Do it in-house: Tie Predictive Value-Based Bidding into your acquisition process

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???

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What is value-based bidding?

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.

Here’s why you should use value-based bidding

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.

• Customer 1 - is worth $100
  
• Customer 2 - is worth $300
  
• Customer 3 - is worth $450

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. 

Historically, there's this urban legend going around the marketing community that Google's tROAS (or VBB, Value-Based Bidding) algorithm is not performing well enough, and that everyone that tries, eventually rolls back to tCPA. Well, let me tell you this, that's plain wrong(!). The legend has good roots for SaaS companies, but the conclusion is totally wrong. VBB does not work for SaaS companies, if they try to assign the first subscription value as the VBB target - as most of the revenue, and the account expansion happens much later, most of the subscribing customers look the same, revenue-wise. If you use predictive VBB, on, say, the 12 months pLTV, then the magic happens, big time.

These are the use cases for B2B SaaS companies to start using value bidding:

• Insufficient conversions: when there is a low number of conversions leads (subscriptions/ free-trial) within the conversion window
  
• A significant drop between soft conversions to paying users
  
• Differentiation between customers long-term LTV

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.

Getting started with value based bidding in-house in only three steps

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:

Step 1: Share better data - Conversion tracking

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:

• We recommend you and your team decide beforehand, exactly which stages of the funnel can define who your quality users are.
  
• Do not disregard your post-funnel conversions
  
• There’s more to your upper funnel conversions than what meets the eye—they are excellent predictors for lower funnel events.
  
• Keep your data up-to-date! Be sure that someone in the team is regularly (meaning on a daily basis) uploading and keeping track of conversions data. According to Google, it is also recommended to share all the important steps of the first 14 days of the customer journey.

Step 2: Assign value to data - Conversion values

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.

Of course the accuracy and effect of the value can be enhanced by using more sophisticated segmentation methods to calculate the expected LTV (as seen in the first and second posts of this series). 

There are different ways to assign different values:

• Static values, as seen in the example above. This is the most simple way to get started, and we recommend starting with this approach if you’re new to value bidding.
  
• Dynamic values - these are changing actual values based on the actual conversion value, such as how much the customer paid. This is recommended when there's high volatility between customers' values/products' pricing. If you have set pricing for products, using static values will be much easier.
  
• Advanced dynamic values- this refers to using predictive LTV models that evaluate and predict a user’s LTV. This, of course, is the most advanced and sophisticated method. Thanks to advances in martech, you can partner with a service that can assist with LTV optimization, and other elements of predictive marketing. If you’re considering going that route, here are nine questions to ask before onboarding a predictive solution.

Step 3: Transition to ROAS - Value-based smart bidding

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. 🙂

• During the transition period, set your budget to 1.2x your desired spend
  
• Choose a set of up to 3-5 conversions
  
• Gradually adjust tCPA bid and budget over the four-week period
  
• Choose 2-3 campaigns with low impression share for the initial test
  
• When switching to tROAS after a few weeks, figure out the ROAS you’d like, then start at ~ 60% of that.
  
• Once you see it succeeds in spending most of the budget and generating conversions, start increasing the tROAS by no more than a relative 20% each time, repeating every 3-4 days if the campaign can keep spending. This is needed as tROAS is much more sensitive to extreme budget or bid changes, and may “choke” if those are changed too aggressively.

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!

The benefits of Predictive Bidding

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:

• Machine Learning empowers marketers to further break their audiences down to the minimal size, enabling the generation of viable predictions on a team/account level
• Granular, value-based segmentation unlocks growth
• Ability to make better, more informed business decisions
• Ability to scale UA, while maintaining healthy unit economics with Predictive tROAS smart bidding
  

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.