Some of the targeting tactics generate prepackaged audiences to help you quickly create useful segmentations.
These prepackaged targeting tactics are available in the targeting UI:
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Lifecycle audiences
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Purchase personas*
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Product affinity*
- Churn prediction audiences*
* Included in the Extended AI package—contact your Voyado representative for details.
NOTE
To ensure good results, these tactics will only appear when the correct configuration has been done and there is enough data available, mainly concerning transactions and product metadata.
Here we'll look at these targeting tactics and their building blocks in more detail.
Your customers can be divided up according to where they are in their shopping journey with this merchant. The building blocks available in this targeting tactic are:
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Never purchased: Customers who have not made any purchase yet.
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First time buyers: Customers who have recently made their first purchase.
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Recurring customers: Reliable customers with repeat purchases.
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Win-back customers: Previously inactive customers who have recently shown renewed interest.
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Defecting customers: Customers showing signs of reduced interaction and engagement.
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Lost customers: Customers who were once active but have ceased their engagement.
This tactic leverages RFM scoring which has existed in Engage for a long time. The Purchase personas building blocks make it much easier to use that existing data in a useful and intelligent way.
Your contacts can be divided into purchase personas based on the way they shop:
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Top Tier: Your most valuable customers, making recent, frequent, and high-value purchases. RFM values: R=4-5, F=4-5, M=4-5. Reward these customers with exclusive offers, loyalty programs, or personalized incentives to maintain their loyalty and encourage further spending. They can become early adopters for new products and will help promote your brand.
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Active buyers: Moderately active, showing consistent purchase behavior with moderate recency, frequency, and monetary value.R=3-5, F=3-5, M=1-5. Reward their consistent and steady behavior with tailored recommendations with the feeling of exclusive offers or upsell opportunities. Consider a loyalty scheme. Combined with some product affinity understanding you might push them to complementary products rather than they usually purchase.
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Potential gems: These show promising signs with good frequency but lower monetary value. They have the potential to become valuable if motivated properly. RFM Values: R=4-5, F=4-5, M=1-2. Motivate them to purchase with targeted promotions, upselling or cross-selling relevant products, or offering discounts on higher-value items to increase their average order value. Increase their spend by introducing related products and other relevant product recommendations. Offer them something to boost their spending, such as “3 for 2” offers.
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Occasional high spenders: These customers show high spend behavior whenever they make purchases, although they do it very often. RFM Values: R=1-5, F=1-2, M=4-5. For new customers, build the relationship with onboarding support and special offers to increase their visits. For old customers, nudge them into your commercial channels, such as your e-commerce web site or physical store, by offering personalized reactivation promotions, organizing special events, or offers with short validity or a touch of FOMO (soon out of stock etc.).
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Dormant: Have low recency, frequency, and monetary value, indicating a risk of churn or disengagement.RFM Values: R=1, F=1-2, M=1-2. Implement strategies such as personalized re-engagement campaigns, exclusive discounts or offers, or proactive customer support to prevent them from leaving and win back their interest and loyalty. Remember to exclude new customers from this segment before approaching them with a reactivation message.
Product affinity is an AI-powered smart tactic and has its own page here.
Churn prediction audiences
This tactic identifies different customer audiences with a predicted likelihood to churn, calculated using a churn scoring metric ranging from 0.0 to 1.0. This score is derived from various factors, including purchase behavior, email engagement, transaction history, and communication channel reachability. The tactic helps marketers proactively address customer retention by targeting groups based on their risk level. Read more about the predictive scoring mechanisms here.
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