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Alliant

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"Enhances marketing with data-driven audience insights."

Case Study
Frequently Asked Questions
This company specializes in leveraging data to boost advertising efforts. They gather and analyze consumer data to provide businesses with insights for better audience targeting, data enrichment, marketing optimization, and credit solutions, making marketing more effective and streamlined.
Alliant Stated Claims
These are public claims Blurbs AI believes to have been made by Alliant.
Unique Data Cooperative
Alliant claims that their data cooperative aggregates diverse transactional data from over 500 brands for unique insights.
Advanced Audience Solutions
Alliant claims that their platforms deliver superior targeting and performance for clients using data-driven strategies.
Future-Proofed Marketing Technology
Alliant claims that their solutions are future-proofed by integrating extensive identity graphs for flexible delivery.
PERFORMANCE MARKETING
PEOPLE-BASED MARKETING
CREDIT SOLUTIONS
OPTIMIZATION
TRANSACTIONAL DATA
ETAIL WEST 2025
DATA COOPERATIVE
TRANSACTIONAL DATA ANALYSIS
AUDIENCE TARGETING
CREDIT DATA SOLUTIONS
MULTI-CHANNEL MARKETING
DATA ENRICHMENT
BlurbSTAR Case Study
Alliant & Collectibles Company
Data-driven mail reactivated inactive collectibles customers effectively.
5X
Exceeded expected order rates
10.83%
Group 1-10 actual order rate
This profile remains unclaimed. Blurbs can only offer a partial, unverified case study.
1.
Situation
Inactive customer reactivation was imperative.
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Direct mail lacked engagement clarity.
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Reactivation was critical for cost efficiency.
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Needed precise method to target.
A leading collectibles company with a vast inventory of rare coins, currency, and other collectibles faced the challenge of reactivating their inactive customers for its direct mail campaigns. These inactive customers had not engaged with the company for 12 to 48 months, and revitalizing this customer segment was a cost-saving priority that the company needed to tackle efficiently. The company realized that it needed to pinpoint the most qualified inactive customers to focus efforts on to optimize their reactivation and reduce wasted resources. Working with Alliant was seen as a strategic move to overcome this hurdle, leveraging data-driven insights to ensure that marketing investments were effective.
2.
Task
Predictive model to identify responders.
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Data analysis for customer inactivity.
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Segment customers into actionable groups.
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Predict likely responders for reactivation.
The collectibles company tasked Alliant with developing a data-driven approach to identify and reactivate inactive customers. Alliant's Data Science team needed to analyze customer data spanning 12 to 48 months of inactivity. They aimed to segment these customers effectively, crafting a model that could predict which customer groups were most likely to respond positively to direct mail efforts. The challenge was to create a precise classification of inactive customer segments to test and implement varying direct mail strategies for better reactivation rates. This task required comprehensive data analysis and segmentation to create actionable insights for reactivation.
3.
Action
Segmentation improved direct mail targeting.
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Developed prediction models for reactivation.
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Classified customers into actionable segments.
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Tested focused mailing strategies.
Alliant developed a custom optimization model focusing on reactivating the company's inactive customers. They analyzed the brand's data, creating a predictive model to classify the 12-48 month inactive customers into 20 groups based on their likelihood to respond to direct mail. The collectibles company tested three different segments: Groups 1-10, Groups 11-13, and Groups 14-15. By doing so, Alliant allowed the company to implement tailored mailing strategies to these segments, ensuring resources were allocated toward the most promising customer groups and reducing costs.
4.
Result
Exceeded reactivation expectations successfully.
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Order rates greatly exceeded predictions.
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Improved resource allocation in direct mail.
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Significant boost in customer engagement.
The collaboration between the collectibles company and Alliant resulted in significantly improved direct mail response rates. The use of Alliant’s custom optimization solution led to reactivation order rates exceeding expectations. For Groups 1-10, the actual order rate soared to 10.83%, against an expected 2%. Similarly, Groups 11-13 achieved a 5.85% rate, and Groups 14-15 realized a 4.20% rate. These results reflect the effectiveness of precise customer segmentation and data-driven strategy, allowing the company to efficiently allocate direct mail resources, significantly improving engagement with previously inactive customers.
Keywords
COLLECTIBLES REACTIVATION
DATA-DRIVEN DIRECT MAIL
CUSTOMER SEGMENTATION
INACTIVE CUSTOMER ENGAGEMENT
MARKETING PERFORMANCE
RESPONSE OPTIMIZATION
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