The Power of Granular Marketing Insights: How Polaris Research Drives Success at Every Level (DMA, Store, Product)
Imagine you’re a major retailer spending millions on advertising across the country. You know your campaigns are working overall, but are they performing equally well in Chicago versus Miami? Are certain stores driving more sales from your TV ads? Is one product flying off the shelves because of your social media push? Polaris Research, Inc., a leader in marketing analytics, has a powerful answer. By using advanced AI and their Box-Jenkins Transfer Function model, Polaris drills down into marketing performance at the DMA (Designated Market Area), store, and even product level. These granular insights help big advertisers optimize their budgets like never before, saving millions and boosting sales. Let’s explore why this level of detail matters and how it transforms advertising for large companies.
What Are Granular Marketing Insights?
Granular marketing insights mean analyzing advertising performance at a very specific level, think individual cities, stores, or even products. Instead of just knowing that your $10 million ad budget increased sales overall, Polaris can tell you:
DMA Level: How your ads perform in specific media markets, like New York versus Los Angeles.
Store Level: Which of your retail locations are seeing the biggest sales boost from your campaigns.
Product Level: Which specific products (say, a new sneaker line versus a clothing collection) are driving revenue thanks to your ads.
These insights are powered by Polaris’s core econometric model, the Box-Jenkins Transfer Function, which analyzes historical ad spending, sales data, and economic trends over time. By adding artificial intelligence (AI), Polaris takes this model to the next level, creating detailed, localized analyses that roll up into a bigger-picture view of your entire marketing portfolio.
How Does Polaris Do It?
Polaris starts with the same data as their broader model: weekly ad spending by channel (TV, social media, radio, etc.), weekly sales and revenue, and economic indicators like consumer confidence or unemployment rates. But their AI-powered approach allows them to break this data down into smaller segments:
AI for Precision: AI helps Polaris analyze massive datasets quickly, identifying patterns in specific regions, stores, or product lines that might be missed by traditional methods. For example, AI can spot that a social media campaign worked better for a product in urban stores than rural ones.
Granular Models: Polaris builds separate models for each DMA, store, or product, showing how ad spending in each area or for each item drives sales. These models are interconnected, so they “cascade” up to the overall portfolio model, ensuring the big picture stays clear.
Time Series Analysis: Like their broader approach, these granular models use time series data to understand how ads influence sales over weeks or months, factoring in local or product-specific trends.
This combination of AI and econometric modeling lets Polaris deliver insights that are both hyper-specific and aligned with your company’s overall strategy.
Why Granular Insights Matter
For large advertisers, understanding performance at the DMA, store, or product level is a game-changer. Here’s why:
Optimize Spending by Region: Not all markets respond the same way to ads. A TV campaign might drive huge sales in Dallas but fall flat in Seattle. Polaris’s DMA-level insights let you shift budgets to high-performing regions, avoiding waste in areas where ads aren’t resonating.
Boost Store Performance: If one store is outperforming others thanks to a specific ad channel, Polaris can pinpoint why. Maybe your radio ads are driving foot traffic to a suburban store but not an urban one. With this knowledge, you can tailor campaigns to each store’s strengths.
Focus on Winning Products: Some products benefit more from certain ads. For example, Polaris might find that your new energy drink sells better with online ads, while your snack line thrives with in-store promotions. This lets you allocate marketing dollars to the products and channels that drive the most revenue.
Save Millions: By identifying underperforming regions, stores, or products, you can cut wasteful spending. For example, if a product isn’t responding to radio ads in certain DMAs, you can redirect that budget to a more effective channel or market, potentially saving millions.
Tailored Strategies: Granular insights allow for hyper-targeted campaigns. Instead of a one-size-fits-all approach, you can create ads that speak to local audiences or highlight specific products, making your marketing more relevant and effective.
Real-World Example
Let’s say you’re a national retailer with 500 stores selling apparel and electronics. You spend $20 million annually on ads across TV, social media, and billboards. Polaris’s analysis might reveal:
In the Chicago DMA, TV ads drive 50% more apparel sales than social media, but electronics sales are flat.
Your Miami stores see a huge spike in electronics sales from Instagram campaigns, but billboards have little impact.
At the product level, your new headphone line is selling out thanks to online video ads, while your clothing line isn’t moving despite heavy radio spending.
With these insights, you could shift your Chicago budget toward TV for apparel, focus Instagram ads on electronics in Miami, and scale back radio for clothing nationwide. The result? Higher sales, less waste, and a leaner, more effective marketing strategy.
Why Polaris’s Approach Stands Out
Many marketing tools focus on broad metrics, like total clicks or overall sales. Others rely on “closed-loop” data, such as tracking online ad clicks to online purchases. But these approaches miss the full picture, especially for big advertisers with complex campaigns across multiple channels. Polaris’s granular models:
Look at all ad channels together, not just one.
Use AI to handle huge datasets and uncover hidden patterns.
Connect local insights to your overall strategy, ensuring every decision aligns with your big-picture goals