Splattr is built for any sales team that wins by reaching the right people with the right message, from founders and individual reps to full revenue teams. It's especially powerful for considered, research-heavy sales like consulting, staffing, and IT services, but customers across a wide range of industries use it to prioritize their market and personalize outreach at scale.

How is Splattr different from Apollo, ZoomInfo, Seamless, Clay, and Instantly?

Those tools are mostly contact databases and sending engines. They hand you names, emails, or a way to blast them. Splattr sits a step earlier: it's a targeting and scoring engine that figures out who is most likely to buy right now and why, then tells you how to start the conversation. Instead of a generic ICP list, Splattr researches every prospect through the lens of what you actually sell, layering in buying signals, hiring data, and tech stack to score and rank them.

How does Splattr find and score the right prospects?

You train an agent on what you sell, and Splattr scores prospects across your market by analyzing publicly available data like buying signals, hiring and job-posting activity, and technology footprint, all measured against your specific offer. Each prospect gets a score plus clear reasoning for why they ranked where they did, so you can prioritize with confidence.

Can I customize how prospects are scored?

Yes. Scoring is driven by the agent you train on each offer, so the criteria reflect your product, your differentiators, and your ideal buyer, not generic out-of-the-box filters. Companies selling multiple products can run a separate scoring lens for each offer.

What intelligence do I get on each prospect?

Every prospect comes with a score and the reasoning behind it, specific leverage points for your outreach, detected business signals (financial, strategic, and hiring/job changes, with sources), hiring metrics, and the company's technology footprint. It's the research that used to take 30+ minutes per prospect, ready in seconds.

Does Splattr integrate with my CRM and outreach tools?

Yes. Splattr offers a direct HubSpot integration so your scored prospects flow straight into your CRM, with Salesforce support coming soon. You can also export any list to CSV to use in other outreach tools.

How does pricing work?

Splattr is $12 per seat per month to keep your account active, and everything else runs on credits, so you only pay for the prospecting you actually do. You can go fully pay-as-you-go at $0.0299/credit, or save with a credit bundle (down to about $0.017/credit). All features are included on every plan.

What does each action cost?

Different actions use different amounts of credits: Find & score a prospect — 8 credits Look up a LinkedIn profile — 4 credits Find contacts at a company — 2 credits Research report — 8 credits Company report — 20 credits Email lookup — 6 credits Phone lookup — 15 credits Chatting with Splattr's AI assistant also uses credits, based on how much you use it.

Is there a free trial, and how do I get started?

Yes. You can score your first prospects free, no credit card required. Just tell Splattr what you sell, answer up to nine quick questions to train your agent (about 3 minutes), and you'll have scored, prioritized prospects in under 10 minutes.

How quickly will I see results?

Most users go from signup to their first qualified, scored prospects in under 10 minutes, and start personalizing outreach the same day.

How is my data handled and kept secure?

Splattr scores prospects using publicly available data, and we treat your account data as your own. We don't sell your data or use your content to train models that benefit other customers. Your data is hosted on Amazon Web Services in the United States, encrypted in transit and at rest, with role-based, least-privilege access controls. Payments are handled by our PCI-compliant processor, and we never store your card details. Full details are in our Privacy Policy and Data Processing Addendum.

Can my whole team use Splattr together?

Yes. Teams get a shared full-team view and can share campaigns across users, so everyone works from the same prospecting engine. On the Business plan you also get a shared credit pool, per-user spending controls, and dedicated support.

Buying Signals in B2B Sales: What Actually Signals a Prospect Is Ready

Every rep has more names than time. The question is never "who could I contact." It is "who is worth contacting this week." Buying signals are how you answer that. A buying signal is an observable change at an account that shifts the odds. It does not guarantee a deal. It tells you the timing just got better, and it usually hands you a reason to reach out that is specific to that company right now. ## Fit is the easy part A list built on firmographics answers one question: does this company fit the profile. That is necessary, but it is not enough. Thousands of accounts fit. Fit tells you who could buy eventually. It says nothing about who is moving. Signals add the second dimension: timing. When you layer what just changed on top of who fits, your list stops being a flat directory and becomes a ranked queue. You work the accounts that are both a good fit and showing movement, and you leave the rest for later instead of burning outreach on them now. ## The five kinds that matter Not every change is a signal. These are the categories worth watching. - **Hiring.** New roles, a shift in what they are hiring for, or a change in hiring velocity. What a company staffs up for is what it is investing in. - **Financial.** A funding round, an earnings move, a new facility or lease. Money changing hands means new budget and new problems to solve. - **Strategic.** A new initiative, a leadership change, a market or category move. Shifting priorities open doors that were closed a quarter ago. - **Product.** What their product or engineering team is building next. A roadmap move often creates the exact gap your offering fills. - **Competitive.** A rival's move, a market shift, or a change that puts a whole segment on the clock at once. ## A signal without context is just noise The trap is treating any headline as a reason to reach out. Two filters keep signals useful. **Relevance.** Does this change actually connect to what you sell? A funding round is exciting, but if the money is going somewhere unrelated to your offering, it is not your signal. The strongest signals map directly to the problem you solve. **Recency.** A hiring surge from eight months ago is history, not timing. Signals decay, so freshness has to be part of how you rank. If you cannot tie a signal to a source you can point at, treat it with suspicion. A real signal has a real origin: a filing, a posting, an announcement. Timing you cannot trace is a guess wearing a costume. ## From signal to first line The best part of working from signals is that the signal is also your opening. It gives you a reason to reach out that is true and specific, which is the opposite of a generic template. Compare two openers. "I wanted to introduce our platform" says nothing and could be sent to anyone. "Saw you just brought on a new head of revenue operations and opened three roles on that team" says you did the work and you are reaching out for a reason that exists. The signal is the angle. Lead with it, then connect it to the problem you solve. ## Making it repeatable Signals only compound if you work them as a system, not as the occasional lucky find. 1. Start from fit. Define the profile so the signals you chase are on accounts that could actually buy. 2. Layer timing. Rank fitting accounts by whether they show a relevant, recent signal. 3. Tie every signal to a source. If it cannot be cited, it does not count. 4. Build the message around the signal. The reason to reach out is the opening line, not an afterthought. 5. Review weekly. Signals move. Your priority list should move with them. ## What this means in practice Volume treats every account as equally ready and hopes. Signals tell you who is actually moving and hand you the reason to reach out. That is the difference between sending activity and building pipeline. Sort your list by what just changed, not by job title.

Put this into practice