This post on digital behavioral analysis is the second in a series that outlines ideation techniques to develop customer-centric test hypotheses. Use these methods to power your experimentation platform and your a/b testing program.
This blog series complements our free, in-depth guide to using structured ideation methods to generate better ideas, better tests and better decisions.
All You Need to Know about Powering Your Tests with High-Value Ideas.
GET THE GUIDEWritten in partnership with Conductrics, the guide is comprehensive, and it sprung from the gospel of charismatic Conductrics CEO and Founder Matt Gershoff:
Most of the work – and value – in experimentation is in how well the problem is defined upfront; how well the experiment is designed; and how well the team is able to understand and interpret the experiment’s results
Our first post used “how well the problem is defined up front” as a jumping off point to discuss what structured ideation is, why it matters, and how to start yourself off right. The first actual method (out of six methods in the guide) that we’ll discuss here is Digital Behavioral Analysis.
Chapter 2: Digital Behavioral Analysis
If you’re not happy with your current optimization program ROI, you might want to look at the quality of the research supplying your idea generation, and gathering data from behavioral analytics to understand your customer journey is an excellent place to start righting your ship.
Digital behavioral analysis is the only ideation method that both helps you identify problems on your site and estimate how much lift you create by addressing them. In our guide, you’ll learn how to tap into a foundational understanding of user behavior, traffic makeup, and page interactions to uncover when and how users are interacting with your site, where a site (or page) is having problems, and the potential size of those problems.
Digital behavioral analysis reveals key trends, patterns, occurrences, and similar characteristics of your digital customers, which can help you generate strong data-backed hypotheses from your existing data. With these high-value ideas in hand and the data to assess potential impact, you can ensure only the most important problems and hypotheses are prioritized.
In this chapter, we discuss how to work with your analytics team to get visibility and access to analytics variables related to your requests, how to structure your plan for analysis, how to use insights to develop and prioritize testing hypotheses, and how to communicate your findings.
Maximizing optimization program ROI with Search Discovery & Conductrics
Companies are starting to move beyond thinking about how to best run experiments to consider how to best set up and run larger (often decentralized) experimentation programs. This is the way to get from running a few scattered a/b tests to providing, say, personalization at scale. The strongest optimization programs are the ones that are useful for decision-making and that can measure and maximize their ROI.
Search Discovery’s team of research and experimentation experts work across data transformation services with our SEO, digital media, analytics, and data science teams to help build your program’s processes and automation, and Conductrics provides world-class optimization technology—a unique partnership in the market that helps you fill the gap without building in-house. Our partnership helps you
- Identify the most important problems
- Expand your program’s impact across the organization
- Increase conversions and improve customer experience
- Apply statistical rigor to experimental design and analysis
Stay tuned for more chapter summaries, and get the guide today for all the details summarized here, plus helpful, downloadable bonus materials in nearly every chapter.
