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Accelerate Your Micro-Moment Marketing With Machine Learning

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Accelerate Your Micro-Moment Marketing With Machine Learning

Data collection has become remarkably easy for businesses with the emergence of several digital communication channels.

Using machine learning in digital marketing enables organizations to harness data to improve their micro-moment marketing strategies.

Marketing has always been about connecting the gap between you and your target audience. To achieve that objective, you need to know exactly what your customers need. For several years now, marketers have used existing trends and consumer demand patterns to create ad campaigns and long-term marketing strategies. However, there are two main problems with trend-based marketing. Firstly, in the age of viral memes and stories, trends may not be as clear-cut as they were a decade ago. Secondly, and more importantly, not every customer of yours will be heavily into existing trends. For example, there may be a large part of your target audience that may not be into sports. So, they may not connect with sports-themed marketing content, even when an ongoing global sporting event like the Olympics is going on. Additionally, a 2015 study found that demographic-based marketing does not guarantee success as it does not focus on the existing customer moods and requirements in great detail. As a result, the businesses that used demographic-based marketing risked losing about 70% of potential mobile shoppers as per a recent study.

This is where micro-moment marketing enters the picture. The dictionary defines a micro-moment as an “internet-rich moment” which indicates that a customer is looking to purchase a product or service either immediately or in the near future. Specific customer interests or requirements at any given point in time create such micro-moments. Capitalizing on such moments is necessary for your business to have the edge over your market rivals, many of whom may be chasing micro-marketing perfection too.

The involvement of machine learning in digital marketing has increased in recent years. The technology allows organizations to identify target customers, curate marketing content, create marketing strategies and regulate dynamic pricing based on customer behavioral patterns. Machine learning can similarly be impactful for micro-moment identification and exploitation.

Identifying and Using the Right Micro-Moments

Unlike, say, a decade ago, internet connectivity is readily available to a large percentage of your target audience today. Therefore, about 91% of all smartphone users turn to the internet whenever they need to complete any task. As a result, people use online searches to find information about the most trivial of things. Therefore, not every “internet-rich” consumer micro-moment will be useful for your business. Therefore, identifying the moments that will have critical value for you is necessary for your long-term marketing operations. This segregation and classification of micro-moments will ultimately be useful to create accurate and targeted campaigns for your audience. According to Google, there are four types of micro-moments that carry particular significance for your business:

Identifying_and_Using_the_Right_Micro-Moments.png

a) I-Want-To-Know Micro-Moments

This moment involves consumers making Google searches regarding certain products or services. Such searches are generally done only out of curiosity and nothing else. These micro-moments can be categorized as exploratory or research-based.

b) I-Want-To-Go Micro-Moments

At times, customers may simply make a Google search to get information about the availability of certain products near their location. Such micro-moments can be indicative of customers stepping out of their houses to visit local stores or shopping malls to purchase said products in the immediate future.

c) I-Want-To-Do Micro-Moments

These micro-moments involve netizens making Google searches when they intend to perform a new action. These micro-moments are indicative of audience interests that can range from being cuisine-related, interest-related or style or creativity-related. Generally, such customers look for articles, blogs or YouTube videos that begin with the words “How to.”

d) I-Want-To-Buy Micro-Moments

The micro-moments classified under this category are indicative of individuals being interested in purchasing your products or services. Such consumers click on sales touchpoints available on social media sites or other websites. Additionally, they can directly visit your website for the same purpose. To visit your website or click on one of the touchpoints, customers may use different devices, but smartphones are seemingly the number one choice for customers everywhere.

As stated above, the number of smartphone users in the world has risen dramatically in recent years. As a result, mobile phones are one of the most dominant data points for the collection of micro-moment-related data. Past studies have found that about two-thirds of customers carry out I-want-to-buy decisions using their mobile phones. Due to this high tendency of users to prefer smartphones instead of other devices, businesses may make their content more mobile-friendly. Most big corporations have their own dedicated mobile apps that literally bring the act of purchasing products and services to consumers’ fingertips. Using specially-trained machine learning tools, your business can identify the customers who have done searches that feature the name of your business, the products or services you provide or the name of your direct market rivals. Such tools generally use session cookies to extract this data. Machine learning-based tools perform pattern recognition on the data from such session cookies to accurately predict customer intent.

Using Big Data for Predictive Micro-Marketing Analytics

As stated above, identifying and collecting data related to micro-moments and consumer intent dynamically is impossible without the use of machine learning in digital marketing processes. Over a period of time, such data keeps getting bigger and more diverse. AI and machine learning tools are useful for big data analytics over the long term. The involvement of machine learning in digital marketing is especially helpful for analyzing past trends in purchase records and consumer demand to make forecasts for the same in the future. This type of predictive analytics can be invaluable for micro-moment marketing too.

Several companies over the past decade or so have understood the value of data and predictive analytics to guess customers’ moods and micro-moments. Therefore, businesses are hiring data scientists, engineers, and developers to make their micro-moment marketing more data-driven. The skills of such workers will prove to be instrumental in the growth and development of such businesses through strong micro-moment marketing campaigns moving forward.

