AI driven Approach by Unilever

Unilever was formed in 1930 by the merger of Dutch margarine producer, Margarine Unie and British soap maker, Lever Brothers. Today, the consumer goods giant sells food, home care, refreshments, and personal care products in over 190 countries. Unilever has headquarters in London, United Kingdom and Rotterdam, the Netherlands, and subsidiaries in over 90 countries. The company employs more than 170,000 people. In 2012, Unilever reported more than €51 billion in revenue.

Unilever, the multibillion-dollar multinational consumer goods company is looking to create a culture and organization which is data-intelligent and can predict the future .

How Unilever Uses AI Technology?

Unilever has been able to accelerate the search process for market influencers via AI-centred influencer marketing platform known as Popular Chips. This technology not only aids the company to identify certain influencers with fake followers but also pairs up Unilever with legitimate ones. The detection and pairing are based on demographics including country, age, and gender.

Recruitments :

Unilever partnered with AI firm Pymetrics in order to construct an online hub deploying technology to evaluate a candidate’s aptitude, logic, reasoning, and appetite for whatever role they have applied for.

The next round of recruiting process involves video interview in which candidate’s speech and body language are examined by AI.

According to Unilever, this embracement has chopped down nearly 70,000 hours from interviewing and analyzing procedure.

Marketing :

Unilever relies heavily on for its AI for marketing efforts is the Google Cloud Vision API, a product that, according to Google’s webpage for it, provides easily modifiable pre-trained machine learning models that can analyze objects, images and text.

This essentially enables Unilever to gather massive amounts of metadata from social media posts to help power its AI for marketing campaigns.

Analyzing social media

The Vision API can decode all the user-generated content across social media, Owens said, while the Natural Language API analyzes user comments.

the Natural Language API can perform analysis and annotation on text, including sentiment analysis, which can help decode emotion; entity analysis, which can help discern what the text refers to and syntactic analysis, which can help determine the makeup of a sentence.

I’m an undergraduate student at IIIT Ranchi, pursuing my B-Tech in Electronics and Communication Engineering.