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June 19, 2020

Customer behavior today bears little resemblance to customer behavior yesterday, pre-pandemic. The predictive analytics retailers used to depend on for customer insights relied on historical information, which does not exist for the COVID-19 outbreak. As a result, businesses must restart their data collection and analysis processes.

Existing data is no longer relevant because everything has changed: how and where we work, how and where we shop, how and where we socialize.  We need to collect new data and create new models based on that new data that reflects when the stay-at-home mandates went into effect to better align with market realities as they unfold.

As if the retail sector wasn’t under enough pressure before the pandemic!  The highly volatile and complex retail world is now even more so.  This makes understanding and responding to quickly changing customer needs in relevant, personalized ways, critical to a retailer’s success.

Today, it’s important to track how customer behavior continues to change depending on such factors as:

  • When the state and executive orders lift
  • Whether or not customers have jobs
  • What behaviors acquired during the lock down will continue post COVID-19
  • How social media and purchasing behavior change as the political and social movements change
  • And more

Data collected just a few weeks ago most probably will not reflect what is happening now. Hence your models are, well, useless.  And Garbage in is Garbage Out (GIGO), so you cannot trust the insights your data is giving you today.

Given this reality, organizations need agile data management and IoT analytics that adapt to new information faster and easier.

That is why a scalable, IoT-enabled platform built for big data that delivers agility is required to compete in today’s dynamic environment. One that offers retailers:

  • The ability to collect and analyze data from the widest array of sources to get a deep understanding of customer needs and desires across their end-to-end customer journeys
  • Pre-built data models and proprietary algorithms to deploy custom analytics models quickly with little or no programming required so you can respond in promptly to changing customer needs
  • Easy partner integration with APIs, platform services, and data models for fast, lower cost deployment because your data alone is not enough. You need third party data for input to forecasting models to make them more accurate
  • Continuous innovative IP in contextual stream analytics and machine learning for more relevant, innovative, and memorable solutions
  • Modular architecture that re-uses common functionality and offers plug and play capabilities to complement existing IT investments for improved operational efficiency
  • Extensible Domain Data Models for blending machine data with customer model, supporting flexibility and automation in extending the core models to meet customer and partner needs.
  • Adaptive Analytics Engine for self-serve analytics. Business users can use the guided UI to execute advanced machine learning models and speed time to market for campaigns.
  • Complex Event Processing for real time actions on data streams based on business rules and analytics algorithms.

Data management and analytics during the COVID-19 pandemic are essential to managing the current disruption and the move to a new normal. Having an agile data management and IoT analytics platform can become your new strength, so you can address changing behaviors before your competitors do.


Kathleen Holm is Marketing Director of the TCS Digital Software & Solutions (DS&S) Group. She has more than 25 years of experience marketing technology software and services to enterprises worldwide. She leverages her extensive background in enterprise software technology to help organizations develop effective marketing strategies, create targeted messaging and positioning, and implement effective go-to-market plans to improve corporate performance. Prior to joining TCS, Kathleen was a Senior Principal of technical product marketing for Oracle Fusion Middleware where she was responsible for defining the marketing strategy based on industry maturity and customer trends. She also held positions at IBM including Market Manager for WebSphere Developer Programs, Market Manager for Tivoli Integrated Service Management and Tivoli Brand Specialist. Prior to joining IBM, Kathleen worked with four high-tech startups.


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