June | 2020
Executive Summary
As COVID-19 transforms the way consumers behave, it will affect demand estimation, customer targeting, and product and service fulfilment strategies. Executives that refine demand predictions with search trends, personalize targeting by continuously sensing customer responses and optimize product fulfilment by injecting local market intelligence will emerge stronger in the post-crisis world.
This paper looks at how Analytics & AI can help companies transform into Intelligent Enterprises by adjusting demand and supply strategies in response to changing customer habits and sentiments.
A Three-Part Action Plan to Demystify Demand Forecasting
As COVID-19 becomes the new normal, companies are facing unprecedented challenges to rapidly forecast demand based on new customer needs and preferences. Consumers are shifting the way they purchase, what and how. The traditional inside-out view of customer needs and anecdotal outside-in view of the market is no longer enough to improve business performance post the crisis. As an immediate response to this crisis, companies should refine demand predictions, improve targeting and optimize product fulfilment based on area demand. Here is a three-part action plan that can help get it right:
Combining continuous feed of outside-in intelligence into the existing inside-out view of the customer helps create a holistic view of the demand for enhanced business decision-making (see figure.1).
Figure 1: Outside-in and Inside-out views unite to form a holistic view
Figure 2: [Asset 1] Market Impact Monitor to flag changing preferences
Fine-tune targeting by continuously sensing customer response
Once companies have refined search trends, they should shift focus to fine-tuning the brand messaging based on today’s fluid geo-political-social situation. Online forums and group communications are emerging as viable ways to understand the right consumer attitudes, services and brand positioning that companies need to adopt. The classic sender-receiver approach (test and learn) helps position the brand based on the ground reality that considers consumers’ fear, uncertainty and doubts. In addition, a survey based approach combined with real-time monitoring of the Web and social media-verse helps personalize services and products by monitoring sales performance and customer preferences. For instance, Wipro’s Return on Spend Studio leverages modular AI based accelerators to help companies sense consumer preferences through a lab setup.The studio uses Google trends data for understanding demand and service requests such as Google, Apple Mobility and COVID-19 case time series. This helps get insight into the affected areas with enhanced ability to understand the possibility of stabilization (see figure 3 and 4).
Figure 3: [Asset 2] Return on Spend Studio - Performance View
Figure 4: [Asset 2] Return on Spend Studio - Customer View
At the same time, companies should plan to rapidly utilize financial indicators and stock markets data for competitive trending. Marketing spend should be allocated to sentiment and brand analysis for improved customer targeting through social media. It is also crucial to analyze Google trends for revamping the brand. Companies can use Google trends analysis Natural Language Processing (NLP) to categorize tweets and their emotional variance for improved targeting across brand categories. Similarly, advanced technologies such as Mobility Exploratory Data Analysis, Google BERT based conversational AI system, Facebook Prophet and predictive monitoring can help companies to better understand the geographic impact, query articles and papers, and forecast COVID-19 infections.
All these actions can help companies optimize their spend, continuously monitor return on their marketing and advertising spend, and refine messaging to customers to boost traction.
Optimize product fulfilment by injecting local market intelligence
As most countries shift their attention to reopening economies post lockdown, companies will need to take a strategic approach to business recovery based on industry-specific demand-supply drivers (see Figure 5). This will equip companies to re-target their customers with the right products, at the right time, in the right location, and through the right channel. A market recovery monitor that considers
Figure 5: Unique data sets for uncovering local market intelligence
Figure 6: Market Recovery Monitor to assess market strength
Figure 7: [Asset 3] Market Recovery Monitor to understand success of social distancing
Figure 8: [Asset 3] Market Recovery Monitor to assess mobility levels
Figure 9: [Asset 3] Market Recovery Monitor to assess consumption per local market
Redefining demand prediction, personalization and product optimization in the post-pandemic normal
Companies that systematically integrate local market intelligence based on an organic open data ecosystem will gain access to a much better view of demand. They can then target better and optimize fulfilment. Companies that fail to do this will miss the boat as far as understanding where the demand lies is concerned, in the uncertain post COVID-19 world.
Tanusree Saha is a Partner with Analytics and AI Consulting. She brings more than 15 years of experience in designing and leading advanced analytics programs to the table. She leads the Marketing Sciences Lab (MSL) proposition for DAAI.
Her areas of expertise include data science, translating business challenges into data science problems, defining AI strategies, and running innovation labs for clients in marketing and customer management, supply chain, and operations. Her domain experience spans travel & transportation, media & telecommunication, retail and financial services.
Tanusree can be reached by writing to tanusree.saha2@wipro.com.
Shamit Bagchi works as a data scientist specializing in predictive and prescriptive analytics, machine and deep learning, and AI consulting. He brings to the table both technical acumen and business consulting expertise based on over 16 years of experience in the big data and software industry, building value propositions for clients in Europe, US and India. He holds a Master of Science degree in Complex Adaptive Systems from Chalmers University, Sweden and an MBA in Marketing & Strategy from the Indian Institute of Management, Bangalore. Shamit can be reached by writing to shamit.bagchi@wipro.com.
Kiran Singh is a Partner with Analytics and AI Consulting. He works out of Wipro’s New Jersey office. Kiran leads the Analytics Consulting charter for Consumer Goods and Retail clients in the North America region. His primary areas of interest are consumer sentiments and marketing channel analytics, along with retail labor operations. He has also authored US patent in labor demand forecasting and optimization.
Kiran can be reached by writing to kiran.singh@wipro.com.
The authors wish to thank Jayapriya Dey, Jayaraman Srinivasan, Vinodhini Gunasekaran and Anish Prakash for their contribution to this paper.
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