Objective

To implement Adobe Target for delivering personalized content and offers based on real-time customer behavior and data, enhancing the overall customer experience and improving conversion rates.

​ ​ ​ ​ ​ ​ ​ ​Solution Overview

Step 1: Adobe Target Setup and Configuration

•  Environment Setup:

The first step involved setting up Adobe Target in the client’s environment, including configuring properties and mboxes to enable targeted content delivery.

# Initialize Adobe Target environment

adobe-target --init --env=production

•  Integration with AEP:

Adobe Target was integrated with Adobe Experience Platform (AEP) to leverage real-time customer profiles for personalized content delivery. This integration allowed the client to use data from various sources, such as browsing history and service usage, to drive personalization.

 @Component(immediate = true)

public class TargetService {

 @Activate

    protected void activate(TargetConfig config) {

        this.clientCode = config.clientCode();

        this.environmentId = config.environmentId();

    }

}
Step 2: Audience Segmentation and Personalization

• Creating Audience Segments:

We used data from Adobe Analytics and AEP to define key audience segments. For example, one segment included customers who had recently browsed broadband upgrade options but had not yet purchased.

var broadbandBrowsers = {

    "segmentId": "seg_broadband_browsers",

    "criteria": {

        "pageViews": "broadband_upgrade_page",

        "purchaseStatus": "not_purchased"

    }

};

adobe.target.getSegments().then(segments => {

    segments.push(broadbandBrowsers);

});

• Personalizing Content and Offers:

Adobe Target’s Visual Experience Composer was used to create personalized content and offers. For example, customers in the broadband browsers segment were shown tailored offers for broadband upgrades when they visited the website.

adobe.target.applyOffer({

    "mbox": "broadband-offer",

    "params": {

        "segmentId": "seg_broadband_browsers"

    },

    "offer": {

        "content": "Upgrade to our fastest broadband plan and save 20%!"

    }

});

Step 3: A/B Testing and Experience Optimization

• A/B Testing Different Offers:

To determine the most effective offers, we set up A/B tests in Adobe Target to compare different promotional strategies. For example, one test compared a discount offer with a free installation offer.

adobe.target.createABTest({

    "mbox": "broadband-offer-test",

    "variants": [

        {

            "name": "Discount Offer",

            "content": "Save 20% on your broadband upgrade!"

        },

        {

            "name": "Free Installation",

            "content": "Upgrade now and get free installation!"

        }

    ],

    "metrics": {

        "conversionRate": "purchase"

    }

}); 

• Optimizing User Experience:

The results of the A/B tests were analyzed to optimize the user experience. For instance, if the discount offer performed better, it was rolled out to the entire segment, while the free installation offer was adjusted or replaced.

public class ExperienceOptimizationService {

    public void optimizeExperience(String testId) {

        Report report = adobeAnalytics.getABTestReport(testId);

        adobeTargetClient.updateExperience(report.getWinningVariant());

    }

}

Step 4: Real-Time Personalization

• Real-Time Offer Delivery:

Adobe Target’s real-time personalization capabilities were leveraged to deliver offers based on user behavior in real-time. For example, if a customer visited the mobile plans page after browsing broadband options, they were shown a bundled offer.

adobe.target.getOffer({

    "mbox": "real-time-offer",

    "params": {

        "userBehavior": "browsed_broadband_then_mobile"

    }

}).then(offer => {

    document.getElementById("offer-container").innerHTML = offer.content;

});

• Cross-Channel Consistency:

The personalized offers were synchronized across the client’s website, mobile app, and email campaigns to ensure a consistent experience. This was achieved through tight integration between Adobe Target, AEP, and the client’s CRM system.

public class CrossChannelSyncService {

    public void syncOffersAcrossChannels(String customerId) {

        Offer offer = adobeTargetClient.getRealTimeOffer(customerId);

        syncToWeb(customerId, offer);

        syncToMobileApp(customerId, offer);

        syncToEmail(customerId, offer);

    }

} ​ ​ ​

Step 5: Data Analytics and Continuous Improvement

• Analyzing Conversion Data:

Adobe Analytics was used to track the performance of personalized offers and A/B tests. Metrics such as conversion rates, average order value, and customer lifetime value were analyzed to measure the success of the personalization strategies.

public class ConversionAnalyticsService {

    public void analyzeConversionData(String segmentId) {

        Report report = adobeAnalytics.getSegmentReport(segmentId);

        report.getMetrics().forEach(metric -> {

            System.out.println(metric.getName() + ": " + metric.getValue());

        });

    }

}

• Iterative Optimization:

The insights gained from analytics were used to iteratively optimize the personalization strategies. For example, if a particular segment was not responding well to a specific offer, the content and timing were adjusted based on the data.

adobe.target.optimizeOffer({

    "mbox": "broadband-offer",

    "params": {

        "segmentId": "seg_broadband_browsers",

        "metric": "conversionRate",

        "threshold": ">=10%"

    },

    "offer": {

        "content": "Switch to our premium broadband and enjoy exclusive benefits!"

    }

});