The involvement of AI and machine learning in digital marketing allows businesses to treat the internet as a collection of millions of data points. Gathering dynamic information about every customer’s actions and purchase intent can be handled adeptly with the inclusion of machine learning in the digital marketing operations of businesses. Machine learning provides insights by studying every customer’s digital journey that consists of thousands of micro-moments. Such insights then go on to shape your micro-moments marketing campaign and other business strategies.

By investing in AI and machine learning in digital marketing, organizations can personalize their ad campaigns to draw in their customers. There are several examples of businesses using machine learning in digital marketing to optimize their micro-moment marketing efforts. One such example is of French retail company Sephora. Sephora deployed the data collection and analysis resources to understand the potential of I-want-to-buy micro-moments. After assessing the data collected by such resources and tools, the marketing department of the business found that their consumers would tend to go online and compare two products to select the best one based on price, preference, brand attraction, amongst other factors. This knowledge was used by Sephora to create a specialized mobile app that allowed customers to compare two goods, provide information regarding product reviews and ratings, and virtually try out items like make-up accessories to see how it suited them. Additionally, the mobile app could also be used to push relevant information regarding a product’s past purchase history, ratings, and other details that could be used by customers to make clear purchase choices.

Micro-moment marketing seems to be the way forward for businesses as it helps them understand audience requirements on a real-time basis. In a way, micro-moment marketing is the natural replacement for significant but dated concepts such as trend analysis and demographics-based marketing. More importantly, this type of marketing allows organizations to get over the concept of boxing customers into specific types. Human interest keeps changing, and micro-moment marketing reflects those shifts in the real world.

The involvement of machine learning greatly helps micro-marketing by performing in-depth data analytics of available consumer information. Carrying out micro-moments marketing is not possible even if your business employs a big team and several workers in your marketing department as it involves large amounts of evolving data. Hence, the use of machine learning in digital marketing is the solution to harnessing the true potential of micro-moment marketing.


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Next-gen chips, Amazon Q, and speedy S3

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AWS re:Invent, which has been taking place from November 27 and runs to December 1, has had its usual plethora of announcements: a total of 21 at time of print.

Perhaps not surprisingly, given the huge potential impact of generative AI – ChatGPT officially turns one year old today – a lot of focus has been on the AI side for AWS’ announcements, including a major partnership inked with NVIDIA across infrastructure, software, and services.

Yet there has been plenty more announced at the Las Vegas jamboree besides. Here, CloudTech rounds up the best of the rest:

Next-generation chips

This was the other major AI-focused announcement at re:Invent: the launch of two new chips, AWS Graviton4 and AWS Trainium2, for training and running AI and machine learning (ML) models, among other customer workloads. Graviton4 shapes up against its predecessor with 30% better compute performance, 50% more cores and 75% more memory bandwidth, while Trainium2 delivers up to four times faster training than before and will be able to be deployed in EC2 UltraClusters of up to 100,000 chips.

The EC2 UltraClusters are designed to ‘deliver the highest performance, most energy efficient AI model training infrastructure in the cloud’, as AWS puts it. With it, customers will be able to train large language models in ‘a fraction of the time’, as well as double energy efficiency.

As ever, AWS offers customers who are already utilising these tools. Databricks, Epic and SAP are among the companies cited as using the new AWS-designed chips.

Zero-ETL integrations

AWS announced new Amazon Aurora PostgreSQL, Amazon DynamoDB, and Amazon Relational Database Services (Amazon RDS) for MySQL integrations with Amazon Redshift, AWS’ cloud data warehouse. The zero-ETL integrations – eliminating the need to build ETL (extract, transform, load) data pipelines – make it easier to connect and analyse transactional data across various relational and non-relational databases in Amazon Redshift.

A simple example of how zero-ETL functions can be seen is in a hypothetical company which stores transactional data – time of transaction, items bought, where the transaction occurred – in a relational database, but use another analytics tool to analyse data in a non-relational database. To connect it all up, companies would previously have to construct ETL data pipelines which are a time and money sink.

The latest integrations “build on AWS’s zero-ETL foundation… so customers can quickly and easily connect all of their data, no matter where it lives,” the company said.

Amazon S3 Express One Zone

AWS announced the general availability of Amazon S3 Express One Zone, a new storage class purpose-built for customers’ most frequently-accessed data. Data access speed is up to 10 times faster and request costs up to 50% lower than standard S3. Companies can also opt to collocate their Amazon S3 Express One Zone data in the same availability zone as their compute resources.  

Companies and partners who are using Amazon S3 Express One Zone include ChaosSearch, Cloudera, and Pinterest.

Amazon Q

A new product, and an interesting pivot, again with generative AI at its core. Amazon Q was announced as a ‘new type of generative AI-powered assistant’ which can be tailored to a customer’s business. “Customers can get fast, relevant answers to pressing questions, generate content, and take actions – all informed by a customer’s information repositories, code, and enterprise systems,” AWS added. The service also can assist companies building on AWS, as well as companies using AWS applications for business intelligence, contact centres, and supply chain management.

