Search will get very smartIn the past year, online search has had several AI and machine learning developments. Google is leading the pack with exciting applications in information retrieval. For example, Google’s BERT technology can process a word in the context of all the other terms in a sentence. BERT also enables anyone to train their own state-of-the-art question answering system. Customization of search results and the results page based on learnings from past interactions and preferences of a user is another application of machine learning used in the search.
AI-driven personalisation of messagingSeveral adtech companies have been focussing on using AI and machine learning to find the right audience to write better ads than humans and to increase conversion rates and engagement. There are also several AI-led developments in the area of creating dynamic ads to personalise marketing messages on the fly. AI has an application in terms of determining the logic of personalisation, using techniques such as natural language generation (NLG).
Use of machine learning in campaign operationsPlatforms such as Google and Facebook have been at the forefront of AI/ML applications in marketing. Starting from smart bidding and smart campaigns to auto-generated ads, Google is making it easy for advertisers. Smart bidding options such as TROAS, TCPA, and others use advanced machine learning algorithms to train on vast data to make accurate predictions on bid amounts impacting conversion and assist advertisers in optimising without getting into too many details. Google factors signals to predict user behavior and to influence auction time bidding as per the goal set by advertisers. Facebook has also incorporated machine learning across campaign planning and execution, as also in ad placements and ad delivery. Similarly, on the organic search side, machine learning-based product ALPS reverse engineers Google’s ranking algorithm and is able to accurately quantify ranking drivers, provide precise recommendations for changes, and predicts the impact of SEO actions before they are implemented. Similar technology to drive improved ad copy testing in digital marketing exists. These help in evaluating ad copies and landing pages on various parameters like relevancy, use of action promoters/inhibitors, urgency inducers, page layout, load times, etc., to gauge the impact on ad relevance, expected CTR, and landing page experience.
Future trends AI
will also have additional application in digital marketing with the uptick in the adoption of technologies such as VR and AR, as commercial use cases of these technologies find wider adoption in retail and other sectors.Many retailers are also testing AI and VR/AR technologies together to make the user experience personalised to an individual. Other areas of impact include voice search. We will increasingly see ads about things which we just said or talked about but haven’t searched for yet. Similarly, image search is also being used by many brands for their consumers to identify products.
This article is written by Aditya Saxena & Ajay Kumar Rama and originally published here