Consumers are more empowered than ever before and expect brands to provide fast and helpful experiences. That’s why leading marketers are 50% more likely to increase investments in capabilities like machine learning to help them win.1 In fact, brands like Rappi and AutoGravity are already using machine learning in AdWords to reach their most valuable app users and grow their businesses. In our final installment of this series, we explore how machine learning is being applied to bid optimization to help businesses make sense of the data around them and get better results at scale.
It’s more than a bid
The days of predictable web sessions are over, replaced by bursts of digital activity throughout the day on multiple devices. Your bids now have to take into consideration a wide range of contextual signals that impact ad performance, including a user’s device, location and time of day. That’s where machine learning can help.
AdWords Smart Bidding uses Google’s machine learning to help you set the right bid for every auction through three core capabilities:
- Auction-time bidding: Smart Bidding sets bids for each individual auction, not just a few times per day. AdWords Smart Bidding evaluates the relevant contextual signals present at each of those auctions—such as time of day, specific ad creative being shown, or user’s device, and browser—to identify the conversion opportunity, and set an optimized bid tailored to each auction. This allows Smart Bidding to set millions of bids per second, something even an army of marketers wouldn’t be able to do.
- Cross-signal analysis: Smart Bidding understands how signal combinations impact conversion rate. For example, a retailer might notice their mobile conversion rates are 20% higher than their desktop conversion rates, and set a mobile bid adjustment of +20%. However, this doesn’t account for the times of day where mobile conversion rates are even stronger, like in the mornings, when people are researching during their commute. Smart Bidding analyzes billions of these types of signals to identify meaningful correlations, and calculates bids based on how likely a conversion will occur.
- Query-level learning: Smart Bidding maximizes performance for new and low-volume keywords. By looking at performance data across similar auctions in your account, Google’s machine learning platform makes informed bidding decisions and helps reduce performance fluctuations even when data is scarce. For example, let’s say you just added a new keyword “cheap flights to NYC.” If that query was already matching to another part of your account and similar auctions, Smart Bidding simply applies what it’s learned about that query to set the best possible bid.
Focus on the next big opportunity
Brands around the world are using Smart Bidding to unlock growth for their business and reinvesting their time and money into new opportunities.
Harmoney, a peer-to-peer lending service in New Zealand, teamed up with its agency, First Digital, to find more, qualified applicants while still hitting an aggressive ROAS goal. They used Target ROAS across their non-brand Search campaigns to reach customers who were most likely to apply and be approved for a personal loan. As a result, Harmoney saw a 219% growth in high-value accounts at a 37% lower cost-per-acquisition (CPA). Importantly, Smart Bidding freed up 5 hours per week for the team to focus on high-value tasks like testing ad copy and learning more about their best customers.
FirstPoint is a Swiss-based digital agency that wanted to maximize its client’s Search budget while driving more conversions. After testing Smart Bidding, the agency moved away from manual bidding in favor of Maximize conversions. It increased conversions by 2.4x, increased conversion rates by 12%, and decreased CPA by 59%.