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CPG Analytics
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Predictive Analytics Use Cases In Consumer Product Goods (CPG) Industry

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The global consumer packaged goods industry (CPG ) is witnessing waves of transformation. Powered by changing consumer demographics, advancing technology and changed shopper behavior, these new dynamics are demanding companies to rethink their business models.

This is offering consumer packaged goods giants an opportunity to revamp their marketing and operations using predictive analytics. By utilizing advanced analytics techniques such as predictive analytics, consumer packaged goods companies can move beyond simple reactive operations and take proactive decisions.

What are the four major trends happening in the CPG industry?

This helps insightful Consumer Goods companies get in a better position to systematically allocate R&D investment and to maximize their supply-chain efficiency.

CPG Data Analytics allows the players to avoid the push strategy in the market and move towards the pull strategy to attract their customers, with improved returns on their marketing efforts.

Challenges And Opportunities For Predictive Analytics For Consumer Packaged Goods Enterprises

To capitalize on the opportunities that advanced analytics provides, organizations must cut the barriers that limit the flow of information. Predictive analytics can only deliver reliable results if it is modeled on clean and optimized external/ internal data. This requires a coherent data management system in place beforehand. One noteworthy use case in having a data management system in place in the CPG industry before analytics is Kraft Heinz. Kraft Heinz has moved from the Hadoop platform on-premises to the Snowflake Data Cloud. Showing how important a data warehouse or data lake services are important before thinking about adopting Analytics.

Advanced analytics will help CPG companies to correlate between decisions and business outcomes in real time. It will provide insights to help you dynamically respond to consumer tastes.

Predictive analytics built on time-series regression and advanced analytics machine learning approaches can provide valuable and actionable recommendations for your business. It will prescribe the optimal decision which maximizes your revenue while cutting down on inefficiencies. All of this offers incremental growth opportunities to CPG manufacturers.

Predictive Analytics Use Cases In CPG Industry

1. Personalized Offers To Increase Engagement With The Brand

To deliver a truly personalized experience to the customer, you need to actively respond to their taste, needs, and preferences. Predictive analytics helps you make proactive strategies based on complete shopper insights. This will help you understand the customer’s behavior in the shopping cycle and create pin-point personalized offers to improve their response rates.

Use customer information such as buying history, demographics, etc. to build predictive models like market basket analysis and deliver results to the frontline in order to make targeted bundled offers depending on the preferences of the customer. This will enhance your processes, leading to best-in-class consumer service and delivering a competitive advantage.

This builds customer loyalty and creates a better brand association leading to increased business revenue. It will reduce the customer’s propensity to churn and increase the average customer lifetime value.

2. Revamp The Business Model With Data-Driven Insights At Every Stage

The product manufactured by CPG companies goes through various phases in its journey from the factory to the consumer. At each of these phases, data can be extracted. This includes data from shipments (which tracks the journey from warehouse to a distributor or consumer), scan track (at retailer POS), survey (collected on the field and consisting of qualitative & quantitative data), digital data, household panel data (using registered users to track their purchase).

This helps you make pricing and cost decisions across your entire portfolios and channels by understanding the price elasticity or the competitive landscape and connecting marketing and sales like never before. And tailor the optimal assortment of products and merchandise for better customer experience in-store.

3. Efficient Inventory Management & Lean Supply Chain Using Predictive Insights

Keeping a track of inventory from the raw material or the WIP or the final product is critical to the success of CPG companies. Predictive analysis helps manage not only the forward logistics but also the reverse logistics to maintain a “lean” inventory. This will drive value through your whole supply chain and help identify cost-reduction opportunities by delving into deep-impact issues.

This will keep your supply chain seamless & without disruptions. Predictive analytics will help you identify optimal inventory levels by factoring in demand-supply economics, varying safety stock levels, product shelf-lives, segment behavior, lead times and cycle times, and share-of-wallet for the different products.

This will require organizations to consolidate data from ERP systems and build on sales pipeline insights. Reduced excesses in inventory will ultimately lead to reduced costs and an improved bottom line.

4. Harnessing New Customer TouchPoints For Sales And Marketing Actions

With the growth of social media, the internet and mobile, the way people shop today has completely changed. Those days are gone when people would read about a product in newspapers and circulars. And enticed by the promised features, they would make the journey to a store to buy. Millennial shoppers’ distrust direct advertising and are more likely to purchase based on friends’ and peers’ recommendations.

Today, there are multiple touchpoints between when the customer first learns about the product and when he makes the final purchase.

predictive analytics cpg industry infographic

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Analysis of such data provides CPG companies the opportunity to influence decision-making across these many touchpoints. Predictive analytics can help design campaigns using multi-channel marketing insights. This leads to an optimized shopper experience and guides the prospect on the buyer's journey to his final purchase.

Taking this advantage requires the companies to work on unstructured and semi-structured data from social media and the internet along with the traditional structured customer information. The company that does this will have a massive advantage over its peers in activating and influencing the shoppers’ decisions.

This will help CPG manufacturers deliver superb customer experiences and design lean operations. This will guide the CPG manufacturers to meet their objectives of better understanding their consumers to enhance their experience, reduce costs, streamline the supply chain and enhance the relationships with retailers.

How can Polestar Solutions help?

With our consulting expertise in CPG Analytics in Data Management, Visualization and advanced analytics for the industry we strive to provide personalized solutions that are the right fit for your company to be right partner in this digital transformation journey.

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Want to know more about how to leverage data to optimize needs for the CPG industry or need help with implementation of analytics?

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