Selected work

Three systems, and the operational problems behind them.

Each started as something broken on a floor. A quality program that only ever reviewed the calls that worked. Contact rates falling at every attempt with no evidence for why. Numbers that took an hour to defend in a meeting. All three ran in production on a Fortune 500 program, and the interfaces below are reconstructions built to the real design specs and filled with synthetic data.

Three tools, one mindset.

Quality · Caliber AI

Every call scored, not a sample.

An effective QA reviewer needs about two hours to score one hour of calls, and that capacity goes to validating sales. The blindspot it leaves is the shorter calls and the opportunities that never closed. This scores every call instead, and the scorer is built so it can't invent what it didn't see.

Node.js · Ollama · MSSQL · PHP

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 engagement	41%
Ownership	52%
Effort reduction	28%
Needs discovery	17%
Solution framing	31%
Customer appreciation	58%

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

Item	Yes	No	N/A
Products positioned	91%	6%	3%
Products explained	84%	11%	5%
Rate disclosure	62%	31%	7%
Overall bill changes	44%	9%	47%
Monthly recurring charges	78%	14%	8%
Non-recurring charges	39%	12%	49%
Term and agreement	71%	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 · click a header to sort

#	Step	Effectively	Partially	Did not demonstrate
1	Positive greeting	74%	21%	5%
2	Set the agenda	66%	24%	10%
3	Discover needs	19%	36%	45%
4	Link the need to a product	27%	41%	32%
5	Assume the sale	48%	29%	23%
6	Engage the objection	22%	33%	45%
7	Re-frame and re-close	18%	30%	52%
8	Confirm the order	57%	26%	17%
9	Deliver compliance	63%	22%	15%
10	Order summary	34%	28%	38%
11	Close with appreciation	69%	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

Type	Count	Handled effectively
not interested	2,240	21%
price	1,388	71%
need	981	43%
timing	794	37%
competitor	752	59%
satisfaction	357	47%
vague	298	33%
spouse	231	55%
contract	174	66%
deferred	6	33%

"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

Product	Pitched	Sold
Mobile Unlimited @ $0/mo	926	36
Premium channel @ $7/mo	317	22
Internet — unspecified tier	312	3
Mobile Unlimited @ $45/line	301	15
Mobile — unspecified	256	24
Mobile Unlimited @ $15/line	223	6
Higher-tier internet @ $25/mo	215	27
Unlimited data @ $30/mo	156	27
Higher-tier internet — unspecified	142	3
Mobile Unlimited — unspecified	141	7

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

Carrier	Mentions	As current provider	As objection basis
National wireless · A	922	787	21
National wireless · B	612	440	25
National wireless · C	547	452	16
Regional cable · A	194	154	12
Regional cable · B	159	96	23
Prepaid MVNO · A	72	64	2
Prepaid MVNO · B	63	53	2
Fiber overbuilder	34	23	3
Satellite TV	28	25	—
unknown / unparsed	8	5	3

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.

47/100Call score · recomputed live

John SmithAgent · Site 02 · Sup: A. Morales

2026-07-07105 lines · caller id withheld

No sale — price objectionOutcome · from dialer disposition

PassRed flag ·

Coaching summary — incomplete discovery, generic pitch

Agent opened with name usage (line 1) and set the agenda (lines 8–12), but missed the cost pillar despite the customer volunteering bill detail (line 21). Product tied to usage (lines 50–54) but delivered generically. Customer objected on price (line 73); agent acknowledged but never uncovered the reason, leading to no sale. Closing compliance delivered (lines 85–91) with no order summary.

What worked

Used the customer's name early (line 1) — pre-call prep evident
Built rapport during discovery (line 34)

Opportunities

Discovery missing the cost pillar (line 21) — ask for the current bill
Pitch not tied to the stated problem (lines 17–20)

Critical

Pricing figure misstated (line 78) — compliance exposure
Required rate disclosure omitted

Composite

Behaviors17.5 / 60

Compliance24.0 / 30

Call-flow steps5.0 / 10

Change any override below and the composite recomputes here. Set every compliance item to N/A and its 30 points renormalize across the other two sections rather than penalizing the agent.

Behaviors6 items · weighted to 60

Behavior	Wt	AI rating
Active engagement	10	Partially demonstrated
Ownership	10	Partially demonstrated
Effort reduction	10	Did not demonstrate
Needs discovery	15	Did not demonstrate
Solution framing	10	Partially demonstrated
Customer appreciation	5	Partially demonstrated

Compliance7 of 11 shown · N/A drops out

Item	AI rating	Override
Products positioned	Yes
Products explained	Yes
Rate disclosure	No
Overall bill changes	N/A
Monthly recurring charges	Yes
Non-recurring charges	N/A
Term and agreement	Yes

Call-flow steps6 of 11 shown

#	Step	AI rating
1	Positive greetingUsed the customer's name (line 1) but delivered the greeting in a neutral tone (lines 1–5), without warmth or a natural connection.	Partially demonstrated
2	Set the agendaClearly stated sales intent (lines 8–12) and the purpose of the call before moving into discovery.	Effectively demonstrated
3	Discover needsUncovered usage (line 25) and devices (line 35) only — missed users and the cost pillar despite the customer volunteering bill detail (line 21).	Did not demonstrate
4	Link the need to a productProduct tied to usage, but delivery was generic (lines 50–54) and never connected back to the buffering the customer raised.	Partially demonstrated
5	Assume the saleMade a confident assumptive ask (line 92) without hesitation.	Effectively demonstrated
6	Engage the objectionAcknowledged the price objection (line 73) but never uncovered the reason behind it, so there was nothing to re-frame against.	Did not demonstrate

Every rating here is already post-rules. Quote verification, forced N/A, and red-flag validation run in the worker before a result gets written, so the dashboard never shows a raw model opinion. Override is the human gate on top of that, and the composite above recomputes from the rubric's own weights the moment one changes. Sale credit does not appear on this page at all, because conversion comes from the dialer disposition, which the client already audits.

A. MoralesSupervisor · Team 04 · Site 02

11Agents on team

33Calls in this synthesis

16%Team sale rate · site 19%

17%Needs discovery · site 34%

Coach's ReadAI synthesis of 33 calls · what will help this team sell more

Discovery is the biggest lever this team has. Without it every pitch downstream lands generic.

Needs discoveryDiscovery deficit

Agents skip asking about users, usage, and cost, relying on closed questions like “Do you have internet?” instead of open-ended discovery. That produces a generic pitch that misses the customer's actual pain point.

Costs sales because: Without discovery there is nothing to tailor the offer to, so the offer reads as irrelevant and the customer defers. Incomplete discovery is the most common root cause in lost-sale review.

Marcus Reed — Asked “Who is your internet provider?” instead of uncovering household usage.

Say this: “How many people in your household use the internet most often?”

Dana Whitfield — Asked the cost pillar only (line 12), missing usage and devices.

Say this: “What do you mainly use the internet for at home?”

Huddle drill: Role-play discovery. Partner A asks open-ended questions about users and usage; Partner B answers with real needs. Rotate every five minutes.

Solution framingPitch not tied to the need

Agents pitch generically without linking the feature back to something the customer said, using “I'm offering you internet” rather than connecting the product to the problem already on the table.

Costs sales because: A pitch that isn't tied to a stated need feels transactional. Customers decline offers they cannot see themselves in, and the call ends in a deferral rather than a decision.

Priya Raman — Pitched the top tier without tying it to the streaming issue raised at line 36.

Say this: “Since you're streaming on three devices at night, the higher tier is what stops the buffering.”

Tomas Vidal — Pitched a mobile line without referencing the coverage complaint at line 39.

Say this: “Because your current carrier drops at home, this line runs on our network and your wifi.”

Huddle drill: Practice “because” statements: “I recommend X because <the thing they told you>.” Use real quotes from yesterday's calls.

Effort reductionScripted language

Agents fall back on closed questions and stock phrasing instead of adapting to what the customer actually said, which makes the interaction feel processed rather than personal.

Costs sales because: Scripted language erodes trust and invites disengagement. Roughly six in ten deferrals happen on calls where the agent never adapted their wording.

Alina Boateng — Asked “How many devices do you connect?” rather than adapting to the answer given.

Say this: “Which of those devices matters most when the connection slows down?”

Huddle drill: Take three scripted lines and rewrite each to open with “What” or “How” instead of “Do” or “How many.”

Quick winsAsk “Who in your household uses the internet most?” instead of “Do you have internet?”Tie the pitch to discovery: “Since you stream nightly, the higher tier prevents buffering.”Replace “How much do you pay?” with “What's your biggest frustration with your current service?”

Regenerated nightly for every scope with newly scored calls — agent, supervisor, brand, and overall.

