How ChargeMate Gets 5–8x Higher Reply Rates With Signal-Based Outreach

The challenge

Before Gojiberry, ChargeMate’s outbound motion was fragmented and highly manual.

Brad used Apollo to find and enrich contacts, experimented with tools like PhantomBuster and Waalaxy for LinkedIn, and personally wrote messages to high-value prospects.

The most personalized outreach worked best — but it was also the hardest to scale.

LinkedIn and email lived in separate workflows. Contacts had to be manually tracked. Sequences often felt formulaic. And maintaining consistent outreach required Brad to repeatedly block time in his calendar for prospecting and follow-ups.

As Brad explains:

“Most of what was successful for us in getting a reply or initiating a meeting was very thoughtful manual outreach, which obviously is great, but doesn't scale.”

The goal wasn't to automate everything.

It was to automate the repetitive work while preserving the human element that made the outreach effective.

The solution

ChargeMate implemented Gojiberry to shift from traditional list-based prospecting to signal-based outbound.

Instead of building a static ICP list and contacting everyone on it, Gojiberry continuously identifies prospects based on signals that suggest there may be a relevant reason to start a conversation now.

New leads are surfaced for Brad to review. He decides who is worth contacting, adjusts the messaging when necessary, and lets Gojiberry handle the connections, sequence timing and follow-ups.

As Brad describes it:

“I might spend 15, 20 minutes reviewing some leads. And then I know that those are going to be sent off at the right time. And it kind of works almost on autopilot for me.”

The result is a workflow that scales outbound without removing Brad from the parts where his judgment matters.

Using signals to reach prospects at the right moment

For ChargeMate, the biggest change wasn't simply automating more outreach.

It was improving when that outreach happened.

Brad uses several types of signals to identify potential opportunities:

• engagement with influential people in the EV and energy ecosystem

• competitor engagement

• company and executive followers

• fundraising

• job changes

• hiring activity

• layoffs

• warm lookalike prospects

Some of the strongest results have come from tracking engagement around prominent executives, customers and companies in ChargeMate's industry.

One more unexpected signal has also proved useful: layoffs.

Changes in federal subsidies have put pressure on parts of the EV charging industry. Some operators need to manage growing infrastructure without proportionally increasing headcount.

For ChargeMate, that creates a natural moment to discuss how AI can help teams manage more assets with fewer operational resources.

Rather than reaching out simply because someone matches the ICP, ChargeMate can reach out when there is context behind the conversation.

The workflow

ChargeMate combines automation with human review throughout the acquisition process:

Signal detection

↓

Gojiberry identifies relevant prospects

↓

Brad reviews and approves leads

↓

AI-assisted personalization

↓

LinkedIn + email sequences

↓

Prospect replies

↓

Brad takes over the conversation

The repetitive execution is automated, while messaging and conversations can remain human-controlled.

“We're automating the things that are time consuming, but still putting a human in the loop [for] the things that deserve a human touch.”

That balance is particularly important for ChargeMate because it sells into a specialized B2B market where relationships matter and burning through a limited ICP with generic messages would be costly.

Personalizing outreach with Claude + MCP

Brad has taken the workflow a step further by connecting Claude with Gojiberry through MCP.

He created custom Claude skills that understand the type of sequence, copy and tone he wants.

He can take batches of five or ten LinkedIn profiles, use Claude to create contextual messaging, and push those updates into Gojiberry through MCP.

His messaging philosophy is deliberately simple: lead with curiosity rather than immediately entering sales mode.

If a prospect's company has rapidly increased the number of assets it manages, for example, the message can start by asking how the team is handling that additional operational volume.

The goal isn't to make AI write a more elaborate sales pitch.

It's to use available context to open a conversation that feels relevant and natural.

Results

The most significant improvement has been reply rates.

Brad reports seeing 5–8x higher reply rates compared with what he previously experienced using Apollo and more traditional outbound methods.

ChargeMate has also been able to compare signal-based outreach directly against static lists.

Brad uploaded existing Apollo and Unify prospects into sequences. These contacts could still match the ICP well, but because there was no timely signal behind the outreach, they generated a lower hit rate.

“When it's driven by a signal and it's timely, the response rate is quite a bit higher.”

That comparison reinforced the core idea behind ChargeMate's outbound strategy: fit tells you who to target; signals help tell you when to target them.

5+ hours saved every week

Gojiberry also saves Brad approximately five hours per week.

Previously, keeping outbound moving meant manually finding prospects, sending messages, managing connections and checking who had responded.

Now he can spend roughly 15–20 minutes reviewing a batch of leads and let the sequences continue running in the background.

For a founder managing product, fundraising, customers and sales simultaneously, the consistency matters as much as the raw time saved.

Outbound no longer depends on Brad remembering to block another prospecting session in his calendar.

From replies to meetings

Within less than a month of using Gojiberry, ChargeMate had already started conversations with potential customers and partners around the world.

Among the relevant prospects who had replied or expressed interest, Brad estimated that roughly half had progressed to the meeting stage.

Instead of forcing prospects directly toward a demo, the process can start with a relevant message, continue with one or two human exchanges, and naturally progress toward a meeting.

Why this approach works

ChargeMate's experience highlights a simple distinction between static prospecting and signal-based outbound:

Matching the ICP doesn't mean it's the right time to reach out.

A static list tells you that someone could be a customer.

A signal gives you context for why a conversation might make sense today.

That signal might be a fundraising event, a hiring change, a layoff, engagement with an industry thought leader or interaction with a relevant company.

Brad saw the difference directly: prospects discovered through timely signals consistently generated stronger response rates than similar contacts coming from static Apollo or Unify lists.

For a company operating in a relatively focused market, that also means ChargeMate can avoid repeatedly cold-messaging the same limited pool of prospects.

What’s next

ChargeMate plans to continue testing different types of signals rather than relying on a single prospecting strategy.

One agent can focus on competitor and industry influencer engagement, while another tracks company-level buying events such as fundraising, hiring or other organizational changes.

The goal is to compare not just which signals generate replies, but which ultimately generate the most meetings and customers.

Brad also wants to push personalization further, with workflows that gradually learn from the way he edits messages and handles conversations.

The ideal system isn't one that removes the founder from sales.

It's one that handles more of the repetitive work around him so he can focus on the conversations where a human actually makes the difference.

Final takeaway

ChargeMate didn't need more prospects.

It needed a better way to identify when those prospects were worth contacting.

By combining signal-based discovery, AI-assisted personalization, multi-channel sequences and human review, ChargeMate has built a founder-led outbound engine that is both more scalable and more relevant.

The early results:

5–8x higher reply rates.

5+ hours saved every week.

~50% of relevant positive conversations progressing to meetings.

For founder-led sales teams, that points to a different way of thinking about outbound:

Don't just find the right person. Find the right reason and the right moment — to start the conversation.