Equipped with the right information, business leaders become superior decision-makers. This is especially important for executives in the highest echelons of an organization. These business leaders are increasingly pressed to make quick decisions on issues that could impact their entire company and need data to inform or support those decisions.
One essential area of insight for the C-suite today is customer data. Through their behavior, what they buy or don't buy, what they say when contacting a call center, and the kind of complaints they are filing, customers are telling a story. Customer data gives top executives the needed insight to make broad changes within their company that will help improve business results. Insight into customer feedback, behavior, and value enlightens the C-suite on what is needed to enhance customer relationships, which, in turn, helps to increase profit.
Today, C-level executives have more direct access to increasingly meaningful and insightful data, especially through the readily available visualization tools that make the presentation of this information easier and more user-friendly. This is enabling business leaders to ask the relevant questions that can be answered by analyzing the mountains of data that most companies are gathering from their customers, be it through surveys and questionnaires, call center communications, or social media content.
While the availability of illustrative data is a necessary starting point, it is not enough. C-level executives also need to have a central role in creating a customer analytics–driven enterprise: They must demand the resources necessary to acquire the insight needed not only to inform their decisions, but also to measure the impact of these decisions.
In addition, it's imperative that C-level executives become good users of analytics. If they demonstrate that they are tracking and using customer and market information when making decisions, and asking questions to get additional insight as necessary, they will motivate the analytics team to delve deeper into the data.
Using customer data successfully
An increasing number of C-level executives are recognizing the importance of gathering and analyzing data for themselves, as well as for their organizations. As a result, their companies have invested in structures to enable them to successfully do so and are making effective use of the results. Etisalat, a telecommunications operator in the Middle East and Africa that operates in 18 countries, used the power of data and analytics to more than double the results of its customer retention efforts, by proactively understanding and acting on consumer behavior, developing targeted offers, and enhancing the company's reactive capabilities across customer interaction channels and the point of sale.
Another example is color-matching company X-Rite, which turned to analytics to understand the drivers of customer experience for various client groups in different countries, prioritize investments accordingly, and ensure an exceptional experience for its consumers. The U.S.-based company used an analytical model based on research and customer behavior analysis to determine the requirements for enhancing the customer's experience and forecasting the impact of doing so, and, as a result, envisioning the order in which it would tackle the various opportunities that were identified.
A third example is Belgium-based international bank BNP Paribas Fortis, which wanted to capture the potential represented by its youth customer group. The bank sub-segmented its young customers into specific and actionable groups, based on potential, needs, and behavior. The results paved the way for targeted strategies to increase the value per customer and reduce attrition.
Despite the evident benefits of a strong data gathering and analytics structure like those at Etisalat, X-Rite, and BNP, its application is still not as widespread as it should be. Some companies are still not effectively leveraging the data they are already gathering. Sometimes—especially when it comes to larger enterprises—because data is siloed across multiple areas of their organization, they are finding it difficult to connect this information and apply it to the company as a whole, despite the availability of sophisticated technology that makes it easier to create these links. But as long as there is drive within the organization, the challenge of information saturation can be overcome.
Key steps towards creating an analytics strategy As a starting point, C-level executives need to put an analytics strategy on their organization's agenda and make it central to their corporate culture. This begins with understanding the maturity of an organization in the analytics journey and a thorough assessment of existing capabilities and gaps. Our maturity model defines three stages that lead to what we call an info|SMART company (see page 12). Next, senior leadership should examine four main areas—people, processes, data, and technology—essential to embarking on a program to establish a data- and analytics-centered culture within a company.
People: Charting goals and responsibilities will enable accountably and ensure results. Every organization needs structures in place; most critically, a management team to support the use of analytics for decision-making and communicate its importance. This is essential to create a company-wide analytics culture, which is not solely restricted to a small group of people who create mathematical models, but spans from top to bottom, enabling everyone to strive to collect more data and use it in their daily work.
Often, the biggest challenge is to find and retain talent that combines analytical rigor and business insight. This is a key reason companies turn to outsourcing to conduct some analytical tasks or to apply a "build-operate-transfer" model. The preferred model of operation is for analysts to be embedded within the organization's business units and made part of the decision-making process.
Processes: There are two types of processes to consider here. The first is related to how the organization collects, stores, and uses data, including data quality, distribution, and security. The second relates to processes, particularly customer-facing ones, that can be improved in efficiency or effectiveness by better and more timely use of data—for example, how customer touchpoints can use customer value data to expedite a customer request or complaint. This would in return drive new requirements for the data-related processes, creating a continuous improvement loop.
Data: Organizations must also consider such information issues as what data they need to answer the questions they have and drive the relevant processes, what granularity and quality is required, and what data elements need to be derived from existing ones. The ongoing challenge is ensuring the collection of the right data, in terms of type, quantity, and quality, as well as regularly updating that information.
Technology: Organizations need to determine their technology requirements based on the previous three elements. Technologies should include not only tools that help to create a holistic view of customer data, but also analytics applications like predictive analytics software that can help to provide deeper, more actionable insight or data visualization and reporting applications.
The right technology will help to ensure that information is accessible and usable by a wide variety of end users. It will also help to avoid data integration issues. A well-thought-out data infrastructure strategy is necessary to avoid long projects that produce little intermediate benefit and create fatigue throughout the organization. Finally, selecting technology based on people, process, and data needs will help to make certain that an organization's investments in technology solutions will produce significant returns.
Investing for the long term
Once this strategy becomes part of the organization's agenda, and key employees know what information is being generated and what other tasks need to be done, the process becomes a virtuous cycle, and the results will be more tangible.
Ultimately, although the technology is available to bridge data silos, create a holistic view of customer data, and support an analytics-driven culture, a data strategy will stall without executive-level support. This means senior executives must continually support the build-up of the analytics capabilities, continually communicate their importance to the organization, and hire employees with a strong business understanding of various analytics techniques to support the organization's analytics efforts. Business leaders who create a culture of analytics and nurture employees who are committed to supporting that culture will have access to a powerful asset: customer data that can inform the decisions that drive business success.