Reshaping the CPG Industry with AI

In the current battle for market shares in the consumer packaged goods (CPG) sector, larger companies are being challenged by smaller niche players that have a data advantage and direct consumer connections.

The CPG sector has increasingly more data at their disposal, both from traditional enterprise data and internet of things (IoT) devices; however companies still struggle to use this data and turn AI investments into profits.

A joint study conducted by BCG and Google found that by using AI and advanced analytics at scale, CPG companies can generate more than 10% revenue growth through more predictive demand forecasting, more relevant ­local assortments, personalized consumer services and experiences, optimized ­marketing and promotion ROI, and faster innovation cycles.

– BCG, Unlocking Growth in CPG with AI and Advanced Analytics

 

From product design to supply chain and marketing, use cases abound, but change to a data-driven enterprise doesn’t happen overnight. The process requires significant changes to ways of working throughout the organization, from board-level decision making to daily operations.

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High-Value Use Cases 

 

Consumer feedback: Getting feedback from the end customer at scale generally requires leveraging natural language processing (NLP) techniques for sentiment analysis. At its core, NLP is about training a machine to interpret the meaning of raw text. It is extremely valuable, but more complex and resource intensive than working with structured data (think: data in rows and columns, like a CSV).

But analyzing customer sentiment is only the first step; a full AI-driven system also incorporates ways to deliver this feedback to the right people so that necessary adjustments can be made.

 

Marketing use cases: From assessing the value of in-store promotions to marketing spend optimization, AI has the opportunity to bring a data-driven approach for marketers in the CPG industry.

For example, AI enables organizations to effectively use data collected from past trade promotions, measure effectiveness, and make recommendations for future promotion based on calculated predictions. Getting started means first putting data in the hands of marketers through self-service data programs can be the first step toward larger-scale, operationalized projects.

 

Planning and forecasting: In the age of AI and algorithms, older modeling techniques fail to incorporate the wide variety of data sources needed to produce results precise enough for the modern enterprise. AI-based systems can more precisely predict the quantity of product necessary and the delay in which they will need to be restocked.

AI-based supply chain optimization: In order to predict shortages in stock and arrange new shipments,  CPG businesses can monitor product location and inventory using both IoT devices as well as other sources like transaction data and even third-part sources, such as the weather. AI tools can eliminate manual effort and increase efficiency in logistics. 

 

Dataiku Solves CPG’s Biggest Challenges

Dataiku is the platform democratizing access to data and enabling CPG companies to build their own path to AI.

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By making AI accessible to a wider population within the enterprise, facilitating and accelerating the design of machine learning models, and by providing a centralized, controlled, and governable environment, Dataiku allows CPG organizations to massively scale AI efforts:

  • To successfully operationalize and scale, data teams need far more than just good data – they also need staff, structure, efficiency, automation, and a deployment strategy; Dataiku facilitates these requirements (and much more).

 

  • CPG organizations often struggle to harness the power of personal data without infringing individuals’ rights to privacy. Dataiku, paired with solid governance practices, facilitates privacy-compliant data projects and brings explainable and responsible AI to the enterprise.

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