Search is an engrained part of the consumer journey, and consumers expect instant, relevant responses. It is gaining the capacity to predict a consumer’s needs and wants and this behavioural data is becoming available.
According to Internet World Stats, Google processes more than 40,000 search queries from around the world every second, all of them relying on keywords. During 2017, search engines will begin to factor additional behavioural data into their results, such as the user’s history of searches and locations, and previously captured conversations.
Artificial intelligence will use this information to power predictive search results. This will improve the quality of search results and provide new insights into consumers’ behaviour and the moments which matter to them. Search will evolve into tailored recommendations.
Several of the largest online platforms already use machine learning to improve search results. For example, Google’s RankBrain technology adds rigour to search results by understanding the context in which the consumer has entered the query. Over time, RankBrain will learn further from user behaviours. Amazon’s DSSTNE (pronounced ‘destiny’) learns from shoppers’ purchasing habits and browsing behaviour to offer better product recommendations, which Amazon can offer before a consumer has even entered anything into the search bar.
What are the implications for brands?
The ability to improve prediction, driven by technology, has major implications for brands. The consumer journey will become a much more dynamic entity, both as a brand tool and as a real experience. Humans will not become redundant in this shift. Someone will need to make faster decisions to act on the implications of prediction at scale. By understanding the context of consumers’ searches, marketers will be able to serve relevant content to help people make better purchasing decisions, and cross-sell their products and services more effectively.
Download Zenith’s 2017 Trends report.
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