Signal-based selling: the new GTM fad we’ve totally embraced - and it’s working!

Signal-based selling is the latest hype in B2B marketing and sales.

And it’s easy to see why everyone’s excited about it.

Signal-based selling arises from the idea that not all right-fit prospects are ‘in-market’.

That is, not all companies meeting your ideal customer profile (ICP) criteria are ready to buy your product. In fact, it’s claimed that less than 5% of your target market is looking to buy at any one time.

Signal-based selling focuses on targeting that 5% by not only considering the right fit but also the right time, which entails identifying, interpreting and responding to buying signals. 

These signals are behaviors and actions which suggest a buyer’s interest in your product, and can be anything from website visits, demo requests on a website to third-party intent data signals, such as G2 buyer intent and LinkedIn Ads engagement.

In this post, we’ll explore the growing phenomenon of signal-based selling, what it is, how we at Dreamdata are putting it into practice, and what we’ve learned.

Contents

• What is signal-based selling?

• Signal-based selling Dreamdata example

• Why signal-based selling is here to stay

What is Signal-based selling?

As mentioned above, signal-based selling emerges from the idea that less than 5% of your market is looking to buy at any one time.

Signal-based selling focuses on targeting that 5% by considering both the right fit and the right time.

The right fit: Ideal Customer Profile

The right fit concerns the more common action of defining and targeting your Ideal Customer Profile - something which has long been an essential component of B2B go-to-market and goes to the heart of a business’s positioning.

It’s the theoretical ‘perfect’ customer that your product solves for - think size, industry, tech, revenue, location, etc.

You should already have a clear picture of your current ideal customer profile, if not, check out this conversation with positioning legend April Dunford for some inspiration.

Now, once your ICP is sorted you can zero in on the right time side of things, which, as mentioned entails identifying, interpreting and responding to buying signals.

The right time: What are signals?

The label ‘Signals’ is derived from the long form: buying intent signals.

These are data points that indicate a potential buyer’s interest or readiness to engage with your product or service, including anything from a pricing page visit to a G2 product category.

Confusion often stems from determining which activities are signals and which aren’t - as not all touchpoints are signals.

Signals are the touchpoints that most suggest a level of buying intent.

How do you know what signals suggest a level of buying intent, you may ask?

Well, ideally these are touches that have proven to be consequential to customer journeys in the past. And for this, you to be able to perform a historic analysis of the influence of different touchpoints.

Using Dreamdata, we look at the total influence of an ‘event’ (touchpoint) and the influence per session metric to determine the impact of touches on previous customer journeys.

Where this isn’t possible, you have to make educated assumptions about what touches suggest buying intent. Pricing page visits, G2 comparisons, and demo requests are obvious candidates.

But even then, not all signals are created equal. Engaging with an ad on LinkedIn is typically less of an indicator than a visit to a pricing page.

Unfortunately, there’s no universal signal ‘strength’ barometer, it will vary from company to company. Once again, it is only by analyzing and measuring signals against business outcomes that businesses can decide which activities are uniquely ‘strong’ to them.

Ok, enough theory.

Let’s take a look at a live example of signal-based selling to see how it works in practice.

How we’ve made our outbound motions signal-based

So at Dreamdata, we’ve embraced signal-based selling with open arms. A great example of how we’re doing is with our outbound efforts, which looks like this:

• Fully automated Dreamdata intent engine picks up and processes signals

The first, and most important step involves tracking the signal data. This takes place behind the scenes. 

To capture all the relevant signals we’re relying on our own platform. Dreamdata collects intent data through its proprietary first-party intent data tracking and integrations setup which you can read all about in this post → 

The data then goes through a process of qualification on BigQuery where our signals are defined, ICP criteria applied and engagement and lead scores added.

We then feed this data back to HubSpot to update company properties with the signals. We then have a workflow to further qualify these and alert our Sales team on our ‘#outbound-opportunities’ Slack channel.

We’ve got all the details on how to set this up in this post →

The Slack notification includes a ‘fit’ score (as per our ICP criteria) - which has been filtered to only include ‘Good’ or ‘Excellent’ fit companies, the Lead Score, time of latest interaction, and the signals performed over the last 7 days - as well as other firmographic details.

