Senior Director of Operations

Bridging what the operation needs and what agentic tools can actually do.

Fifteen years leading multi-site call center operations. Quality programs stood up from scratch, sites launched in the Dominican Republic and Belize, 400+ staff across two Fortune 500 companies. Now bringing agentic tools to reporting, analytics and pipeline problems.

See the workGet in touch

Open to operations leadership roles, full-time or contract. Also on LinkedIn.

15+Years leading operations
400+Staff supported
3Sites launched across two countries
5Systems built and running in production

What I built

Each one replaced something.

Quality scoring, campaign analytics, and telesales performance. All three replaced something that was costing the floor hours a week, or costing the client its confidence in a number. Open any of them to see it working.

Quality · Caliber AIEvery eligible call scored against a structured rubric

Caliber was built out of the idea that QA shouldn't be a shot in the dark. Human grading leaves a blindspot that I wanted to solve.

See it working →
Caliber AIProgram A · Telecom
Quality — brand home

Program A / Outbound

8,412 calls scored · 214 agents · 19% sale rate
8,412Calls scoredrolling 30 days
96%Population coveragewas ~2% sampled
19%Sale ratedialer-derived · 1,598
214Agents with scored calls3 sites
31Auto-failsred flag · review queue
32%Compliance rateapplicable items only
Behaviors% of scored calls
Active engagement41%
Ownership52%
Effort reduction28%
Needs discovery17%
Solution framing31%
Customer appreciation58%
Effectively demonstratedPartially demonstratedDid not demonstrate

Needs discovery sits at 17%, the weakest behavior on the board, and every later step leans on it. It also carries the heaviest weight in the rubric — 15 of the 60 behavior points. An agent who never found the need has nothing to tie the offer to.

Complianceapplicable items
ItemYesNoN/A
Products positioned91%6%3%
Products explained84%11%5%
Rate disclosure62%31%7%
Overall bill changes44%9%47%
Monthly recurring charges78%14%8%
Non-recurring charges39%12%49%
Term and agreement71%18%11%

N/A means the item could not apply to that call, so it drops out and its weight renormalizes across the rest. Rate disclosure fails on 31% of the calls where it did apply, which puts it in regulatory territory rather than coaching.

Call-flow steps11 steps · full population
#StepEffectivelyPartiallyDid not demonstrate
1Positive greeting74%21%5%
2Set the agenda66%24%10%
3Discover needs19%36%45%
4Link the need to a product27%41%32%
5Assume the sale48%29%23%
6Engage the objection22%33%45%
7Re-frame and re-close18%30%52%
8Confirm the order57%26%17%
9Deliver compliance63%22%15%
10Order summary34%28%38%
11Close with appreciation69%20%11%

Discovery, engaging the objection, and re-closing are the three weakest steps, and they run back to back. Sort by Did not demonstrate and they surface together at the top. A call that never discovered anything has nothing to re-frame against when the objection lands.

Objectionsclick any count to open those calls
TypeCountHandled effectively
2,24021%
1,38871%
98143%
79437%
75259%
35747%
29833%
23155%
17466%
633%

"Not interested" comes up more than any other objection and gets recovered 21% of the time. Price, which every training deck is built around, is already handled on 71% of calls. The coaching budget is pointed at the objection reps solved years ago.

Productsclick any count to open those calls
ProductPitchedSold
92636
31722
3123
30115
25624
2236
21527
15627
1423
1417

The scorer pulls the offer and the price the agent quoted, so one product shows up at several price points, and as unspecified when nobody named a figure. Those rows convert worst. 312 pitches of an unnamed internet tier returned three sales.

Competitive landscapeclick any count to open those calls
CarrierMentionsAs current providerAs objection basis
92278721
61244025
54745216
19415412
1599623
72642
63532
34233
2825
853

Roughly 85% of competitor mentions are the customer telling you who they are with. About 2% become the basis of an actual objection. Train every rep to "handle the competitor" on every mention and you have spent the coaching hour on the wrong 85%. Carrier labels genericized.

Reconstruction · synthetic data
Analytics · Dialer IntelligenceTwenty million attempts, and where the yield stops paying

A call center's lists are its inventory, and almost nobody can tell you how theirs are performing. The answers were already sitting in twenty million rows nobody had a way to look at.

