Tech Review: RocketReach’s contact finder vs Wiza

At a recent roundtable, RocketReach was described as a better way to find contacts at scale than one that NichePublisher had been using, Wiza.

So for a new project – reaching out to new executives in a partially developed B2B niche – we decided to give RR a try.

Spoiler alert: For this use case, RR was a better overall workflow for finding unique, niche-oriented contact data for B2B at scale – if you knew the contact. WIZA  stayed our go to for uploading company names and searching by job title, since they only reference LinkedIn.

Here’s the new approach using RR vs Wiza and how they compared along 6 criteria.

1. # of Steps to find new contacts

Wiza requires a simple search on LinkedIn Sales Navigator that delivers results at scale, and only valids are charged against credits. While there is a learning curve to using Sale Navigator search features, no imports are required, and no starting list is needed.

RR requires some kind of starting list of data, and it has to been imported in a specific format.

+1 Wiza for simplicity + big number of contacts, fast, or if you have a list of company names.

2. Validity of email contacts

While Wiza strictly uses LinkedIn data, RocketReach uses other sources. LinkedIn as self-reported data is more current than anything that can be found on the web. So, RR’s additional reach may be returning older data. However, much of its data does come through LinkedIn, too.  Both received 5% to 10% cleared from the list after running through NeverBounce. 

WIZA wins this round by a nose.

3. Finding phone numbers

Both tools find phone numbers; however, WIZA phone-finding was too expensive for us to use and somewhat limited. RR just found a lot of phone numbers, no big deal.   However, in this use case, phone numbers were only valuable for potential future use, but not immediately critical.

RR wins if phones are needed.

4. CRM

Both have built-in CRMs. Most publishers have their own –  including this use case – so this is a wash.

5. Additional data-cleaning

This was the real gut punch that moved the needle into the RR category for this use.

While Wiza’s LinkedIn contacts without company names were up-to-date, the data was over-inclusive.

Because of the limited filtering options approach LinkedIn’s filtering allows, it can be nearly impossible to get a clean list for some use cases, including this one.

For example, in LinkedIn, “books and periodicals” is the only category to look for magazines. Putting “magazines” in the search bar eliminates some newspapers, but not books, which also have title= publishers.

Other use cases have much cleaner search outcomes in Sales Navigator, but if not, this can be a real problem.  Cleaning data after the export takes at least 5 to 100x times what it would take to find the right lists for RR in the beginning. This is especially true if the data set is large – WIZA will find and export up to 2500 at a time.

But what a company saves in time finding a really good list in advance to enrich with contacts using RR, they may spend on the backend cleaning off bad contacts,  produced by WIZA, even if the data, including emails themselves, is valid. In short, RR eliminated the “aftershock” of finding bad data that needed to be cleaned in the export list.

Note that WIZA does now allow data uploads, but it still over-reports, adding companies that were not uploaded (!). That did  not take advantage of WIZA’s other strengths – big lists, easily at scale – in this case.

RR still won this criterion if contact names are available. We now use GPT to find job title names first, if the niche is very specific. 

6. Cost

The costs of the two tools were not significantly different compared to the other costs, especially the opportunity cost of NOT having the right data, and the cost of damage to the domain’s quality score if bad data is allowed in.

We considered this criterion a wash, without further context.

Conclusions

In the end, for this use case, we selected to use  RR for most searches. We have yet to solve the over-inclusive issue with WIZA, and customer service did not respond to the “why are you giving me companies I did not upload” issue.

To make it faster for an offshore researcher without a full understanding of the heuristic issues to find them – and use RR to enrich at scale with no duplications with our own list – we decided to use vibe-coding to build a piece of software that took the guesswork out of finding companies and contacts.

First, we imported the current contact/email list, as well as the taxonomy, into Replit‘s Vibe code app builder.

The contractor then exports contacts without email addresses (all auto-saved) and uploads them to RR for a new search.  In this case, better data was worth it – after some adjustments, about 50 to 60% of B2B emails found are valid after running through NeverBounce.

And that’s a wrap.