The morning after I connected Sales Navigator to my Dynamics 365 trial, the Match Data Report delivered its verdict: 0% of accounts matched, 0% of contacts, 0% of leads. Zero. If you saw that number on a production org you’d assume the integration was broken. It wasn’t — and twenty-four hours later, without my touching a thing, the same report read 100% of accounts and 69% of leads. The contacts stayed at 0%, and they always will, for a reason worth understanding. This post is about reading that report like a diagnostician instead of panicking like a project sponsor.
If you haven’t met the Match Data Report yet, it lives on the CRM sync Summary page and regenerates every 24 hours — I walked through the page itself in What Actually Syncs?. Today we go one level deeper: what the percentages mean, why they’re low, and the three levers that raise them.
Why the number matters more than it looks
Matching is the load-bearing wall of the whole integration. Auto-save only imports records it can match. Activity write-back only lands on matched records. Data validation only flags matched contacts. The embedded profile widget matches per-record on the form, but everything that happens in bulk, in the background flows through this report. An unmatched contact isn’t slightly less useful — it’s invisible to every time-saving feature you turned the integration on for. A 40% match rate means you bought a tool for 40% of your database.
Step 1 — Read the Report Without Flinching
Three numbers, three different stories. Accounts at 100%: companies are easy — Sales Navigator matches them on name, website, and industry, and real companies exist on LinkedIn. Leads at 69%: my trial’s lead records carry real-looking names and companies, and LinkedIn found plausible matches for eleven of sixteen. Contacts at 0%: my contacts are Microsoft demo characters with @treyresearch.net email addresses. They don’t exist on LinkedIn because they don’t exist at all. Your production numbers will tell different stories, but the diagnostic method is the same: each percentage is a statement about the data you’re feeding the matcher, not about the matcher.
Step 2 — Download the Evidence
Don’t guess at why records failed — the report will tell you. The Sample unmatched data dropdown exports the actual unmatched records, and when you open that file the pattern is almost always embarrassing in its simplicity: empty email columns, first-and-last names crammed into one field, job titles that say “Decision Maker” instead of anything a human puts on LinkedIn, companies stored as abbreviations. The matcher isn’t failing; it’s being asked to identify people from records that barely describe them.
Step 3 — Know What the Matcher Actually Looks At
On the CRM Mapping page, change the view from All fields to Fields for matching and the mystery evaporates: for contacts, matching runs on first name, last name, email address, and the address fields. That’s the priority list for your data-hygiene sprint, in order of impact. Email is the heavyweight — names collide, work emails don’t. If your org stores emails or names in custom columns, this is also where the Edit button earns its keep: remap the Sales Navigator field to the column where your data actually lives. A perfect matching engine pointed at the wrong column scores zero.
Step 4 — Don’t Forget to Match Your Sellers
Records aren’t the only thing being matched. The CRM User Match column in the Summary page’s user table tells you whether Sales Navigator knows which Dynamics user each seller is — and it needs to know, because activity write-back attributes InMails and calls to a CRM user. The match runs on email address, so a seller whose LinkedIn login email differs from their Dynamics primary email shows “Not Matched” and their activities have nowhere to land. The fix lives here on the User tab of CRM Mapping. In my trial, my LinkedIn identity and the trial’s admin account will never share an email — a limitation worth knowing if you’re evaluating on a trial tenant like I am.
The Repair Playbook, In Order of Payoff
- Fill emails first. One populated work-email column moves the contact match rate more than every other fix combined.
- Split mangled names. “Baker, Alex” living in the first-name column defeats the matcher. A one-time Power Automate cleanup or a bulk edit pays for itself overnight — literally, since the report regenerates nightly.
- Remap custom columns. If matching fields point at empty standard columns while your data sits in custom ones, Edit the mapping. Five minutes, massive effect.
- Fix your sellers’ user match. Ten seconds per user, and write-back starts attributing correctly.
- Re-check tomorrow, not in five minutes. The report is a nightly batch. Make your fixes, close the tab, and read the verdict with tomorrow’s coffee.
⚠️ Important: a low match rate on a trial with demo data is expected — fictional people don’t have LinkedIn profiles. Don’t evaluate the integration’s matching quality against Contoso characters; seed a handful of real-company test records if you need a realistic read before buying.
Wrapping Up
The Match Data Report went from 0% to 100% on accounts overnight not because anything was fixed, but because the batch hadn’t run yet — and my contacts will sit at 0% forever because the records describe people who don’t exist. Between those two extremes sits every production CRM I’ve ever seen: a match rate that is really just a data-quality score wearing a LinkedIn badge. Read the report, download the unmatched sample, feed the matcher the fields it eats, and match your sellers. The integration was never broken. It was hungry.
