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Artificial Intelligence and the Digital Path to Purchase

July 28, 2015


“Ecommerce is a complex, convoluted thing,” writes Simon Le Gros Bisson (@sbisson).[1] She reminds us that e-commerce began humbly with retailers complementing paper catalogs with online catalogs and with beefed up call centers that could handle consumer traffic. “What started as a way of putting catalogues online has now become something much more involved,” she writes. “In the past we built ecommerce engines out of databases, with a little shopping cart magic wrapped around them. We generated static content for Google to search, and redirected users to our dynamic sites as soon as they clicked on a link. Manual curation was the watchword, much like the paper catalogues the web had replaced.” In fact, the digital path to purchase has become so complex that manual curation can no longer keep up with any robust omnichannel business. In order to handle order fulfillment in today’s omnichannel world, Bisson reports that businesses have turned to machine learning and cloud-scale processes. Kurt Marko (@krmarko), an IT analyst and proprietor of MarkoInsights, agrees with Bisson that the e-commerce world has changed dramatically. “We are in a time of unprecedented flux in consumer behavior,” he writes. “Customer expectations and company business models, created by technologies that simultaneously disrupt established businesses and spawn new ones,” are changing everything.[2] And, like Bisson, he notes that machine learning is playing a central role in the digital path to purchase.


Machine learning is a branch of artificial intelligence that is becoming ubiquitous in the world of omnichannel retail. Amber Kemmis (@AmberKemmis) explains, “Artificial intelligence (AI) is the use of computer systems to mimic human decision making, problem solving and reasoning. It is an area of computer science that attempts to reach the level of intelligence in humans through the use of computers and software. In some instances, you may see artificial intelligence used interchangeably with machine learning; however, the terms do differ. Machine learning is an area of artificial intelligence that aims to use behavioral and learning principles in algorithms to help achieve artificial intelligence in software without programming.”[3] As Kemmis notes, machine learning is most-often used to help businesses understand customer behavior so that customers can be better served along their path to purchase. While writing her article, Bisson spoke with Raj De Datta (@rdedatta), CEO of BloomReach, a company that uses machine learning to understand what attracts people to a website and how they find what they are looking for once they get there. She discovered:


BloomReach’s first app [was] a tool for driving traffic to sites via organic search. Under the hood of a service that uses machine learning to manage on-site navigation, is a ‘web relevance’ engine that uses user data — which has been collected at scale — to understand demand. This isn’t data about you, per se, it’s the aggregate high-level data about all the users like you. If you liked blue sheets, that data suggests you’re also likely to like a certain type of scented candle, an approach very similar to that used by machine-learning giant Amazon. And if you don’t like it, and don’t buy it, that information becomes an input to the next iteration of machine learning rules. The result is a set of highly optimised web pages built on the fly, and delivered to users as they navigate around a site.”


Bisson notes that marketing lies at the heart of the digital path to purchase and “marketing needs relevant content to work effectively.” Kemmis reports that AI can help in that department as well. She explains:


Inbound marketers have at their disposal a mass amount of data that can be mined and analyzed to help make predictions and decisions as to where to focus future marketing campaigns. These goals have driven a need for more complex technology solutions. Technology solutions that are evolving to encompass artificial intelligence. For example, InboundWriter.com is a software that allows marketers to gain predictions in performance on content ideas before publishing them. The software’s algorithm uses real-time web data to take the guesswork out of content. As this example demonstrates, AI is already making a subtle impact on inbound marketing and has been since inbound’s inception.”


Marko agrees that content is important as is alignment across every touchpoint of omnichannel operations. He asserts that artificial intelligence is essential to make that happen. He explains:


The omnichannel discussion often focuses on retail, but its implications on customer service and support are equally significant. Used wisely, omnichannel can turn frustrating, unfruitful customer interactions into delightful, loyalty-building experiences. Collecting, correlating and analyzing data from customer interactions across channels is the key to transforming the customer experience from nightmare to nirvana. The nexus of big data and machine learning in all its forms, including predictive analytics and even neural network deep learning, are the underpinnings of well informed, highly efficient and deeply satisfying interactions that benefit both customers and business. … Remember, the goal isn’t to merely provide an integrated omnichannel experience, but to be proactive not reactive: anticipate customer needs and prevent problems, don’t just solve them.”


Mark van Rijmenam (@VanRijmenam), founder of Datafloq, believes that the emergence of the Internet of Things (IoT) will add an entirely new dimension to how AI can and will be used in the digital path to purchase arena.[4] In the past, he notes, manufacturers could determine “where their products where bought, but would not receive detailed information about who bought the product and/or where the product would be used.” He continues:


The Internet of Things radically changes this and offers the marketer with a lot of new data to understand their customers and who their market is. Instead of just Point of Sales data, marketers will have access to detailed information on their customers, where they live, who they are, what their preferences are (if they link social media accounts) and, … how they use the product. All this information enables marketers to create truly personalized messages that will have a lot higher conversion rate than today’s generic messages.”


From manufacturing to marketing to customer service, artificial intelligence (especially machine learning) will play an increasingly important role. Marko explains why that will be the case. He writes:


There are two elements to a predictive omnichannel system: optimization and self-learning. The best predictive systems improve over time, learn from previous events, adapt to changing conditions and optimize to improve key performance metrics. These attributes are especially important in customer support systems, where the customer mix, channel usage, quality and quantity of data, and business priorities are quite dynamic. Ultimately, first-class customer support in the omnichannel era means analyzing volumes of data to understand their needs and connect the dots along a customer’s journey. The business mantra must be: learn, anticipate and simplify.”


Accenture analysts agree that artificial intelligence will help companies improve in the digital era, but they go further and predict that cognitive computing systems are going to provide “the ultimate long-term solution” for many of businesses’ most nagging challenges.[5] Cognitive computing systems can deal with many more variables in order generate actionable insights that are helpful to both companies and consumers. At Enterra Solutions® we believe that only a system that can Sense, Think, Act, and Learn® is going to be up the challenges faced by digital enterprises.


[1] Simon Le Gros Bisson, “Artificial intelligence in your shopping basket: Machine learning for online retailers,” ZDNet, 3 October 2014.
[2] Kurt Marko, “Using Big Data And Machine Learning To Enrich Customer Experiences,” Forbes, 8 April2015.
[3] Amber Kemmis, “The Impact Of Artificial Intelligence On Inbound Marketing,” Business 2 Community (B2C), 24 June 2015.
[4] Mark van Rijmenam, “3 Ways How the Internet of Things Will Revolutionize Marketing,” Datafloq, 5 May 2015.
[5]From Digitally Disrupted to Digital Disrupter,” Accenture Technology Vision 2014.

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