Rubric — version 17.6

The rubric is the engine

Edit this definition and the pipeline inherits it on the next run. No deploy, no developer.

Composite weightingno compliance item applies

Behaviors	60
Compliance	30
Call-flow steps	10

Any red flag forces the composite to zero. Tick the box above to see the other rule: when no compliance item applies to a call, those 30 points renormalize proportionally across the remaining 70 rather than penalizing the agent for a section that could not apply.

Red flagsauto-fail · 6

Misrepresentation of price or terms
Failure to disclose a required term
Rebuttal after a firm customer refusal
Discourtesy or dismissiveness
Unverified account access
Commitment the company cannot honor

Definition sourcesingle source of truth

BEHAVIORS = [
  { id: "b1", name: "Active engagement",     weight: 10 },
  { id: "b2", name: "Ownership",             weight: 10 },
  { id: "b3", name: "Effort reduction",      weight: 10 },
  { id: "b4", name: "Needs discovery",       weight: 15 },   // heaviest — the sales lever
  { id: "b5", name: "Solution framing",      weight: 10 },
  { id: "b6", name: "Customer appreciation", weight:  5 },
];

STEPS      = [ /* 11 call-flow steps */ ];
COMPLIANCE = [ /* 11 items, each N/A-able */ ];
RED_FLAGS  = [ /* 6 — any one forces composite to 0 */ ];

The production worker does not reimplement this. It extracts the definition and runs it in a sandbox, checking the expected exports on load, so a restructure fails loudly at startup instead of quietly scoring every call against half a rubric. A new program needs a new definition and nothing else.

Reconstruction · synthetic data

Four surfaces in the dark bar above: the population dashboard, one scored call, a supervisor's coaching digest, and the rubric definition itself. No real agent, customer, client, or caller information appears anywhere on this site.

Analytics · Dialer Intelligence

Where the yield stops paying for itself.

Contact rates were falling at every attempt and there was no evidence for why. The answers were already in the dialer, in twenty million attempt records nobody had a way to look at. This is the platform that made them legible, and what it found on the first pass.

SQL Server · PHP · Chart.js · Vanilla JS

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

att	dials	→contacts	rate
1	42,100	8,930	21.2%
2	27,400	5,180	18.9%
3	18,200	2,985	16.4%
4	11,900	1,740	14.6%
5	7,600	1,005	13.2%
6	4,700	555	11.8%

View attemptsall

DialsContacts

Fall-off detail

Per-attempt numbers · cumulative shares

att	dials	→contacts	rate	new/1k	reach∑	dials∑
1	42,100	8,930	21.2%	212.1	51%	38%
2	27,400	5,180	18.9%	158.4	76%	62%
3	18,200	2,985	16.4%	119.8	88%	78%
4	11,900	1,740	14.6%	96.6	95%	89%
5	7,600	1,005	13.2%	80.3	98%	96%
6	4,700	555	11.8%	66.0	100%	100%

Goals by attempt

Bars = leads whose first goal landed at that depth

212

Att 12.4%

148

Att 22.9%

Att 33.2%

Att 43.5%

Att 53.7%

Att 64.0%

att	first goals	ans conv
1	212	2.4%
2	148	2.9%
3	96	3.2%
4	61	3.5%
5	37	3.7%
6	22	4.0%

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

att	first goals	goals∑	ans conv	goal/1k
1	212	37%	2.4%	5.04
2	148	63%	2.9%	5.40
3	96	79%	3.2%	5.27
4	61	90%	3.5%	5.13
5	37	96%	3.7%	4.87
6	22	100%	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 at	keep reached	keep goals	save dials
Attempt 1	51%	37%	62% (69,800)
Attempt 2	76%	63%	38% (42,400)
Attempt 3	88%	79%	22% (24,200)
Attempt 4	95%	90%	11% (12,300)
Attempt 5	98%	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.

Rotation health

Attempt penetration per caller ID · unique leads reached against dials placed

caller id	dials	unique leads	att / lead	burn	contact %
Toll-free · AToll-free	41,200	12,840	3.21	82	5.6%
Toll-free · BToll-free	28,900	9,610	3.01	78	6.1%
Toll-free · CToll-free	19,470	6,980	2.79	71	6.8%
Local · 206Rotationlow	742	681	1.09	9	22.4%
Local · 618Rotationlow	719	664	1.08	8	21.1%
Local · 724Rotationlow	706	648	1.09	9	20.7%
Local · 469Rotationlow	698	641	1.09	8	19.9%
Local · 913Rotationlow	688	634	1.09	8	19.4%

5 of these sit under the 1,000-dial floor, so their rates are tagged and greyed — a single local number never places enough dials in a month to rank on rate. The comparison that survives the floor is the grouped one below, where 497 rotation numbers aggregate to 69% of volume. Three toll-free numbers carry the other 31% and see each lead about three times; the rotation pool reaches a lead roughly once. Same list, same agents, same script.

CID leaderboard

Click a caller ID to see which states it actually dialed

caller id

area code region

dials

contact %

sales / 1k

▸

Toll-free · AToll-free

—

41,200

5.6%

2.1

▸

Local · 206Rotation

Seattle, WA

742

22.4%

9.1

▸

Local · 618Rotation

Southern IL

719

21.1%

8.6

▸

Local · 724Rotation

Western PA

706

20.7%

8.4

Open Local · 206 and look for Washington. A Seattle area code that never dialed a Seattle number. Same for 618 and Illinois, 724 and Pennsylvania. That is why area code cannot stand in for lead geography, and why the grouping had to come from the lead's own state instead.

CID summary

Grouped by type · both groups clear the 1,000-dial floor

group	cids	share of dials		contact %	sales / 1k
Toll-free	3	31%		6.0%	2.4
Local rotation	497	69%		20.8%	8.7

Unique-lead counts are a DISTINCT per month and must never be summed across months. A lead dialed in both June and July counts twice, which inflates unique leads and deflates attempts-per-lead. The cross-month figure comes from its own endpoint after the merge. That was a P0 in my own audit of this dashboard.

Timezone × local hour

Zone derived from lead state · a good hour means nothing until the zone is resolved

8a9a10a11a12p1p2p3p4p5p6p7p8pDials

Eastern7.18.49.28.86.97.48.19.611.213.414.112.89.749,200

Central6.47.88.98.56.27.07.79.110.812.913.612.19.170,400

Mountain5.87.28.17.95.96.67.28.410.112.212.811.48.460,100

Pacific6.17.58.68.26.16.87.58.810.512.613.211.88.861,300

5.8%14.1%Contact rate

Every zone peaks between 5 and 7pm local and troughs at lunch, but the peak is worth 2.3× the trough. Switch the tint to Dials and the mismatch shows: volume is spread far flatter than yield, so the best hours are under-dialed and the lunch trough is over-dialed. Dialing a Pacific list on an Eastern clock spends the best hours on the worst window.

Day of week × local hour

Contact rate · aggregated across zones

8a9a10a11a12p1p2p3p4p5p6p7p8p

Mon6.57.98.98.56.47.17.89.210.913.013.712.39.2

Tue6.78.29.28.96.77.48.19.511.313.514.212.79.5

Wed6.78.19.18.86.67.38.09.411.213.414.112.69.5

Thu6.58.09.08.66.57.27.99.211.013.213.812.49.3

Fri6.07.38.27.85.96.57.28.410.012.012.611.38.5

Sat5.26.37.16.85.15.76.37.48.710.511.09.97.4

Sun4.55.56.25.94.54.95.46.47.69.19.58.56.4

The evening peak holds every day, but the whole surface sinks toward the weekend — Sunday's best hour is worth less than Tuesday's worst. A weekend shift is not a cheap way to add dials.

Ranked states

Volume floor applied · below-floor rows sink

state	zone	dials	contact %	sales / 1k
AZ	Mountain	38,400	11.8%	11.6
OK	Central	22,800	12.4%	10.2
CA	Pacific	34,100	10.2%	9.7
VA	Eastern	17,200	12.9%	8.9
LA	Central	19,600	11.1%	8.1
NV	Pacific	14,900	9.6%	6.4
RIlow	Eastern	840	16.2%	14.1

Switch the metric and the board reorders. Virginia answers best and converts fourth; Arizona answers third and converts first. Contact rate measures whether someone picks up, not whether they buy, so a target list ranked on answer rate alone sends volume to the wrong states. Separately: an 840-dial state showing the best sales rate on the board is noise, so anything under a thousand dials gets tagged and sinks below the eligible rows. Ranked lists without a volume floor promote whichever row got lucky.

Fall-off by attempt depth, cumulative reach, and what capping the campaign at each depth costs in goals against what it saves in dials. Caller-ID numbers, client names, and campaign identifiers are removed. Volumes are perturbed.

Reporting · Performance Intel

One campaign, two true percentages.

