Artificial Intelligence Uses In The Consumer Product Goods Industry​

The potential of Advanced Analytics (AA) and Artificial Intelligence (AI) is becoming evident. Several Consumer Product Goods companies have deployed AA and AI with excellent success over the last few years. This has led to enhanced sales, reliable data processing and better customer targeting. 


Many CPG firms, on the other hand, are having trouble harnessing AA and AI efficiently. They may excel at marketing but lack the ability in large-scale analytics or digitization initiatives. Many CPG firms are autonomous and matrixed, which adds to this. The framework has resulted in marketing and product creation prowess. This can stifle a company’s willingness to invest in data and analytics systems. Also from investing in the agile operating practices needed to scale them.  


CPG companies lack access to granular sales data (viz. retail sell-in and sell-out data) or first-party data (information on individual consumer purchasing behaviour). These gaps also discourage companies from aggressively investing in AA and AI applications.    

CPG firms that have excelled in AA and AI have concentrated on three key fields:   

 > Creating the right talent pool and organizational model  

 > Maintaining strong data strategy and governance  

 >Constructing the correct data and digital channels  

CPG businesses that are yet to master these tools should learn from those who have led the way.    

If a CPG Organization wants to maximize profits & enhance sales it needs to harness technology. Combining technology with the human aspects of the market drives a specific range of business outcomes. They need to honour the following five primary areas:   


  1. Proof of value:   

Companies must create a sound business case for deploying AA and AI. This includes detailing the necessary costs and the expected benefits. They should first define the problem or pain point they want to address. Then create the necessary tools and solutions. Many AA and AI efforts aren’t linked to company goals, resulting in minimal benefits. One must prioritize business cases to ensure optimal attention and resource distribution.    

  1. Changes in Management:  

Companies must dissuade workers from seeing AA and AI efforts as the IT department’s responsibility. AA & AI should be projected as critical to Management policy and vision. Rather than being built for IT, the Marketing Team could lead this transformation.   


  1. Talent Base and Operating Model:  

Data scientists in conjunction with domain experts plan a mechanism for each department including HR. They identify processes for recruiting, maintaining, and deploying the best people. They also create an operating model that facilitates the scaling of such AA and AI solutions. Agile models focusing on cross-functional teams and fast sprints are usually preferred. At times a centralized team supporting various departments may be the preferred model.   

  1. Data Strategy and Governance:  

Businesses must have a coherent data collection approach. This ensures that they have access to high-quality, structured data, which is the basis for AA and AI. Companies may use large, uniform data sets in AA and AI applications. They develop a policy and governance approach driven by focused initiatives.    

  1. Data and Digital Platform:

To help effective AA and AI application, having the correct data and digital platform is a must. Data and digital platforms sometimes need to unite the various systems used by the firm. At times there are sluggish updates because of legacy IT systems. Companies can solve this by distinguishing between AA & AI data and digital media.    


Working in these essential areas makes a CPG company a pioneer in the industry for years to come. Its agility and flexibility to adapt new technologies will also encourage future generations.