Retailer resilience through intelligent operations: A conversation with CommerceIQ's CMO

Listen to the full conversation with Sai Koppala on The Agile Brand podcast, hosted by Greg Kihlström:

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The outdated playbook brands can't afford anymore

Economic pressures like tariffs and inflation are forcing retail brands into an impossible choice: raise prices and risk losing customers, or maintain prices and watch margins disappear. Deep discounts and across-the-board price hikes represent the old playbook—tactics that damage both profitability and consumer trust in equal measure.

Sai Koppala, CMO at CommerceIQ, joined Greg Kihlström on The Agile Brand podcast to discuss how leading brands are breaking free from this false choice. With over 20 years in marketing and strategy, including leadership roles at SheerID, Apigee (acquired by Google), and SAP, Koppala brings a unique perspective on how AI is transforming retail operations from reactive to resilient.

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How brands are rewriting the P&L playbook

Traditional pricing strategies treat all products and all consumers the same. This one-size-fits-all approach ignores the complexity of consumer behavior and market dynamics.

Koppala explains how leading brands now use AI to identify exactly where price increases are viable:

• Combining multiple data sources: Sales operations, retail media performance, and shopper behavior
• Analyzing price elasticity by segment: Products in higher price bands have seen 10%+ increases, while lower-priced items remain flat
• Micro-targeting pricing strategies: AI analyzes click-through signals, sales data, and promotional performance to identify specific SKUs where price increases won't trigger customer flight
• Building trust through transparency: Instead of shrinkflation, brands are being upfront about necessary price adjustments

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The retail media efficiency opportunity

While brands scrutinize revenue opportunities through smarter pricing, they're simultaneously attacking costs through more efficient retail media spending.

Key strategies brands are using:

• Evaluate organic performance first: If a product already ranks well organically for a search term, redirect media dollars elsewhere
• Optimize in real time: Process 50+ data signals including CPC, share of shelf, and inventory levels
• Focus on incremental gains: During Prime Day, 10+ brands achieved 100-140% increases in incremental ROAS while CPCs dropped

Real results:

One auto care brand working with CommerceIQ:

• Grew traffic 3X
• Increased sales 200%
• Kept retail media spend flat

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Why manual optimization can't keep pace

Manual optimization hits a wall quickly. Consider the math:

• 500 SKUs across 20 retailers = 10,000 individual combinations to monitor
• 50+ real-time data signals per combination
• Market conditions shifting minute-by-minute during major shopping events

Koppala emphasizes: "If I'm a brand with 500 plus SKUs and I'm selling across 20 different retailers in the US, it's manually impossible for me to optimize each and every aspect of it."

What AI handles automatically:

• CPC rates
• Share of shelf metrics
• Inventory levels
• Competitor pricing
• Search trends
• Demand curve identification
• Investment recommendations

Automation doesn't remove humans from decisions—it shifts human focus from data gathering to strategic decision-making.

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The digital shelf content challenge

In categories where 30-40% of sales are digital or digitally influenced, product detail pages become critical conversion points. Maintaining consistency across retailers remains a massive operational challenge.

The scale of the problem:

• 500 SKUs × 10 retailers = 5,000 product pages to manage
• Each requires specific imagery, messaging, attributes, and descriptions
• Search trends shift, requiring constant updates
• Manual processes can't keep pace

How AI solves this:

1. Monitors trending keywords in specific categories
2. Identifies products where those attributes should be highlighted
3. Generates updated content for marketing team review
4. Deploys approved updates automatically across all retailers

Example: If "protein" becomes a major search term in pet food, the system flags relevant products and updates content accordingly.

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From dashboards to AI teammates

Traditional business intelligence tools present data in dashboards. Humans interpret, decide, and execute manually. AI teammates function differently.

The AI teammate workflow:

1. Analyze: Process data across multiple sources
2. Identify: Pinpoint specific actions that'll drive business outcomes
3. Recommend: Present options for human approval
4. Execute: Implement approved changes automatically

Example use case—out of stock management:

• AI identifies SKUs that are out of stock
• Recommends pausing retail media spend on those items
• Human reviews and approves
• System executes changes immediately across all campaigns

Koppala describes this through CommerceIQ's "Ally" AI teammates: teams shift from chasing data to focusing on strategic decisions and collaboration.

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The metrics that actually matter

Growth for growth's sake loses appeal when margins compress. Volume increases that erode profitability don't build sustainable businesses.

Key metrics for AI-driven operations:

• Incremental ROAS: Only sales that wouldn't have happened without ad spend
• Net profit per million impressions: True profitability, not just volume
• Contribution margin: Whether growth is actually profitable

Why incremental ROAS matters:

Traditional ROAS measures total sales per dollar spent. Incremental ROAS measures only the truly additive impact—the sales that wouldn't have occurred otherwise. This distinction becomes critical for optimizing spending efficiency.

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Building consumer trust in uncertain times

Consumer trust becomes currency when markets feel unstable. Shrinkflation damaged that trust by obscuring price increases. Transparency rebuilds it.

How major brands are handling this:

• Walmart: Publicly committed to keeping prices as low as possible while acknowledging necessary increases
• Procter & Gamble: Announced price increases across 25% of portfolio, specifically citing tariff-driven costs
• Leading brands: Using AI to raise prices where elasticity exists while holding prices for price-sensitive segments

Brands that communicate openly about pricing rationale maintain customer relationships even during difficult adjustments.

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The future of brand-retailer collaboration

Koppala envisions a transformation in how brands and retailers work together. Today's joint business planning involves manual processes, PowerPoint presentations, Excel spreadsheets, and slow negotiation cycles.

Tomorrow's AI-mediated workflow:

1. Brand's AI identifies top 10 optimizations to improve conversion on retailer's website
2. Brand team reviews and approves recommendations
3. Changes automatically flow to retailer's systems
4. Retailer's AI agent implements updates

Additional opportunities:

• Enhanced personalization: Combining shopper behavior data with product data for targeted recommendations
• Real-time optimization: Both sides working from unified data signals
• Faster execution: Minutes instead of weeks for strategy adjustments

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Building teams for the AI era

Hiring strategies must shift alongside operational strategies. Koppala emphasizes two critical attributes when building teams:

1. Psychological safety

• Team members can experiment without fear of failure
• Testing and iteration become standard practice
• Intelligent risk-taking drives discovery of what works

2. Growth mindset

• Willingness to approach problems from first principles
• Comfort with "I haven't done this before, but I'm open to trying"
• Rejection of "we've always done it this way" thinking

Koppala models this approach himself, experimenting with AI workflows to understand how automation can help his marketing team. Leaders who remain hands-off with new technologies can't effectively guide teams through transformation.

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The choice facing brands today

Economic pressures aren't disappearing. Tariffs, inflation, supply chain disruptions—these challenges persist. The question isn't whether brands face difficult decisions, but whether they'll make those decisions with intelligence or instinct.

What AI-driven operations deliver:

• Surgical precision in pricing adjustments
• Real-time media spending optimization
• Digital shelf presence maintained at scale
• Thousands of micro-optimizations impossible through manual work

What happens without adaptation:

• Forced choice between margins and customers
• Inability to compete with operationally sophisticated competitors
• Lost ground across every dimension that matters

The technology exists. The strategies are proven. What remains is the decision to adapt.

If you would like to see how the Intelligent Content Engine works in practice, we would be delighted to show you real examples from leading CPGs and help identify where connected content operations can unlock growth for your brand. Reach out here.