The database was solid and so was the infrastructure. On top sat canned reports, a live tool, and a spreadsheet I maintained by hand, so directors and supervisors spent their day assembling numbers instead of acting on them. This is the platform that replaced the assembling, and it became the reference implementation for the next one.

React · Vite · PHP · MSSQL

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

Program	Hours				Homes				Internet			Mobile
Program	Plan	Actual	/ Day	Rem	Plan	Actual	/ Day	GPH	Plan	Actual	/ Day	Plan	Actual	/ Day
New Household · RetailAC-1140, AC-1142 · New Business	5,950	5,808	48	142	2,575	2,477	33	0.69	1,596	1,512	28	412	402	4
New Household · DirectAC-1141 · New Business	1,600	1,602	+2	—	608	611	+3	—	377	384	+7	97	94	1
Mobile PropensityMX-1215, MX-1217 · Installed Base	4,275	4,394	+119	—	2,073	2,133	+60	—	1,286	1,088	66	332	461	+129
Shared PlanMX-1216 · Installed Base	1,100	706	132	394	374	231	48	0.36	232	231	1	60	78	+18
Premium BundleUT-1194 · Revenue Uplift	1,250	1,317	+67	—	395	388	3	—	245	244	1	63	66	+3
TOTAL	14,175	13,827	180	348	6,025	5,840	84	0.53	3,736	3,459	96	964	1,101	+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

#	Program	Hours Plan	Hours Actual	Homes Goal	Homes Actual	% to Goal
1	Mobile Propensity	4,275	4,394	2,073	2,133	102.9%
2	New Household · Direct	1,600	1,602	608	611	100.5%
3	Premium Bundle	1,250	1,317	395	388	98.2%
4	New Household · Retail	5,950	5,808	2,575	2,477	96.2%
5	Shared Plan	1,100	706	374	231	61.8%
	TOTAL	14,175	13,827	6,025	5,840	96.9%

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.

Day-of-week performanceAll Programs

Mon309avg sales/day0.410GPH754avg hrs4 days

Tue360avg sales/day0.450GPH801avg hrs3 days

Wed306avg sales/day0.428GPH714avg hrs4 days

Thu344avg sales/day0.435GPH790avg hrs4 days

Fri232avg sales/day0.387GPH598avg hrs4 days

Tuesdays are the strongest day (avg 360 sales, 0.450 GPH) while Fridays lag (avg 232 sales, 0.387 GPH).

Daily performance breakdownAll Programs

Region

Program

19Days worked

13827.0Total hours

5,840Total sales

0.422Avg GPH

3,459Total Internet

1,101Total Mobile

—% to goal

Product code columns

Date

Day

Agents

Hours

Sales

GPH

Internet

Mobile

% to Goal

▸ 04-22

Wed

836.8

370

0.442

238

102

—

▸ 04-23

Thu

788.4

329

0.417

215

—

▸ 04-24

Fri

591.6

226

0.382

141

—

Wk 04-20

~95

2216.8

925

0.417

594

223

—

▸ 04-27

Mon

100

814.0

320

0.393

206

—

▸ 04-28

Tue

831.7

366

0.440

229

—

▸ 04-29

Wed

797.9

364

0.456

214

—

▸ 04-30

Thu

833.7

357

0.428

215

—

▸ 05-01

Fri

648.1

240

0.370

146

—

Wk 04-27

~96

3925.4

1,647

0.420

1,010

353

—

▸ 05-04

Mon

756.9

312

0.412

186

—

▸ 05-05

Tue

838.1

376

0.449

218

—

▸ 05-06

Wed

494.3

158

0.320

—

▸ 05-07

Thu

788.5

350

0.444

195

—

▸ 05-08

Fri

584.2

232

0.397

133

—

Wk 05-04

~91

3462.0

1,428

0.412

830

224

—

▸ 05-11

Mon

716.2

304

0.424

167

—

▸ 05-12

Tue

731.8

339

0.463

192

—

▸ 05-13

Wed

727.4

330

0.454

186

—

▸ 05-14

Thu

750.4

339

0.452

190

—

▸ 05-15

Fri

567.7

228

0.402

130

—

Wk 05-11

~82

3493.5

1,540

0.441

865

263

—

▸ 05-18

Mon

729.3

300

0.411

160

—

Wk 05-18

~84

729.3

300

0.411

160

—

TOTAL

13827.0

5,840

0.422

3,459

1,101

—

% to Goal is empty until a program is chosen, and that is the product rather than a gap: both pages pass a null SPH goal and the panel returns null while the filter reads All, because a business running five SPH plans has no single rate to divide by. Pick a program and the column fills. Where a program is dialled at two sites on two different plans the rate here is plan-weighted; the source reads whichever agent row it finds first, which reports one of the two plans as if it were both. Hours print to a tenth with no separator on every row of this table and rounded with one on Trends — two tables, two formats, both the source's own. The date drill-down opens the day at the filter you are looking at, and its % to Goal divides GPH by the campaign's plan SPH, the same measure as the column above it; the source divides by a prorated daily goal this schema does not carry. Agent mode never collapses a name into two campaign rows, because nobody here carries two campaigns in a month and the expansion that exists for that cannot fire.

Site

Campaign

Week-by-week performanceCurrent vs prior month

			Sales			Hours			GPH			Internet			Mobile
Week	Days	Agents	Cur	Prior	Δ	Cur	Prior	Δ	Cur	Prior	Δ	Cur	Prior	Δ	Cur	Prior	Δ
▸ Wk 104-22 – 04-24 / 03-23 – 03-27(prior matched to 3d)	3/5	107/93	925	918	+7	2,217	2,223	−7	0.417	0.413	+0.004	594	587	+7	223	213	+10
Wed04-2203-25	1/1	97/93	370	359	+11	837	823	+14	0.442	0.436	+0.006	238	223	+15	102	97	+5
Thu04-2303-26	1/1	94/93	329	329	0	788	794	−6	0.417	0.414	+0.003	215	208	+7	84	92	−8
Fri04-2403-27	1/1	93/93	226	230	−4	592	606	−14	0.382	0.379	+0.003	141	156	−15	37	24	+13
Mon—03-23	0/1	—/91	—	285		—	735		—	0.388		—	195		—	80
Tue—03-24	0/1	—/87	—	332		—	760		—	0.437		—	219		—	92
▸ Wk 204-27 – 05-01 / 03-30 – 04-03	5/5	107/107	1,647	1,503	+144	3,925	3,649	+276	0.420	0.412	+0.008	1,010	921	+89	353	301	+52
Mon04-2703-30	1/1	100/93	320	299	+21	814	741	+73	0.393	0.403	−0.010	206	190	+16	64	61	+3
Tue04-2803-31	1/1	95/89	366	330	+36	832	767	+65	0.440	0.430	+0.010	229	202	+27	90	81	+9
Wed04-2904-01	1/1	89/88	364	331	+33	798	773	+25	0.456	0.428	+0.028	214	198	+16	92	82	+10
Thu04-3004-02	1/1	97/96	357	334	+23	834	813	+21	0.428	0.411	+0.017	215	201	+14	81	69	+12
Fri05-0104-03	1/1	99/86	240	209	+31	648	556	+93	0.370	0.376	−0.006	146	130	+16	26	8	+18
▸ Wk 305-04 – 05-08 / 04-06 – 04-10	5/5	107/107	1,428	1,470	−42	3,462	3,651	−189	0.412	0.403	+0.010	830	850	−20	224	202	+22
Mon05-0404-06	1/1	92/93	312	282	+30	757	726	+31	0.412	0.388	+0.024	186	171	+15	52	41	+11
Tue05-0504-07	1/1	95/93	376	320	+56	838	789	+50	0.449	0.406	+0.043	218	183	+35	77	52	+25
Wed05-0604-08	1/1	91/89	158	347	−189	494	767	−272	0.320	0.453	−0.133	98	197	−99	13	65	−52
Thu05-0704-09	1/1	91/92	350	295	+55	789	761	+28	0.444	0.388	+0.056	195	169	+26	59	36	+23
Fri05-0804-10	1/1	88/96	232	226	+6	584	609	−25	0.397	0.371	+0.026	133	130	+3	23	8	+15
▸ Wk 405-11 – 05-15 / 04-13 – 04-17	5/5	97/107	1,540	1,414	+126	3,494	3,518	−24	0.441	0.402	+0.039	865	772	+93	263	137	+126
Mon05-1104-13	1/1	85/90	304	281	+23	716	690	+26	0.424	0.407	+0.017	167	162	+5	44	29	+15
Tue05-1204-14	1/1	80/87	339	295	+44	732	720	+12	0.463	0.410	+0.054	192	161	+31	71	32	+39
Wed05-1304-15	1/1	78/90	330	322	+8	727	760	−32	0.454	0.424	+0.030	186	172	+14	67	46	+21
Thu05-1404-16	1/1	84/93	339	296	+43	750	754	−3	0.452	0.393	+0.059	190	157	+33	62	24	+38
Fri05-1504-17	1/1	83/96	228	220	+8	568	595	−27	0.402	0.370	+0.032	130	120	+10	19	6	+13
▸ Wk 505-18 – 05-18 / 04-20 – 04-21(prior matched to 1d)	1/2	84/87	300	257	+43	729	657	+72	0.411	0.391	+0.020	160	141	+19	38	15	+23
Mon05-1804-20	1/1	84/87	300	257	+43	729	657	+72	0.411	0.391	+0.020	160	141	+19	38	15	+23
Tue—04-21	0/1	—/93	—	303		—	752		—	0.403		—	156		—	20
Total	19/22		5,840	6,482	−642	13,827	15,945	−2,118	0.422	0.407	+0.016	3,459	3,841	−382	1,101	1,060	+41

