Every COD confirmation operation eventually hits the same wall. Order volume grows, the confirmation backlog grows with it, and the only lever anyone seems to pull is hiring more agents. Payroll rises, training never stops, quality varies from shift to shift, and the cost per confirmed order barely moves.
This guide is about the other levers. It is written for call-center directors, delivery companies that offer confirmation as a service, and e-commerce operators in Morocco, Algeria, Tunisia, Egypt and the Gulf who want to handle the same — or more — confirmation volume with a smaller, better-paid, more stable team.
We are deliberately careful with numbers. You will not find "cut your team by 50%" here. You will find a method to measure where your agents' time goes, the changes that typically recover it, and an illustrative model you can fill with your own data.
Step 1: measure where agent time actually goes
You cannot reduce headcount responsibly without knowing what your agents do all day. Take a representative week and split each agent's logged-in time into four buckets:
- Talk time — actual conversation with a customer.
- Dial and wait time — dialing, ringing, voicemail, busy tones, no-answers.
- After-call work — typing the status, notes, address corrections, switching screens.
- Idle / unavailable — waiting for the queue to refill, breaks, technical issues.
Most phone systems and call-center tools can export call logs with start time, answer time and end time. If your agents use personal phones or click-to-call from a spreadsheet, estimate with a stopwatch on a sample of 50–100 attempts per agent. Rough numbers are fine; the goal is to see proportions.
What you will usually discover in a COD confirmation center:
- Talk time is the minority of the shift.
- Dial and wait time — especially no-answers — is a large chunk.
- After-call work is bigger than expected because statuses live in a different tool than the call.
- Idle time spikes at the start of shifts and when the order flow from ads slows down.
Each bucket has a different fix.
Step 2: remove dial and wait time with parallel dialing
The single largest recoverable bucket is usually dial and wait time. A predictive dialer launches several calls in parallel and only connects an agent to customers who answered. No-answers are logged automatically, invalid numbers are detected and classified, and the waiting happens on the machine's side.
In Speedliv, the agent presses "Start work" and a customer is already on the line; when the call ends and the agent presses "Next", the next answered customer is usually connected. The dialer can launch many calls at once — for example up to 20 — with the intensity tuned per team.
This is the change that most directly affects headcount, because it converts waiting time into conversation capacity. Our predictive dialer guide covers sizing and pitfalls in detail.
Step 3: shrink after-call work by putting the call and the order in one place
After-call work grows when the agent must:
- copy the outcome from the phone tool into a spreadsheet or a separate order system,
- look up the order on another screen during the call,
- type the same note in two places,
- message the hub on WhatsApp to tell them an order is confirmed.
When the confirmation queue, the call and the parcel record are the same system, the status is written once and the hub, the driver app and inventory see it immediately. That is a core design choice in Speedliv: the call center sits on the same parcel record as shipping, COD settlement and warehouse.
Practical tips:
- Use standardized statuses with clear meanings: confirmed, cancelled (with reason), no answer, callback at a time, out of zone, wrong address, invalid number. Free-text notes take longer and are harder to report on.
- Pre-fill what you can. Product, price, address and previous attempts should be on screen when the call connects.
- Permission transitions. Not every agent needs to be able to move an order to every status; fewer choices means faster, more consistent work.
Step 4: fix idle time with routing and scheduling
Idle time between orders is a planning problem. It shows up when:
- all agents start at 9:00 but most orders arrive in the evening after ad campaigns,
- a team dedicated to one merchant or country sits idle while another team drowns,
- callbacks are scattered instead of being returned to the right team at the right time.
Call routing (distribution) solves the second and third points: organize teams by country, language, merchant or shift, and let the system distribute work and callbacks. Supervisors can then move agents between queues when one market spikes. For the first point, align shift start times with your order arrival curve — your order timestamps tell you exactly when volume arrives.
Step 5: stop wasting attempts on orders that will never be confirmed
Some orders are not worth multiple human attempts:
- Invalid numbers — classified automatically by the dialer, then sent to a correction path.
