The ultimate guide to CDPs for retail
Learn everything there is to know about CDPs in retail, including global trends, key insights, use cases, how leading retailers are leveraging personalization, and a framework for evaluating CDP vendors.
Why are retail companies adopting CDPs?
Retail has incredibly low margins and customers have more options than ever before on where and when to shop. With 80% of revenue coming from just 20% of customers,1 customer loyalty is everything. Data is your only advantage to stand out in a sea of virtually endless options.
Retail industry leaders differentiate themselves with personalization
Retail use cases
Acquisition campaigns
Drive new customer acquisition using browsing data to target potential customers and untapped lookalike audiences with promotions like free shipping or first-purchase discounts, increasing sign-ups and purchases.
Suppression campaigns
Reduce wasted ad spend by using loyalty program data to suppress frequent buyers from acquisition campaigns and reallocate budget toward lapsed or new customers, optimizing ROAS and efficiency.
Retargeting campaigns
Re-engage users who browsed or abandoned their cart by serving dynamic ads featuring their abandoned items and offering limited-time discounts, driving incremental purchases and recovering lost revenue.
Cross-sell campaigns
Promote complementary products to existing customers by leveraging purchase data to serve targeted ads featuring relevant add-ons, increasing wallet share and fulfilling unmet shopping needs.
Referral campaigns
Leverage existing customers to attract new shoppers through referral promotions that offer incentives for both the referrer and the new customer, driving cost-effective acquisition and brand advocacy.
Localized campaigns
Increase store foot traffic by targeting users near a store with personalized ads highlighting exclusive in-store discounts, encouraging in-person visits and boosting sales.
Seasonal campaigns
Maximize seasonal revenue by promoting holiday collections and early-bird discounts to create urgency and increase conversions during peak shopping periods.
Conversion APIs
Share online and offline conversion data with ad platforms, enabling algorithms to better identify and target users with a higher likelihood of converting to improve campaign performance.
Retail media networks
Monetize customer insights and first-party data by offering anonymized audience segments to brand partners for targeted advertising, generating incremental revenue through premium audience data sales.
Upsell & cross-sell campaigns
Promote complementary products using SKU-level purchase data to recommend related items, increasing AOV and encouraging customers to buy more.
Reactivation campaigns
Re-engage lapsed customers with personalized emails offering exclusive discounts to those who haven’t shopped in months, driving repeat purchases.
Seasonal campaigns
Promote holiday-specific products and early-bird discounts, capturing seasonal demand and increasing sales during peak shopping periods.
Price drop notifications
Notify customers about price drops on previously viewed or favorited items, increasing conversion rates by leveraging urgency.
Localized promotions
Use geo-targeted campaigns to promote in-store-only sales or events, driving foot traffic and boosting store-level revenue.
Replenishment reminders
Send timely reminders based on purchase frequency to encourage repeat purchases of consumable items, driving recurring revenue.
Loyalty program engagement
Offer double points, exclusive discounts, or early access to products to drive program participation and increase repeat purchases.
Post-purchase nurturing
Send product care guides, styling tips, or how-to videos after purchase, improving customer satisfaction and reducing returns.
Milestone celebrations
Reward loyalty milestones with exclusive discounts or gifts, strengthening long-term customer relationships.
Dynamic home page content
Display personalized banners and featured products based on browsing history, purchase behavior, or seasonality to increase engagement and conversions.
Dynamic app experiences
Customize navigation and category prioritization based on user preferences, making it easier for shoppers to find relevant products and improve retention.
Dynamic Search Results
Tailor search filters and results to highlight best-sellers, in-stock items, and relevant recommendations based on past purchases, improving search-to-purchase conversions.
Recommended products
Use purchase history and browsing data to suggest complementary items, increasing AOV through effective cross-selling.
Next best action
Encourage high-value customer actions such as joining a loyalty program, completing a wishlist, or adding more items to the cart to boost engagement and revenue.
Proactive cart nudges
Reduce cart abandonment by notifying users of low-stock items, sending reminders about items left in the cart, or offering limited-time discounts to drive conversions.
Savings goal tracking
Show progress toward free shipping thresholds, loyalty rewards, or spending milestones to encourage additional purchases and improve customer satisfaction.