See it working →
dialer-intelligence19.7M attempts · 2026-02 → 2026-07synthetic data

Attempt fall-off · contacts

No cliff — yield stays above 1 contact/1k through the measured range

42.1K
8.9K
Att 1
27.4K
5.2K
Att 2
18.2K
3.0K
Att 3
11.9K
1.7K
Att 4
7.6K
1.0K
Att 5
4.7K
0.6K
Att 6
DialsContacts

Fall-off detail

Per-attempt numbers · cumulative shares

attdials→contactsratenew/1kreach∑dials∑
142,1008,93021.2%212.151%38%
227,4005,18018.9%158.476%62%
318,2002,98516.4%119.888%78%
411,9001,74014.6%96.695%89%
57,6001,00513.2%80.398%96%
64,70055511.8%66.0100%100%

Goals by attempt

Bars = leads whose first goal landed at that depth

212
Att 12.4%
148
Att 22.9%
96
Att 33.2%
61
Att 43.5%
37
Att 53.7%
22
Att 64.0%

Conversion is the share of that depth's answers that converted — the denominator moves with the bar, which is why the two read in opposite directions.

Goals detail

First goals · conversion by depth

attfirst goalsgoals∑ans convgoal/1k
121237%2.4%5.04
214863%2.9%5.40
39679%3.2%5.27
46190%3.5%5.13
53796%3.7%4.87
622100%4.0%4.68

Attempt outcomes

What each dial hit at each depth · share of dials

Att 1
Att 2
Att 3
Att 4
Att 5
Att 6
ContactedAnswering machineBurnedNo answer & other

Answering machine climbs at every depth while contact falls. The dials past attempt two are not finding fewer people at random — they are landing in voicemail.

Cap planning

If dialing stopped at each depth

cap atkeep reachedkeep goalssave dials
Attempt 151%37%62% (69,800)
Attempt 276%63%38% (42,400)
Attempt 388%79%22% (24,200)
Attempt 495%90%11% (12,300)
Attempt 598%96%4% (4,700)

Keep-goals is the same quantity the Goals detail prints as goals∑, and dials saved is computed against the derived total rather than by summing the rows past the cap — the curve is truncated at the 99th percentile of depth, so summing undercounts.

Reporting · Performance IntelAttainment against a goal that moves during the month

Directors and supervisors were spending their week assembling numbers instead of acting on them. I built one place to look, so that compiling wasn't anyone's job anymore.

See it working →
Homes5,840goal 6,025 · 96.9%
Hours13,827plan 14,175 · 97.5%
SPH0.422plan 0.425 · 99.4%
Mobile1,101goal 964 · 114.2%
Unit7,170goal 7,231 · 99.2%
Executive Summary

Business-wide: 5,840 of 6,005 homes sold (97%). 3 business days remaining, month is 86.4% elapsed. Operations are ahead of pace.

Strongest program: Premium Bundle at 103% to goal. Needs attention: Shared Plan at 62% to goal.

Site A: 4,430 sales, 0.426 GPH across 10,403 hours. Site B: 1,410 sales, 0.412 GPH across 3,424 hours.

Projected EOM: 6,762 homes (113% to plan).

Daily rates — Homes: 307/day (need 273/day — on pace). Internet: 182/day (need 169/day — on pace). Mobile: 58/day (need 44/day — on pace). Units: 377/day (need 328/day — on pace).

Every figure above divides by the plan of record — the goals as written on day one. The KPI strip and the gainshare panel divide by the goal those plans became once a funder burned past its planned hours. So homes reads 6,005 here and 6,025 there, and one program leads this list on its plan while placing third on its goal. Both readings are correct and neither is meant to reconcile with the other: one is what was promised, the other is what is being measured.

Goals vs Plan — Business Wide

Pacing — Business Wide

Fiscal 2026-04-22 → 2026-05-21 · data through 2026-05-18

19Days In
3Days Left
22Total BDays
Month elapsed86.4% of fiscal month
Total Hours13,827of 14,175 plan
98%to date
348remaining
116.0per day
PROJ. EOM16,010
113% of plan▲ +1,835
Total Homes5,840of 6,005 plan
97%to date
165remaining
55.0per day
PROJ. EOM6,762
113% of plan▲ +757
Total Units7,170of 7,207 plan
99%to date
37remaining
12.3per day
PROJ. EOM8,302
115% of plan▲ +1,095
Internet3,459of 3,724 plan
93%to date
265remaining
88.3per day
PROJ. EOM4,005
108% of plan▲ +281
Mobile Lines1,101of 961 plan
115%to date
+140 over plan
PROJ. EOM1,275
133% of plan▲ +314

Plan on these cards is the plan of record — the number written on day one. The KPI strip above divides by the goal, which is what the funder gate turned that plan into once a funder burned past its planned hours. Homes therefore reads against 6,005 here and 6,025 there, on the same actual. That gap is the point, not a rounding error.

