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January 6, 2021

As the world entered the pandemic, COVID-19 forced many organizations to boost their digital transformation initiatives, making some fall prey to half-baked analytics. All businesses tried to implement this game-changing technology in the name of digital transformation, defying any barriers to adoption. COVID-19 has made everybody witness Analytical Intelligence reset the economy world over.

This led many organizations to convert their legacy non-digital legacy systems to digital ecosystems to drive new revenues and increase cost savings. Because of the rush to transform their businesses, many organizations end up with data silos with different LOBs and departments not getting an integrated, big picture view of their customers’ end-to-end journeys.

An organization might implement the best analytics intelligence algorithms, but what matters most is how well the data flows through inter-departmental arteries so that all stakeholders have a comprehensive and contextual analytics-driven view of the customer experience.

Data democratization across departments done right makes an organization better at effective decision making.

Analytics done on such data silos often results in inaccurate decision making because of lack of context flow. Companies today not only need marketing & operational analytics, they need 360-degree marketing and 360-degree operational analytics to get the data-driven, actionable and contextual insights required to run their business.

Cases in point:

Some problems creep in because of a missing context or the bigger picture.

  • Example 1: Misguided ad targeting

We see many organizations using analytics in their marketing strategy to decide how their ads will get shown to their prospects. One such recent encounter was with an educational organization, using programmatic marketing intelligence & targeting for serving display ads on blogs related to higher education (MBA). The organization was using analytics to find the best way to target its MBA admission ads. The absence of overall contextual knowledge of the blog content made its ad display on a blog article discussing how MBA degrees bring snob value but no real success!

Giving us is a perfect example of using analytics or intelligence in its half-baked form without considering the full context, leading to a lost customer acquisition opportunity.

Organizations should ensure that they not only implement analytics, but they implement 360 degrees analytics for connected intelligence based on data from departments across the enterprise when ad targeting is getting more and more automated and targeted [1] when we know that the advertising industry has a problem: People Hate Ads [2]

  • Example 2: Unintelligent buy recommendation and order delivery

Another example of half-baked analytics decision-making is about an online purchase experience on Amazon when a customer orders the first best buy recommendation from Amazon fulfilling all the needs on cost, features, etc. Later on, to figure out that the product was being packed and shipped using road transportation from another distant state nearly over 1,093 km away from the ordering location left the customer with deep guilt as a responsible customer for not taking care of the environmental implications of ordering. Alternatively, the product could have been bought from the very next door market by the customer. The company could have used coordinated analytics capabilities to understand what to pitch to a customer based on the requirements and other related factors that the customer values without negatively impacting the environment. Had the company implemented marketing analytics well with supply chain operational analytics, the chances of getting a happy and loyal customer would have been much more with increased customer satisfaction and experience. It becomes even more imperative when we know how companies are trying to improve on their carbon footprint and sustainability efforts the world over. [2]

Implementing 360-degree cross-organizational analytics to analyze the data holistically with a full contextual understanding of your overall business can help solve this issue.

To read more about how to merge and transform data from multiple sources across your organization into trusted data insights, check out the TCS CIP platform. CIP can source interdepartmental data into a single data lake for more informed and collaborative analytics, helping you build back even better post-COVID-19 and thrive in the post-COVID-19 era.

To understand how you can better know your customers’ needs and preferences check-out TCS Connected Intelligence & Insights software solutions for Retail & Banking.





Nitu Sharma is a technology products and solution marketer with TCS Digital Software & Solutions (DS&S) with more than 13 years of experience. Her entire career spans marketing of technology software & services for Life Sciences, Manufacturing, BFSI, and Retail domains. She is passionate about the latest technologies such as AI, ML, IoT, and Smart Cities and their impact on all the three pillars of sustainability including economy, people & environment. She likes analyzing corporate brands and their marketing communication, especially from a sustainability perspective. She loves to spend her spare time investing in stocks and engaging in speaking opportunities on podcasts & university sessions related to the latest marketing technology & trends.


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