Roadmap to Success: CX Opportunities in Big Data

Learning Fast: Turning Insights into Action

It’s one thing to set up a customer insight data and analytics framework, but quite another to turn those insights into action—ultimately yielding successful outcomes. While decision making occurs throughout the product development lifecycle, it is surprising how few organizations track and measure the success of the decisions that they make.

The Opportunity

Organizations that are armed with appropriate—and measurable—intelligence need not fear making mistakes. Mistakes, after all, are inevitable. What is important is identifying them quickly and trying out other alternatives to correct them. Anne Milgram’s Ted Talk “Why Smart Statistics Are the Key to Fighting Crime” provides a great illustration of how leveraging data in a timely manner can enable an organization to achieve great outcomes.

NG conference participants also voiced their confusion about how—and when—to draw on qualitative versus quantitative customer insights at varying times. Over-reliance on one or the other of these two approaches to research can be problematic. We like to remind our clients:

• Qualitative techniques are best for hypothesis generation and quick discovery.
• Quantitative techniques are best for validating hypotheses and model data at a segment level.

While quantitative data is often much more persuasive in supporting decisions in the C-suite, this work is often underpinned by earlier qualitative research.

Real-time Access and Usage

Timeliness of decision making can also have a huge impact on business performance. Immediate access to data and insights enables project stakeholders to pivot at a faster rate, thus reducing the impact of poor performance in the discovery process of best practice outcomes.

The Opportunity

Often organizations fail to be responsive to customers because they are paralyzed by a lack of structure or confidence in existing systems. A/B and multivariate testing are approaches that provide confidence in results.

At U1, we often suggest such testing to clients to assess the immediate impact of design changes on their Web sites. Concurrently testing the performance of two or more design iterations side by side provides an apples-with-apples comparison that facilitates rapid decision making. We have also used this approach to forecast performance for businesses in a live environment, demonstrating expected outcomes and fostering support.

Capability and Resourcing

The market for skilled resources, capable of working on a data and analytics framework, is limited. This fact makes the process of establishing a framework—as well as its ongoing support—more difficult. So how do organizations build—and appropriately resource—a data and analytics framework, while effectively leveraging competitive advantage?

The Opportunity

Ultimately, capability and resourcing must tie into a broader organizational strategy and governance framework. For the most part, organizational needs and the availability of a supply of skilled resources drive the decision to insource or outsource roles—or go with a combination of the two.

Investment in such a framework is, by its nature, highly measurable. So, however organizations set up their structure, the results should speak for themselves. But establishing a data and analytics framework requires diligence and maintenance, so unfortunately, there are no shortcuts.