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Roadmap to Success: CX Opportunities in Big Data

December 22, 2014

From my discussions with fellow UX and CX professionals, it’s clear that we share a sense of pressure and frustration when it comes to big data. Regardless of the organization or industry, we encounter similar challenges around grappling with, understanding, and leveraging big data in our efforts to realize exceptional customer experiences.

Attending the NG Customer Experience Summit in Canberra recently—alongside many executives from Australia’s leading corporations—reinforced my awareness of this sentiment. Although big data has been a huge focus of industry discussion for quite some time, most large corporations have not yet embedded a framework into their operations that would let them harness its real potential.

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Seizing the Opportunities That Big Data Bestows

Everyone at the conference echoed this message: The clock is ticking. If we don’t leverage the power of the huge amount of data at our disposal and derive insights from it soon, our competitors will! However, as Baruch Sachs says on UXmatters, the reality is: “Most companies have the data, they just don’t possess the will, time, or money to make it work elegantly for someone who is trying to access it.”

In response, I’d like to share with you the top challenges and potential pitfalls that the U1 Group┬áhas identified and our experience working with clients to establish insight frameworks within organizations.

Navigating Myriad Technical Considerations

Big data, in essence, is all about harnessing data from a multitude of sources and interactions. The aim is to form a clearer understanding of customers—their activities, desires, and motivations—and with this in mind, develop strategies to better satisfy their needs.

The challenge here, however, is how to sort the wheat from the chaff. With the plethora of technical considerations that exists, including software for CRM (Customer Relationship Management), CMS (Content Management Systems), and analytics, deciding how best to invest time and resources is overwhelming.

The Opportunity

Choosing the right technology is just one part of the overall planning-and-implementation picture. Making the most of available data—and working out what is missing—requires tying┬ástrategic reviews directly to specific business goals.

It’s crucial to evaluate the business value that data tools offer and to keep your long-term objectives in mind. At U1, we recommend a broader business-needs consultation that includes engaging with stakeholders at different levels to understand business goals. By conducting this research, we can identify business needs at a granular, or divisional, level and, therefore, can prioritize these needs appropriately.

Data Governance

Single source of truth echoed throughout the conference, addressing data accuracy and integrity. (In other words, it is paramount that all stakeholders in an organization are singing from the same songbook.) This requires incubating cultural change and ensuring that decisions are based on appropriate and measurable insights—as opposed to other, less clear criteria. The degree to which knowledge transfer occurs and internal stakeholders become aligned underpins an organization’s success.

The Opportunity

Organizations must appropriately provision capability, capacity, and governance to maintain an effective insight framework. Agreeing on roles and responsibilities is essential. Implementing an organization-specific RACI model helps to ensure that proposed frameworks align to business goals.

In the past, we have facilitated such an approach for Telstra. To find out about this, take a look at the presentation we gave at the 2014 NG Customer Experience Summit and 2014 UX Australia. It covers our collective effort, implementing an operationalized customer insight governance structure around customer advocacy.

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. 

Co-founder of Loop11

Melbourne, Victoria, Australia

Shefik BeyShefik is the leader and cofounder of U1. Previously Global Vice President of the online market research company RedSheriff, Shefik broke away to bring his niche passion for usability to the Australian marketplace. One of the country’s true UX pioneers, he is constantly seeking out knowledge and is an expert in all things business management and project development.  Read More

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