A week that is not finished must not be read against a whole one, so a partial week compares against only the prior days that share its weekdays and says so — (prior matched to 3d). This month opens on a Wednesday and closes on a Monday, so the first and last weeks carry the label and the three in the middle line up exactly and do not. The Total row is the two full months and is deliberately not matched: it is the same comparison the MoM tab reports, 5,840 against 6,482. Click a week to open its days; a prior weekday with no current day to sit beside gets its own row rather than being dropped.

SPH % to goal · week over weekAll sites

Axis fixed 80–115%

Site A · solid line, circleSite B · dashed line, diamond100% = at goal

New Household · Retailplan SPH 0.433

8090100at goal110A 93%B 101%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	103%	0.447	0.433	267	597
Site A	Wk 2	04-27 – 05-01	103%	0.445	0.433	480	1079
Site A	Wk 3	05-04 – 05-08	99%	0.427	0.433	437	1023
Site A	Wk 4	05-11 – 05-15	104%	0.451	0.433	510	1132
Site A	Wk 5	05-18 – 05-18	93%	0.404	0.433	104	257
Site B	Wk 1	04-22 – 04-24	88%	0.382	0.433	123	322
Site B	Wk 2	04-27 – 05-01	89%	0.386	0.433	216	560
Site B	Wk 3	05-04 – 05-08	89%	0.385	0.433	180	467
Site B	Wk 4	05-11 – 05-15	100%	0.431	0.433	141	327
Site B	Wk 5	05-18 – 05-18	101%	0.438	0.433	19	43

New Household · Directplan SPH 0.380

8090100at goal110A 97%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	99%	0.377	0.380	91	241
Site A	Wk 2	04-27 – 05-01	101%	0.382	0.380	163	426
Site A	Wk 3	05-04 – 05-08	98%	0.372	0.380	143	385
Site A	Wk 4	05-11 – 05-15	104%	0.394	0.380	177	449
Site A	Wk 5	05-18 – 05-18	97%	0.368	0.380	37	101

Mobile Propensityplan SPH 0.485

8090100at goal110A 100%B 108%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	109%	0.528	0.485	218	413
Site A	Wk 2	04-27 – 05-01	109%	0.530	0.485	383	722
Site A	Wk 3	05-04 – 05-08	105%	0.511	0.485	331	648
Site A	Wk 4	05-11 – 05-15	108%	0.526	0.485	390	742
Site A	Wk 5	05-18 – 05-18	100%	0.483	0.485	80	166
Site B	Wk 1	04-22 – 04-24	82%	0.399	0.485	133	334
Site B	Wk 2	04-27 – 05-01	84%	0.406	0.485	237	584
Site B	Wk 3	05-04 – 05-08	88%	0.429	0.485	187	436
Site B	Wk 4	05-11 – 05-15	102%	0.493	0.485	150	304
Site B	Wk 5	05-18 – 05-18	108%	0.524	0.485	24	46

Shared Planplan SPH 0.340

8090100at goal110A 106%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	92%	0.312	0.340	33	106
Site A	Wk 2	04-27 – 05-01	95%	0.323	0.340	61	189
Site A	Wk 3	05-04 – 05-08	93%	0.317	0.340	58	183
Site A	Wk 4	05-11 – 05-15	101%	0.343	0.340	64	187
Site A	Wk 5	05-18 – 05-18	106%	0.361	0.340	15	42

Premium Bundleplan SPH 0.300

8090100at goal110A 93%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	98%	0.294	0.300	60	204
Site A	Wk 2	04-27 – 05-01	98%	0.293	0.300	107	365
Site A	Wk 3	05-04 – 05-08	96%	0.287	0.300	92	320
Site A	Wk 4	05-11 – 05-15	102%	0.306	0.300	108	353
Site A	Wk 5	05-18 – 05-18	93%	0.280	0.300	21	75

Charted are the two programs both sites run — the campaign filter above reaches the other three. Each point is a week's own SPH over that program's plan SPH, and a week under ten hours is not drawn, which is the source's own rule. The axis is fixed at 80–115% over every filter state rather than recomputed from what is on screen: the source rescales per render, which makes two different readings look like one change. Site A leads Site B on both shared programs for four weeks and loses the last one — the one-day week its ten remaining agents carry without the fifteen who had already stopped dialling. That is the wind-down read as a rate rather than a headcount.

Site Comparison · Month to Datemonth to date · agent counts today

SITE A· 8 agents

Goal99.2%4,430 / 4,466

GPH0.426goal 0.436

Hours101.5%10,403 / 10,250

Above goal5of 8 (62.5%)

CPS$50.254,430 sales

SITE B· 2 agents

Goal85.7%1,410 / 1,645

GPH0.412goal 0.419

Hours87.2%3,424 / 3,925

Above goal0of 2 (0.0%)

CPS$51.971,410 sales

Site A leads goal +13.5pts, GPH +0.01, hours +14.3pts, above-goal agents +5, CPS −$2.

Same numerator, two denominators

Goals filtered to Site A first101.6%4,430 ÷ 4,360

Divided by the combined plan73.8%4,430 ÷ 6,005

Goals must be filtered by site before attainment is computed — otherwise a single site's number ends up divided by the combined plan.

Homes4,430goal 4,466 · 99.2%

Hours10,403plan 10,250 · 101.5%

GPH0.426goal 0.436 · 97.7%

Q1 Rate40.5%34 of 84 agents

Unique agents848 on the floor today

Site Summary — Site A

Goals vs Plan — Site A

Pacing — Site A

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 Hours10,403of 10,250 plan

101%to date

+153 over plan

PROJ. EOM12,046

118% of plan▲ +1,796

Total Homes4,430of 4,360 plan

102%to date

+70 over plan

PROJ. EOM5,129

118% of plan▲ +769

Total Units5,453of 5,233 plan

104%to date

+220 over plan

PROJ. EOM6,314

121% of plan▲ +1,081

Internet2,679of 2,704 plan

99%to date

25remaining

8.3per day

PROJ. EOM3,102

115% of plan▲ +398

Mobile Lines837of 698 plan

120%to date

+139 over plan

PROJ. EOM969

139% of plan▲ +271

Plan on these cards is Site A's plan of record — every goal row at the number written on day one, dependents derived per row and summed. The KPI strip above divides the same homes actual by the gate-corrected goal, so homes reads against 4,360 here and 4,466 there. That gap is the point, not a rounding error.

Gainshare — Site ASite Table

Net Bonus+1.75%

Proj. Bonus+2.60%▲ 0.85% gain at pace

Mobile Attainment

117%proj. 118%

6 lines to next tier (+2.60%)

> 125%900+3.50%

118–125%843+2.60%PROJ.

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

97%proj. 98%

202 internet to next tier (+0.90%)

> 125%3,491+3.50%

118–125%3,269+2.60%

111–117%3,075+1.75%

104–110%2,881+0.90%

96–103%✓0.00%

89–95%✓-0.90%

82–88%✓-1.75%

75–81%✓-2.60%

< 75%✓-3.50%

Cost Per Attainment

102%proj. 103%

122 units to next tier (+0.20%)

> 125%6,754+0.90%

118–125%6,325+0.70%

111–117%5,950+0.45%

104–110%5,575+0.20%

96–103%✓0.00%

89–95%✓-0.20%

82–88%✓-0.45%

75–81%✓-0.70%

< 75%✓-0.90%

SPH Attainment

98%proj. 98%

284 sales to next tier (+0.20%)

> 125%5,712+0.90%

118–125%5,349+0.70%

111–117%5,032+0.45%

104–110%4,714+0.20%

96–103%✓0.00%

89–95%✓-0.20%

82–88%✓-0.45%

75–81%✓-0.70%

< 75%✓-0.90%

Hour Gate

101%proj. 118%

≥ 100.0%✓0%

< 99.9%✓-1.60%

Band boundaries and bonus values are perturbed; the ladder's shape is real. Projecting re-runs the funder gate at projected hours over Site A's rows alone: all three of its funders cross, so every projected goal grows with them. Mobile is the one metric whose numerator outruns its own denominator, which is why it is the only column drawing a projected row. The reading worth keeping is the gate — Site A clears it at 101.5% while the business-wide panel fails the same gate at 97.5%. The difference is Site B winding down, and it is the plainest argument on this page for filtering by site before dividing.