- Duplicates — the same customer ordering twice from two ads.
- Obvious fakes — test orders, insults in the name field, impossible addresses.
Filtering these before they reach agents reduces load without touching real customers. AI-assisted confirmation in Speedliv helps flag suspicious or incomplete orders so agents spend their time where a conversation can actually change the outcome.
Step 6: use recordings to raise first-call resolution
Every time an agent fails to close a confirmation on the first connected call — because they forgot to confirm the size, did not check the address, or could not answer a question — that order comes back, costing another attempt. Call recording lets supervisors listen to calls that ended in callbacks or cancellations, find the recurring gaps and fix the script or training. Fewer repeated calls means fewer agents needed for the same volume.
The illustrative headcount model
Here is a simple model you can apply to your own measurements. It is an illustration, not a measured Speedliv result.
Inputs:
- N = current number of confirmation agents
- W = share of agent time spent on dialing, waiting and no-answers (measured in step 1)
- R = share of that waiting time the dialer absorbs
Model:
- Talk-capable time per agent today = 1 − W
- Talk-capable time with dialer = (1 − W) + W × R
- Agents needed for the same number of conversations = N × (1 − W) / [(1 − W) + W × R]
Example (assumptions stated): N = 20 agents, W = 40%, R = 70%.
- Today: 60% of the shift available for conversations.
- With dialer: 60% + 28% = 88%.
- Agents needed for the same conversations: 20 × 0.60 / 0.88 ≈ 13.6.
If you also reduce after-call work (step 3) the gain compounds; if your W is lower — say 20% — the gain is smaller (20 × 0.80 / 0.94 ≈ 17). That is exactly why measuring first matters.
What the model ignores, and you should not:
- Breaks and fatigue. More conversations per hour is more intense; plan breaks.
- Peaks. Size for your busy hours, not your average.
- Ramp-up. Agents need a few days to adapt to a continuous flow.
- Attrition and absences. Keep a buffer.
Try the interactive version on our call center software page.
Reduce headcount — or grow without hiring?
In practice, many operations do not fire anyone. They stop hiring while volume grows, let natural attrition do the rest, and reinvest part of the savings into better pay for the best agents, supervisors and quality control. Others use the freed capacity to add services: upsell during confirmation, a second language team, or confirmation for a new country.
Whichever route you choose, communicate with your team. Agents who understand that the dialer removes the boring part of the job — waiting — tend to adopt it faster.
What not to do
- Don't cut before measuring. If you remove agents first and then discover your W was low, you will create backlogs.
- Don't push the dialing ratio to compensate. Customers who answer and hear silence lose trust in the merchant.
- Don't track only calls per hour. Track confirmation rate, delivered rate (orders that actually got delivered), and cost per confirmed or delivered order.
- Don't promise AI will replace the team now. In Speedliv, AI assists confirmation today; AI voice agents that handle a larger share of calls and escalate to humans are planned for phase 2 and are not live yet.
KPIs to follow before and after
| KPI | Why it matters |
|---|---|
| Talk time share | Direct measure of recovered waiting |
| Conversations per agent per hour | Capacity per agent |
| Time from order to confirmation | Speed, affects same-day dispatch |
| Confirmation rate | Must not drop when you go faster |
| Delivered rate of confirmed orders | The real business outcome |
| Cost per confirmed order | Payroll + telephony ÷ confirmed orders |
| Callbacks per order | First-call resolution |
A 30-day plan
- Week 1: measure time buckets; define clean statuses and retry rules.
- Week 2: pilot the predictive dialer with one team, conservative ratio; enable recording.
- Week 3: tune ratios; set up routing by country/merchant; review recordings of callbacks and cancellations.
- Week 4: compare KPIs with week 1; decide staffing for next month (hiring freeze, reallocation, or new service).
If you want to go through this with your own data, book a meeting with us or message us on WhatsApp at +212 661-962932.
