Predictive Analytics Use Cases In Consumer Product Goods CPG Industry
The global consumer 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 Product Goods giants an opportunity to revamp their marketing and operations using predictive analytics. By utilizing advanced analytics techniques such as predictive analytics, consumer product goods companies can move beyond simple reactive operations and take proactive decisions.
This helps insightful Consumer Product Goods companies get in a better position to systematically allocate R&D investment and to maximize their supply-chain efficiency. Instead of aggressively pushing their products in the market, CPG players can now encourage their consumers to pull their products. It is leading to improved returns on marketing efforts.
Challenges and Opportunities for Predictive Analytics for Consumer Product 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.
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 taste.
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.
#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 and deliver results to the frontline in order to make targeted offers. This will enhance your processes, leading to best in class consumer service and deliver 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 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 and connect 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
Efficient inventory management is critical to the success of CPG companies. Predictive analysis helps manage the warehouse by suggesting optimal inventory levels. 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 product.
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 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. The 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.
This gives CPG company 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 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 their 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.