Funder gate — Site A

A goal is not a constant. A campaign's goal scales by its own hours — but only once the whole funding source has burned past its planned hours. Below plan, every campaign inside it keeps the goal it opened the month with. Expand a funder to see what its campaigns are measured against.

Funder

Plan hrs

Actual hrs

Hours %

Gate

Scale

Plan goal

Scaled goal

▸ New Business

5,500

5,690

103.5%

OPEN

1.035

2,363

2,449

▸ Installed Base

3,500

3,396

97.0%

SHUT

1.000

1,622

Campaign	Scale	Plan goal	Scaled goal	Actual	% vs plan	% vs goal
Mobile PropensityMX-1215	1.000	1,248	1,248	1,402	112.3%	112.3%
GATE SHUT Burned 112.1% of its own plan hours while Installed Base totalled 97.0%. The gate is at the funder, so this campaign keeps its unscaled goal and reads 112.3%. Had the funder crossed, it would read 1,399 goal · 100.2%.
Shared PlanMX-1216	1.000	374	374	231	61.8%	61.8%

▸ Revenue Uplift

1,250

1,317

105.4%

OPEN

1.054

375

395

TOTAL

10,250

10,403

101.5%

—

4,360

4,466

A funder's Scale is its own hours ratio, which is the gate test — above 1.000 and the gate is open. Each campaign inside it then scales by its own ratio, so a funder's scaled goal is the sum of its campaigns' scaled goals rather than its own factor applied to its own plan: 2,363 × 1.035 would give 2,445, and the column adds to 2,449. TOTAL is 4,360 of plan turned into 4,466 of goal against 4,430 sold — 101.6% one way and 99.2% the other.

Supervisor ranking — Site Asite SPH goal 0.436

Supervisor

Program

Hours

Sales

GPH

% Goal

CPS

▸ A. PetrovaTOP

Mobile Propensity

2,690

1,402

0.52

112.3%

$41.06

▸ J. Meunier

New Household · Direct

1,602

611

0.38

100.3%

$56.11

▸ D. Osagie

Premium Bundle

1,317

388

0.29

98.2%

$72.64

▸ R. Castillo

New Household · Retail

4,088

1,798

0.44

97.7%

$48.66

▸ K. Ibarra

Shared Plan

706

231

0.33

61.8%

$65.40

TOTAL

10,403

4,430

0.43

99.2%

$50.25

Agent trays show today's snapshot — the only agent-level figures this dataset models. Ranking rows above are month to date, so a tray will not sum to its row. Each supervisor here owns exactly one campaign, so the tray's Program column repeats down the tray; in the product a supervisor can span several.

Daily Targets — Site A

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

TARGET

FUNDING

Campaign	Hours				Homes				Internet			Mobile
Campaign	Plan	Actual	/ Day	Rem	Plan	Actual	/ Day	GPH	Plan	Actual	/ Day	Plan	Actual	/ Day
New Household · RetailAC-1140 · New Business	3,900	4,088	+188	—	1,840	1,798	14	—	1,141	1,102	13	294	294	0
New Household · DirectAC-1141 · New Business	1,600	1,602	+2	—	609	611	+2	—	378	384	+6	97	94	1
Mobile PropensityMX-1215 · Installed Base	2,400	2,690	+290	—	1,248	1,402	+154	—	774	718	19	200	305	+105
Shared PlanMX-1216 · Installed Base	1,100	706	132	394	374	231	48	0.36	232	231	1	60	78	+18
Premium BundleUT-1194 · Revenue Uplift	1,250	1,317	+67	—	395	388	3	—	245	244	1	63	66	+3
TOTAL	10,250	10,403	+153	—	4,466	4,430	65	—	2,770	2,679	34	714	837	+123

Plan in the Homes group is the gate-corrected goal from the funder table — which is why Mobile Propensity's plan reads 1,248 and not the 1,399 its own hours would have bought. Internet and Mobile goals are derived from each row's own homes goal, so they scale with the gate too. The tier buttons scale all three sales groups; hours are never scaled, at any tier.

Quartile distribution — Site A

34Q1 ≥100% to goal34 total · 40.5%

21Q2 ≥80% to goal21 total · 25.0%

22Q3 >0% to goal22 total · 26.2%

7Q4 0% to goal7 total · 8.3%

Day-of-week performanceSite A Site

Mon240avg sales/day0.411GPH584avg hrs4 days

Tue272avg sales/day0.456GPH596avg hrs3 days

Wed229avg sales/day0.434GPH527avg hrs4 days

Thu260avg sales/day0.440GPH592avg hrs4 days

Fri175avg sales/day0.388GPH451avg hrs4 days

Tuesdays are the strongest day (avg 272 sales, 0.456 GPH) while Fridays lag (avg 175 sales, 0.388 GPH).