Gainshare — Business WideOverall Table

Net Bonus-0.75%
Proj. Bonus+0.85%▲ 1.60% gain at pace
Mobile Attainment
114%proj. 118%

37 lines to next tier (+2.60%)

> 125%1,215+3.50%
118–125%1,138+2.60%
111–117%+1.75%
104–110%+0.90%
96–103%0.00%
89–95%-0.90%
82–88%-1.75%
75–81%-2.60%
< 75%-3.50%
Internet Attainment
93%proj. 95%

128 internet to next tier (0.00%)

> 125%4,708+3.50%
118–125%4,409+2.60%
111–117%4,147+1.75%
104–110%3,886+0.90%
96–103%3,5870.00%
89–95%-0.90%
82–88%-1.75%
75–81%-2.60%
< 75%-3.50%
Cost Per Attainment
99%proj. 102%

351 units to next tier (+0.20%)

> 125%9,112+0.90%
118–125%8,533+0.70%
111–117%8,027+0.45%
104–110%7,521+0.20%
96–103%0.00%
89–95%-0.20%
82–88%-0.45%
75–81%-0.70%
< 75%-0.90%
SPH Attainment
99%proj. 99%

273 sales to next tier (+0.20%)

> 125%7,406+0.90%
118–125%6,935+0.70%
111–117%6,524+0.45%
104–110%6,113+0.20%
96–103%0.00%
89–95%-0.20%
82–88%-0.45%
75–81%-0.70%
< 75%-0.90%
Hour Gate
98%proj. 113%

348 hrs to clear gate

≥ 100.0%14,1750%PROJ.
< 99.9%-1.60%

Band boundaries and bonus values are perturbed — the ladder's shape is the real one, its numbers are not. The overall table's hour gate is a flat penalty below 100%, so it reads as two rows rather than the site table's four bands. Purple marks a projection, never a current state. Projecting re-runs the funder gate at projected hours: all three funders cross, so every goal grows with them and all four metrics land back in the band they already occupy — no projected row is drawn on any of them. The gate itself is the exception, and its recovery is the whole projected swing.

Daily Targets — Combined

Required per day to finish on goal · 3 business days remaining

TARGET
ProgramHoursHomesInternetMobile
PlanActual/ DayRemPlanActual/ DayGPHPlanActual/ DayPlanActual/ Day
New Household · RetailAC-1140, AC-1142 · New Business5,9505,808481422,5752,477330.691,5961,512284124024
New Household · DirectAC-1141 · New Business1,6001,602+2608611+3377384+797941
Mobile PropensityMX-1215, MX-1217 · Installed Base4,2754,394+1192,0732,133+601,2861,08866332461+129
Shared PlanMX-1216 · Installed Base1,100706132394374231480.3623223116078+18
Premium BundleUT-1194 · Revenue Uplift1,2501,317+67395388324524416366+3
TOTAL14,17513,8271803486,0255,840840.533,7363,459969641,101+137

A / Day reading of +N means the metric is already past plan — red on Hours, where overage is cost, green everywhere else. TOTAL / Day adds up the figures in the column above it, counting a program that is already past plan as zero rather than as credit against the ones still owing: finishing early on one campaign does not reduce what another campaign owes tomorrow. TOTAL Rem is the one column that does net, because it reports a single combined balance.

Program attainment — ranked on the gate-corrected goal

#ProgramHours PlanHours ActualHomes GoalHomes Actual% to Goal
1Mobile Propensity4,2754,3942,0732,133102.9%
2New Household · Direct1,6001,602608611100.5%
3Premium Bundle1,2501,31739538898.2%
4New Household · Retail5,9505,8082,5752,47796.2%
5Shared Plan1,10070637423161.8%
TOTAL14,17513,8276,0255,84096.9%

Premium Bundle leads the Executive Summary's ranking at 103% and places third here at 98.2%. Nothing about the campaign changed between the two lists. It is the only program whose funding source burned past its planned hours, so it is the only one measured against a goal that grew — 375 homes promised, 395 homes measured, the same 388 delivered. The other four are ranked against goals identical to their plans, which is why their two positions agree.

Reconstruction · synthetic data

All three are reconstructions filled with synthetic data. No real agent, customer, client, or caller information appears anywhere on this site.


Case studies

The arguments behind them, in full.

All work

How it gets built

One operations director, five production systems.

Not a methodology I invented, just a way of working I settled into. Writing the code was never the constraint. Knowing what "correct" means on a Saturday shift is the part no tool ships with.

01

Start at the data

It starts with the data, or with a question about the data. The instinct for wide, awkward datasets came from years of building campaign trackers by hand, long before any of this was software.

02

One feature at a time

A goal stated in a sentence, then the problem worked one question and one dataset at a time. Slowing down to plan out exactly what I want has consistently beaten starting to build and finding out.

03

Check it against what I know

The math is checkable because I've computed these numbers by other means for years. Dated specs, plans and validation logs come out of the tooling rather than a process I designed, but I keep them. In six months nobody remembers why a rule exists, including whoever wrote it.

Let's talk about building together.

Open to operations leadership and applied-AI work, full-time or contract. Based in Miami, happy to work anywhere.

What's this about?

Or email hello@joshuaedgecomb.com, or find me on LinkedIn.