Introducing Adlucent Index™ – Bring Control into Automation

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Automation in paid search and social advertising is beneficial for most marketers but comes with some challenges. Real-time bidding algorithms are designed to spend as fast as possible and have very few meaningful settings to help you guide what data is taken into account. If you care about profit, it doesn’t matter how well automated bidding maximizes revenue if it all comes from low-profit products and low-value customers.

Welcome to Adlucent Index™, our proprietary smart bidding technology created to help you acquire high lifetime value customers profitably. Let’s look deeper into the challenges of real-time bidding, how Adlucent Index™ addresses those issues, and how to get started.

THE CHALLENGES OF REAL-TIME BIDDING ALGORITHMS

With many changes in the marketplace, real-time bidding (RTB) is taking the front seat. RTB is used across display and programmatic, especially with Google and Facebook, which are moving towards an automation first methodology for campaigns. These automated technologies – Smart Bidding and Smart Shopping – are beneficial for driving better performance for retailers and marketers. The problem is that with this gain comes some drawbacks with having a degree of control, visibility, and reporting that advertisers are used to having.

Real-time bidding algorithms like Google Smart Shopping and Performance Max (PMax) are simple to use but challenging to control. With that consolidation, advertisers have less transparency and less visibility into where clicks are coming from and the audiences that are getting the most results – so they’re not built to help you better understand what’s truly going on behind the scenes. Real-time bidding algorithms are not tuned to help you perform better but instead are adjusted to spend as fast as possible.

On the topic of measurement, Smart Bidding campaigns optimize to the wrong data. For example, if you were to look at the conversion values inside Google, they might not match up to what’s in your bank account. Over 50% of surveyed marketers prefer optimizing to data that isn’t accessible to Google. This data is never taken into account by Google’s Smart Bidding algorithm.

ENTER ADLUCENT INDEX™: HUMAN + MACHINE

What if you could drive better performance with real-time bidding? And drive improvements in margin and reduce customer acquisition cost (CAC) simultaneously?

Bringing control into automation is what Adlucent Index™ is all about. It helps turn real-time bidding algorithms into your algorithms. Rather than bidding on clicks, it takes control of how you value each conversion. Features include:

• Working in tandem with Google’s automation solutions
• Available for Google text ads, Smart Shopping, Performance Max (PMax), and Facebook
• Fully customizable to your business goal(s)
• Multiple testing structures can be run in sequence or concurrently with other strategies

Before advertisers would bid on clicks and set different bid modifiers, with Adlucent Index™ the focus shifts to conversion value. Asking ourselves, “how do we value a conversion that just occurred?” By feeding that information back into real-time bidding algorithms, they can optimize towards those conversion values that we configure. 

Adlucent developed this solution in tandem with Google and extended it to other platforms like Facebook to drive a layer of performance value and connected data on top of these real-time bidding algorithms. With this approach, we can run multiple testing structures. Maybe we optimize towards margin, or maybe optimize towards a new customer vs. an existing customer. All of those levers are at our disposal with this approach. 

THE BEST OF BOTH WORLDS

This is combining the best of both worlds. We’re able to use the data we have in Deep Search™, connecting it with our clients’ data, and feed it into a layer of control and adjustments to conversion value on these platforms. Smart bidding has access to all sorts of signals from users, which it utilizes to optimize at the time of click. With frequent adjustments, we can reinforce the value of specific signals to nudge the algorithm to learn the right things. 

This allows us to take advantage of real-time bidding algorithms with signals that we don’t have in the auction. As well as gives us insights into what is driving efficient clicks but optimizing to the data that matters to our retailer clients. 

IN PRACTICE

Effectively, this can look like an existing conversion could occur, and maybe, before we would just be sending that from you onto the engines. The problem is that it doesn’t take into consideration the attribution model or advertiser goals.

By connecting multiple data sources from Deep Search™ and first-party data, platforms can enrich that signal. We can send information to our team of experts to decide how to optimize and deliver on the advertiser goals. For example, maybe we bid up new customers by 50% as having higher conversion values, so instead of a $100 purchase, we send that it was a $150 purchase. Or maybe we optimized to a day of the week because we have insights in doing different regression analyses that Fridays are less efficient, so we bid down the conversion values by 20%. 

We run different tests and iterate to send signals to the engines. By sending those different conversion values, real-time bidding goes and finds the higher-value customers and optimizes to those adjusted conversion values. This allows us to bring real control to automation and drive better performance. 

DATA-DRIVEN RESULTS FOR A NATIONAL RETAILER

For one retailer, who was looking to optimize their shopping program toward margin and not just revenue, we were able to implement Adlucent Index™ and train Google automation to optimize their Smart Shopping campaigns towards margin instead of just revenue. By using this approach, we saw an improvement of 12% higher return on ad spend (ROAS) over a month at a 52% higher conversion value. These results were achieved by applying Adlucent Index™ target ROAS, in addition to using our custom integration with Google Ads, versus a standard target ROAS.

HOW TO GET STARTED WITH ADLUCENT INDEX™ 

The only requirement to get started with Adlucent Index™ is to provide the necessary data that Adlucent doesn’t already have. You can send us this data in several ways:

• Any product data (ex. margin) can be added to the product feed.
• For data available at check-out, the Deep Search™ pixel can be configured to include information like customer type.
• Other data that isn’t available immediately or needs to be put into a model can be provided in files via SFTP. In most cases, if we’re doing data modeling, we’ll want two years of historical data.

By getting all of this data together, the Adlucent team can begin to aggregate and connect how the different goals you have will be optimized with an Adlucent Index™ setup.

It usually takes a few weeks to get the data collection set up to get started. If we’re doing predicted customer lifetime value (pCLV), we run training models and run different tests to ensure that Adlucent Index™ is driving the performance we strive for. Running this test can take anywhere from 1-2 months to gather an analysis to provide recommendations on the next steps. This process continues, and our team runs different tests to uncover what strategies are working for your program.

CONCLUSION

Adlucent Index™ is giving you the best of both worlds. We’re taking advantage of real-time bidding but using signals and first-party data these platforms do not have. This allows you to get ahead of the curve and adopt automation faster. Combining human and machine, Adlucent Index™ takes advantage of our team of experts to experiment and identify what works best for your program consistently. Reach out today if you want to learn more and get started with Adlucent Index™!

Matt Zeiger

Matt Zeiger has over 20+ years of digital marketing experience working on Fortune 500 brands and has started and successfully exited multiple tech startups. With an Executive MBA from UCLA Anderson, and a Software Engineering degree, he currently serves as Adlucent's VP of technology helping define the vision for our technology products and helping consult with our clients on their marketing and technology challenges.