Daily performance breakdownSite A Site

Program

19Days worked

10403.0Total hours

4,430Total sales

0.426Avg GPH

2,679Total Internet

837Total Mobile

—% to goal

Product code columns

Date

Day

Agents

Hours

Sales

GPH

Internet

Mobile

% to Goal

▸ 04-22

Wed

591.4

269

0.455

176

—

▸ 04-23

Thu

552.1

236

0.427

160

—

▸ 04-24

Fri

417.2

164

0.393

105

—

Wk 04-20

~67

1560.7

669

0.429

441

159

—

▸ 04-27

Mon

592.5

238

0.402

157

—

▸ 04-28

Tue

590.8

269

0.455

173

—

▸ 04-29

Wed

545.3

256

0.469

156

—

▸ 04-30

Thu

590.1

259

0.439

163

—

▸ 05-01

Fri

462.5

172

0.372

110

—

Wk 04-27

~68

2781.2

1,194

0.429

759

264

—

▸ 05-04

Mon

540.1

229

0.424

140

—

▸ 05-05

Tue

606.5

274

0.452

163

—

▸ 05-06

Wed

367.8

118

0.321

—

▸ 05-07

Thu

594.7

264

0.444

152

—

▸ 05-08

Fri

449.7

176

0.391

102

—

Wk 05-04

~68

2558.8

1,061

0.415

633

169

—

▸ 05-11

Mon

561.4

235

0.419

132

—

▸ 05-12

Tue

591.6

272

0.460

156

—

▸ 05-13

Wed

602.7

271

0.450

154

—

▸ 05-14

Thu

630.2

282

0.447

159

—

▸ 05-15

Fri

476.3

189

0.397

108

—

Wk 05-11

~68

2862.2

1,249

0.436

709

213

—

▸ 05-18

Mon

640.1

257

0.401

137

—

Wk 05-18

~74

640.1

257

0.401

137

—

TOTAL

10403.0

4,430

0.426

2,679

837

—

Campaign

Week-by-week performanceCurrent vs prior month

			Sales			Hours			GPH			Internet			Mobile
Week	Days	Agents	Cur	Prior	Δ	Cur	Prior	Δ	Cur	Prior	Δ	Cur	Prior	Δ	Cur	Prior	Δ
▸ Wk 104-22 – 04-24 / 03-23 – 03-27(prior matched to 3d)	3/5	79/60	669	612	+57	1,561	1,427	+133	0.429	0.429	−0.000	441	396	+45	159	140	+19
Wed04-2203-25	1/1	69/60	269	242	+27	591	532	+59	0.455	0.455	−0.000	176	151	+25	74	64	+10
Thu04-2303-26	1/1	66/60	236	216	+20	552	511	+42	0.427	0.423	+0.004	160	138	+22	59	60	−1
Fri04-2403-27	1/1	66/59	164	154	+10	417	385	+32	0.393	0.400	−0.007	105	107	−2	26	16	+10
Mon—03-23	0/1	—/61	—	197		—	491		—	0.402		—	137		—	56
Tue—03-24	0/1	—/53	—	208		—	464		—	0.448		—	137		—	56
▸ Wk 204-27 – 05-01 / 03-30 – 04-03	5/5	79/68	1,194	1,018	+176	2,781	2,363	+418	0.429	0.431	−0.001	759	633	+126	264	201	+63
Mon04-2703-30	1/1	73/63	238	215	+23	593	504	+88	0.402	0.426	−0.025	157	141	+16	49	44	+5
Tue04-2803-31	1/1	68/58	269	223	+46	591	498	+93	0.455	0.448	+0.007	173	137	+36	68	53	+15
Wed04-2904-01	1/1	61/53	256	204	+52	545	465	+81	0.469	0.439	+0.030	156	123	+33	65	49	+16
Thu04-3004-02	1/1	69/64	259	233	+26	590	542	+48	0.439	0.430	+0.009	163	142	+21	61	49	+12
Fri05-0104-03	1/1	71/55	172	143	+29	463	355	+107	0.372	0.402	−0.031	110	90	+20	21	6	+15
▸ Wk 305-04 – 05-08 / 04-06 – 04-10	5/5	79/68	1,061	971	+90	2,559	2,320	+239	0.415	0.419	−0.004	633	562	+71	169	129	+40
Mon05-0404-06	1/1	66/56	229	180	+49	540	437	+103	0.424	0.412	+0.012	140	110	+30	39	28	+11
Tue05-0504-07	1/1	69/61	274	213	+61	607	517	+90	0.452	0.412	+0.040	163	121	+42	56	33	+23
Wed05-0604-08	1/1	68/58	118	234	−116	368	500	−132	0.321	0.468	−0.147	76	133	−57	11	40	−29
Thu05-0704-09	1/1	69/58	264	195	+69	595	479	+116	0.444	0.407	+0.037	152	111	+41	45	23	+22
Fri05-0804-10	1/1	68/61	176	149	+27	450	387	+63	0.391	0.385	+0.007	102	87	+15	18	5	+13
▸ Wk 405-11 – 05-15 / 04-13 – 04-17	5/5	79/68	1,249	955	+294	2,862	2,271	+591	0.436	0.421	+0.016	709	524	+185	213	95	+118
Mon05-1104-13	1/1	67/61	235	200	+35	561	467	+94	0.419	0.428	−0.009	132	116	+16	33	22	+11
Tue05-1204-14	1/1	65/53	272	190	+82	592	439	+153	0.460	0.433	+0.026	156	103	+53	57	21	+36
Wed05-1304-15	1/1	65/60	271	221	+50	603	507	+96	0.450	0.436	+0.013	154	120	+34	55	30	+25
Thu05-1404-16	1/1	71/58	282	195	+87	630	469	+161	0.447	0.416	+0.032	159	102	+57	53	18	+35
Fri05-1504-17	1/1	70/63	189	149	+40	476	390	+87	0.397	0.382	+0.014	108	83	+25	15	4	+11
▸ Wk 505-18 – 05-18 / 04-20 – 04-21(prior matched to 1d)	1/2	74/57	257	180	+77	640	432	+209	0.401	0.417	−0.016	137	99	+38	32	13	+19
Mon05-1804-20	1/1	74/57	257	180	+77	640	432	+209	0.401	0.417	−0.016	137	99	+38	32	13	+19
Tue—04-21	0/1	—/57	—	198		—	459		—	0.431		—	103		—	15
Total	19/22		4,430	4,339	+91	10,403	10,227	+176	0.426	0.424	+0.002	2,679	2,591	+88	837	705	+132

SPH % to goal · week over weekSite A only

Axis fixed 80–115%

Site A · solid line, circle100% = at goal

New Household · Retailplan SPH 0.433

8090100at goal110A 93%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	103%	0.447	0.433	267	597
Site A	Wk 2	04-27 – 05-01	103%	0.445	0.433	480	1079
Site A	Wk 3	05-04 – 05-08	99%	0.427	0.433	437	1023
Site A	Wk 4	05-11 – 05-15	104%	0.451	0.433	510	1132
Site A	Wk 5	05-18 – 05-18	93%	0.404	0.433	104	257

New Household · Directplan SPH 0.380

8090100at goal110A 97%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	99%	0.377	0.380	91	241
Site A	Wk 2	04-27 – 05-01	101%	0.382	0.380	163	426
Site A	Wk 3	05-04 – 05-08	98%	0.372	0.380	143	385
Site A	Wk 4	05-11 – 05-15	104%	0.394	0.380	177	449
Site A	Wk 5	05-18 – 05-18	97%	0.368	0.380	37	101

Mobile Propensityplan SPH 0.485

8090100at goal110A 100%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	109%	0.528	0.485	218	413
Site A	Wk 2	04-27 – 05-01	109%	0.530	0.485	383	722
Site A	Wk 3	05-04 – 05-08	105%	0.511	0.485	331	648
Site A	Wk 4	05-11 – 05-15	108%	0.526	0.485	390	742
Site A	Wk 5	05-18 – 05-18	100%	0.483	0.485	80	166

Shared Planplan SPH 0.340

8090100at goal110A 106%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	92%	0.312	0.340	33	106
Site A	Wk 2	04-27 – 05-01	95%	0.323	0.340	61	189
Site A	Wk 3	05-04 – 05-08	93%	0.317	0.340	58	183
Site A	Wk 4	05-11 – 05-15	101%	0.343	0.340	64	187
Site A	Wk 5	05-18 – 05-18	106%	0.361	0.340	15	42

Premium Bundleplan SPH 0.300

8090100at goal110A 93%

Wk 1Wk 2Wk 3Wk 4Wk 5

% to goal by week
Site	Week	Dates	% to goal	SPH	Plan SPH	Sales	Hours
Site A	Wk 1	04-22 – 04-24	98%	0.294	0.300	60	204
Site A	Wk 2	04-27 – 05-01	98%	0.293	0.300	107	365
Site A	Wk 3	05-04 – 05-08	96%	0.287	0.300	92	320
Site A	Wk 4	05-11 – 05-15	102%	0.306	0.300	108	353
Site A	Wk 5	05-18 – 05-18	93%	0.280	0.300	21	75

Charted are the two programs both sites run — the campaign filter above reaches the other three. Each point is a week's own SPH over that program's plan SPH, and a week under ten hours is not drawn, which is the source's own rule. The axis is fixed at 80–115% over every filter state rather than recomputed from what is on screen: the source rescales per render, which makes two different readings look like one change. This page is scoped to Site A, so only its line is drawn. The comparison these two programs exist to carry is on the Overview tab, where the other site's wind-down reads as a rate rather than a headcount.

ComparevsPacing: 19/22 biz days (86.4%) through 2026-05-18

Agent comparison · month over month

Every agent's goal here is their share of their campaign's plan of record — the goal that campaign published before any funder gate — prorated by the hours they worked, because that is the denominator the source's comparison carries on the agent row. The Site tab divides by the gate-corrected goal at its own site's scope. So the same sales read two percentages: the agents on New Household · Retail add up to 102.5% of the plan they were given and 97.7% of the goal Site measures them against, and Premium Bundle is 103.5% here and 98.2% on Overview. Neither is a rounding error — a denominator moved, and it is the same pair the Overview's executive-summary footnote already names.

Current agents110vs 107 prior

Avg Δ % to goal−5.5%35 improved · 57 declined

Current sales5,840vs 6,482 prior

Current hours13,827vs 15,945 prior

Executive Summary — Current vs Prior

All agents month over month: 110 agents carried hours this month against 107 prior. Sales down 642 (6,482 to 5,840), hours down 2,118 (15,945 to 13,827).

GPH moved from 0.407 to 0.422 (+0.016). 35 agents improved their % to goal while 57 declined, with an average delta of −5.5%.

Top improvers: F. Magalhaes (+17.7%), R. Benoit (+15.2%), K. Madsen (+13.9%).

Biggest declines: O. Mahfouz (−52.9%), N. Garrido (−51.1%), C. Fitzgibbon (−49.5%).

Current against Prior: the average agent moved −5.5 points while the business as a whole moved +1.0, 96.2% to 97.3%. Not a contradiction — the business total is weighted by goal and the average is not, and this month the hours moved: the best quartile of converters took 30.7% of the hours against 25.4% last month. 35 agents improved, 57 declined, 18 are new and 15 are gone. Mobile is the only volume line that rose while sales fell, which is where the gainshare panel's single positive band comes from.

Month-over-month metrics

Metric	Prior	Current	Paced	Change
Hours	15,945	13,827	16,010	▼ 2,118
Sales	6,482	5,840	6,762	▼ 642
GPH	0.407	0.422	0.422	▲ 0.016
Internet	3,841	3,459	4,005	▼ 382
Mobile	1,060	1,101	1,275	▲ 41

Every row is coloured higher-is-better, which is the source's rule and not a choice made here — on the Hours row that reads as volume, not as cost. Paced divides the left month's total by the share of that month which has elapsed — the source derives the elapsed fraction from the left month's own dates, not from today, so switching the pair switches which month is being paced. A closed month is 100% elapsed and has nothing left to pace, so with Prior on the left this column equals that month's actual. That is the product's behaviour, reproduced rather than tidied away.

