14 min read
Edward Chenard
Case Study
Case Study Best Buy January 2026 • 8 min read

Build vs. Buy: How We Outperformed a $30M Vendor Quote in 90 Days

When Best Buy needed an enterprise personalization platform, vendors quoted $20-30M and 18-24 months. We built it for $3.2M in 90 days—and it generated $1B+ in revenue.

Edward Chenard
Edward Chenard
Former Head of Emerging Technologies & Data Products, Best Buy
RESULTS SUMMARY
$3.2M
Build Cost
90 days
To Production
$1B+
Revenue Generated
1% → 17%
Conversion Rate
vs. vendor quotes of $20-30M and 18-24 months

The Challenge

In 2011, Best Buy faced an existential threat. The mandate was clear: implement an enterprise-grade personalization platform to stabilize the digital ecosystem and compete with Amazon.

The traditional path was obvious—buy from an established vendor. But the quotes came back sobering:

$20-30M
Vendor quotes
18-24 mo
Delivery timeline

We chose a different path: build in-house.

The Speed-to-Market Framework: 3 Pillars

When an organization chooses to build in-house, the goal is not to replicate a vendor's "Ferrari"—it's to build a "Pickup Truck" that runs immediately. This success was predicated on three core operational shifts.

Pillar 1: Ruthless Prioritization (The 80% Rule)

Vendors sell perfection and feature-completeness, which leads to "scope bloat."

THE STRATEGY

We identified the smallest possible set of features that would move the needle for our top use cases.

The Rule: Solve for the 80% of users now; ignore the 20% edge cases until the platform is revenue-positive.

This meant saying "no" to feature requests that would have added months to the timeline but only served edge cases. The discipline was uncomfortable—but it was essential.

Pillar 2: Radical Ownership (The Small Team Advantage)

Large vendor projects often drown in "steering committees" and cross-departmental handoffs.

THE TEAM

We utilized a dedicated team of 12 people who owned the outcome end-to-end.

No "innovation lab." No middle-management layers to hide behind. The team moved 10X faster than traditional enterprise cycles.

When ownership is clear, decisions happen fast. When decisions happen fast, momentum compounds.

Pillar 3: Outcome-Driven Metrics

We ignored vanity metrics and focused on the three KPIs that mattered to the P&L:

1% → 17%
Conversion Rate
The primary metric for personalization effectiveness
4 months
Time-to-Value
Achieved positive ROI in just 4 months after launch
$1B+
Total Impact
Life-of-platform revenue ($120M in year one)

The AI Warning: Don't Wait for Perfect

A Cautionary Tale

The biggest risk in the current AI landscape isn't building an imperfect tool—it's waiting for "perfection" while the market window closes.

In 2022, I proposed an AI logistics strategy at Shipwell that some considered "too early." We shipped it anyway—18 months before ChatGPT. Today, that early mover advantage is irreplaceable. The companies that waited are now "also-rans."

"Speed is a feature. Momentum is a strategy."

The Cost Comparison

Factor Vendor Quote Our Build
Total Cost $20-30M $3.2M
Timeline 18-24 months 90 days
Time to ROI 24+ months 4 months
Cost Savings 85%
This project won the Tekne Award (Minnesota's highest technology innovation honor) and qualified for US Innovation Tax Credits.
THE BOTTOM LINE

The goal isn't to build a "cheaper vendor." It's to build a focused solution that ships fast, iterates faster, and generates revenue while competitors are still in procurement meetings.

Edward Chenard

Are you paying for a Ferrari when you need a Pickup Truck?

I specialize in helping Fortune 500 and PE-backed firms replicate this high-velocity "Build" mindset. Whether you're auditing a vendor quote or building a proprietary AI stack, my frameworks deliver production-ready results in 90 days.

Book a Build vs. Buy Strategy Sprint
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Edward Chenard
Edward Chenard
AI Revenue Strategist

I spent 20 years building AI and data products at Best Buy, Target, C.H. Robinson, and Olo. I've launched 100+ products, built teams from 2 to 300+, and contributed to over $2.5B in AI-driven revenue — including the data architecture for Olo's $3.6B IPO. Now I publish the frameworks so other leaders can skip the expensive mistakes.