This is how it looks like once all the automation is done 👇

In this example, we see that the company has performed a number of signals that we have previously determined suggest buying intent, specifically a visit to one of our blogs, a visit to a relevant G2 category and our G2 profile, and engagement with our LinkedIn Ads.

You can also run this process manually (with no tech knowledge)!

Although our process is fully automated, Dreamdata makes all the signal intent data available on our Reveal report - which is available out-of-the-box.

In the Reveal Report you can manually search for (ICP) companies engaging with your brand and filter by Signal Type, engagement score, and exclude any with Account Owners.

• Sales reps review the customer journey to gain full picture

Once the notification appears on our Slack channel, our (awesome) Sales team steps into action.

The notification includes a direct link to the company’s customer journey map on Dreamdata which helps the Sales reps see who has performed those signals and when, as well as see any other relevant touches.

This is invaluable to the team in formulating highly personalized and timely sequences.

Let’s work through an example journey.

Here, the Sales rep will find that two separate stakeholders have performed those actions. If the contacts have been identified, the Sales rep can then determine whether they are the ideal buyer persona. 

Otherwise, the Company Journey view includes direct links to LinkedIn profiles which our reps use to gain a better understanding of who else might be working in that company plus any other clues that can help tailor the outreach.

On the timeline itself, the reps can see what blog post was read, which ad campaigns were engaged and any other touches that may have taken place.

This qualitative analysis offers the ideal context for the rep to then truly personalize their outreach.

• Marketing sets up retargeting campaigns

This isn’t a sales-only affair. With access to this signal data, our marketing team also jumps into action with tailored retargeting on LinkedIn.

The team has an audience sync setup with our Audience Hub feature, which picks up all these accounts and sends the audience data directly back to the LinkedIn Ads campaign manager, where they enter a specific retargeting campaign.

Once again, this can also be done manually by downloading audience lists and uploading them to LinkedIn or Google Ads.

Why signal-based selling is here to stay

Just think about what the above process would look like without the signals.

How many companies would be contacted blindly through cold outbound and ad spend wasted on target accounts when they’re not even remotely ready to buy?

With signals, you are putting your resources on accounts that indicate some level of interest.

Here’s what it’s meant for us.

1. Putting resources where they are most likely to succeed

Signal-based selling is ultimately about putting your resources on the most-likely-to-close accounts.

By helping target the right accounts at the right time, we’ve limited sales and marketing time and budget spent chasing accounts that are not ready to buy.

This is already being reflected in our Win rates, and ROAS on our retargeting campaigns.

2. Ahead of the competition

Signals are an obvious competitive advantage. None of us are operating in isolation. By identifying buying intent signals as soon as they happen, we’re able to get to these leads as soon as possible. Which means ahead of our competitors.

In a growing portion of our conversations from the outbound motion described above we are the first solution engaging with the companies.

3. Identify in-market prospects (not on your target account list)

Signals aren’t only performed by listed target accounts. Sometimes prospects from outside your target list can jump on the radar. In this way, collecting and interpreting signals becomes invaluable to inbound efforts.

4. Better prioritization of pipeline

Signals are also useful in prioritizing accounts already in the Sales pipeline. Again here, we want Sales Reps to put their limited time into those opportunities that are most likely to close.

Additionally, in much the same way as our outbound example above, signals equip sales teams with actionable insights to engage prospects with highly personalized and timely messaging. For instance, have they gone back to G2 to compare against a competitor? Have they read a particular blog post you can use to segue to a feature?

The end of other marketing approaches?

Do these benefits spell the end of traditional go-to-market strategies?

Of course not.

Sure, signal-based selling addresses some of the limitations of ‘traditional’ approaches by providing the timing and level of intent for each account. 

But this doesn’t mean commercial teams should drop target account lists and running ABM campaigns. It certainly doesn’t mean stopping traditional demand generation activities… after all, signals don’t just magically appear. 

A target account cannot send out a G2 signal if you’re not present (and active) on G2. So there must be omnichannel go-to-market in the first place.

It’s more a case of enhancing processes and making them way more efficient by using data that is readily available.

Want to get started with Signal-based selling?