Here it is—the third installment of our “Do it in-house” series!
There are for sure plenty more to come, but the first three here are their own trilogy-of-sorts. So before I dive into the unique contents of this post, I’ll quickly go over what the first two posts covered, leading up to this one.
The first “Do it in-house” post, which is titled Transitioning from 1-CAC-fits-all, to the value modeling approach, discussed the current state of B2B campaign optimization, and the formula to project CAC using past conversion data. It also went over how subscription SaaS B2B companies can easily switch to the value modeling approach to better evaluate their ad campaigns—in-house!
The second “Do it in-house” post, which is titled Use LTV projections as part of the value-based acquisition process, went over how to take on value modeling in-house using LTV projections. Of course, it also included details on where common practices fall short, the benefits of projected LTV, and advanced projections using segmentation.
You don’t have to read the previous two posts before reading this one, but doing so would certainly help in terms of tying everything into one neat package, as this post will go over how you can use Predictive Value-Based Bidding in-house, to ramp up your value-based acquisition efforts. The previous two posts were more focused on improving campaign decision making. In this post, we will go over how to take this data, and leverage it on ad platforms, with a special focus on Google campaigns.
So let’s jump right in, shall we???
Value-based bidding, which is also commonly referred to as “smart bidding” refers to any of the strategies used to let Google automate your bidding. tROAS, tCPA, Max Conversions and Max Conversion Value are the options there. tROAS is also dubbed "Value-Based bidding" by Google. This is done by placing special focus on each customer, instead of all customers—and naturally placing greater emphasis (proportional to their value) on the customers that are likely to be the most profitable. By extension, the ROAS on campaigns stemming from value-based bidding is significantly higher than those without.
When using Google Ads for value-based bidding, Google gains a better understanding of your customer’s value, and in turn builds better target lists for your ads that are based on your business objective. The objectives themselves can range from increasing market share, to increasing sales volume, to growing revenue and profits.
As the name suggests, everything about value-based bidding stems out of assigned customer values. The starting point is knowing which type of customers you’re looking to target with each ad campaign, then developing, and acting on your growth strategy based on customer value, or LTV data. Down to the individual-level.
When you use value-based bidding, you are prioritizing and enforcing more lucrative conversions, and deploying your marketing capital in the most efficient way.
The purpose of value-based bidding is to signal ad network algorithms towards goals that align with your company's business objectives. You can help ad networks “understand” which users are the most valuable by feeding ad networks with additional data (user value data). In turn, this will enable your marketing team to improve their UA efficiency by bidding differently on different customers, in accordance to how much revenue they are expected to generate. You can think of it as getting more bang for your buck, instead of having to pay based on the average value of all customers.
If you’re wondering what’s wrong with treating all customers the same, it boils down to the fact that you are over-investing on lower-quality customers, while under-investing on those that are inclined to be more profitable. When segmentation takes place based on customer value, the returns become maximized, as seen below.
Now let’s go over the most valuable element of value-based bidding with an example:
Let’s assume your company has three typical customer types, each of which manifests different values to your company. These values can be due to LTV, plan price or probability to convert.
Instead of paying the same amount for Customer 1 as Customer 3, even though Customer 3 is worth four times more, you can bid differently for each of those customers, in accordance to their potential value, while also prioritizing users that will manifest higher value.
Ecommerce brands have been using value based bidding for a long time, and we now see that B2B companies are beginning to use this approach as well.
These are the use cases for B2B SaaS companies to start using value bidding:
Now that we have that established, let’s talk about how you can get started with value-based bidding, so you can make the most of your marketing budget to target higher value customers.
At this point, value-based bidding is for most every brand. The only exception would be companies with brand new campaigns all around, as they aren’t able to give ad networks enough data to build up from.
But since you’re still here reading this post, I imagine your team is part of a more sophisticated company. So without further ado, here’s how you can get started with using value based bidding on Google Ads—in only three steps:
Choose the right conversions and track them in accordance to your unique funnel (to your BI or a tracking platform like Google Analytics).
The purpose of this is to gain as much zero- and first-party data as possible, which could indicate a customers propensity to convert.
From our experience, it’s not only funnel events that can indicate the user’s value. One must also factor the actions that customers take after they register. That data can oftentimes indicate a user’s value. Some data points worth looking into include whether they invited a team mate, or whether they started a new project/dashboard. And let’s not forget the value of any first party data that is collected before/after the onboarding stage. This primarily consists of answers to questions such as the customer’s job function, company size and more—each of which can be very indicative of a customer's value.
Here are some pointers that are worth keeping in mind as you map out your funnel:
The second step is to assign different values to different conversions, based on your historical data.
I’ll explain how it’s done.
Let’s say you want to calculate the value of signups. To start you need to know, and be able to calculate the average value of a paying customer/team. You would then need to calculate the average conversion rate from sign ups, to paying customers/team. The final step is to multiply the average value by the conversion rate percentage. That will provide you with the conversion value.
There are different ways to assign different values:
The final step on Google Ads is to set the campaign up as a tCPA, then switch to tROAS.
You start this by reporting your conversion value to Google Ads on an ongoing basis, four weeks before opting in. This is done by creating a new conversion on Google Ads, and applying value to the conversion. This includes choosing whether the value is constant or dynamic.
After four weeks, you can launch a new campaign(s) on tCPA, optimizing toward this set of conversions. It’s recommended to keep the campaign as tCPA for about four weeks, or three sales cycles. Why four weeks? Well, mainly because of the learning curve. You need to give Google’s algorithm some time to process the data.
After the waiting period is over, and Google’s algorithms have collected enough data to predict user behavior, you can switch to tROAS.
I’ll just throw in a few extra tips regarding transitioning to tROAS, as an FYI. 🙂
Now if you’re really ready to spice things up, you might want to consider adding Predictive Bidding into the mix. This branch of predictive marketing takes value-based bidding to a whole new level.
Read on for more on that!
Let’s start at the very beginning. What is Predictive Bidding in the first place?
Predictive Bidding, in the context of growth marketing, refers to the usage of predictive models that can analyze your brands zero- and first-party data, to accurately determine each user's future LTV in real time. By extension, your team can use this to accurately gauge each user's chance of conversion and retention, based on defined behaviors.
For marketing teams, Predictive Bidding can be considered the holy grail in the value modeling approach. Here are some of the main reasons why:
Simply put, the usage of Predictive Bidding will drive the best value for your campaign budget in a sustainable manner, locking in long-term profitability. All in addition to reaching and exponentially exceeding team goals.
Subscription SaaS companies have much to gain from value-based bidding, as it gives static/dynamic values to different types of conversions so that Google can bid on a target ROAS, as opposed to a fixed target cost per acquisition. This is precisely why there is no time like the present to make the switch to value-based bidding, to maximize your conversion value. And if you choose to capitalize on LTV optimization on top of that though predictive modeling—the sky's the limit because that sets your campaigns up to succeed from the onset.