Data-infused & Integrated approach toward superior customer experiences
Multi-dimensional nature of human experiences captured in unidimensional data
Today data science and its applications have grown by leaps and bounds, and rightly so. The primary purpose of data is to make informed decisions. The data that we use in tech-driven businesses is quantitative and absolute. But the world where this data comes from has humans with transient emotions, distinct information models, and unique preferences.
And so, making decisions using a data-only approach may need deliberate optimization. We need to evolve the decision-making frameworks to use data but intersperse it with context, reasoning, and an enriched understanding of human behavior to gain a multi-dimensional knowledge of the opportunity space to create meaningful experiences.
We can associate a superior customer experience with something that:
- Meets a purpose for the customers — helps achieve a goal.
- Makes meaning — what and how a business offering adds value, leaves the customers fulfilled
- Creates affordance (easy to use without errors), simple to learn, understand, and remember
Creating such experiences at scale requires much work and involves a heady mix of science, art, and engineering, drawing inputs from several multi-disciplinary areas. Today, we refer to customer experience as a strategic business term and often position it inward from the view of the respective business capabilities. As a result, we see different CX characterizations emerging from tech, data, and design lenses. Even in large organizations with functional capacities and a customer-centric approach, the alignment of the functions happens much later, only in the tactical layers.
This model leaves the organization with far less scope to create what could be a unique and superior experience for its audiences.
The data capture silos and missing links in the interpretation
It also provokes inquiry about the unidimensional nature of the input parameters in the form of data. Further, it instigates us to reimagine its characterizations that inform strategic choices — that drive the decisions through engineering, design, and the build.
When we refer to data in the technology industry, we refer to the big data generated in massive volumes by tracking people’s digital behavior from various sources. Data analytics examines data sets to find trends and draw conclusions about the information they contain. Businesses use three types of analytics to drive their decision-making; descriptive analytics, which tells us what has already happened; predictive analytics, which shows what could happen; and prescriptive analytics, which informs what should happen in the future.
A data-driven approach enables organizations to examine and structure the data to serve customers better and create superior experiences. Such an approach implies that strategic decision-making is an outcome of the data interpretations that inform “what is” while presenting an outlook on the possibilities for the future.
It is a commonly adopted practice to capture CX performance through relationship surveys and other survey-based measurement systems that eventually shape major strategic decisions. Although the tools and methods are integral to conducting research, evidence suggests that in silos, they are inadequate in providing contextual, holistic, and diagnostic viewpoints and result in compromised business expectations of superior and distinct customer experiences.
Moreover, using a data-only approach for all situations has a fundamental limitation in understanding the distinct dimensions of the stages involved in business outcomes, from conceptualization to the build. In contrast, a data-based process works well in situations that involve evaluation. In other words, it is a preferred approach when we have clarity around the deliberations: is a particular choice preferred over the other, or is a specific design decision working? A data-driven approach works well at the tactical layers of strategy implementation.
While in the exploratory and generative phases, this approach comprising data interpretations from unidimensional models might constrain the holistic understanding of the futuristic possibilities & opportunities. And result in satisficed strategic business decisions.
Capturing, processing, and interpreting data using aging measurement systems are limited by dimensions.
Need for an integrated model to create superior customer experiences.
As our understanding of the different elements of data & technology continues to evolve, we also see a rise in companies shifting to a data-centric approach — getting the right kind of data to build high-quality, high-performance machine learning models. Evolving the data-led decision-making frameworks to harness high-quality data factoring in human elements involves: -
- a detailed study of the missing links in the existing model
- exploring the feasibility of adopting anthropological methods such as ethnography at strategic layers
- an integrated outlook to CX.
To create experiential outcomes, we need to rise beyond absolutes. Unlike natural science, which is related through quantifiable facts, the social reality comprising humans has different attributes. Understanding social structures requires qualitative reasoning and a deeper understanding of motivations, deeply held beliefs, and cultural orientations.
As the focus shifts to getting high-quality data for training models, it is also time to define the yardstick for “high quality.” And deliberate on the minimum viable capabilities organizations must build to capture such data and what capacities and systems can help acquire enriched data sets.
The process starts with familiarizing the business with the diverse nature of human outlooks, behaviors, and motivations in the real world — it involves creating awareness about the different human ontological stances — what is social reality as viewed from objective and constructivist lenses, the diversity in philosophical viewpoints, and the other epistemological approaches to knowledge creation. Most importantly, the awareness of the ethical implications of missing the holistic view in the unidimensional data models.
The challenges we face today are multi-faceted. The complex problems that we face today warrant integrated thinking. Diversity and inclusion are imperative strategic actions to strive for creating collective intelligence rather than an adjunct.
The intentional blending of human elements in the big data we gather gives us the full advantage to tap into the untapped potential of diagnostic, prescriptive, or predictive analytics to help make mindful strategic decisions.
The foundation for achieving superior experiences is at the intersection — sourcing enriched data as input, using technology as an enabler, and design as the understanding, reasoning & engineering tool to federate the various critical pieces.
