Warm up your pipe gen efforts with signals

There is so much noise in the software market. Buying and selling software has never been harder because of the sheer volume of noise from vendors. Inboxes are full of cold outreach from SDRs, logging into LinkedIn means an insurmountable number of connection requests from vendors, and god forbid you download a piece of content from a vendor because you’ll be inundated with messages. 

Over the last fifteen years, go-to-market teams selling software have put their faith in the notion that volume equals success. The predictable revenue model taught us that a GTM organization's goal is to build a scalable process that yields high volume output. For every thousand emails or cold calls you do, you could predictably generate hundreds of thousands in pipeline. 

As we all ran after this model of revenue predictability, we created immense noise for buyers, who eventually tuned it out. What’s left is a broken pipeline generation engine that no longer works for the modern SaaS business. 

What is the antidote to the cold outbound, high volume model?

Focusing on warm, hyper-relevant outbound instead. 

In this blog, we’ll break down: 

• What are the 4 key pillars of warm outbound
• How to use intent signals to fuel pipeline 
• Popular warm outbound playbooks 
• Tools and process needed to build a warm outbound engine

What is warm outbound? 

At its simplest, warm outbound is reaching out to prospects or customers who either have 1) previous engagement with your brand or 2) signals that indicate they are in the market for your product. 

Warm outbound may look different from company to company because the warm outbound playbooks you run will depend on your go-to-market motion (more product-led vs. more sales-led) and your goals as a business (new logos vs. expansion). 

Regardless of your goals with warm outbound, 4 key components must exist to build a warm outbound engine: 

1. Well defined TAM
2. Authority
3. Relevance
4. Make it personal

Well defined TAM

A good warm outbound strategy starts with a solid foundational understanding of your target market and ICP. All the pipeline strategies in the world will feel like throwing spaghetti at a wall if your team doesn’t understand who your best fit customers are and how you segment them. 

To start building your warm outbound engine, begin by defining your total addressable market (TAM) and then segmenting that further based on your current best fit customers. What industries or verticals are you seeing success within? What size of company do you best serve? What stage of company? 

Authority 

Outbound efforts falling flat despite doing all the right things? You might have an authority problem. The best brands build scalable warm outbound engines in their space because they build authority in parallel. What is authority? Authority is establishing your brand as the trusted source, expert, or leader in your space.

If you’re not building trust with your audience of target customers through thought leadership content and community, outbound will be an uphill battle. Your outreach may not land because the audience doesn’t know your brand or maybe they’ve heard of you but don’t trust your expertise yet. 

There are many ways to build brand authority, but the primary levers are thought leadership and community. Build authority by becoming the go-to source for educational content in your space, position your leaders and customers as thought leaders, or build a community of practitioners. 

Relevance 

Both TAM and authority are foundational aspects of the warm outbound engine before you even begin to think about building playbooks. Then, the key to warm outbound playbooks is relevance. 

For years, the conventional wisdom has been to emphasize personalization as they key to good outbound programs. If you can nail personalization, then you’ll see higher open rates and better conversion to meetings. The problem is we’ve overemphasized personalization and under utilized the more important lever - relevance. 

Relevance is what it sounds like. Instead of taking a spray-and-pray approach, prioritize outreach based on signals that create relevance like seeing a spike in website visits from an account to your pricing page or noticing that job descriptions reference a keyword for your solution. Create relevance by tapping into signals about how customers already use your product (product usage), who they are (firmographic or demographic signals), how they engage with marketing content (website visits, content downloads, community engagement), and more.  

We’ll dive into relevance more below.

Make it personal 

Finally. The last piece of the puzzle is personalization. With data about your target audience’s customer fit and relevant signals you’ll have enough insights to create personalized outreach that goes beyond basic parameters like {company_name}. 

It’s easy to spend an hour personalizing 5 emails. The key to building a warm outbound engine that scales is speeding up your reps time through automation. There are a few areas you can automate to make personalization at scale possible without it becoming generic. 

Areas of automation:

• Automate account research: Instead of reps taking an hour to deep dive into an account, use AI tools to generate quick summaries of an account. 
• Personalized templates: Create dynamic fields in outreach that can be dynamically populated with data like product usage stats to show the prospect you did your research. 

Using signals to fuel relevance 

Relevance is created by analyzing signals. 

What are signals?

Signals are attributes that indicate the level of engagement or interest a customer has with your product or brand based on who they are (firmographic/demographic) or actions they’ve taken (product or marketing engagement). They inform the level of intent shown by a customer and can be used to prioritize where your team spends effort. Signals can be grouped into 1st party and 3rd party signals.

1. First-party signals: This is data you own that was collected from a user engaging with your product or a prospect engaging with your brand. Examples include customer data from the CRM, product usage data from the data warehouse, marketing engagement data like content downloads or event attendees, community engagement, and more. 
2. Third-party signals: This is data from a 3rd party provider, typically a data vendor like Clearbit, 6Sense, Bombora, and others. Some of these 3rd party vendors explicitly sell “intent” to help you understand where there is a buying signal within your target accounts. Other providers offer firmographic data, funding data,  tech stack, etc., that could help you infer intent or buying signals. 

Go back and read this article on building a signal-based GTM tech stack. 

Here’s a simple example of creating relevance by using signals that inform intent. 

On Tuesday I got an email from a company. I opened the email to see that it was for a product I already use and the messaging was about how to get started. The relevance of this message was near zero - I already use this product and I’ve already “gotten started.” Sending this email was a complete waste of time for that rep. 

