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Four Ambitious Analytics Projects for 2014

Business leaders are often challenged to choose the best analytics projects in which to invest their dollars and resources. Experts recommend four analytics initiatives that will pack the biggest punch for organizations this year.

Data has become an essential currency for organizations. With companies striving to capture information stemming from every single customer interaction, business leaders are faced with one major dilemma—how to make the most of this constantly growing mountain of information.

According to IDC, the digital universe is expected to hit 40,000 exabytes by 2020. To put this gargantuan number into perspective, one exabyte equals one billion gigabytes, which would require more than 15.6 million 64 gigabyte iPhones to store. While data provides an opportunity for organizations to tap into important information, it also poses a challenge, with many companies struggling to make the most of data. As Gartner notes, a staggering 85 percent of Fortune 500 organizations “will be unable to exploit big data for competitive advantage” through 2015.

Further, Big Data shows no sign of slowing down. In December, IDC forecast that the Big Data technology and services market will experience a 27 percent compound annual growth rate through 2017, reaching $32.4 billion. That, IDC notes, is about six times the growth rate of the overall ICT market.

With so much data in hand, business leaders are doing their best to leverage this information across their whole organization. "There’s a lot of data out there," notes Niren Sirohi, a leader in TeleTech’s Analytics Services Group. Unfortunately, this sheer amount of data is, at times, overwhelming organizations which are unsure of where to start to try and make sense of all the information that’s available to them. "They are stuck in the mindset of collecting and storing Big Data, not analyzing it," Sirohi explains.

According to Allen Bonde, partner and principal analyst at Digital Clarity Group, hype around the concept of Big Data has pushed organizations to really think about making the most of the information they collect. "But relating this effort to their day-to-day needs requires a new way of thinking," he says. Although a lot of effort has been made to turn Big Data into actionable insights, not all analytics initiatives are sound ones that will reap results. In fact, several organizations are falling into the trap of biting off more than they can chew and ending up right where they started, even after making substantial investments.

 

Anatomy of an Ambitious Analytics Project

Srividya Sridharan, senior analyst for customer insights professionals at Forrester, notes that an ambitious analytics project is one that strains the marketing execution system; uses unstructured data sources, like text, voice, and images, that are hard to analyze; uses Big Data technologies and machine learning algorithms to produce and execute analytics at scale; finds use cases and applications of existing analytics work in new areas, for example human resources analytics and supply chain optimization; and uses a method that is unusual to the analytics sourcing strategy. However, the type of projects that an organization can tackle depends on where that company is on its analytics journey. While some companies have a good handle on data, others are still at the point of understanding where all their data resides and how they get value out of their existing data investments. In essence, this varies between organizations and mostly depends on their resources, whether technological, financial, or human. While some organizations can embark on cutting-edge analytics initiatives, others need to focus more on what they can achieve with existing resources and make sure they can gain the most return on investment. Instead, experts share four projects that organizations should focus on this year.

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Business leaders are eager to make the most of their Big Data and turn it into actionable insights. But, just like an architecturally interesting house will crumble unless it is built on the right foundation, analytics projects will also fail unless the necessary data is in place. This means that organizations need to focus on the preliminary steps or face future problems.

As author and academic Tom Davenport notes, the preparatory steps are "hard, time-consuming, and expensive." However, they're necessary. These preparatory steps include integrating data from across different channels and bridging information silos, which is still a problem many companies struggle with. The challenge stems from two main reasons—lack of collaboration between different departments and the inability to identify customers across different channels. "Multichannel integration is a difficult and ambitious project, especially as channels continue to proliferate," Davenport notes. Experts agree that the projects that cause the most pain for organizations are those that require significant coordination across multiple units in their organizations. As a result, many companies shy away from analytics projects that don’t easily fit into their day-to-day world, including those that require organizational change.

 
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Segmentation exercises might sound basic, but they're not. When done properly they can reap important results that organizations can continue building on. Effectively, a segmentation exercise can be as ambitious and elaborate as an organization wants it to be.

In fact, an effective segmentation project will not focus on channel-specific data, but rather it will build on multichannel integration and build customer segments that span across different touchpoints, notes Digital Clarity Group’s Bonde. "Start stitching together richer profiles," he recommends. "The aim is to have individual profiles based on preferences and history, social interactions, comments, reviews, etc." From a marketing perspective, creating a single view of a customer, including information from disparate social channels, is considered the Holy Grail— something that many organizations are trying to achieve.

 
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Companies shouldn’t be discouraged if they don’t have all the information they need for an ambitious analytics project in place. With so much focus on Big Data, organizations tend to forget the importance of small data, notes Ron Wince, president and general manager at Peppers & Rogers Group. He notes that after investing heavily in technology and outside resources to get a data strategy together, companies often don’t get the results they intended. "Take a step back," he recommends. Channels like the contact center are important sources of information.

Similarly, Bonde encourages organizations to make the most of the small data they have available. "If you focus solely on the larger data sets and the bigger prizes, you might forget the smaller data that’s produced every day," he notes. For example, Twitter mentions of a brand might be only a part of what’s being said about the organization but hide essential insights that shouldn’t be forgotten.

However, focusing on small data might require a change of perspective within the organization, especially among the technical division which might be motivated to push for Big Data projects. This change can be triggered through showcasing successes, especially ones that don’t take a long time to achieve. "Take advantage of the data you already have and show value without long waiting times," Bonde recommends.

 
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Good data visibility will allow organizations to make timely predictions that will not only save them money, but also help improve the customer experience. Experts believe that emphasis over the past years surrounded collecting information and using it to gain visibility into the customer journey across different channels. The next step will include a predictive element in that process. Sprint is one company leveraging data to make early predictions. For example, the company realized that customers activating a new smartphone were often contacting Sprint a few days later to inquire about reactivating their old devices for another family member or about recycling options. Sprint added a step to the activation process, asking customers what they wanted to do with their old devices, thus eliminating thousands of calls every year, increasing customer satisfaction, and also positively impacting revenue.

Apart from identifying problems before they happen, predictive analytics can also help organizations determine the next best offer for individual customers. As Davenport notes, recommendations require a clear sense of customer behavior across multiple channels. Further, knowing customers isn’t enough. "You also need to know a lot about your products," Davenport stresses. Zappos, for example, has three departments focused on determining the attributes of different products so that the company can make relevant recommendations to customers based on different criteria, for example style, brand, and size.

Finally, business leaders need to be wary about embarking on analytics projects simply for the sake of doing something with their data. Instead, they need to make sure such initiatives are geared towards solving an existing problem. Therefore, business leaders need to take an inward look and identify the business problems that need solutions. Sridharan agrees, emphasizing that the first step before embarking on an analytics project is to understand the organization’s analytics maturity. She also stresses that organizations need to set aside an experimentation budget for testing before embarking on a new analytics method.