Hardware Retail Store Database of South Dakota Latest August 2026 Updated

Stress-tested it during a client pitch. Email coverage is the highest I've seen for United States B2B data. Bought it to handle hardware retail store lead-gen for the United States region. Worth the price.

Wanted a United States list with verified phones. Encoded UTF-8 with no character-set surprises in Excel. Bought it to scope a United States territory in the hardware retail store segment. Sales team is happy.

Skeptical at first, given the price. Catchment overlap with our existing CRM was minimal — almost all net-new leads. Bought it to scope a United States territory in the hardware retail store segment. Sales team is happy.

Bought this for a cold outbound push. Cross-checked 50 entries against Google Maps and 47 matched cleanly. Otherwise no complaints.

Picked it up for cold outbound. Bounce rate stayed under 4% on a 1,000-contact email send. Bought it as the seed list for a hardware retail store outbound push in United States. Workable, the LinkedIn coverage could be better.

Bought for a one-time project. Address quality is the strongest column in the file. Bought it for hardware retail store prospecting across United States. One column I'd add next refresh: secondary email.

Decent dataset for the price. Bounce rate stayed under 4% on a 1,000-contact email send. Connect rate could have been higher with cleaner mobile numbers.

Reasonable value for what we paid. Google rating column came included, which we hadn't expected. One column I'd add next refresh: secondary email.

Honest reaction after a week of use: Hours-of-operation field meant my SDRs called during open windows. Recommended.

Bought this for a cold outbound push. Connect rate jumped noticeably once we filtered by category. Will be the default vendor for us now.

Bought this for a cold outbound push. ROAS improved roughly 30% versus the lookalike audience we ran before. Bought it for our hardware retail store sales team's United States pipeline. Couple of small gaps but workable.

Field-tested by my team. Website URLs were populated for ~80% of rows, far better than the competition. Money well spent.

Picked it up for a one-off campaign. Imported into HubSpot in two clicks, every column mapped automatically. Bought it while building a hardware retail store prospecting workflow for United States. Money well spent.

Did the job for our campaign. Connect rate jumped noticeably once we filtered by category. Otherwise no complaints.

Onboarded a junior with this dataset. Categorisation was granular enough to segment by sub-vertical without regex. Recommended.

Ran the CSV through our deduper first. Address quality is the strongest column in the file. Bought it while building a hardware retail store prospecting workflow for United States. Renewed for another quarter already.

Met most of what we were looking for. Google rating column came included, which we hadn't expected. Bought it for hardware retail store prospecting across United States. Minor cleanup needed but nothing major.

Field-tested by my team. The phone column was live for 95% of the rows I tested. Decent value, would tighten one or two fields.

Bought a sample first. ROAS improved roughly 30% versus the lookalike audience we ran before. Bought it to support a hardware retail store rollout in United States. Sales team is happy.

Onboarded a junior with this dataset. Refresh cadence means the data feels current, not 2-years-old. Bought it for our hardware retail store outreach in United States. Worth the price.

Bought a sample first. Loaded into Power BI without a single import warning. Bought it for a United States hardware retail store market-sizing project. Renewed for another quarter already.

Field-tested by my team. Dedup pass only flagged 1.2% — cleanest dataset I've worked with. Bought it as part of a wider hardware retail store expansion into United States. Genuinely useful.

Looking to scope a regional rollout. Address quality is the strongest column in the file. Bought it for hardware retail store prospecting across United States. Saved us hours of manual cleanup.