# What's the best sales intelligence software for SDRs who need to build targeted B2B prospect lists by industry and company size?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hey G2 community! I'm putting together a piece on what's the best<a class="a a--md" elv="true" href="https://www.g2.com/categories/sales-intelligence"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/sales-intelligence">sales intelligence</a> software for SDRs who need to build targeted B2B prospect lists by industry and company size, and I wanted input from people doing this day to day. The tool that works for a well-resourced ops team is usually not what works for an SDR managing their own list-building without data team support, and that gap isn't discussed enough.</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/apollo-io/reviews"><strong>Apollo.io</strong></a><strong>:</strong> Users describe it as putting the repetitive parts of outbound on autopilot, combining deep filtering by role and company size with sequencing in one platform so lists never need exporting elsewhere. Do you find the credit limits a constraint during heavy prospecting weeks?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/gtm-workspace-powered-by-zoominfo/reviews"><strong>GTM Workspace by ZoomInfo</strong></a><strong>:</strong> Users cite stacking multiple job titles alongside industry and company size as the core workflow, though data going stale faster on smaller accounts is the most repeated complaint. Which company-size range have you found most reliable?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/cognism/reviews"><strong>Cognism</strong></a><strong>:</strong> Mid-market SDRs focused on European markets consistently point to phone-verified mobile number accuracy as what actually changes connect rates, not just contact volume.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/wiza/reviews"><strong>Wiza</strong></a><strong>:</strong> Small business users highlight the browser extension and one-click LinkedIn list downloads for SDRs whose prospecting runs mostly through LinkedIn profiles.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/seamless-formally-seamless-ai/reviews"><strong>Seamless (Seamless.AI)</strong></a><strong>:</strong> Comes up for high-volume list-building where speed matters more than deep firmographic precision.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Which of these has worked best for your SDR team, and what's the single filter that most affects the quality of the lists you actually get out?</p>

##### Post Metadata
- Posted at: 3 months ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;&lt;span style=&quot;color: rgb(0, 0, 0);&quot;&gt;The GTM Workspace powered by ZoomInfo and Apollo.io both let you filter and build lists by industry and company size natively. D&amp;amp;B Hoovers is another established option for firmographic targeting.&lt;/span&gt;&lt;/p&gt;

##### Comment Metadata
- Posted at: 11 days ago



### Comment 2

&lt;p&gt;Good addition, and the two interact in a way that makes your sample check even more worth doing. Title and company size aren&#39;t independent: Head of Sales at a 30-person company is a founder&#39;s first hire with a card and no process, and at 3,000 people it&#39;s a layer of management three steps from the decision. Same string, two different people. So a list that&#39;s clean on both fields can still be a blend of personas who need completely different first lines. When I&#39;ve sampled lists, the useful cut has been by size band rather than overall, because the accuracy problem and the relevance problem cluster in different bands. Cognism getting singled out for phone-verified mobiles is interesting in that light, since connect rate is the one metric that punishes a blended list immediately rather than weeks later.&lt;/p&gt;

##### Comment Metadata
- Posted at: 12 days ago
- Author title: Tech Consultant



### Comment 3

&lt;p&gt;I’d test company-size accuracy alongside job-title normalization. Employee-count bands move as companies grow, shrink, or get acquired, and one stale company-size field can quietly push an otherwise perfect prospect outside the intended ICP. A useful comparison would run the same narrow industry, role, geography, and size filters across tools, then manually verify a sample rather than judging by list volume alone.&lt;/p&gt;

##### Comment Metadata
- Posted at: 12 days ago
- Author title: Writer



### Comment 4

For an SDR building lists alone, the quiet quality-killer is how each tool sorts job titles. The same VP of Sales might be filed as &quot;VP Sales&quot; in one tool, &quot;Sales Director&quot; or a generic &quot;Sales&quot; bucket in another. So, when you filter on one title, you silently drop everyone tagged with the others and pull maybe two-thirds of a persona thinking it&#39;s the whole thing. Without an ops person to sanity-check the list, that doesn&#39;t show up as an error; it shows up weeks later as soft reply rates. Credit limits get complained about more, but those cap how much you can pull, not how good the list is.

##### Comment Metadata
- Posted at: 3 months ago
- Author title: Tech Consultant





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