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市場調查報告書

機器學習和行銷的邂逅

Machine Learning Meets Marketing

出版商 Frost & Sullivan 商品編碼 327774
出版日期 內容資訊 英文 21 Pages
商品交期: 最快1-2個工作天內
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機器學習和行銷的邂逅 Machine Learning Meets Marketing
出版日期: 2015年04月01日 內容資訊: 英文 21 Pages
簡介

本報告提供新的解決方案 - 機器學習 (ML) 為基礎的案源計分 - 為現有的行銷自動化方式和相關市場、企業帶來的影響、新型解決方案的功能的辦法,及各供應商的配合措施案例、對各企業的行銷/採購負責人來說的意義和教訓等相關調查與考察。

摘要整理

簡介

機器學習與案源計分

機器學習 (ML) 為基礎的案源計分:新業務的案例 (共4件)

  • Mintigo:與CustomerDNA ™的合作
  • Radius Intelligence:中小企業 (SMB) 的企業的開拓Targets Enterprises Selling to SMBs
  • Infer:以與預見性建模的結果比較,與大企業對抗
  • Fliptop:諮詢性方法的採用

Lead的新供給來源、傳統型的軟體、供應商利用記錄

  • PerfectLeads:質樸的解決方案
  • LeadiD:應對Lead品質方面的課題
  • 現有的軟體的目標再設定,與供應商利用記錄 (Vendors of Record) 的今後支援預測
    • Salesforce:IP及AppExchange
    • Adobe Enhancements:B2C關連上最恰當的東西
    • Oracle:企業收購的體制強化

各企業的買主和銷售負責人,能對應機器學習為基礎的案源計分到哪個程度?

結論

圖表一覽

目錄
Product Code: 9A37-00-1C-00-00

This report explains why these new solutions represent a major improvement over existing marketing automation measurements, how they work, and how different vendors are exposing machine learning capabilities in their lead scoring solutions. This report should be of interest to buyers, sellers, and current users of marketing automation (MA) and customer relationship management (CRM) solutions.

Table of Contents

Executive Summary

Introduction

Machine Learning for Lead Scoring

Four Startups That Provide ML-Based Lead Scoring

  • Mintigo Identifies CustomerDNA™
  • Radius Intelligence Targets Enterprises Selling to SMBs
  • Infer Challenges Enterprises to Compare Predictive Modeling Results
  • Fliptop Takes a Consultative Approach

New Lead Sources, Old Software, and Vendors of Record

  • A Simple Solution from PerfectLeads
  • How LeadiD Addresses Lead Quality Issues
  • Repurposing Existing Software, Waiting on Vendors of Record
  • Salesforce Has the IP and the AppExchange
  • Adobe Enhancements Are Most Relevant in B2C
  • Oracle Gears Up through Acquisitions

How Ready Are Buyers and Sellers for ML-based Lead Scoring?

The Last Word

List of Exhibits

  • Exhibit 1: Generating a Machine Learning Model (Classifier)
  • Exhibit 2: Mintigo Process
  • Exhibit 3: Radius Segment-Building Screen
  • Exhibit 4: Infer External Signals Screen
  • Exhibit 5: What Fliptop Signal Data Looks Like in Marketo
  • Exhibit 6: External data sources are not very popular with large enterprises
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