Top improvers

F. Magalhaes125.4% → 143.0%

+17.7%

R. Benoit112.9% → 128.1%

+15.2%

K. Madsen146.7% → 160.5%

+13.9%

Biggest declines

O. Mahfouz149.1% → 96.2%

−52.9%

N. Garrido202.1% → 151.0%

−51.1%

C. Fitzgibbon127.5% → 77.9%

−49.5%

Agent movers12 of 92

Top 6 each way on Δ % to goal · cut at +11.2 / −27.2

Agent

Prior hrs

Hours

Sales

GPH

Internet

Mobile

Prior sales

Prior GPH

Prior %

Current %

Δ▼

▸ F. MagalhaesMX-1215MOVED#11(▲12)

185

171

113

0.661

0.454

125.4%

143.0%

+17.7%

▸ R. BenoitMX-1215#24(▲12)

124

139

0.590

0.565

112.9%

128.1%

+15.2%

▸ K. MadsenAC-1140MOVED#1(▲10)

153

178

122

0.685

0.575

146.7%

160.5%

+13.9%

▸ P. ChibaAC-1141#82(▲15)

185

108

0.250

0.205

52.1%

65.9%

+13.8%

▸ K. NakamuraAC-1140#19(▲7)

186

168

0.577

0.527

122.5%

134.7%

+12.2%

▸ I. EgbunikeMX-1215#3(▲10)

160

153

110

0.719

115

0.719

143.8%

154.9%

+11.2%

▸ M. AlvarezMX-1215#16(▼12)

146

169

107

0.633

120

0.822

164.4%

137.2%

−27.2%

▸ A. AmundsenAC-1140#17(▼14)

133

178

104

0.584

0.707

164.9%

135.1%

−29.8%

▸ L. MarchettiAC-1142MOVED#92(▼42)

157

162

0.265

0.363

95.0%

55.8%

−39.2%

▸ C. FitzgibbonMX-1216#63(▼42)

143

145

0.414

0.455

127.5%

77.9%

−49.5%

▸ N. GarridoUT-1194#6(▼5)

159

178

0.433

0.610

202.1%

151.0%

−51.1%

▸ O. MahfouzAC-1142MOVED#44(▼36)

143

166

0.458

0.573

149.1%

96.2%

−52.9%

Hours utilization

—

—peer average

Metric	Prior	Current	Change
Days	—	—	—
Hours	—	—	—
Goal	—	—	—
Sales	—	—	—
% to goal	—	—	—
GPH	—	—	—
Internet	—	—	—
Mobile	—	—	—

This table shows 12 rows, not 110 — the 6 biggest gains and the 6 biggest declines in % to goal, drawn from the 92 agents who carried hours in both months (35 of them up, 57 down). Six from each end rather than the twelve largest movements outright: ranked on size alone this month's twelve are all declines, and a movers table that cannot show a gain is a losers table. The cut falls at +11.2 on the gaining side, where the next agent out moved +10.9, and at −27.2 on the losing side, where the next moved −25.9. 18 agents joined and 15 left; a month you were not in is not a movement, so neither group can be ranked here and neither appears. Every other figure on this panel — the four cards, the metric table, the summary and the two mover lists — is still measured over the whole 110 and 107.

The comparison is per agent, which is what the source component does — it filters by campaign and ranks the agents inside the filter. 110 agents carried hours this month against 107 last month; 84 of this month's were on the floor at the live view's last refresh, 11 more were marked absent and the rest had already stopped dialling. The column set is the source's own Agent Detail set in this build's vocabulary. Three of its columns count products this schema does not model, and they are absent rather than invented; one more repeats a value the table already shows two blocks earlier, and it is dropped because a repeat is not information. The campaign code beside each name stands in for the source's job-type filter, which is not reconstructed here.

Prior agents107vs 110 current

Avg Δ % to goal+5.5%57 improved · 35 declined

Prior sales6,482vs 5,840 current

Prior hours15,945vs 13,827 current

Executive Summary — Prior vs Current

All agents month over month: 107 agents carried hours this month against 110 prior. Sales up 642 (5,840 to 6,482), hours up 2,118 (13,827 to 15,945).

GPH moved from 0.422 to 0.407 (−0.016). 57 agents improved their % to goal while 35 declined, with an average delta of +5.5%.

Top improvers: O. Mahfouz (+52.9%), N. Garrido (+51.1%), C. Fitzgibbon (+49.5%).

Biggest declines: F. Magalhaes (−17.7%), R. Benoit (−15.2%), K. Madsen (−13.9%).

Month-over-month metrics

Metric	Current	Prior	Paced	Change
Hours	13,827	15,945	15,945	▲ 2,118
Sales	5,840	6,482	6,482	▲ 642
GPH	0.422	0.407	0.407	▼ 0.016
Internet	3,459	3,841	3,841	▲ 382
Mobile	1,101	1,060	1,060	▼ 41

Top improvers

O. Mahfouz96.2% → 149.1%

+52.9%

N. Garrido151.0% → 202.1%

+51.1%

C. Fitzgibbon77.9% → 127.5%

+49.5%

Biggest declines

F. Magalhaes143.0% → 125.4%

−17.7%

R. Benoit128.1% → 112.9%

−15.2%

K. Madsen160.5% → 146.7%

−13.9%

Agent movers12 of 92

Top 6 each way on Δ % to goal · cut at +27.2 / −11.2

Agent

Current hrs

Hours

Sales

GPH

Internet

Mobile

Current sales

Current GPH

Current %

Prior %

Δ▼

▸ O. MahfouzAC-1143MOVED#8(▲36)

166

143

0.573

0.458

96.2%

149.1%

+52.9%

▸ N. GarridoUT-1194#1(▲5)

178

159

0.610

0.433

151.0%

202.1%

+51.1%

▸ C. FitzgibbonMX-1216#21(▲42)

145

143

0.455

0.414

77.9%

127.5%

+49.5%

▸ L. MarchettiAC-1143MOVED#50(▲42)

162

157

0.363

0.265

55.8%

95.0%

+39.2%

▸ A. AmundsenAC-1140#3(▲14)

178

133

0.707

104

0.584

135.1%

164.9%

+29.8%

▸ M. AlvarezMX-1215#4(▲12)

169

146

120

0.822

107

0.633

137.2%

164.4%

+27.2%

▸ I. EgbunikeMX-1215#13(▼10)

153

160

115

0.719

110

0.719

154.9%

143.8%

−11.2%

▸ K. NakamuraAC-1140#26(▼7)

168

186

0.527

0.577

134.7%

122.5%

−12.2%

▸ P. ChibaAC-1141#97(▼15)

108

185

0.205

0.250

65.9%

52.1%

−13.8%

▸ K. MadsenAC-1141MOVED#11(▼10)

178

153

0.575

122

0.685

160.5%

146.7%

−13.9%

▸ R. BenoitMX-1215#36(▼12)

139

124

0.565

0.590

128.1%

112.9%

−15.2%

▸ F. MagalhaesMX-1216MOVED#23(▼12)

171

185

0.454

113

0.661

143.0%

125.4%

−17.7%

Hours utilization

—

—peer average

Metric	Current	Prior	Change
Days	—	—	—
Hours	—	—	—
Goal	—	—	—
Sales	—	—	—
% to goal	—	—	—
GPH	—	—	—
Internet	—	—	—
Mobile	—	—	—

This table shows 12 rows, not 107 — the 6 biggest gains and the 6 biggest declines in % to goal, drawn from the 92 agents who carried hours in both months (57 of them up, 35 down). Six from each end rather than the twelve largest movements outright: ranked on size alone this month's twelve are all declines, and a movers table that cannot show a gain is a losers table. The cut falls at +27.2 on the gaining side, where the next agent out moved +25.9, and at −11.2 on the losing side, where the next moved −10.9. 15 agents joined and 18 left; a month you were not in is not a movement, so neither group can be ranked here and neither appears. Every other figure on this panel — the four cards, the metric table, the summary and the two mover lists — is still measured over the whole 107 and 110.

The comparison is per agent, which is what the source component does — it filters by campaign and ranks the agents inside the filter. 107 agents carried hours this month against 110 last month; 84 of this month's were on the floor at the live view's last refresh, 11 more were marked absent and the rest had already stopped dialling. The column set is the source's own Agent Detail set in this build's vocabulary. Three of its columns count products this schema does not model, and they are absent rather than invented; one more repeats a value the table already shows two blocks earlier, and it is dropped because a repeat is not information. The campaign code beside each name stands in for the source's job-type filter, which is not reconstructed here.

