It’s been said that “hindsight is 20/20,” meaning that it’s easy to see and understand things clearly after they’ve already happened. The same is true of business analytics. They’re helpful, can confirm past decisions and certainly add clarity. But they’re also capable of so much more. Indeed, the real power of analytics is predicting events and prescribing a path for obtaining specific outcomes.
Many businesses today still fall short when predicting customer behaviors and improving decision-making. The main reason is organizations still lack clearly defined customer outcomes. Because they’re not sure of their goals for analytics, they revert back to historical precedent and focus on reporting rather than intelligent forecasting. There is hope, however. Our six-step guide can help organizations break through the analytics barrier and gain new customer insights with the help of predictive recommendations. Explore our interactive graphic below. Click or tap each of the six steps for more details.
Step 1: Define Outcomes
If you don’t know where you are going, it doesn’t matter where you turn. The problem with most analytics operations today is that they lack directional goals. Hence the first step to becoming predictive is to identify and strive for concrete goals, such as decreasing customer churn by 5% or increasing net promoter score by 10%. Regardless of maturity level, all businesses must begin their analytics by defining their desired customer or other business outcomes.
Step 2: Integrate Big Data
Big data architectures can bring analytics to life when they use smart information management design. While most companies still rely on data warehouses to stockpile records ranging from billing and enterprise resource planning (ERP) to customer relationship management, such all-purpose repositories are insufficient for the enormous volumes of information required to manage customer experience. Hence, organizations must adopt a big data architecture that supports both structured data, such as online and of offline transactions, and unstructured data, such as e-mails, images, customer service calls, and social media sentiment. Only then can they realize the insights they desire.
Step 3: Rethink Journey Maps
Understanding the customer journey is at the heart of achieving predictive analytics. Instead of making guesses based on IP addresses and geographic locations, organizations can develop journey maps to identify connecting points and how those points influence whether customers stay or go. In short, journey maps are powerful visual tools that trace customers’ steps as they travel through the organization. They understand the big picture by following customers through channels, decision paths, and most importantly, emotions. For example, if the goal is to improve customer retention, then each touchpoint should provide a positive experience that makes customers feel in control of their experience.
Step 4: Digitize More Data
Too many organizations associate analytics with rolling out costly business intelligence systems and data warehouses. But in actuality these programs require only focusing on needed data sets. Therefore, analytics is fundamentally a business challenge first, and an IT initiative second. Since many businesses are just beginning to take their first steps to automate standard functions — such as accounts payable or claims submissions — they (as of now) only have a limited amount of data to feed into their analytics initiatives. To use analytics to shape the customer experience, however, they’ll need to digitize manual data processes, which will also reduce costs and enable them to gain customer insights more quickly.
Step 5: Serve Only One Customer
At every turn of analytics, organizations must ask themselves: Have we kept the customer at the center of our analytics effort? Have we focused on specific outcomes that will drive more value? Established companies often have a data infrastructure that can take on new business problems but lacks the flexibility that marketing efforts need to move quickly. Young companies typically have the opposite problem. While focused on one business problem, they’ve gotten very good at one solution, and their speed and agility land them high on the analytics maturity model. But they lack the infrastructure to tackle the next business problem. No matter the size of the business or analytics sophistication, the focus remains the same.
Step 6: Regularly Measure Outcomes
Testing is an often neglected yet vitally important component when moving from diagnostic to predictive analytics. 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, however, businesses can automate and gain the clarity they are looking for. Doing so lets them monitor and measure whether business and technical activities actually add value.