Today’s organizations face rising customer expectations in a fragmented marketplace amidst stiff competition. This landscape is one that presents opportunities for a modern data-driven organization to thrive. At the nucleus of such an organization is the practice of accelerating time to insights, using data to make better business decisions at all levels and roles. In […]
Today’s organizations face rising customer expectations in a fragmented marketplace amidst stiff competition. This landscape is one that presents opportunities for a modern data-driven organization to thrive. At the nucleus of such an organization is the practice of accelerating time to insights, using data to make better business decisions at all levels and roles.
In the first of two blog posts, we delve into customer analytics to examine where data makes a difference in delivering an exceptional customer experience.
Understanding and anticipating the needs of customers is key to organizational success, whether it is a small family owned business or a large multinational company. Understanding what customers want, their motivations, interests and preferences, and subsequently being able to predict and influence their behaviour makes the difference between just scraping by and being a market leader
The key to knowing customers is listening to them, connecting with them both directly and indirectly through a burgeoning number of communication channels.
We have increasingly moved purchases online in recent years fueled by the COVID-19 pandemic and general trends in Digital Transformation. Today, interactions with a brand spans omnichannel touchpoints. We may browse items online, ‘like’ a post containing a product, provide feedback, ask a chatbot, recommend an item, write a review. The list goes on. Combining disparate but connected data points over time about where and who customers are and what they are doing during each of these exchanges is clearly an impossible task to be performed manually. It entails capturing exponentially increasing amounts of data, on a variety of data types, each of which are potentially moving at different speeds. This data needs to be harmonized in a way that presents a unified view of the customer journey and ideally a real-time view of their propensity to perform certain actions, with suggested options to increase or decrease that propensity.
Like most labels, “data-driven” is not a binary, black and white measure of capability. In reality, organizations live on a continuum, varying in how sophisticated their data is and the extents to which it influences management decisions.
Any article on what it means to be “Data Driven” makes references to Data Strategy, Data Culture and Decision Culture. One esoteric term leads to another.
Data Strategy. I’ve heard countless definitions of what strategy is and it gets appended to just about everything to make it seem more important. The definition that I have found most useful is that by Richard Rummelt, Emeritus Professor of Business and Societyat the Los Angeles Anderson School of Management:
“The kernel of good strategy consists of a diagnosis of the challenge, policies for dealing with it and cohesive actions.”
One of the most important factors influencing Data Strategy is an organization’s cloud strategy. In particular, a common emerging theme within enterprise organizations is CIOs are challenging CDOs to move a significant portion of existing enterprise data platforms to the cloud in the next 18 to 24 months. A cloud-first policy might therefore be one of the guiding policies within your organisation and therefore a crucial part of your data strategy.
Data and decision culture. Related to the policy or principles of strategy, we have behavior and values. Peter Drucker famously said “Culture eats strategy for breakfast”. A world class strategy, which does not align with views, social norms and how people and teams work together is not going to be effective. Data culture is
“Senior leaders setting and reinforcing a set of shared beliefs and values around data that shape employee perceptions, behaviours and understanding of how to use data to make better decisions.”
While this definition may seem a little high-level, this is one area that has a strong and clear connection to how technology can support decisions.
A good starting point on how to manage and draw insights from data begins with having a clear view of the customer.
Cloudera worked with Experian, a global leader in consumer and business credit reporting which gathers, analyzes, combines and processes data on over one billion people and businesses. Legacy processes and a legacy environment could no longer keep up with the volume and velocity of data while the business needed to meet customers’ demands for better data coverage and data accuracy. Experian leveraged Cloudera Data Science Workbench for its B2B business unit to build and launch six different maintenance apps to resolve issues with machine learning and automation, such as de-duplication and classification of data. The implementation of this solution brought productivity gains up to 3,000% and average unit cost reductions of up to 99% compared to when manual processes were used. Apart from maintaining its competitive edge, Experian has vastly improved client experiences.
Providing teams with access to data insights at the right time, supported by a culture of making data- driven decisions leads to an overall improved customer experience. In other use cases, real-time data collected on issues can be translated into insights which are made available to customers and support staff. In doing so customers are able to “help themselves” more often and solutions to recurring issues are developed only once, reaping significant reductions in support costs and issue resolution times .
Reaching new levels of customer centricity is only possible through becoming increasingly data driven. This requires a strong and clear data strategy supported by a commitment to develop and nurture a data and decision culture. By believing that customers are the focus of every business, the reason it exists, these improvements to the bottom line should then be reinvested into innovation and developing better products and services for customers. All paths should lead to delivering the best value to customers.
This data-driven culture extends to other functional departments within an organization that each play a direct or indirect role in delivering value to the customer, be it marketing, sales, consulting, support, IT or product development. These will be discussed in the second blog post.
If you are interested in learning about how a modern Enterprise Data Cloud can support the goal of being increasingly data-driven, please join me for my upcoming webinar .
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