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Data-Centric Transformation in the Age of Disruption

If the pandemic has made one thing clear, the only constant is transformation. For successful companies, it’s no longer about adopting digital strategies that get them closer to the customer. It’s about significantly altering businesses in ways that place the customer at the heart of the enterprise. With consumers having more choices than ever before, businesses must work aggressively to establish new means of engagement and loyalty. This can only happen when:

  • Access to data becomes decentralized and the C-Suite works in partnership to innovate ecosystems built upon next generation insights and emotionally optimized experiences that intuit customer needs and place them at the nexus of a business
  • Brands recognize where they have overshot or under-delivered on using data to help improve people’s lives and begin working toward establishing hyper-personalized experiences
  • Credible actions are taken to make customers feel in control, and willing to opt in to share their data.

In order to get closer to customers, gathering intelligence related to how they behave has never been more important. As a result, insights must quickly migrate from being a marketing tool, to one of the most critical currencies fueling the future of business transformation. Additionally, as we enter the new age of consumer permissioning, the end game of it all will be creating the type of brand experiences that make people want to opt in and share their data. This is where CMOs must foster collaborative synergy with their Data Science team to effectively operationalize data, all with the goal of best advocating for customers to create the winning experiences of tomorrow.

How?

Integrating first-party data with alternative data from other providers can unlock patterns and insights which are sometimes unexpected and can help drive effective transformation.  For example, consumer purchase behavior data from credit and debit card transactions can address questions like:

  • What are the e-commerce trends in our industry?
  • Which competitors have a much higher e-commerce rate of growth, compared to our business?
  • What is the profile and behavior of these online buyers for our competitors?
  • Which locations should we be opening more stores and which should we consider closing?
  • What is our consumer share of wallet compared to competing brands, and how is it changing over time?
  • What is the profile of our brand loyalists and how does it differ from brand loyalists of our key competitors?
  • What percentage of our most valuable shoppers also spend significant dollars at our competing brands, and what are the characteristics of such “dual shoppers”?

Real-time Adaptation to Customer Behavior

As the pandemic ensues, many brands are struggling to identify consumer behaviors which have permanently shifted amidst COVID vs. those that are apt to snap back. Among the changes unlikely to ever fully revert to previous levels are the quantity of in-person interactions across commerce. As a result, critical to success will be an ability to:

  • Identify hybrid engagement models that effortlessly join the physical and digital;
  • Place a greater emphasis on augmenting emotional understanding in ways that close some of the gaps created by the loss of 1:1 human interaction

These milestones will only be possible if consumer behavior data is gathered, cleansed, integrated, and leveraged to generate intelligence that is embedded in key business processes across the company – in other words, data-centricity.

For example, if there is a consumer demand pattern change which can be observed immediately, the supply chain can be dynamically adjusted to match the demand and avoid bottlenecks. 

Figure 1 depicts how grocery shopping patterns changed due to the COVID-19 pandemic and stayed for the long term as the pandemic continued and remote work became the norm for high income knowledge workers. The chart compares the behavior of grocery shoppers on Sundays versus weekdays in 2019 and 2020, and there is a distinct shift from Sunday shopping to weekday shopping (Saturdays remained flat and are not shown). 

This is the type of data brands can use to adjust shipping days and retailers could apply this information to change their restocking days.  Clearly these types of cross segment and cross industry trends cannot be observed purely using one’s own organizational data and requires integration of reliable alternative data obtained externally, as well as flexible business processes which have this integrated centralized data as the fuel to power them.

Figure 1: Grocery Store & Supermarkets Shopping Trends (Source: Affinity Solutions)

Brand experiences

Data-centric solutions are also key for experiential marketing by brands, which can delight consumers with personalization and the right messaging at the right time. Consumers are more inclined to share their interactions, likes/dislikes and profiles in return for such experiences, thereby generating more permissioned data for brands to utilize. One example I experienced personally was when an airline offered me a free meal and beverage while waiting at the terminal at Newark Airport sitting at a kiosk in the waiting area, based on my frequent flier profile, location, and purchase history with them.  Clearly this required consumer permissioned data like my frequent flier profile and location, and collaboration/integration with the airport food vendors, so all the relevant data elements were available centrally to be leveraged by the airline application installed on my mobile phone.  

The future is now, and it is time for organizations to integrate data into their business models, going beyond serving as an asset for improving marketing.  Being data-centric is about weaving several internal and external data assets into the fabric of strategic decision-making processes across all functions and at all levels of the organization. Now is the time for businesses to shift from being data-driven to data-centric to achieve customer-centric business transformation that yields hyper growth in this era of continuous change.

About the Authors

Atul Chadha, Chief Technology + Operations Officer, Affinity Solutions. He heads the Engineering, Data Science, Analytics, Program Management and Quality functions at the company. Prior to joining Affinity Solutions, Atul held various senior leadership positions in Big Data and Cloud technology and strategy at IBM Silicon Valley Lab. He has authored nine patents in data mining and related areas, including one which was rated among the top 5% of most valuable patents for IBM. Atul is a data focused entrepreneur having co-founded a behavioral data platform startup, and has over 25 years of experience in the field of software engineering, data engineering, data mining and cloud technologies, with senior roles at Wipro, Good Technology (acquired by Blackberry), PeopleSoft (acquired by Oracle), and IBM. He holds an MS in Computer Science from University of California San Diego, and an MBA from California State University East Bay.

Arun Rajagopal, Principal Data Scientist, Affinity Solutions. Arun brings a decade of experience in building large-scale distributed analytical and ML/DL systems. Previously, he advised major financial institutions and large retailers on AI strategy while at EY CIO Advisory and TCS Retail Innovation Lab. In his current role, Arun is building an AI-based transaction labeling product and a marketing insights platform.”

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