The first time you login to Cloudingo you may look around and wonder how to dedupe and where to start. Should you look at Leads first? What’s the best filter to use?

Before we tell you what we recommend, keep in mind that if you don’t follow our order, it’s okay – your final results will be the same. Each person has different data, problems, and goals. You won’t mess up your end result by coming up with a different plan, but here’s what we tell our clients for the most efficient workflow.

Single-Table First: Accounts, Contacts, then Leads

For the most efficient workflow, we suggest working with single-table filters first. Specifically the most logical place to start is with Accounts, then move on to Contacts, then dedupe Leads last.

By starting with Accounts, all duplicate contacts will be consolidated under a single, correct and complete Account. This eliminates the decisions about which Account your contacts should be affiliated with and reduces the risk that new leads or contacts will get attached to incorrect Accounts.

Once you’ve completed these single-table filters, move on to multi-table filters, deduping Leads to Contacts and Contacts to Accounts. No matter which records you use, remember that all related objects (notes, attachments, and opportunities, both closed and lost) will end up on the final record. Cloudingo wouldn’t want you to lose those!

Use Tight Filters to Limit Manual Review

Start with the tightest filters that deliver exact matches on one or two fields, such as Last Name and Email. There’s a high probability that records with the same Last Name and Email are in fact, true duplicates. Using tight filters like this will require less manual review, if any at all. There’s less decision making needed. You’ll feel confident in letting automation merge the records without you having to look to make sure they’re true duplicates.

After you’ve run tight filters, be thorough and rescan using looser filters, like matching on First N Characters. Keep in mind that the looser the filter, the more likely manual review is needed for each duplicate group. We highly recommend using fuzzy matching last, after you’ve done a few passes of your data.

Experiment

Overall a good rule of thumb is to focus on the number of groups that your filter finds. If your filter found 300 duplicate groups, but you know there are at least 800 dupes, consider using different matching criteria. Your filter may be too specific. On the other end of the spectrum, if your filter is finding too many duplicates, tighten your filter.

Don’t Panic

Here are few tips to keep in mind:

  • Each time you build or edit a filter, look at a couple of the duplicate matches in the merge preview grid and see what your final record will look like. Are you satisfied with the result? If not, revise or add more matching criteria.
  • To ensure you find all of your dupes, perform multiple scans of your data, varying the criteria each time.
  • Don’t just do a one-time cleanse. Continual ongoing maintenance is key. As your data changes and as more records are added, more maintenance is required.
  • Breathe. Don’t be scared. A lot of people are hesitant at first to change their data, especially in bulk or via automation. That’s understandable, but can easily be overcome with time the more you use Cloudingo and trust that your filters are performing to your needs.