Personalization is crucial to success across industries and organizations of all sizes, including independent e-commerce businesses, user- and professionally-generated content communities and media companies. According to a recent report from management consulting firm McKinsey, 71% of consumers expect companies to deliver personalized interactions - and 76% become frustrated when this does not happen. This pays off in improved outcomes; companies that grow faster drive 40% more of their revenue from personalization than their slower-growing counterparts. In addition, professional services firm Accenture released a Pulse Check that revealed 91% of consumers were more likely to shop with brands that recognize, remember and provide relevant offers and recommendations, while 83% were willing to share their data to enable a personalized experience (as long as businesses are transparent about how they are going to use that data and that consumers retain control).
Product, service and content recommendations are crucial to an effective personalization strategy and the lesson for organizations and developers is clear: move away from providing generic product or content recommendations to users of websites and applications, to personalized recommendations based on data about user behaviors, preferences, interactions, locations and other relevant factors.
Unfortunately, delivering personalized recommendations across thousands to millions of often fast-changing pages and content items is too important to leave to outdated, unproven or manual solutions. The answer lies in AI and machine learning, which enables organizations and developers of websites and applications to associate users with content, and to use algorithm-driven recommendations to provide products, services or information to achieve objectives and targets.
Many organizations may initially view the cost and resources required to develop industry-specific AI and machine learning models based on large datasets to generate personalized recommendations as prohibitive. However, an end-to-end AI and machine learning-powered personalized recommendation solution – delivered from the cloud, featuring industry-specific templates and proven across some of the world’s largest e-commerce sites and events – provides an easy way for organizations and developers to improve user engagement, conversions and experience.
For e-commerce businesses, such a solution can help improve performance across critical metrics such as gross merchandise volume/gross merchandise value – a critical indicator of an e-commerce site’s performance as it enables like-for-like comparison of performance between one period and another – and enables them to implement measures to address issues such as cart abandonment, as well as increase loyalty and drive repeat purchases.
For news organizations today, 'cutting through' and remaining relevant among a tsunami of online content generated by competing outlets, individuals and organizations, on social platforms, websites and applications is a critical challenge.
A cloud-based, AI and machine learning-powered personalized recommendation solution can help them remain a trusted, go-to source of information. Intelligent recommendations delivered through models developed from industry-specific templates help ensure users remain engaged with news websites, increasing the time spent within their ecosystems and consequently growing exposure to information and advertisements, with potential to improve conversion rates and reduce churn.
Furthermore, with much of the content around ‘hot’ issues updated in real-time, AI and machine learning-powered personalized recommendations can help organizations identify and accurately distribute within seconds articles, videos and podcasts to recommend to individual users or groups based on their interests, interactions and other relevant factors.
Rapidly identifying and distributing recommended content from the vast pool of user- and professionally-generated content generated daily is another formidable challenge for organizations.
The ability to post self-generated content such as audio, images, text and video online enables consumers to rate and review products and services, and brands to benefit from the fact prospective customers typically trust these recommendations more than corporate content.
Meanwhile, complementary professionally-generated content can enable organizations to position themselves and their products in a disciplined, brand- and message-oriented fashion, as well as providing unfiltered practical information about how and when to use those products effectively.
When users consume content to learn and build knowledge, they can provide positive ratings through 'likes', 'favorites', forwards and comments, or negative ratings through 'dislikes' and similar approaches. With a cloud-based, AI and machine learning-powered solution, organizations can quickly distribute recommended content based on user interests and increase user engagement with their digital properties.
So where can organizations and developers find a cloud-powered recommendation engine powered by big data and AI – and built on service accumulations across e-commerce, content, news, live streaming and social media – that can streamline and expedite end to end the process of delivering high quality, personalized recommendations?
Alibaba Cloud Artificial Intelligence Recommendation (AIRec) leverages Alibaba’s big data and AI technologies to provide personalized recommendation services, proven extensively through use to deliver real-time, personalized recommendations on the homepage of Alibaba-owned online shopping platform Taobao and across a number of Double 11 promotional events. Its performance showcases the capacity of its architecture and algorithm models to handle high numbers of concurrent queries and large volumes of behavioral data.
AIRec provides:
A news template that enables organizations to recommend information with news attributes, such as an author, location of publication and time of publication;
A content sharing template that enables content sharing platforms to recommend content such as text, articles, images or a combination, that has attributes such as ‘like’ and ‘forward', and;
An e-commerce template that enables the recommendation of items that have commodity attributes, such as logistics information and sales information, with the recommendations guiding users through direct transactions and featuring specific requirements on the click-to-purchase ratio.
AIRec also provides an end-to-end solution to stream processing that provides operation and maintenance and management, with businesses able to create an AIRec instance in a few hours. The product also delivers provisioning of end-to-end features, including auxiliary features through data interaction, testing, debugging, effect observation and stable use, with no extra development required.
Furthermore, AIRec's architecture and algorithm models can handle high numbers of concurrent queries and large volumes of behavioral data.
Overall, AIRec can deliver a recommendation performance up to 100% higher than self-managed algorithms.
Finally, the platform provides a variety of features based on years of business operations experience and combines operations logic with recommendation algorithms to open platform capabilities and implement rapid business customization, while it isolates user data, encrypts sensitive information, delivers stable and reliable services, and responds to requests in milliseconds.
The opportunity to gain a competitive edge is there for organizations that adopt the right strategy, technologies and products – analysts Gartner recently revealed nearly two-thirds (63%) of digital marketing leaders were continuing to struggle with delivering personalized experiences to customers – while only 17% to date were using AI and machine learning broadly across the marketing function.
With AIRec, organizations can leverage AI and machine learning to increase engagement, generate revenue and accelerate growth through personalized recommendations.
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