XDL Pricing Flashcards

(32 cards)

1
Q

What are the two questions that set an XDL subscription price?

A

1️⃣ Fleet size → choose a Volume-Bucket row (Pilot…Enterprise). 2️⃣ Analytics depth → choose a Feature Package column (Core→Insight→Insight-Plus→Enterprise AI). Price = row × column.

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2
Q

Why did we split price into rows & columns instead of one flat fee?

A

Rows align price to infrastructure cost curve (Redshift/Bedrock). Columns align to customer value (dashboards, AI allowance). Together they protect gross margin and create upsell lanes.

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3
Q

Which fleet-size bucket is most common in our funnel?

A

Growth-B (60 k – 140 k endpoints) – mid-size co-ops & cities; default 4 RPUs and 75 k free AI queries.

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4
Q

What default bundle distinguishes Insight-Plus from Insight?

A

Insight-Plus includes one dashboard pack of choice + 25 k extra AI queries (no à-la-carte hunting).

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5
Q

When does an account qualify for ‘Enterprise’ row pricing?

A

When its fleet exceeds 700 k endpoints (≈500 k for discount trigger)—after infra cost flattens so GM ≥ 55 %.

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6
Q

Name one phrase to explain the grid to execs in <10 s.

A

“Rows are size, columns are sophistication; multiply and you’re done.”

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7
Q

Old 2022 fee for water & gas meters?

A

$0.40 per meter per year (≈ 3.3 ¢/mo).

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8
Q

Why did Houston-Water lose money under the old fee?

A

Unit COGS (Redshift+S3+Bedrock ≈ $0.43/EP/yr) exceeded 40 ¢ list → –8 % GM.

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9
Q

New per-endpoint price range for Insight tier (most deals)?

A

$0.05 – $0.09 per EP per month depending on volume bucket.

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10
Q

ARR uplift across the 4 pilot utilities (old → new)?

A

+$603 k (+80 %), from $755 k to $1.36 M in Year 1.

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11
Q

New blended GM across those utilities?

A

≈ 45 %, compared to 6 % (and negatives) before.

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12
Q

Three pain points fixed by matrix pricing.

A

① Loss-making water/gas, ② No upsell path, ③ Ad-hoc discounts for large fleets.

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13
Q

What prevents AI-token cost overruns in new model?

A

Free query allowances per tier + $0.002/query overage SKU → COGS passes through.

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14
Q

Why pilots convert faster under new pricing?

A

Pilot row starts at $1.5 k/mo floor, avoiding $50 k minimum PO hurdles.

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15
Q

One-sentence value pitch to Finance.

A

“We doubled ARR and locked > 40 % margin without raising a single utility above 1 % of meter O&M.”

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16
Q

List the four dashboard packs and their personas.

A

GRID-PK (reliability), NETOPS-PK (network ops), CAP-PK (capacity planning), CUST-PK (customer service & revenue).

17
Q

Pack list-price per endpoint per month?

A

$0.010 / EP / mo (≈17–22 % cheaper than two singles).

18
Q

How many dashboards ship in each pack?

A

Two (e.g., GRID-PK = IEEE Outage Metrics + Power Quality).

19
Q

What SKU prefix do all packs share?

A

XDL-PK-* (e.g., XDL-PK-CAP).

20
Q

Base build price for any single dashboard?

A

8 PS credits = $4 200 (standard build).

21
Q

Standard delivery timeline for a dashboard?

A

4 weeks (Kick-off → Build → UAT → Go-live → Training).

22
Q

Automation Basic add-on fee (per EP)?

A

$0.010 / EP / mo; nicknamed “nightly PDF & email alerts.”

23
Q

What % of dashboard build is templated vs. configurable?

A

80 % templated, 20 % configurable for repeatable margin.

24
Q

Max Pilot discount?

A

20 % (Pilot row only; $10 k cap).

25
Annual-prepay discount cap?
2 % (stacks with others if GM ≥ 55 %).
26
Partner margin bands?
15 % standard, 20 % strategic.
27
Bundle discount rule?
≤ 10 % when subscription + ≥ 1 dashboard ordered together.
28
GM floor after all discounts?
55 %.
29
Sales quote script in ≤30 s.
“Count endpoints → pick the row. Need dashboards day-one? Pick the column. Multiply endpoints × price and you’re done.”
30
Why Enterprise row price tapers to $0.04?
Infra cost (Redshift RPUs, S3) plateaus beyond 500 k endpoints; volume discount preserves GM while staying sub-$1 EP/yr.
31
Primary upsell levers post-go-live (3).
More dashboard packs, AI-query overage, storage add-ons (HOT+/COLD).
32
One-liner for Board Q&A on complexity.
“It’s only two decisions for the buyer—size and sophistication—everything else is optional value stacking.”