Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World (2nd Edition) (Que Biz-Tech)
暫譯: 數位行銷分析：在數位世界中理解消費者數據（第二版）（Que Biz-Tech）

Distill Maximum Value from Your Digital Data! Do It Now!

Why hasn’t all that data delivered a whopping competitive advantage? Because you’ve barely begun to use it, that’s why! Good news: neither have your competitors. It’s hard! But digital marketing analytics is 100% doable, it offers colossal opportunities, and all of the data is accessible to you. Chuck Hemann and Ken Burbary will help you chop the problem down to size, solve every piece of the puzzle, and integrate a virtually frictionless system for moving from data to decision, action to results! Scope it out, pick your tools, learn to listen, get the metrics right, and then distill your digital data for maximum value for everything from R&D to customer service to social media marketing!

• Prioritize—because you can’t measure and analyze everything
• Use analysis to craft experiences that profoundly reflect each customer’s needs, expectations, and behaviors
• Measure real digital media ROI: sales, leads, and customer satisfaction
• Track the performance of all paid, earned, and owned digital channels
• Leverage digital data way beyond PR and marketing: for strategic planning, product development, and HR
• Start optimizing digital content in real time
• Implement advanced tools, processes, and algorithms for accurately measuring influence
• Make the most of surveys, focus groups, and offline research synergies
• Focus new marketing investments where they’ll deliver the most value • Identify and understand your most important audiences across the digital ecosystem

“Chuck and Ken lead marketers clearly and efficiently through the minefield of digital marketing measurement. And they do so with a lightness of touch and absence of jargon so rare in this overhyped, much-misunderstood ecosystem.”
—Sam Knowles, Founder & MD of Insight Agents; author of Narrative by Numbers: How to Tell Powerful & Purposeful Stories with Data