How to Break Through the Analytics Barrier

How to Break Through the Analytics Barrier

While many companies pursue analytics initiatives to improve the customer experience, few realize the full benefits of the program. Here are the six steps needed for moving beyond operational reporting to enabling prescriptive insights.

Many businesses today find their analytics initiatives fall short of their promise to help them better connect with customers. The main reason is that many organizations still lack clearly defined customer experience outcomes. Because they’re not sure what their goals are for analytics, they revert back to operating in data silos. 

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Our six-step guide can help organizations break through the analytics barrier and give new purpose to the customer experience. 

  • Step 1: Define the customer experience outcomes. Analytics isn’t one size fits all. Organizations vary tremendously in where they are on their information journey. Many companies remain at Level 1 or 2 as defined by analytics maturity models; they likely use dashboards or scorecards and are still largely dependent on manual processes. Regardless of maturity level, all businesses need to begin by defining their customer experience outcomes.
  • Step 2: Integrate a big data infrastructure. A big data infrastructure is the backbone that enables businesses to deliver the defined outcome, whether it’s next best action or communicating with customers on their preferred channels. Create a backbone capable of delivering the desired outcomes. The big data environment should encompass three layers: data ingestion, data modeling and data analytics.
  • Step 3: Rethink the customer journey. Understanding the customer journey is at the heart of breaking through the analytics barrier. Instead of making guesses based on IP addresses and geographic locations, organizations can develop journey maps to identify connecting points and how the points influence whether customers stay or go.
  • Step 4: Enhance customer insights with digital data and processes. Automation is a key asset to successful analytics initiatives. Many businesses are just beginning to take their first steps to automate standard functions such as accounts payable and receivable or claims submissions. As a result, they have a limited number of data sets to feed into their analytics initiatives. To use analytics to shape the customer experience, they’ll need to digitize manual data entry processes, which will reduce costs and enable them to translate customer experience insight into action much more quickly.
  • Step 5: Construct solutions from the customer’s perspective. Organizations need to ask: Have we kept the customer at the center of our analytics effort? Have we focused on specific outcomes that will drive business value? Do we have the maturity to achieve that value? The right analytics solution depends on the organization’s place in the maturity model, whether it has already proactively assessed and addressed customer behaviors or progressed to the point where its information management processes are repeatable and predictable, and activities are outcome-based. Some organizations may just be beginning their analytics journey.
  • Step 6: Test and measure for outcomes. For most organizations, test and measurement is a bolt-on function. It offers few guideposts for knowing whether employees’ day-to-day activities will directly impact customers and the bottom line. After-the-fact testing has also led to the proliferation of diagnostic analytics in the form of dashboards that require human interaction to decipher the patterns of impact. By embedding testing and measurement into all aspects of analytics, businesses automate the process – and gain the clarity they are looking for. It lets them monitor and measure whether business and technical activities actually add value.

There’s no doubt that analytics can be a powerful business acceleration tool. Using advanced analytics, businesses in all sectors, whether B2B or B2C, are learning more about their customers and turning those insights into revenues and profits. Read more about Cognizant’s analytics practice here.

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