That same day I got an email from a different company. This is also a product I use but the message said “saw that you added someone to your team Sandy, I thought it might be a good time to reach out and tell you that we offer XYZ package for teams of your size.” The relevance of this message was way higher because the signal that triggered the outreach was two fold: 1) 1st party signal that I am an active user of their product and 2) 3rd party signal that I recently hired someone on my team.

Bringing together 1st party and 3rd party signals to create relevance will lead to more compelling outreach and a higher likelihood that your outreach is successful. 

Types of warm outbound signals

So what kind of signals can you use to run warm outbound playbooks? The answer will depend on your goals with warm outbound, but broadly you can think of signals in these broad categories: 

Product engagement

Product engagement signals will be most relevant for teams that want to outbound to their existing customers to achieve goals such as conversion from free to paid plans, seat expansion, cross-selling new products, and preventing churn. 

Product usage is the highest intent signal when running Product-Led Sales playbooks. Since product usage is unique to each company, few generic signals work universally. Here is a small selection to draw inspiration: 

• Senior user joins a workspace 
• IT user joins a workspace 
• Recent usage spike on a particular feature 
• Recent spike in active users in an account
• New title sign-ups for account 

How do you surface these signals to your reps? Use Pocus ⬇️

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Further reading: 

• Top 5 Product-Led Sales playbooks you need to fuel warm outbound
• How to move upmarket with outbound playbooks in PLG. 

Network intros

One of the strongest non-product usage signals is having a concentration of network overlap in a target account. A warm intro into a target account will always convert faster than a cold outbound. Warm intros can come from anywhere in your network: existing customer referrals, investors, your team, or friends in the industry. 

Some common signals for warm outbound via network include:

• Teamlink connections (connection of someone on your team) 
• Investor overlap (portfolio company of your investor)
• Partner intros (customer of your partners)

Marketing engagement

We’re all familiar with the marketing qualified lead (MQL), the original warm outbound signal. This signal has been historically misunderstood. Marketing engagement is rarely a strong enough signal on its own to warrant outreach. 

How many times have you downloaded content and immediately regretted it because of the deluge of irrelevant spam sent afterwards? Marketing engagement, like content downloads, rarely shows enough intent to warrant warm outreach. Instead, focus on signals like:

• Website visits to the pricing page 
• Active community members within target accounts 
• In person event attendance

Firmographic / Demographic 

Like MQLs, firmographic and demographic signals have always been available, but in the era of AI we’ve seen a resurgence of these signals. Instead of basic signals like recent funding announcements, use AI to scrape job listings for keywords relevant to your product, or scrape target account websites to prioritize specific GTM motions (PLG vs. sales-led). The possibilities are endless. 

The same applies to demographic signals. Use powerful signals like job change data to see when product champions move to your target accounts or change roles within the company, which can help drive expansion to a new team. 

Here are a few firmographic and demographic signals rising in popularity:

• Job listing keyword mentions relevant to your business
• Product champion switches jobs 
• Title changes 
• Headcount growth on a particular team 
• LinkedIn follower count growth 

5-steps to build signal-based playbooks

Step 1: Choose best fit target accounts 

Build a target account list of ICP accounts based on firmographic signals like company size, geography, last funding round, and GTM motion. 

Step 2: Score level of existing engagement within target accounts 

Score accounts based on their “warmth” or level of existing engagement with your company or product. Look at metrics like number of contacts with marketing activity, website visitors, active sales conversations, and any other relevant customer or product data. The warmth of the account would determine our goals in the next step.  

Step 3: Define goals for warm vs. cold accounts

Your goals will directly inform which signals you prioritize and the playbooks you run. Goals will depend on lifecycle stage. The goal for a totally cold account with no marketing activity might be simply to get them to open your email. A warm account with tons of existing marketing engagement should have a more aggressive goal like opportunity created.  In turn, the signals you use to prioritize those accounts and create relevance will look different. 

Step 4: Brainstorm signals 

You can do this step a few different ways depending on the level of maturity of your motion. Mature PLG teams may already have analysis from the data team about any relevant product usage signals. Others may use feedback from reps in the field to create an initial list of signals that correlate to the goal. Often it’s a combination of both the quantitative and qualitative approach. In Pocus’ own example, we took a combination of signals we already had evidence worked (investor overlap) and some we had hypothesized based on what we know about our ICP (sales headcount changes) to come up with list.

Step 5: Build and operationalize playbooks

Once signals are aligned to goals, it’s time to build the playbooks. Depending on which team will run the playbook (sales, marketing or CS) your mechanism for surfacing the signals and next best action will change. When surfacing signals to sales, you’ll need to either surface in Salesforce, Slack, custom tools, or even a spreadsheet (or use Pocus to make this process scalable). For scaled playbooks run by marketing, you’ll need to surface signals in marketing automation tools, email platforms, or advertising platforms. 

Bonus: Measure results & iterate

Finally, you’ll want to measure the effectiveness of signals in driving conversion towards the goal. Measure conversion rates for each playbook to double down on what works and retire underperforming signals. 

Learn more about using signals to build your warm outbound engine

Operationalize warm outbound with Pocus

Need a way to surface and manage your signal-based playbooks? Pocus helps you accelerate pipeline based on real buying signals, not your team’s best guess (whether it’s landing new logos or expanding existing ones). Combine all product usage and intent signals your team needs to prioritize the best opportunities and take quick action.

Learn what type of signal-based playbooks you can build in Pocus. 

See it in action👇

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