Recommendations to Increase the Customer Interaction of E-commerce Applications with Web Usage Mining

Rajapaksha, Pamuditha; Asanka, Dinesh

doi:10.1109/inc457730.2023.10263131

Cited by 1 publication

(1 citation statement)

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“…According to 4) , providing recommendations using an interest profile can increase customer interaction on social media by up to 60%. The detected user behavior and purchasing patterns were used to produce recommendations; and this tactic boosts the efficacy and profitable economic position 5) .…”

Section: Introductionmentioning

confidence: 99%

Topic-Based Segmentation in Email Marketing

Surjandari,

Harvey P. Gunawijaya,

Angella Natalia Ghea Puspita

2024

Evergreen

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Email is one of the most common marketing channels businesses use to reach potential customers and advertise their products or services. To maintain the relevance of email messages and the performance of email marketing, personalization, or information from everyone is usually needed. This personalization is often derived from transactional data that reflects the behavior and interests of each customer. However, not all industries have enough transactional data for each of their customers. The challenge is how to use non-transactional data to discover customer interests and preferences. This study proposes to model customer preferences and build customer segmentation in email marketing based on topics and historical email interactions with customers. Biterm Topic Model (BTM) is used in this study for topic modeling because it is suitable for short texts such as email titles. Then segmentation is built based on the generated topics and customer email interaction history. Topic-based segmentation can increase the open rate by nearly two times on average. Marketers can use the findings of this study to develop new strategies and achieve higher email marketing performance.

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Section: Introductionmentioning

confidence: 99%