July 12, 2024 By: Sreenivasa Sunkari
With consumers knowing no alternatives to the physical shopping experience to consumers switching multiple digital channels and considering competing brands with a simple click, the Consumer Packaged Goods(CPG) and retail industry have come a long way.
The predictive analytics market is projected to grow to $41.52 billion by 2028. Businesses now focus on improving digital touchpoints and literally listening to what consumers want. Consumers are at the center of the decision-making process on everything ranging from products to marketing styles, and it has all been made possible because of predictive analytics.
With predictive analytics enriching business intelligence, CPG companies and retailers can assimilate a data mine of consumer insights, industry trends, and competitor strategies. This helps in varied aspects, including hyper-personalization of sales approaches, informing choices on new launches, phasing out old ones, pricing optimization, etc.
This blog will explore how predictive analytics has helped the retail and CPG industry come out on top regardless of the extent of digital penetration in today’s business landscape.
Understanding Predictive Analytics in Retail and CPG
There are certain fundamental strategies that CPG companies use when leveraging predictive analytics to gain maximum revenue. They are as follows:
Have defined objectives: Companies should have clarity on the goals they intend to achieve and collect data that helps them achieve those goals.
Unify data from diverse sources: In the retail and CPG industry, there are diverse sectors that contain relevant data that need to be unified for accurate sales forecasting. For example, retailers need to tap into sales trends, consumer demands, consumer interactions with their brands, consumer demography, and customer feedback. External factors such as location or weather also need to be looked into.
Continuous data maintenance: Storing, maintaining, and updating data collected is crucial for making informed decisions related to any aspect of the manufacturing or sales pipeline. Inaccurate data or missing data can be harmful to market forecasting, leading to revenue loss.
Rely on AI/ML tools: What hyper-automation in retail essentially does is pattern recognition. Identifying correct patterns when conducting consumer behavior analysis can help CPG companies in weeding out non-performing products and prioritizing the popular ones. The integration of machine learning in retail thus can help in supply chain optimization, improve overall operational efficiency and maximize ROI.
Monitor real-time data: Monitoring real-time data can help in the instant rectification of anything in the production and distribution channels.
Challenges to Mitigate
Implementing these key strategies is the best-case scenario that can help retailers’ holistic market intelligence. In practice, leveraging predictive analytics to get accurate data proves to be extremely challenging.
For one, it is difficult to integrate the massive amount of data that has been gleaned. In CPG and retail marketing, segmented research is done for different departments, locations, or products, but there is no cross-sharing of the results gathered from that data.
Data is usually stored in silos, and it consumes an immense amount of time and effort on the part of data d experts to unify. Even when that is accomplished, harmonized data is not scaled up or reused, but the process of assimilating data for new projects starts again. This makes it a fiscal liability.
Not only that, experts use various programming languages to design analytic models that interpret data. This poses an obstacle when it comes to accessing the data when those experts leave. The consequences? Data gets duplicated, stored, and ultimately outdated, which is again financially draining.
But data-driven insights are indispensable. Hence companies must avail experts and scientists to create algorithms that can process massive amounts of complex data. Also, businesses need to invest in high-powered computer processors for AI systems to demonstrate flawless results.
Benefits for Retail and CPG
Nothing is without its faults, and predictive analytics is no exception. But even then, without it, companies would be in the dark about who their target audience is, current market demands, pricing strategies, and any other integral business component. Here’s how companies can leverage predictive analytics in their favor:
Demand forecasting: Demand forecasting in retail entails predicting customer wants at the right time. Companies leverage predictive analytics to examine past and present sales and marketing trends to estimate consumer demand.
Inventory Management: Accurate demand forecasting helps CPG companies prevent overstocking irrelevant products or understocking popular ones. For further inventory optimization, businesses leverage data analytics to keep tabs on real-time demand data, which would help prompt stock shifts as per market dynamics.
Personalized Marketing Strategies: Predictive analytics also takes care of isolating customer demography as per business needs and consequent customer segmentation by personal, social, or psychological factors. This helps them tailor marketing maneuvers as per audience preferences, which enhances customer experience and drives up product sales.
Lowering production costs: Mismanaging the product pipelines or inventory mishandling can lead to wasted resources or products which increases production costs. Predictive analytics helps retail businesses recognize these bottlenecks in the supply chain and remedy them, thus automatically lowering the overall production cost.
Reducing markdowns: Predictive analytics helps in optimal product stocking, reducing the chances of products becoming outdated or unpopular. This minimizes the need for markdowns. Even though predictive analytics can assess when markdowns wouldn’t result in a loss and set you apart from your competitors, the point however is to avoid it.
Competitor analysis: Predictive analytics helps in scouring the market for competing products or trends, thus helping companies understand how to strengthen their own USP to set themselves apart.
Aligning Strategies with Consumer Trends
To keep up with today’s consumers, CPG companies need to embrace the digital boom and build a habitable ecosystem to exist in it successfully. Since technology is reshaping how customers shop and view brands and products, CPG companies must invest in predictive modeling solutions. Predictive analytics incorporating advanced tools such as artificial intelligence or machine learning help companies have an ear to the ground and concoct the perfect recipe for success.
JK Tech, a next-generation business consulting provider, elevates business outcomes with in-house retail IT services. We aim to bring transformation and automation to retail and CPG brands by leveraging retail analytics and AI/ML. Our expertise lies in maximizing revenues, reducing risks, and delivering faster ROI.
