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Role Of Enhanced Predictive Analytics In Serving Consumers During The Pandemic
Consumers have radically shifted their consumption patterns and organizations are responding by moving from precision measurement to prediction.
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Data has become the key to a competitive advantage in a world that becomes smarter each day. Leveraging data in a responsible way to make more informed decisions has become vital for every organization, including Philips. Being able to predict customer demand and improve upon current decisions based on past experiences more accurately, is an invaluable tool. Today, the sheer volume of data is growing at an astronomical rate and being able to analyze such voluminous data to reveal consumer trends and patterns is not easy. But, with the advancement in technology, the responsible application of analytics, machine learning, deep learning and AI over big data, has enabled the identification of actionable insights for transforming predictive analytics.
With a broad data set, analytics can help businesses to better understand their consumer. Predictive analytics traditionally have helped in forecasting and planning sales. It applied statistical techniques on historical sales data to identify commonalities and patterns to answer questions on consumer behavior. Based on the need, a deep dive of these patterns is then conducted to establish the learning that can then be used to better serve customers. This worked well in the past as it relied on the historical behavior of consumers.
The advent of COVID-19 has changed all that. It has impacted consumer habits in such a way that historical trends cannot be used anymore. Decision-makers during COVID-19 have operated in uncharted territory and had to respond to ubiquitous spikes and dips in market demand. Consumers have radically shifted their consumption patterns and organizations are responding by moving from precision measurement to prediction. In such treacherous waters, where trends continue to shift, it should not come as a surprise that it has become essential to view data in new paradigms by applying experimental techniques to problem solving.
Organizations have thus turned to enhance their predictive analytical tools with analytical, machine learning, and deep-learning models - These models use more and more data to make assumptions, test them and learn autonomously and continuously to improve predictions over time. At the same time, new sources of auxiliary data like Google mobility trend report and market sell-out data (even though lagged) have been used to generate insights that help improve their “response times” and make better decisions during the ongoing pandemic.
Such insights can also help drive tactical decision-making during this pandemic. In fact, we analyzed the market data, before and after the pandemic hit. We then statistically quantified and compared this data and were able to identify the effect of “stay at home behaviors” that helped businesses make better-informed decisions. For example, it was found that the lockdown restrictions had a positive impact on the sales of hair grooming products as consumers could not access salons and had to purchase products to use at home. The same restrictions did not affect the consumer behavior related to shavers as it was not a priority while working from home.
Also, using these new data sources and applying the deep learning models it is possible to generate the sellout forecast data not just for your products but also for the competition and the market as a whole. These insights can then be used to forecast how various decisions can lead to business improvements in the organization.
Leaders and organizations can apply the learnings from such rapid analytics to be in a stronger position. They can tap deeper into the value waiting to be unlocked for the consumer and help organizations with the much-required edge in staying ahead of others, in spite of the challenges during the pandemic.
The true strength of data lies in its ability to help us make more informed, more effective, and more intelligent business decisions. Data analytics is how organizations are driving their business growth strategy. This enables them to derive meaningful insights and patterns from user data giving a clearer picture of the customer needs and future market changes. Such insights are helping businesses achieve their goals even faster than ever before. In the current time, when the consumers themselves are evolving during this period, we need to continuously find ways to improve on how we add value for them, which in turn affects the business value.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.