There is a tendency to assess AI in terms of how it can improve discrete elements of the consumer commerce journey, rather than how AI can enhance the journey as whole. Service providers routinely focus their efforts on machine learning (ML)-powered recommendations, which is understandable as these functions can be compelling when well executed and are one of the reasons that Amazon is so successful. But the full impact of AI on commerce extends well beyond recommendations and touches every part of the consumer commerce journey: from discovery and evaluation, through to cart management, transactions, fulfillment, and post-purchase follow-up actions. The various steps are connected and, to be effective, AI commerce implementations must do the same.
How and where AI can shape consumer commerce is summarized in Figure 1, which is by no means an all-inclusive mapping and will be examined in detail in a forthcoming Ovum report published in December(AI Impacts on Consumer Commerce: The Big Picture).
Figure 1: AI impacts every step of the consumer commerce journey
Source: Ovum, AI Impacts on Consumer Commerce: The Big Picture (to be published December 2019)
But some key areas to highlight here are the way that AIisdriving personalized advertising at scale and is enabling adaptive marketing messages across channels, contexts, and connected devices. ML is also improving automated ad bidding while ML inputs are even being used to improve the advertising creative process.
In parallel with this, AI is transforming the shopping experience. AI assistants with a voice interface are providing a new commerce platform to which consumers are warming – of those surveyed in Ovum's 2019 Digital Consumer Insights survey, 56% use their AI assistant to buy goods and services at increasing frequency. Augmented reality applications allow consumers to contextually visualize products in a range of scenarios (online and offline), which can help remove the uncertainty that can halt a purchase. Biometrics techniques (e.g., fingerprints, facial recognition, behavioral) are streamlining the verification and authentication process. ML solutions can monitor behavior to detect anomalies that is indicative of fraudulent activity on transactions. AI is also helping to make post-purchase actions more effective. This is particularly important as service providers often drop the ball once a sale is closed, which is a mistake as post-purchase follow ups can stimulate repeat business. ML algorithms can provide predictive, targeted post-purchase next-best actions. For example, a price discount on a product that intent modelling indicates a consumer is likely to buy.
Service providers must also ensure they get in front of data privacy as the combination of AI and commerce raises the stakes. Information relating to commercial transactions and personal finance is highly sensitive, while AI can increasingly extract and analyze consumer data. Service providers with strong AI/commerce privacy credentials will win consumer trust and gain competitive advantage.
These are just a few of the ways that AI can enhance the shopping journey in terms of consumer experience potential revenues. But this can only be achieved if service providers approach AI in commerce in a joined-up, holistic way. Confining AI to silos is a mistake as it is limits what AI can achieve and how it can scale.
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