Data collection often poses quite the conundrum. Though companies know they must utilize customer insights in order to grow and improve, many organizations have yet to fully grasp how to integrate the proper analytics with their current infrastructure. Typically, the struggle lies with the inability to establish the right set of metrics that will drive business forward, as the constant flow of data leaves marketers and IT specialists scrambling to capture anything and everything that comes into the organization. Instead, companies must focus upon analyzing and transforming this incoming information into actionable insight.
According to a Big Data study conducted by TCS, of the 1,217 companies surveyed, 53 percent undertook Big Data initiatives in 2012, with 43 percent of those respondents predicting an ROI of more than 25 percent. When asked to rate a list of 16 potential challenges, getting business units to share information across organizational silos ranked number one, while dealing with Big Data's three "V's" (data volume, velocity, and variety) came in a close second. However, at number three, respondents rated determining which data to use for various business decisions as one of their primary obstacles.
With numerous channels generating endless insights, data flow can appear rather daunting. It's not until brands determine what they hope to achieve through using this insight that they can move forward and drive the changes that allow them to enhance business and meet changing customer needs. As Mukund Srinivas, client partner at Mu Sigma, notes, there are traditionally two kinds of approaches to determining which metrics are right for any given brand. The problem-driven approach focuses on specific business problems and what the company must do to solve the issue. On average, brands will calculate their hypotheses, collect relevant data, and construct solutions based on these results. The discovery driven approach, however, supports the need to understand what customers are saying across channels, enabling the brand to uncover certain insights and use the key findings to inform their overall strategy.
Alex Algard, founder and CEO of WhitePages, indicates that companies must determine how they want to use the data within the context of their organization. Upon doing so, the company must then ensure staff across departments understands the purpose and analysis of this data, thereby setting goals and metrics that encompass the entire enterprise, for the key to success means overcoming silos, thus applying this insight holistically throughout the organization.
But, as Alan Knitowski, chairman, CEO, and co-founder of Phunware, Inc., emphasizes, in the grand scheme of things, customer data is less about the specific analytics capturing, and more about learning to crawl, walk, and run in the process. In the crawl phase, companies must develop consistent analytics and common data capture, for all the data points being observed must bring greater meaning to the business. The walk phase requires standardization of analytics, meaning brands must establish a platform that allows everyone within the enterprise access to the same information. The run phase then enables brands to personalize and customize the consumer experience using the deep insights gathered. Throughout, these data points must work to support a coherent goal, otherwise "they begin comparing apples to pickup trucks instead of apples to apples and oranges to oranges," Knitowski says.
Yet, while companies look for guidance as to which data sets are right for them, there does not appear to be a "one size fits all" remedy that cures this unavoidable affliction. But, if brands are willing to approach data capture and analysis with an open mind and willingness to evolve, they will realize that the "curse" of data overload is truly a blessing in disguise. Moving forward, companies should keep the following tips in mind in their efforts to learn, understand, and improve:
1. Don't bring bias into the equation.
Srinivas encourages companies to look toward data capture and analysis as an opportunity to discover, for learning is much more important than doing. Brands must determine what they hope to accomplish and understand using the data collected, but they should enter into this process with few biases, if any. Doing so helps companies learn at a faster pace, for they are able to observe how customers engage with the brand and their buying behaviors, thus adapting according to the insights gleaned. If brands begin the journey with a result in mind, they will mainly look to data points that support that hypothesis, neglecting the driving force behind such analysis in the first place.
"The purpose of any analysis is to yield an unbiased result, almost as if conducted by a third party," says Justin Klapprodt, director, strategy and insight at Quaero. "The basis of analytics is to use it as a tool to drive progress and improvement, so there's really no such thing as a bad result. If there is a result that yields something less than desirable, analysis will identify opportunities to improve."
2. Be willing to evolve and admit mistakes.
Data capture and analysis fosters growth. Therefore, companies must be willing to change their metrics as necessary. If the measurements in place do not help move the company forward, the brand must step back and reevaluate where it's headed and where it needs to be, even if it means admitting momentary failure.
"The awareness and identification of a mistake is huge," Klapprodt emphasizes. "If a company can admit that it's doing something poorly, that [recognition] itself is a major accomplishment. Many companies cannot see that they're making mistakes, so they keep making them over and over again. If you can identify the mistake, you can prioritize and try to understand the impact. Hopefully direction will come from there."
Algard also notes that data sets require continuous testing. Brands must measure the results, for data sets need refreshing and cleansing from time to time. It's imperative to go back to the source of the data to get updates regularly. Companies grow-an evolution these metrics facilitate-and it's likely that most of the brand's important operating metrics will change, as well, so be sure to continue to assess the data and its use on a regular basis.
3. Offer value that boosts efficiency.
Ideally, when determining the right data sets, companies will leverage the insight in a way that elevates the brand and brings more efficiency to the business. "A rule of thumb: Don't fall into a rat hole trying to infuse data into each and every business operation," Algard highlights. "Look at it from the standpoint of understanding that the data is there to solve a current problem and you will find that the data will work for you rather than being a confusing mess and work against you." Establish the area of the company that needs improvement and match the metrics accordingly. If these data points don't foster progress, be prepared to reassess.
According to Chris Reynolds, vice president, marketing analytics at Cond?ast, the magazine publisher implemented data analytics over the past two years that specifically focus on gaining better understanding of the audience that frequents the brand's sites. While its consumer database contains the names and addresses of nearly 55 million subscribers, Cond?ast sought to bridge the gap between online and offline audiences in a way that provides marketing support for its luxury brand advertising partners. "Being able to look at specific segments and what influences those segments gives the editorial teams a more prescriptive capability of how to convert all the valuable insights," he says. "We are laying the groundwork to do all of that, and it's bringing a ton of opportunity."
In doing so, Cond?ast not only strengthened customer engagement through online surveys, but the brand now brings rare data on key influencers that help advertisers and teams align their goals and attract the audiences they want, thereby adding value for these partner networks. According to Reynolds, presenting these valuable insights to the teams has helped to focus departments across the enterprise on data. "Having that ability to understand that full experience the user is going through and to customize it and enhance it has helped to focus the organization in terms of how we can support the data side."