order value rose by 10.91%.
  • Revenue grew by 28.37%.
  • CPA fell significantly, improving ROAS.
  • Campaign overlap and cannibalization were eliminated.

Each campaign is now optimized around intent, margin, and audience behavior. Budgets flow where they drive the most profitable conversions.

What’s Next

We’re now moving into deeper segmentation:

  • Refining by brand and product margin.
  • Customizing conversion values even further.

FunnelFlex.ai removes one of Google Ads’ biggest Smart Bidding bottlenecks: lack of reliable conversion data in niche or low-volume segments.

Takeaway

This case shows how a focused AI augmentation layer like FunnelFlex.ai can:

  • Increase data density for Smart Bidding.
  • Enable tighter optimization.
  • Deliver outsized returns on segmented traffic.

It’s not a plug-and-play fix. Success depends on:

  • Proper GTM integration.
  • Clean first-party data.
  • Strategic application of modeled conversion logic.

But when done right, it works. And scales.