If you’re in B2B sales or customer success, you know “customer engagement” is both the holy grail and the biggest buzzword. You want hard data, not wishful thinking. This guide is for folks who want to track and analyze B2B customer engagement in Salesforce’s Einstein CoPilot—and actually find insights you can use, not just dashboards to ignore.
I’ll walk you through what works, what’s just marketing fluff, and how to go from “we’re tracking everything” to “here’s what we’re going to do about it.” No vague advice, no endless lists of KPIs that don’t matter.
Why B2B Engagement Is So Hard to Nail Down
Let’s get real: B2B engagement isn’t like tracking web clicks in e-commerce. One “customer” is often a whole buying committee. Signals are subtle. You might be tracking:
- Email opens and replies (and, yeah, those don’t always mean much)
- Product usage (if you have a SaaS or digital product)
- Event attendance or webinar activity
- Support tickets or customer feedback
- Sales call participation
The real trick isn’t collecting these signals—it’s making sense of them and deciding what’s worth your time.
What Einstein CoPilot Actually Does (and Doesn’t)
First, a reality check. Einstein CoPilot is Salesforce’s AI assistant that sits on top of your CRM data. The promise: smarter insights, less grunt work, and easier analysis. Sounds great, right?
Here’s what’s actually useful: - Centralizes engagement data from across Salesforce (emails, calls, meetings, product usage, etc.) - Can surface “insights” or next-step suggestions based on patterns in your data - Lets you ask questions in plain English (or close to it), like “Which accounts are most engaged this quarter?” or “Show me accounts at risk.”
What it can’t do (yet): - Magically know what “good engagement” looks like for your business—someone has to define the signals - Pull in every external data source easily (think: your webinar tool, third-party platforms). It’s possible, but it takes work. - Replace actual judgment. AI is only as good as your data and your follow-through.
If you’re expecting Einstein CoPilot to hand you a foolproof playbook, you’ll be disappointed. If you want to save time sifting through noise and get nudged toward real patterns, you’re in the right place.
Step 1: Get Your Engagement Data House in Order
Before you can analyze anything, you need your data in the right place—and actually trustworthy. Here’s how to get started:
1.1. Decide What “Engagement” Means for You
Don’t just track what’s easy. Figure out what actually signals a healthy customer relationship in your world. For B2B, this might be:
- Key contacts replying to emails (not just opening them)
- Usage of core product features (not just logging in)
- Attending quarterly business reviews (QBRs)
- Submitting feedback or feature requests
Pro tip: Ignore vanity metrics. Ten email opens from the same person don’t mean much. Focus on actions that show real interest or intent.
1.2. Audit Your Existing Data
- Is the right data in Salesforce? Or is some of it stuck in spreadsheets, inboxes, or another tool?
- Are fields being filled out consistently? Or do you have “unknown” and blank values everywhere?
- Is activity being logged automatically, or does it depend on reps remembering to click buttons?
If your data is a mess, Einstein CoPilot can’t fix that. Clean it up first.
1.3. Connect the Dots with Integrations
You’ll get the most out of CoPilot if you connect all your engagement sources:
- Email/calendar: Use Salesforce Inbox or similar integrations to pull in email and meeting data.
- Product usage: If you have a SaaS product, pipe usage logs into Salesforce (via APIs, middleware, or tools like Segment).
- Events/webinars: Sync attendee data with Salesforce, either natively or via connectors.
- Support: Make sure case/ticket data is tied to accounts and contacts.
What to skip: Don’t bother importing every social media like or retweet. In B2B, that’s rarely meaningful.
Step 2: Set Up Einstein CoPilot to Track What Matters
Once your data’s solid, now you can use CoPilot to make sense of it.
2.1. Configure Engagement Signals
You’ll need to work with your admin or Salesforce owner to define what “engaged” means for your team. This might involve:
- Creating custom fields or activity types for key events (e.g., “QBR Attended”)
- Tagging product usage events that matter (not just “logged in,” but “used advanced analytics module”)
- Setting up scoring models (e.g., 10 points for a meeting, 5 for a product feedback submission)
Skip the default engagement score unless it actually lines up with your reality. Many out-of-the-box models are tuned for generic sales cycles, not your customers.
2.2. Train CoPilot to Understand Your Questions
CoPilot works best if you teach it your language:
- Add key terms to your Salesforce glossary (“QBR,” “POC,” “churn risk”)
- Test queries like “Show me accounts that haven’t attended a meeting in 90 days”
- Adjust when the results look off—sometimes you’ll need to tighten up filters or retrain the model
Pro tip: Don’t expect perfect answers. Use CoPilot as a starting point and refine from there.
Step 3: Analyze Engagement—Without Drowning in Reports
It’s tempting to run every report under the sun. Resist. Instead, focus on these core questions:
3.1. Who’s Actually Engaged (and Who’s Not)?
- Look at last meaningful activity by account/contact, not just generic “last touched.”
- Segment by customer tier (e.g., “Top 20 Accounts” vs. “Long Tail”).
- Flag accounts with no engagement in X days—pick a window that makes sense for your cycle.
3.2. What’s Driving (or Killing) Engagement?
- Compare activity before and after key events: onboarding, price changes, feature releases, etc.
- Look for drop-offs: Has usage tanked after a support issue? Are QBRs being skipped suddenly?
- Don’t just count activities—look for changes in behavior.
3.3. Which Activities Actually Correlate with Renewals or Upsells?
This is where the rubber meets the road. Use CoPilot to dig into:
- Which engagement signals show up most often in successful renewals?
- Are there “red flags” that predict churn (e.g., key contact goes dark)?
- Can you spot patterns in accounts that expand—did they attend more trainings, or submit more feedback?
Don’t get lost in noise: Not every activity matters. Look for patterns over time, not one-off events.
Step 4: Turn Insights Into Action (or It’s All Just Dashboard Filler)
Data is useless if nobody does anything with it. Here’s how to make sure insights become action:
4.1. Build Playbooks, Not Just Reports
- If you spot a churn risk pattern (e.g., no product usage in 30 days), set up a playbook: who should reach out, what should they say, what’s the next step?
- If engaged accounts tend to use a certain feature, make sure all new customers are trained on it.
4.2. Automate Smart Nudges—But Don’t Overdo It
- Use CoPilot to trigger alerts for real signals (“Key contact hasn’t replied in 60 days”), not every activity.
- Set up automated tasks or reminders for your team, but leave room for human judgment.
4.3. Share Learnings with the Whole Team
- Don’t keep insights locked up with data folks. Make it easy for sales, success, and product teams to see what’s working.
- Hold regular reviews: “Here are the engagement trends we’re seeing, here’s what we’re testing next.”
What to Ignore (Or, How Not to Waste Your Time)
- Vanity dashboards: If nobody’s acting on a report, kill it.
- AI “insights” with no context: If CoPilot flags something that makes no sense, dig in before panicking.
- Tracking everything: More data isn’t always better. Focus on what changes your decisions.
Wrapping Up: Keep It Simple, Iterate Often
Tracking and analyzing B2B customer engagement in Einstein CoPilot isn’t rocket science, but it does take some discipline. Get your data right, define what matters, and focus on actions—not just pretty charts. Don’t wait for perfect; start simple, see what works, and tweak as you go.
Remember: The goal isn’t to have the fanciest AI dashboard. It’s to actually understand your customers and take action that matters. If you do that, you’re already ahead of most.