LIVE · auto-refreshes every 5 min · last loaded 3:12 PM

Today's Operations

Showing all agents with data today

84On Floor11 absent · 3 new

612.5Hours Today7.29 avg/agent

268Sales Today0.438 GPH pace

$48.91CPScost per sale

322Unitstoday total

11Absentof 92 last-7-day roster

Product code columns

Performance by campaign · by site

7Campaigns

84Agents

612.5Hours

268Sales

0.438GPH

$48.91CPS

Program	Campaign	Site	Agents	Hours▼	Sales	GPH	CPS	Units	Internet	Mobile Line	Video	Voice	% to Goal	Internet %	Mobile %
New Household · Retail	AC-1140	Site A	31	228.5	104	0.455	$47.02	125	52	21	33	19	101%	50%	20%
Mobile Propensity	MX-1215	Site A	18	133.0	72	0.541	$39.53	86	24	34	18	10	104%	33%	47%
New Household · Direct	AC-1141	Site A	12	88.0	34	0.386	$55.39	41	18	7	11	5	102%	53%	21%
Premium Bundle	UT-1194	Site A	7	51.5	16	0.311	$68.88	19	7	4	5	3	104%	44%	25%
Shared Plan	MX-1216	Site A	6	43.5	14	0.322	$66.49	17	5	7	3	2	95%	36%	50%
New Household · Retail	AC-1142	Site B	6	43.0	16	0.372	$57.51	19	8	3	5	3	93%	50%	19%
Mobile Propensity	MX-1217	Site B	4	25.0	12	0.480	$44.58	15	4	6	3	2	109%	33%	50%
TOTAL			84	612.5	268	0.438	$48.91	322	118	82	78	44	101%	44%	31%

New Household · RetailAC-1140Site A · 31 agents

101%

228.5Hours

104Sales

0.455GPH

$47CPS

125Units

Internet: 52Mobile Line: 21Video: 33Voice: 19

Mobile PropensityMX-1215Site A · 18 agents

104%

133.0Hours

72Sales

0.541GPH

$40CPS

86Units

Internet: 24Mobile Line: 34Video: 18Voice: 10

New Household · DirectAC-1141Site A · 12 agents

102%

88.0Hours

34Sales

0.386GPH

$55CPS

41Units

Internet: 18Mobile Line: 7Video: 11Voice: 5

Premium BundleUT-1194Site A · 7 agents

104%

51.5Hours

16Sales

0.311GPH

$69CPS

19Units

Internet: 7Mobile Line: 4Video: 5Voice: 3

Shared PlanMX-1216Site A · 6 agents

95%

43.5Hours

14Sales

0.322GPH

$66CPS

17Units

Internet: 5Mobile Line: 7Video: 3Voice: 2

New Household · RetailAC-1142Site B · 6 agents

93%

43.0Hours

16Sales

0.372GPH

$58CPS

19Units

Internet: 8Mobile Line: 3Video: 5Voice: 3

Mobile PropensityMX-1217Site B · 4 agents

109%

25.0Hours

12Sales

0.480GPH

$45CPS

15Units

Internet: 4Mobile Line: 6Video: 3Voice: 2

The summary strip above the table is the source's, which draws it only while a site filter is active; it is drawn for All sites too because that is the one place this view carries a business-wide GPH and CPS. Its CPS colour follows the mean of the visible campaigns' attainment, not the aggregate — two different numbers, and the mean is the one the product uses.

Agent leaderboard10 today

sort by headers

Agent	Site	Program	Hrs▼	Sales	GPH	Units	Internet	Mobile Line	Video	Voice	% to Goal	Internet %	Mobile %	Hist Q
M. Alvarez	Site A	Mobile Propensity	8.0	7	0.875	9	2	4	2	1	168%	29%	57%	Q1 EXCEEDING
T. Okonkwo	Site A	New Household · Retail	8.0	6	0.750	7	3	1	2	1	167%	50%	17%	Q1 EXCEEDING
K. Nakamura	Site A	New Household · Retail	8.0	4	0.500	5	2	1	2	—	111%	50%	25%	Q1 EXCEEDING
J. Whitfield	Site A	Premium Bundle	8.0	2	0.250	2	1	—	1	—	83%	50%	—	Q2 NEAR GOAL
R. Benoit	Site A	Mobile Propensity	7.5	5	0.667	6	1	3	1	1	128%	20%	60%	Q1 EXCEEDING
D. Fournier	Site A	New Household · Direct	7.5	3	0.400	4	2	1	1	—	105%	67%	33%	Q1 EXCEEDING
P. Osei	Site A	New Household · Retail	7.5	2	0.267	3	1	—	1	1	59%	50%	—	Q3 BELOW GOAL
S. Haddad	Site B	Mobile Propensity	7.0	3	0.429	4	1	2	1	—	97%	33%	67%	Q2 NEAR GOAL
C. Delgado	Site A	Shared Plan	7.0	—	—	—	—	—	—	—	0%	—	—	Q4 NO ACTIVITY
L. Marchetti	Site B	New Household · Retail	6.5	1	0.154	3	1	1	1	—	38%	100%	100%	Q3 BELOW GOAL
TOTAL (10)			75.0	33	0.440	43	14	13	12	4	96%	42%	39%

M. AlvarezSite A · Mobile Propensity

8.0h · 7 sales · 168% · $24

Internet: 2Mobile Line: 4Video: 2Voice: 1

T. OkonkwoSite A · New Household · Retail

8.0h · 6 sales · 167% · $29

Internet: 3Mobile Line: 1Video: 2Voice: 1

K. NakamuraSite A · New Household · Retail

8.0h · 4 sales · 111% · $43

Internet: 2Mobile Line: 1Video: 2

J. WhitfieldSite A · Premium Bundle

8.0h · 2 sales · 83% · $86

Internet: 1Video: 1

R. BenoitSite A · Mobile Propensity

7.5h · 5 sales · 128% · $32

Internet: 1Mobile Line: 3Video: 1Voice: 1

D. FournierSite A · New Household · Direct

7.5h · 3 sales · 105% · $54

Internet: 2Mobile Line: 1Video: 1

P. OseiSite A · New Household · Retail

7.5h · 2 sales · 59% · $80

Internet: 1Video: 1Voice: 1

S. HaddadSite B · Mobile Propensity

7.0h · 3 sales · 97% · $50

Internet: 1Mobile Line: 2Video: 1

C. DelgadoSite A · Shared Plan

7.0h · 0 sales · 0% · $150

L. MarchettiSite B · New Household · Retail

6.5h · 1 sales · 38% · $139

Internet: 1Mobile Line: 1Video: 1

Ten of the 84 agents on the floor carry a modelled row for today — the same ten the Site tab's ranking trays open on. The other 74 are counted in the KPI strip, the campaign table and the region cards, and are not named on this view; the MoM tab names all 110 who carried hours this month, at month-to-date grain rather than this one. Hist Q is the agent's standing in the 84-agent quartile population, not a reading of today.

Reconstruction · synthetic data

Four surfaces in the bar above: the whole business, one site drilled into, a month against the month before it, and the live day. Sites, programs, funding sources, campaign codes, agents and every figure are invented, and the gainshare percentages are perturbed as well as renamed. The arithmetic is real: the funder gate, the goal scaling it triggers, and the two denominators that come out of it. Controls that go nowhere are rendered disabled and say why.

And how any of it got built

Five production systems, no technical background.

04Method

Shipping production software as an operator

An operations director with no engineering background, and five systems in production that are still running. This is an honest account of how that happened in about five months, what it actually takes, and where the limits are.

2026Read →

The interesting parts are the ones I left out.

Happy to walk through any of it in detail. Architecture, trade-offs, and the bugs I'm least proud of.

Get in touch

Every call scored, not a sample.

Program A / Outbound

The rubric is the engine

Where the yield stops paying for itself.

Attempt outcomes

Cap planning

Rotation health

CID summary

Day of week × local hour

One campaign, two true percentages.

Goals vs Plan — Business Wide

Gainshare — Business WideOverall Table

🎯Daily Targets — Combined

Program attainment — ranked on the gate-corrected goal

Day-of-week performanceAll Programs

Daily performance breakdownAll Programs

Week-by-week performanceCurrent vs prior month

SPH % to goal · week over weekAll sites

Goals vs Plan — Site A

Gainshare — Site ASite Table

Funder gate — Site A

Supervisor ranking — Site Asite SPH goal 0.436

🎯Daily Targets — Site A

Quartile distribution — Site A

Day-of-week performanceSite A Site

Daily performance breakdownSite A Site

Week-by-week performanceCurrent vs prior month

SPH % to goal · week over weekSite A only

Month-over-month metrics

Top improvers

Biggest declines

Agent movers12 of 92

Month-over-month metrics

Top improvers

Biggest declines

Agent movers12 of 92

Today's Operations

Performance by campaign · by site

Attendance vs last 7 days

By region — live

Agent leaderboard10 today

Five production systems, no technical background.

Shipping production software as an operator

The interesting parts are the ones I left out.

Daily Targets — Combined

Daily Targets — Site A