Key Takeaways
The quarterly survey came back fine. Average effort scores were steady, and a few comments mentioned slow responses. Carmen's team almost filed it. Then an analyst pulled a different view: every account that had opened a ticket within 14 days of a previous one on the same topic. The list had 60 accounts. Most of those customers had searched the help center first, found nothing useful, and filed a ticket. A third of the repeat tickets were about the next step after a problem support had already solved. None of that was in the survey.
That's the gap in most advice about customer journey friction points. It treats friction as something to ask customers about. For a B2B customer operations team, most friction is already in the records: who contacted you twice, who got transferred, who searched first. This post covers what the friction research actually measured, where friction sits in your data, and how to fix it.
What the friction research measured
Most friction guides rest on one study. A 2010 HBR article surveyed “more than 75,000 B2C and B2B customers about their recent service interactions in major nonface-to-face channels, including live phone calls, voice prompts, web, chat, and e-mail.” Its headline was that reducing effort matters more than delighting customers.
Two details matter for B2B teams. First, the study measured intent. It defined loyalty as “customers' intention to continue doing business with a company, increase their spending, or say good things about it.” Second, the sample mixed consumers with business customers across contact-centre channels.
The widely quoted figure that 96% of high-effort customers become disloyal comes from the same research. CEB's 2010 press release said “96 percent of customers who put forth high effort to resolve their issues are more disloyal.” That's stated intent, not observed churn.
When researchers later tested feedback metrics against what customers reported two years on, effort didn't hold up. One study in the International Journal of Research in Marketing surveyed a sample “representative of the Dutch consumer market” and followed up on self-reported retention. The researchers found “the CES does not seem to have a strong impact in general.” That's consumer data too. The fair summary: effort predicts what customers say after a service contact. It hasn't been shown to predict whether B2B customers renew. Our post on customer effort score goes deeper on that metric.
What survives: the friction types
The survey outcome is weak evidence. The friction types CEB identified are still useful, because they describe what customers go through. Customers resent “having to contact the company repeatedly (or be transferred) to get an issue resolved, having to repeat information, and having to switch from one service channel to another.”
The same study found service interactions lean negative. “Customers are four times more likely to leave a service interaction disloyal than loyal.” It also found satisfaction a weak guide on its own: 20% of satisfied customers said they intended to leave, and 28% of dissatisfied ones intended to stay. So a good average score doesn't mean the friction isn't there. It may just not be in the survey.
The friction types themselves were common. CEB reported “56% report having to re-explain an issue 57% report having to switch from the web to the phone 59% report expending moderate-to-high effort … 59% report being transferred 62% report having to repeatedly contact the company.”
Newer data shows little change in one of them. Salesforce's 2023 survey found 56% of customers often have to repeat or re-explain information to different representatives. That survey pooled consumers and business buyers.
The friction ledger
Each friction type leaves a trace in records a B2B team already keeps. Here's where to look, and what usually fixes it. The last two columns are our recommendations.
| Friction type | What customers reported (CEB 2010) | Where it already sits in your data | What usually fixes it |
|---|
| Repeat contact | 62% contacted the company repeatedly | Same account and topic, reopened within 7 to 14 days | Resolve the issue and the likely next question |
| Transfer | 59% were transferred | Requests with more than one assignee or team | One owner per issue, with help brought in |
| Re-explain | 56% had to re-explain | Requests where the customer repeats earlier details | Conversation history on the account record |
| Channel switch | 57% switched from web to phone | Tickets opened after a help-center session | Fill the content gap; add a no-dead-end handoff |
| Downstream issue | 22% of repeat calls | A new request on a related topic after one was solved | Answer the next question in the first response |
Source for the percentages: HBR, 2010. The sample mixed consumers and business customers.
Where friction hides in B2B service data
Repeat contacts, not ticket counts
Ticket volume on its own isn't a friction measure. One B2B SaaS case study found a relationship score built from “customer interactions and support tickets” was a strong churn predictor, with a low score signalling a lack of engagement. That's a single company in a master's thesis, so treat it as a caution, not a rule. Some ticket volume is healthy engagement.
Repeat contacts on the same issue are different. CEB recommended counting them directly: “Tracking repeat calls within a specified period (we recommend seven to 14 days) is not only easier than measuring FCR but also casts a wider net.” It warned that “tracking repeat calls instead of using FCR inevitably makes performance appear worse.” That's the point. The number shows friction the first-contact metric hides.
Self-service that ends in a ticket
CEB found “fifty-seven percent of inbound calls came from customers who went to the website first.” Every one of those is a customer who tried to help themselves and couldn't. For a B2B team, that's a content gap with a name and a timestamp.
Knowledge-Centered Service warns against counting self-service like tickets. It notes “we can count activity like searches and page views and sessions, but they don't have a one-to-one relationship to issues pursued and resolved.” One of the measures it suggests instead is how often self-service is used before a case is opened.
KCS also offers a rule of thumb: if people “find helpful information 40-50% of the time, they are likely to use self-service again.” It attributes that to Service XRG research, not its own benchmark. And it sets a design rule: “No dead ends” ensures there is a smooth way to transition from self-service to assisted support. Our guide to the customer success and support handoff covers the handoff side.
The next question after a solved one
This is the friction type most teams miss. CEB found “22% of repeat calls involve downstream issues related to the problem that prompted the original call, even if that problem itself was adequately addressed the first time around.” The first ticket looks resolved. The customer comes back anyway, because the next step wasn't covered.
CEB also found “twenty-four percent of the repeat calls in our study stemmed from emotional disconnects between customers and reps.” The customer didn't trust the answer, so they asked again. Both are fixable in content: answer the likely next question, and explain why the answer is what it is.
Where friction sits at renewal
Friction isn't only in support. The renewal process has its own. TSIA's 2021 survey found auto-renewal improved renewal rates for the long tail by 11.5%, meaning small-dollar, high-volume accounts. TSIA didn't publish the sample size. It still suggests that removing steps from the renewal itself matters for smaller accounts. Our guide to why SaaS churn is higher than it should be covers the usage signals that come before renewal.
How to find customer journey friction points this month
- Pull repeat contacts. List accounts with two or more requests on the same topic within 14 days over the last quarter.
- Pull search-then-ticket. List tickets opened within an hour of a help-center session. Note what the customer searched for.
- Count transfers. List requests that changed owner or team more than once.
- Tag downstream issues. For repeat contacts, mark whether the second request was the same problem or the next step.
- Fix the top five. For each, write the missing article, add the next-step answer, or name a single owner. Then re-run the same pulls next month.
You won't get a friction score out of this. You'll get a list of specific problems, each with a fix, and a way to check whether the fix worked. That's more useful than an average effort rating.
Keeping friction visible in one place
Most teams can't run those pulls easily. Tickets live in the help desk. Search logs live in the help-center tool. Account context lives in the CRM. Joining them takes an analyst and a week.
MatrixFlows keeps them together. Customer requests, accounts and knowledge articles are linked tables in Matrix. App analytics show what people searched for in the help center and what the AI assistant answered, next to the conversations that followed in the Inbox. A view can list repeat requests by account and topic. When the team writes a missing article, the help center and assistants start using it straight away.
Back to the 60 accounts. The survey said effort was steady. The records showed where customers were struggling and what to fix first.
Create a Free Workspace → and pull your repeat contacts and search-then-ticket list this week.
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