10x growth in B2B SaaS with PPC: 9 lessons from 3 years
By: Stefan Secker
Tags: PPC Marketing, Adwords, Search Engine Marketing, Saas Marketing, B2b Marketing

10x growth in B2B SaaS with PPC: 9 lessons from 3 years

More campaigns, more market segments, more traffic? In order to scale paid search for SaaS I found that just doing more of the same is not enough. Each stage of growth requires something new and different. We’ve been working on Google Ads for an Amazon optimization SaaS for the past three years, and these are some of the key things I have learned.

1. Just turning up the volume doesn’t go very far

That’s the first lesson I had to learn just a few weeks in. The campaigns were already running on a small budget and had a few conversions per day. So just add more keywords, ads and get more impression share for more signups:

Hooray, a lot more people signing up, and CPA is decreasing. But two weeks later, this didn’t translate to the same increase of customers. Four weeks later, still not a lot more customers. What’s going on, time to get worried?

2. Understand your activation event

So why didn’t all those leads become customers like before, how are they different? They’re also looking for an Amazon optimization tool and creating an account to test it. It’s easy to focus too much on vanity metrics like getting more traffic, increasing CTR or even leads without realizing that they’re not the right type of user. Getting a higher CTR often doesn’t translate to throughput, if you’re not sure what conversions to optimize for.

Have a look at this signup page for example:

Digging into the data showed, that users clicking the upper button “I have an Amazon Seller Central Account” were about 10x more likely to convert than users selecting the other option. Maybe obvious in hind-sight, but it doesn’t stop there.

Do people actually connect their account immediately to Amazon? If they do, again they’re several times more likely to actually become a customer. So if you you know that a certain activation event actually makes certain users over 2000% more likely to actually become a customer, it becomes clear how important it is what your track, and why just any new account is not enough to predict revenue.

3. How to structure your accounts

There are a million ways how you could structure your accounts: Upper funnel vs. lower funnel, informational queries vs. transactional etc.

As described in lesson 3, every user pretty much goes through the same process during the signup. If you’re selling an all-in-one tool, there’s just a single product people can buy. But users have many different problems and are searching for very different things. In the case of an Amazon tool, this could be how to manage reviews, how to track their rankings, inventory or optimize ads, to name just a few. That’s why we found it makes the most sense to structure accounts based on region/market (e.g. US, DACH, etc.) and feature, because these have similar search intent.

So it will look something like this:

Account level:

US EMEA Asia …

Campaign level:

Amazon Reviews Amazon Inventory Amazon Rankings …

Ad Group level:

Amazon review manager Amazon review software Amazon review tool …

Why having this kind of clear structure based on regions and use cases is so helpful, will become more clear with lesson number 7.

4. Attribution can be misleading… and your brand matters a lot

“Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” — John Wanamaker.

That’s why we love performance marketing, you can just measure everything and stop doing the things that don’t work, right? I wish it worked like that but there are just too many things getting in the way.

Sometimes measuring can even make things worse, because you’ll have false confidence in the numbers and also be biased to only do things that can be measured more easily (e.g. last click attribution)

Some factors make performance marketing more difficult than it seems at first glance.

The most obvious example is missing data: Deleted cookies, different devices, tracking problems etc. The Data is never really clean, but if there is no systematic bias it shouldn’t affect the quality of your decisions too much. GDPR hasn’t made this easier, however and

The other problem is direct traffic and branded searches and not just if you’re looking at a last click attribution model. Brand and direct traffic will often make the bulk of all conversions, but it’s often impossible to track where they’re coming from. Especially if they happen on the first touch point: Was it word of mouth? Did they have a dream and an angel descended from the sky to tell them about your brand?

Brand affinity also has a big effect on your CTR and CVR. When we launched the first display campaign we saw a big uplift in conversion rate for all channels, and even after the campaign the CVR stayed higher than before:

The value of the brand can’t be overstated, but often it has nothing to do with your search ads, and everything with word of mouth, third party review sites and social media groups.

5. Some users are WAY more valuable than others

So point 2. was about filtering for users that do specific actions that act as leading indicator to predict the likelyhood to become a customer. The value of those users will often still show a pareto distribution when segmenting them.

Let’s look at a simple way how to calculate the value of a trial (made up numbers):

Formula:

Value Trial = Trial to customer CVR x (ARPA/Churn)

e.g. 20% x (100€/5%) = 400€

ROAS = 3:1 → aim for CPA < 133€

Which dimensions differ the most on these metrics?

For example:

Region Revenue Number of products Most used features …

So let’s say there are two segments of users:

Value of a trial in Segment A: 5% x (50€/8%) = 31,50€

Value of a trial in Segment B: 25% x (300€/3%) = 2500€

Least valuable : most valuable = 1:79 (!)

This is just an example, but not too far away from what we’ve observed. In order to know where to allocate your budget, it’s crucial to understand what certain user segments have in common and what LTVs and conversion rate you can forecast based on these criteria.

6. CPCs will explode as soon as you have a few real competitors

Consider yourself lucky if you’re doing paid search for a company that’s very early in a new industry. It has it’s downside as well, like not many people searching for what you have to offer, because the whole topic isn’t well known. But adwords is a competitive game and the fewer people are bidding on your keywords, the better for your bank account.

If you’re early, grow as fast as possible with PPC and have a plan in advance to shift to other channels later on.

Have a look at how the CPCs of some of our most important search terms has developed in the past 2 years:

That’s an increase of 1000% — 1500%. Hard to achieve the same level of profitability if you don’t fix all the other variables at the same time.

7. Raise your prices

Many of those variables don’t have anything to do with the paid search campaigns directly. Next to churn, one of the highest leverage points is the pricing. And if done correctly, this shouldn’t affect conversion rates negatively. Of course it is important to analyze the price elasticity of each segment, but with value based pricing there is often a lot of room for growth. Getting this right will allow you to outbid many competitors in the SERPs.

8. You never have enough data

The three main problems with paid search ads in B2B SaaS all have one thing in common: Not enough data…

The target group is very fragmented because you have very different life time values for different segments. So each segment is very small. The overall conversion volume is quite small compared to retail, booking, or comparison sites. The buying journey and decision process is usually long and complex, with many stakeholders being involved.

All of this leads to a chronic lack of data. Without enough data it’s pretty hard to make good optimization decisions in real time and giving the algorithm enough food to figure out automated bidding.

9. Work in Progress: Making bidding automation work

So next steps will be looking at data that correlates with users converting, such as looking at pricing pages and other micro conversions in order to see if optimizing for this will solve the data issue and help the algorithm to bid more effectively.