Kevin Mulcahy, Director-Future of Business, Global Consulting Practice, Tata Consultancy Services
The rapid adoption and convergence of digital technologies is having a profound affect on our lives and businesses. The technology platform of the Internet, social, mobile, cloud and big data/ analytics rapidly continues to expand with technologies such as 3D printing, wearable devices, robotics, nanotechnology, IoT (Internet of Things) and others. And as more of our everyday lives are becoming digitized and instrumented, consumer expectations are elevated. With increasing frequency, they expect the same type of shopping experience as through their mobile device ecosystem where app stores can anticipate, suggest and instantly deliver an information-enabled experience.
"A next generation experience occurs in the home, car, airport, store– anywhere and anytime needs are met".
The challenge not only for retailers, but also across industries, is how to create sustained competitive advantage by delivering differentiated customer (and stakeholder) experiences. Retailers must excel at creating and delivering these differentiated experiences to attract, develop and retain customers. Insight and foresight gained from analytics play a key role for companies to anticipate, deliver and continually improve the customer experience. A fundamental enabler to create differentiated experiences is the use of advanced analytics on an ever-growing stream of data–especially from external sources including social media, location data, IoT, third party API data services and others. Retailers will need to navigate the analytic shift from descriptive to pre-scriptive, along with other key enablers including scenario and ecosystem thinking; systems of engagement; and sense and respond to create and evolve a differentiated customer experience.
Customer experience begins with design thinking, specifically edge-driven design, that takes an “outside in” approach to understand personas, journeys and moments of interaction. Experience in this context refers to stakeholder experience. It’s about creating differentiated customer experiences - but to get there, the experience we create for our employees and partners is critical to that end goal. A next generation experience is anchored in how customers think about it, not the way functional silos do. These experiences are delivered by the stakeholder ecosystem and require experience strategies to include all stakeholders whether internal or external. Next generation experiences step outside of traditional channel and marketing oriented views towards a much broader perspective. A next generation experience occurs in the home, car, airport, store –anywhere and anytime needs are met.
Since the customer experience is defined by moments of interaction that occur at the edge (of the business), retailers need to shift their focus to systems of engagement from systems of record, i.e. become interaction rather than transaction oriented. The insight and foresight needed here also requires a shift in analytic capability - moving from descriptive to predictive, and finally to prescriptive.
On the analytic continuum, descriptive is describing and analyzing outcomes, creating a historical view of data. It is answering the question-what happened? Examples include query, drill-down, reporting, dashboards and scorecards. Advanced Business Intelligence (BI) technologies provide powerful, self-service capabilities allowing business users to query and interact directly with their data, while minimizing their dependency on technical specialists.
Predictive is identifying possible outcomes and answering the question-what will happen? Examples include text analytics, predictive modeling, statistical modeling and link analysis. Here, advanced analytics techniques and technologies, including big data and natural language processing, are providing actionable insight and foresight from a growing volume and variety of data.
Most retailers today, along with companies in other industries, are between the descriptive and predictive levels on the continuum. Ultimately, businesses need to be prescriptive, optimizing outcomes and answering the question–what should happen? Examples include next best action, optimization, simulation and machine learning.
Prescriptive analytics are also a key component of cognitive systems where artificial intelligence progresses along the continuum as well. An example of artificial narrow intelligence is IBM Watson, which is using natural language processing, and machine learning to improve healthcare outcomes. Artificial general intelligence is the ability to solve a variety of complex problems in a variety of com¬plex environments, leading to artificial super intelligence. This is where the convergence of computing power and harnessing the collective intelligence of billions of people produce an intellect that is much smarter than the best human brains in practically every field. In the meantime, here are six practical steps retailers can take to progress the analytic continuum.
1. Develop a roadmap for analytic excellence–
start with a focus on major opportunities and put a plan in place that moves the organization from the current level of analytic maturity (mostly descriptive) to the highest level of maturity (prescriptive). Define the metrics required to assess progress as the evolution to the future state plays out. The roadmap should ensure that data manage¬ment evolves to support the business needs. In aspirational compa¬nies, the ability to capture, analyze and share insight is limited. As maturation occurs, the data management function must strengthen to better enable these capabilities. In the end state, a mature analytics capability uses insight and foresight to make decisions, guide future strategies, enable differentiated customer experiences and guide day-to-day operations.
2. Start with business outcomes –
The traditional approach tends to start with data and quickly gets lost in data management activities. The goal is to enable the business through insight, so it starts with business outcomes, moves to the insight and actions required to enable those outcomes, and then fo¬cuses on the insight and action-enabling data.
3. Embed analytics into operations –
Historically, business intelligence technology has been used to report on operations. This paradigm must shift to enable operations, including the customer experience, through analytics. Embedding analytics into operations is the ultimate goal, enabling both efficiency and effectiveness. Understanding the current environment is the starting point. From there, value creating insight and foresight should be identified and enabled through the right analytic methods and technologies. Lastly, the insight and foresight must be embed¬ded into operations using various approaches like analytic applications that create automated closed-loop systems.
4. Establish Analytic Centers of Excellence –
Leading organizations are creating a centralized analytics unit that makes it possible to share analytic resources efficiently and effectively. These centralized units are the primary source of analytics, providing a home for more advanced skills within the organization. This same dynamic could lead to the appointment of Chief Analytics Officers (CAO) in the future. One of the key focus areas will be advanced analytics skill acquisition, development and retention.
5. Instill a data-driven culture –
The shift from gut-based decision making to insight and foresight-enabled decision-making could be the toughest hurdle. Barriers include management and culture rather than data and technology, and the business must overcome a lack of understanding of analyt¬ics and its ability to improve the business. One effective strategy to overcoming cultural challenges is to focus on some of the biggest business issues like customer experience. The importance of solving those issues ensures executive sponsorship and has proven to move some of the biggest cultural hurdles.
6. Leverage new techniques and approaches –
To inform decisions and actions, new techniques and approaches should be embraced. Examples include: create an experimentation culture and environment and use advanced analytics on massive data sets; combine traditional reporting with forward looking indicators based on predictive analytics; drive optimal decisions and actions using simulation; and deliver insight, foresight and actions through automated closed-loop systems that effectively embed analytics into the customer experience and operations.
Just as the journey is an important component of the customer experience, retailers should embrace their own analytic journey as they evolve to a prescriptive business and culture, where insight, foresight and prescribed outcomes continually anticipate and meet the needs of their customers.