Customers cited as early adopters include Accenture, BMW and Wunderkind.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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HCLTech and Cisco create collaborative hybrid workplaces

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Digital comms specialist Cisco and global tech firm HCLTech have teamed up to launch Meeting-Rooms-as-a-Service (MRaaS).

Available on a subscription model, this solution modernises legacy meeting rooms and enables users to join meetings from any meeting solution provider using Webex devices.

The MRaaS solution helps enterprises simplify the design, implementation and maintenance of integrated meeting rooms, enabling seamless collaboration for their globally distributed hybrid workforces.

Rakshit Ghura, senior VP and Global head of digital workplace services, HCLTech, said: “MRaaS combines our consulting and managed services expertise with Cisco’s proficiency in Webex devices to change the way employees conceptualise, organise and interact in a collaborative environment for a modern hybrid work model.

“The common vision of our partnership is to elevate the collaboration experience at work and drive productivity through modern meeting rooms.”

Alexandra Zagury, VP of partner managed and as-a-Service Sales at Cisco, said: “Our partnership with HCLTech helps our clients transform their offices through cost-effective managed services that support the ongoing evolution of workspaces.

“As we reimagine the modern office, we are making it easier to support collaboration and productivity among workers, whether they are in the office or elsewhere.”

Cisco’s Webex collaboration devices harness the power of artificial intelligence to offer intuitive, seamless collaboration experiences, enabling meeting rooms with smart features such as meeting zones, intelligent people framing, optimised attendee audio and background noise removal, among others.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: Cisco, collaboration, HCLTech, Hybrid, meetings

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Canonical releases low-touch private cloud MicroCloud

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Canonical has announced the general availability of MicroCloud, a low-touch, open source cloud solution. MicroCloud is part of Canonical’s growing cloud infrastructure portfolio.

It is purpose-built for scalable clusters and edge deployments for all types of enterprises. It is designed with simplicity, security and automation in mind, minimising the time and effort to both deploy and maintain it. Conveniently, enterprise support for MicroCloud is offered as part of Canonical’s Ubuntu Pro subscription, with several support tiers available, and priced per node.

MicroClouds are optimised for repeatable and reliable remote deployments. A single command initiates the orchestration and clustering of various components with minimal involvement by the user, resulting in a fully functional cloud within minutes. This simplified deployment process significantly reduces the barrier to entry, putting a production-grade cloud at everyone’s fingertips.

Juan Manuel Ventura, head of architectures & technologies at Spindox, said: “Cloud computing is not only about technology, it’s the beating heart of any modern industrial transformation, driving agility and innovation. Our mission is to provide our customers with the most effective ways to innovate and bring value; having a complexity-free cloud infrastructure is one important piece of that puzzle. With MicroCloud, the focus shifts away from struggling with cloud operations to solving real business challenges” says

In addition to seamless deployment, MicroCloud prioritises security and ease of maintenance. All MicroCloud components are built with strict confinement for increased security, with over-the-air transactional updates that preserve data and roll back on errors automatically. Upgrades to newer versions are handled automatically and without downtime, with the mechanisms to hold or schedule them as needed.

With this approach, MicroCloud caters to both on-premise clouds but also edge deployments at remote locations, allowing organisations to use the same infrastructure primitives and services wherever they are needed. It is suitable for business-in-branch office locations or industrial use inside a factory, as well as distributed locations where the focus is on replicability and unattended operations.

Cedric Gegout, VP of product at Canonical, said: “As data becomes more distributed, the infrastructure has to follow. Cloud computing is now distributed, spanning across data centres, far and near edge computing appliances. MicroCloud is our answer to that.

“By packaging known infrastructure primitives in a portable and unattended way, we are delivering a simpler, more prescriptive cloud experience that makes zero-ops a reality for many Industries.“

MicroCloud’s lightweight architecture makes it usable on both commodity and high-end hardware, with several ways to further reduce its footprint depending on your workload needs. In addition to the standard Ubuntu Server or Desktop, MicroClouds can be run on Ubuntu Core – a lightweight OS optimised for the edge. With Ubuntu Core, MicroClouds are a perfect solution for far-edge locations with limited computing capabilities. Users can choose to run their workloads using Kubernetes or via system containers. System containers based on LXD behave similarly to traditional VMs but consume fewer resources while providing bare-metal performance.

Coupled with Canonical’s Ubuntu Pro + Support subscription, MicroCloud users can benefit from an enterprise-grade open source cloud solution that is fully supported and with better economics. An Ubuntu Pro subscription offers security maintenance for the broadest collection of open-source software available from a single vendor today. It covers over 30k packages with a consistent security maintenance commitment, and additional features such as kernel livepatch, systems management at scale, certified compliance and hardening profiles enabling easy adoption for enterprises. With per-node pricing and no hidden fees, customers can rest assured that their environment is secure and supported without the expensive price tag typically associated with cloud solutions.

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: automation, Canonical, MicroCloud, private cloud

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