Generative AI-Powered Data Transformation — A Game Changer For CPG industry
Generative artificial intelligence (Gen AI) is transforming businesses across industries. For consumer packaged goods (CPG) companies, generative AI unlocks game-changing opportunities to create highly personalized and relevant content, optimize operations, and accelerate innovation.
Let’s look at a few scenarios within the retail and CPG industry where Gen AI can create genuine positive impacts.
- Predict customer choices: Using text-to-image conversion, the choices of customers can be transformed into images of a particular color, texture, fabric and more. This can then be looked up within inventory for availability, and orders can be placed. Natural language processing (NLP) interfaces can aid a lot here in the right product selection. Personalized recommendations and a human-like ordering interface can enhance CX, user traffic and brand loyalty.
- Generate disruptive designs: The entire product design phase for CPG firms can be enhanced by creating newer versions of a design, taking inputs from the company’s design team and packaging department. This can create out-of-the-box design ideas that could in turn create a better customer experience. Text-to-video generation can in parallel create a visually appealing video for the product’s customers. Interesting product descriptions can be created as well.
- Create ‘synthetic customers’: Consent rates for consumers across websites have been reducing significantly — by as much as 30% at times. Ad blockers might prevent potential segments from viewing the ads. As a consequence, doing subsequent customer analysis and refining campaigns and offers gets trickier by the day for e-commerce firms. Generative AI can leverage cluster data from consenting groups of customers, apply ML on top, and come up with ‘synthetic customers,’ who will be a good replica of the original segments, and also bypass the issue of consent. The world of ‘cookieless tracking’ is fast evolving for retail and CPG firms, and Generative AI will play a major role in that.
- Make chatbots human-like: Conversations with chatbots can be made more natural with focused queries and responses. Translation to multiple languages based on how the customer is interacting is also possible, thereby raising customer experience and shortening response times.
- Improve content creation: SEO-optimized copy can be created for blogs, social media posts and landing pages. Product images and models can be created without the need for any photography. This will be a big positive for online catalog updates.
- Enhance track-and-trace for freight: By scraping content around public vessels and freight locations, real-time tracking of vessels for ordered goods is possible. Supply chain bottlenecks can also be pre-empted using data across suppliers, manufacturing, logistics and warehousing nodes. A proactively generated warning mechanism for the end-to-end supply chain will help in making the system more resilient and even anti-fragile.
- Aid retail technologists and developers: Programming languages are but another form of language, meaning that foundation models can be trained to generate codes. This will aid in enhancing programmer productivity, and also minimize bugs and rework efforts.
- Supply-side risk resilience: Using AI-driven news that could impact suppliers to the Nth degree, a model that creates a risk profile for multiple sourcing items within supplier bills of material can be built. This in turn generates text/graphic/visual messages to be sent out to the concerned suppliers for prescriptive actions. Supply-side risk resilience is a key area of consideration for the majority of CPG supply chain organizations.
OpenAI released its latest version of GPT (GPT-4) on March 14, 2023, with the version available via ChatGPT Plus as also an API waitlist. And expectations are already rife that GPT-5 may be launched as early as 2024. With all of the hype around Gen AI, it must be said that we are only scratching the surface of it now, especially in the case of industrial applications in retail and CPG. The industry is watching, and it will warrant an eclectic mix of technology, process, business and people to carve out a champion in this space.
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GenAI use cases for CPG industry
1. Automated Marketing Copy and Creative Generation
Generative AI can create endless personalized variations of marketing copy, social posts, emails, and display ads tailored to each customer. For broader campaigns, it develops optimal creative concepts and assets.
2. AI-Generated Product Descriptions
Detailed, consistent product descriptions are crucial for ecommerce. Generative AI can automatically generate engaging, on-brand product descriptions tailored to each product.
3. Custom AI Avatars and Chatbots
Generative AI can create custom conversational avatars and chatbots with unique personalities and voices aligned to each brand. This improves customer service and brand interaction.
4. Next-Best Action Recommendations
Generative AI analyzes customer data to determine the optimal real-time interaction or offer that will resonate most with each customer, increasing engagement.
5. AI-Powered Product Configurators
Interactive product configurators allow customers to customize options. Generative AI can create the optimal configurator experience for each product.
6. Personalized Product Recommendations
Generative AI models can craft customized product recommendations for each customer journey based on individual interests and purchase history data.
7. Optimized Pricing Strategies
Generative algorithms along with Predict AI can simulate millions of pricing scenarios to determine the optimal pricing strategy for each product across geographies and segments.
8. Accelerated Threat Assessment
Generative AI can rapidly analyze threats like supply chain disruptions or competitive activity and generate action plans to address them.
9. Streamlined Contract Review
Lawyers can use generative AI to help review and summarize contracts and highlight risks, speeding up deal flow.
10. R&D Acceleration
Generative algorithms suggest new product ideas, formulations, packaging, and creative concepts based on latest trends and consumer preferences.
11. Predictive Demand Forecasting
Generative AI and Predictive AI draws insights from disparate datasets to create accurate demand forecasts for better inventory and production planning.
12. Supply Chain Network Optimization
Generative modeling determines optimal supply chain network design and product routing scenarios to lower costs.
13. Automated Reporting and Dashboards
AI instantly generates customized reports, presentations, and data visualizations for business insights.
14. Improved Prediction Modeling
Generative AI can be used to create more accurate predictive models for forecasting, risk analysis, and predictive maintenance.
15. Simulation of New Market Entry
Brands can leverage generative AI to rapidly model and simulate new market entry scenarios and refine expansion strategies.
16.Accelerated Product Testing
CPG companies can use generative AI to simulate endless product test scenarios, reducing physical prototyping and testing.
17. Personalized Customer Experiences
Generative AI tailors messaging, offers, product suggestions, and customer service interactions to each individual.
18. Fraud Detection and Risk Mitigation
AI models identify patterns indicative of fraud or risk, generating strategies to prevent losses.
19. Data Security Enhancement
Generative AI augments cybersecurity by modeling new threats and generating improved security protocols.
20. Edge Computing Enablement
Generative algorithms make edge computing more viable by optimizing models for local deployment and execution.
The Bottom Line
The opportunities for generative AI in CPG are truly boundless. As the technology matures, it will become an indispensable asset across the CPG value chain. Companies that leverage generative AI now will gain a lasting competitive advantage and usher in the next evolution of the consumer goods industry.
Anirban Das
Global Data &AI Innovation Lead
Leads AI innovation, focused on building and implementing breakthrough AI research and accelerating AI adoption in the organization