Dan Cropsey

• 1,700+ — Purchase Preference, Intent, and Lifestyle Audiences

Most brand advertising targeted broad proxy audiences like demographics, or when using purchase data, it was typically targeted to audiences who “bought the product” or “did not buy the product.” Most audience targeting could not reach and specifically influence consumers based on the “why” or the “why not” behind the purchase.

As a solution, I combined the sophisticated analytics behind strategic retailer assortment and brand portfolio decisions with vast amounts of retailer loyalty transactions, purchase panels, location visiting behavior, demographics, store profiles, and detailed product label data to identify the “why behind the buy.”

We created and maintained personality audiences with the following approach:
• Sourced diverse purchase and lifestyle data necessary to understand our lives and why we buy what we buy.
• Linked desperate behavioral data anonymously via PII and 3rd-party agents (clean room).
• Selected shopper personalities to create and commercialize based on industry trends, brand investments, and client feedback.
• Researched and discovered broad-based “choice drivers” via market structure and other consumer-decision tree methodologies.
• Scored 100+ million shoppers for 1,700+ buying personalities based on their historical behaviors.
• Determined the minimal evidence required to qualify as having a buying personality, for example, buying water does not make the person a Vegan.
• Vetted personality propensities against known product buying. For example, gluten-avoiders are 12x more likely to buy Udi’s bread, or cooking-avoiders are 7x more likely to buy mac and cheese.
• Packaged shopper personality households with digital IDs and delivered to industry DSPs, agencies, and other media buying platforms.

We became the first to market with comprehensive choice-driver-based audiences. I enabled new audience targeting capabilities, provided richer insights into purchase affinities, and facilitated highly personalized messaging opportunities, achieving higher ROAS, especially for brands looking to acquire new brand buyers.

• 90% — Major Retailer Adoption Rate

Retailers struggled to understand their local market buying needs and competitive situation beyond major markets like the Chicago metro area. Legacy solutions were fixed, expensive, and too broad for localized product assortment, pricing, and promotion decisions.

I created a solution that would allow retailers to define their own local markets where their stores and competitive stores could be grouped and analyzed based on location and proximity, while maintaining retailer partnership, data cooperation, and confidentiality rules that would not expose individual competitor sales.

The solution involved the following:
• Creating a modern data platform where client-specific aggregations of store data could be managed.
• Building a UX where retail clients could define specific geographics like zip codes or store proximity rules, like a mile from these stores, which define the desired local market.
• Establishing rule vetting certification logic to ensure retailer cooperation and confidentiality rules where maintained.
• Executing high-scale store and product data aggregation capabilities for retailer and competitive store sets based on the local market definition.
• Maintaining and re-vetting the local market data weekly.

We became the first to enable retailers to create their own micro trade area and competitive intelligence markets. I quickly established this new, more granular retailer, market, and competitive intelligence capability across the industry with over 90% market penetration. Retailers were able to better inform individual stores, ad zones, price zones, and other localized decisions. The company was also able to create a new revenue stream, establish a strategic role inside the retailer, solidifying partnership/cooperation, and motivate brands to invest in similar data, sold by company.

Most digital and television advertising was targeted and delivered to media based on demographic proxies rather than actual purchasing behavior, resulting in media budget waste, less relevant messaging, and poor ROAS measurement.

I created a solution that would enable advertisers to target individual consumers or media programs based on what viewers buy and, not just on what broad demographic or interest group they may belong to.

This solution required the following:
• Recruiting and incentivizing a large nationally representative group of retailers to create a massive addressable shopper loyalty platform.
• Establishing a consumer privacy-safe data sharing, household linking, and identity anonymizing capability with 3rd-party agents and clean rooms.
• Appending demographics and other household attributes for cleansing, enriching, and expanding audiences, i.e., look-likes.
• Creating and maintaining syndicated and custom audiences based on client and brand audience definitions.
• Distributing audiences with associated digital IDs to DSPs, agencies, and other media buying platforms.
• Receiving impression logs and other campaign information to enable sophisticated test versus control measurement to provide sales incrementality and ROAS metrics.

I helped revolutionize how CPG advertising is performed and measured. We achieved the first omnimedia addressable targeting and measurement solution based on purchase behavior and the highest market share for CPG-based purchased audiences and measurement.