Level Zero Solvable
Definition
Level Zero Solvable
Level zero solvable (LZS) is the share of customer issues a user could have resolved alone, through a knowledge base, a chatbot, or an account portal, before contacting a live agent. It marks the deflection ceiling of your entire customer experience.
The metric grew out of tiered support models, where “level zero” names the pre-agent layer: everything a customer settles without human help.
A high LZS score means your help content is doing the heavy lifting. A low score means agents keep answering questions your website should already answer for them.
Most contact centers treat LZS as a diagnostic rather than a target. It is a signal — not a scoreboard — and it points straight at where your knowledge base, product design, or in-app messaging fails to guide the customer.
Key takeaways
- LZS measures the percentage of customer contacts that could have been self-served.
- Teams calculate it through ticket sampling, internal testing, or post-contact surveys.
- A rising score means your knowledge base is beating live agents on cost per resolution.
- LZS complements first contact resolution and customer satisfaction scoring; it never replaces them.
- Ownership usually sits with the client’s customer experience lead, with the outsourcing partner supplying the data.
How it works
You calculate level zero solvable by sampling closed contacts and asking one question: could the customer have solved this using the self-help you already publish? Analysts tag every contact solvable or unsolvable, then divide the solvable count by total contacts.
Altura Communication Solutions, a contact center technology integrator, argues in its LZS explainer that the score gives you three things: the deflection ceiling, the quality gap in current self-help, and the priority order for knowledge base spend.
A Harvard Business Review analysis of 2017 service data reported that 81% of customers try to resolve matters themselves before reaching a live representative. That habit is why the deflection ceiling matters.
The arithmetic is simple:
| Input | Description |
|---|---|
| Sampled contacts | a representative slice of tickets or calls over a set period, usually 30–90 days |
| Solvable tags | contacts answerable through existing FAQ pages, knowledge base articles, or automated flows |
| LZS score | (solvable contacts ÷ total contacts) × 100 |
| Review cadence | quarterly on a 200–500 ticket sample for most mid-market teams |
A 2023 industry benchmark places typical LZS scores between 20% and 45% for mid-market SaaS support teams. That band means roughly a third of live agent conversations were technically avoidable, so the sampling work usually pays for itself inside one quarter.
Sampling method matters more than sample size. Pull contacts across weekdays, weekends, and peak billing dates — or you will score a skewed week and mistake seasonality for a content gap. Two reviewers scoring the same batch keeps the tagging honest.
Score alone changes nothing. Rank the solvable contacts by volume, fix the top five reasons in your help center, then resample the same period next quarter. The movement between the two scores is the number your finance team actually cares about.
The term travels under a few names. Some vendors call it deflection potential or self-service opportunity, and the arithmetic barely changes between them. What matters is that everyone in the room agrees which contacts count as solvable.
Examples
LZS shows up in four shapes across the Business Process Outsourcing (BPO) sector: retail, software, telco, and insurance support. Each team uses the score the same way, redirecting budget from headcount toward self-service assets.
- E-commerce returns: a US apparel brand sampled 2,000 chat contacts in early 2024 and found 38% asked about return windows already published on the product page. That is roughly 760 tickets in one sample. Rewriting the returns FAQ cut agent contacts by 22% the next quarter.
- SaaS onboarding: a Manila-based call center supporting a European fintech logged an LZS score of 41% on tier-1 tickets. Password resets, invoice downloads, and two-factor setup dominated. A searchable help center pulled the score to 27% within six months.
- Telco billing: an Australian carrier ran LZS analysis in 2023 and found 30% of billing calls were “where do I find my invoice.” An in-app banner solved most of them within eight weeks.
- Insurance claims: a regional insurer used LZS scoring to justify a chatbot build, showing that 33% of claim status calls could be answered by a self-service status lookup. The score turned a soft hunch into a funded project.
The pattern repeats. In each case the fix was cheap, the finding was uncomfortable, and the savings landed on the client’s side of the contract — not the provider’s. That is why a good outsourcing partner reports LZS honestly instead of burying it.
Related terms
LZS sits inside a wider vocabulary of support metrics. The cluster below covers the scores that appear beside it on a customer experience (CX) scorecard or an outsourcing tender. It stops at pure staffing terms, which measure supply rather than demand.
- First Contact Resolution: the share of issues closed in a single interaction, the operational cousin of LZS.
- Customer Satisfaction: the sentiment score that self-service quality moves directly.
- Chatbot: the automation layer that turns a theoretical LZS ceiling into real deflection.
- Agents: the support staff whose queue LZS is designed to shrink.
- Call Center: the delivery site where sampled contacts are usually logged and tagged.
- Business Process Outsourcing: the delivery model under which most LZS scoring work is contracted.
FAQ
What does LZS stand for?
LZS stands for level zero solvable. The “zero” points at the pre-agent tier of support, everything a customer resolves without a human on the other end. Some teams write it out in full on scorecards to avoid confusion with level one triage.
How is level zero solvable calculated?
Sample a batch of closed tickets, flag each one solvable or unsolvable against your existing self-help content, then divide solvable by total contacts. Most teams run this quarterly on a 200–500 ticket sample. Keep the tagging rules identical between runs.
Is a higher LZS score better?
Not on its own. A high score means self-service could carry more volume, but it also proves customers are still calling instead of self-serving. The goal is closing that gap through better discoverability.
Who owns LZS in an outsourced support setup?
Usually the client’s CX operations lead, with the outsourcing provider supplying raw ticket data. In mature partnerships the provider’s analytics team scores the tickets directly. Write the scoring method into the statement of work either way.
How does LZS differ from first contact resolution?
First contact resolution (FCR) measures how efficiently agents close an issue once contacted, while LZS measures whether that contact should have happened at all.
Explore vetted customer service partners in the Outsource Accelerator directory to benchmark your own LZS scoring.
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