1. Predictive analytic techniques are used to create detailed customer profiles and segments in order to personalize marketing messages and offers.
2. A predictive behavior modeling approach uses recency, frequency, monetary value and other data to forecast customer lifetime value and do dynamic micro-segmentation.
3. Personas representing specific customer segments are constructed based on attributes like goals, motivations and behaviors derived from various data sources.
29. Predictive Behavior Modeling
Simplest one RFM model: Recency, Frequency, Monetary
- Recency - How recently did the customer purchase?
- Frequency - How often do they purchase?
- Monetary Value - How much do they spend?
Web-based (SaaS, massive datasets)
- Continual dynamic micro-segmentation
- Customer lifetime value (LTV) forecasting
- Mathematics
- Secret sauce
*LTV is the predicted sum of all future revenues
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30. Personas
Personas are specific archetypes of people in the target
audience
Persona is a digital identity constructed by a presentation
of oneself
With micro segmentation personas are constructed to be
representative of specific segment
Personas are representations of a cluster of users with
similar behavior
Variety of sources, such as usability testing, surveys, field
studies, interviews, behaviors, goals, motivations and
market research
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31. Personas
Personas are specific archetypes of people in the target
audience
Persona is a identity constructed by a presentation of
oneself
With micro segmentation, personas are constructed to be
representative of specific segment
31
Brainy Smurf Smurfette Diane von Smurfstenburg Jokey Smurf
息 OTIUM-CSR
32. Digital Marketing Funnel
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Acquisition Landing
Page(s)
Content
Conversation
Conversion CRM Retention Loyalty
Unique Audience
Active Reach 10-30%
(Call to act)
Add-to-card 5-8%
Sales conversion rate 1-3%