Personalized rewards tracking
Highlight available loyalty rewards and suggest actions to maximize benefits, increasing participation and engagement in loyalty programs.
AI-powered chat recommendations
Use AI-driven chatbots to analyze user queries and browsing history in real-time, providing personalized product recommendations that improve user satisfaction and conversions.
What retail companies should consider when evaluating a CDP
90%5 of marketers say their traditional CDP does not do what they need, so why do they keep buying them?
| Category | Traditional CDP | Composable CDP |
|---|---|---|
| Architecture | Operates as a separate entity, removed from your company’s data | Integrates directly within your company’s data infrastructure |
| Security & data storage | Data is stored and maintained in the CDP’s data infrastructure | Data is stored and maintained in your existing data infrastructure |
| Data access | Supports user and event data | Supports both online and offline data |
| Data modeling | Uses predefined, generic data models that may not fully capture retail-specific nuances like SKUs or in-store data | Supports tailored data models to handle complex retail-specific data structures and relationships |
| Audience management | Supports broad audience segmentation and targeting but struggles with retail-specific data objects and hierarchies | Enables granular segmentation leveraging any data point, including product SKUs, stores, preferences, traits, and custom data science models |
| Customer journey customization | Provides standard customer journey templates that fit common use cases like email campaigns or cart abandonment | Powers fully adaptable journeys tailored for omnichannel retail paths, including cross-channel purchase cycles |
| Identity resolution | Relies on out-of-the-box identity resolution algorithms, which may not fully integrate across retail-specific systems and use cases | Supports custom algorithms to match and unify customer profiles across diverse data sources like POS and e-commerce |
| Pricing | Bundled pricing: dependent on monthly tracked users (MTUs) & feature add-ons | Unbundled: individually priced features with no MTU billing |
| Average implementation time | 6-12 months | 1-4 months |
- ArchitectureIntegrates directly within your company’s data infrastructure
- Security & data storageData is stored and maintained in your existing data infrastructure
- Data accessSupports both online and offline data
- Data modelingSupports tailored data models to handle complex retail-specific data structures and relationships
- Audience managementEnables granular segmentation leveraging any data point, including product SKUs, stores, preferences, traits, and custom data science models
- Customer journey customizationPowers fully adaptable journeys tailored for omnichannel retail paths, including cross-channel purchase cycles
- Identity resolutionSupports custom algorithms to match and unify customer profiles across diverse data sources like POS and e-commerce
- PricingUnbundled: individually priced features with no MTU billing
- Average implementation time1-4 months
- ArchitectureOperates as a separate entity, removed from your company’s data
- Security & data storageData is stored and maintained in the CDP’s data infrastructure
- Data accessSupports user and event data
- Data modelingUses predefined, generic data models that may not fully capture retail-specific nuances like SKUs or in-store data
- Audience managementSupports broad audience segmentation and targeting but struggles with retail-specific data objects and hierarchies
- Customer journey customizationProvides standard customer journey templates that fit common use cases like email campaigns or cart abandonment
- Identity resolutionRelies on out-of-the-box identity resolution algorithms, which may not fully integrate across retail-specific systems and use cases
- PricingBundled pricing: dependent on monthly tracked users (MTUs) & feature add-ons
- Average implementation time6-12 months
Why Hightouch for retail?
Your CDP vendor should mold to your data — not the other way around. Hightouch is purpose-built to handle the complexity of retail.
Leverage any data point in your warehouse – not just users, accounts, and events.
Build and activate audiences directly from your warehouse.
Integrate with your existing data infrastructure on your warehouse.
Leverage any data point in your warehouse – not just users, accounts, and events.
A complete CDP for retail
Members
Overlap
High LTV Email Abandoners
2,988 members
1,974 members
Cart Abandoners 2,988
Newsletter Subscribers 1,974
Member overlap 378 or 12.65%
Customer Data Platform resources
What is a Composable CDP?
Learn why Composable CDPs are seeing such rapid adoption, how they work, and why they're replacing traditional CDPs.
Free CDP RFP template: streamline your vendor evaluation
Building a tailored CDP RFP is key to choosing the right platform for your business. Use our free template and evaluation framework to focus on the most important features and values for your goals.
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