CMO Advisory Service Best Practice Series: Predictive Analytics for Marketing

Published by: IDC

Published: Jan. 6, 2013 - 21 Pages

Table of Contents

IDC Opinion

In This Study

Methodology

Situation Overview

Predictive Analytics for Marketing

Top Applications of Data Analytics for Marketing

Building a Marketing Analytics Function

A Functional Model for Analytics Organizations

Measures of Business Analytics Competency

IDC's Key Drivers of Success

Getting Started

IDC Recommendations

Expanding on Success — Big Data at SAP

Ways That SAP Uses for Insight-Driven Marketing

IDC Recommendations

Institutionalizing Analytics — Predictive Analytics at Intuit

Key Challenges

Formalizing Analytics Culture in Marketing

Intuit Marketing Analytics Competency

Essential Guidance

IDC Recommendations

future outlook

Learn More

Related Research

Synopsis

Figure: Propensity Segmentation in Banking

Figure: 2012 High-Tech Pipeline Conversion Benchmarks

Figure: The Basic Structure of the Typical Analytics Function

Figure: The Data Services Organization at Intuit

Figure: The Data Science and Analytics Team at Intuit

Abstract

This IDC study provides insight into how analytic models are being applied to marketing and sales processes to increase conversion rates, improve productivity, and provide enhanced customer experiences. Analytics is a rapidly emerging area of B2B marketing. BI and analytics have long been applied to B2C sales and marketing practices and to Web traffic and ad-related optimization. However, high-tech B2B companies are just beginning to learn how to apply the power of these models as well as their inherent limitations in the broader context of B2B customer creation.

"Analytics holds the potential to create quantum leaps in marketing funnel and sales pipeline performance," according to Gerry Murray, research manager in IDC's CMO and Sales Advisory Services. "Early advantages in analytics will put a few visionary companies on a long-term performance curve that will be almost impossible for laggards to cross. It takes too long to build the data, skills, processes, and cultural orientation to support data-driven marketing and sales. Companies with leadership that lack the vision to support investment in analytics will face significant marketplace penalties."

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