Exploring the benefits which MNCs are getting from AI/ML
Emphasizing the enhancement of AI provided to their products and make them the top notch companies of this generation.
The last decade has brought about a huge revolution in the form of Artificial Intelligence (AI) and Machine Learning (ML) cutting across industries. These changes brought an evolution in the overall operating scenario of companies by providing them insights to improve their product and service offerings. It wouldn’t be wrong to say that AI made lives easier through chatbots, algorithms, recommendation engines, hardware infrastructure, language processing and much more. Now, the industry is expected to experience some strategic shifts from enterprises.
The AI industry witnessed a tremendous growth in 2019. According to the Deloitte report from late last year, 9 out of 10 companies investing in artificial intelligence. Whereas 70% of such companies also accepted to have seen minimal or no impact from their investments in AI. Over the last decade, while organizations actively associated with AI companies, implementation of models has remained a challenge. 2020 will see a visible shift towards intelligent automation changing the face of all major sectors right from the Indian Government to Startups and Small Medium Entrepreneurs.
Artificial intelligence and its applications have made a significant impact on nearly every industry. Defined as a technique enabling machines to mimic human behaviour, brands are using AI to automate processes at an increasing rate. We see this at many points of brand interaction site suggestions on our search engine, lane assistance in passenger vehicles, and app troubleshooting, to name a few.
AI isn’t a new phenomenon. It has been around for almost 50 years, learning constantly, almost on a daily basis. As we evolve and become more efficient, and artificial intelligence learns to better emulate human intelligence, businesses benefit from increased process and operational efficiencies. As just one example, analysis by PWC predicts that AI could contribute up to $15.7 trillion to the global economy as soon as 2030. Of this, $6.6 trillion will likely come from increased productivity; $9.1 trillion, from consumption side effects.
Machine Learning
Imagine that you were in charge of building a machine learning prediction system to try and identify images between dogs and cats. As we explained above, the first step would be to gather a large number of labeled images with “dog” for dogs and “cat” for cats. Second, we would train the computer to look for patterns on the images to identify dogs and cats, respectively.
Trained machine learning system capable of identifying cats or dogs.
Once the machine learning model has been trained , we can throw at it (input) different images to see if it can correctly identify dogs and cats. As seen in the image , a trained machine learning model can (most of the time) correctly identify such queries.
for example, the image search and translation tools use sophisticated machine learning. This allows the computer to see, listen and speak in much the same way as humans do. Much wow!
Google uses machine learning algorithms to provide its customers with a valuable and personalized experience. Gmail, Google Search and Google Maps already have machine learning embedded in services.Google is the master of all. It takes advantage of machine learning algorithms and provides customers with a valuable and personalized experience. Machine learning is already embedded in its services like Gmail, Google Search and Google Maps.
1. Machine Learning Case Study on — Dell
The multinational leader in technology, Dell, empowers people and communities from across the globe with superior software and hardware. Since data is a core part of Dell’s hard drive, their marketing team needed a data-driven solution that supercharges response rates and displays why certain words and phrases outperform others.
Dell partnered with Persado, the world’s leading technology in AI and ML generated marketing creative, to harness the power of words in their email channel and garner data-driven analytics for each of their key audiences. As a result of this partnership, Dell noticed a 50% average increase in CTR and a 46% average increase in responses from customers. It also generated a 22% average increase in page visits and a 77% average increase in add-to-carts.
Excited by their success and learnings with email, Dell was eager to elevate their entire marketing platform with Persado. Dell now uses machine learning to improve the marketing copy of their promotional and lifecycle emails, Facebook ads, display banners, direct mail, and even radio content.
2. Machine Learning Case Study on — Sky
Sky UK transforms customer experiences with the help of machine learning and artificial intelligence through Adobe Sensei.
“We have 22.5 million very diverse customers. Even attempting to divide people by their favorite television genre can result in pretty broad segments.” said the Head of Digital Decisioning and Analytics, Sky UK.
This will:
· Create hyper-focused segments to engage customers.
· Use machine learning to deliver actionable intelligence.
· Improve relationships with customers.
· Apply AI learnings across channels to understand what matters to customers.
The company was able to make sense of its large volumes of customer information with the help of machine learning frameworks to recommend them with products and services that resonated the most with each customer.
“People think of machine learning as a tool for delivering experiences that are strictly defined and very robotic, but it’s actually the opposite. With Adobe Sensei, we’re drawing a line that connects customer intelligence and personalized experiences that are valuable and appropriate” says McLaughlin.
3. Machine Learning Case Study on — Trendyol
Trendyol which is a leading e-commerce company based in Turkey faced threat from global competitors like Adidas and ASOS, particularly for sportswear.
To help gain customer loyalty and enhance its emailing system, it partnered with vendor Liveclicker, which specializes in real-time personalization.
Trendyol used machine learning and artificial intelligence to create several highly personalized marketing campaigns. It also helped to distinguish which messages would be most relevant to which customers. It also created an offer for a football jersey imposing the recipient’s name on the back to ramp up personalization.
By creatively using one-to-one personalization, the retailer’s open rates, click-through rates, conversions, and sales reached all-time highs. It generated a 30% increase in click-through rates for Trendyol, a 62% growth…
4. Machine Learning Case Study on — Harley Davidson
The place we are in today is where it is difficult to break through traditional marketing. For a business like — Harley Davidson NYC, Albert (an artificial intelligence-powered robot) has a lot of appeal. Powered by machine learning and artificial intelligence, robots are writing news stories, working in hotels, managing traffic, and even running McDonald’s.
Albert can be applied to various marketing channels including social media and email. The software predicts which consumers are most likely to convert and adjusts personal creative copies on its own.
Harley Davidson is the only brand to make use of Albert. The company analyzed customer data to determine the behavior of previous customers whose actions were positive in terms of purchasing and spending more than the average amount of time on browsing through the website. With this information, Albert created segments of customers and scaled up the test campaigns accordingly.
Results show that Harley Davidson increased its sales by 40% with the use of Albert. The brand also had a 2,930% increase in leads, with 50% of those from high converting ‘lookalikes’ identified by artificial intelligence and machine learning.
5. Machine Learning Case Study on — Yelp
While Yelp might not seem to be a tech company at first glance, it is taking advantage of machine learning to improve users’ experience.
For an entire generation today, taking photos of their food has become second nature and thanks to these people because of whom Yelp has such a huge database of photos. Its software uses techniques for analysis of the image to identify color, texture, and shape. It means that it can recognize the presence of say, pizzas, or whether a restaurant has outdoor seating.
As a result, the company is now able to predict attributes like ‘good for kids’ and ‘classy ambiance’ with more than 80% accuracy. It is also planning to use this information to auto-caption images and improve search recommendations in the future.
These were all the machine learning case study examples.
Summary
These case studies of machine learning listed above would have been almost impossible to even think as recently as a decade ago, and yet the pace at which scientists and researchers are advancing is nothing short of amazing. In the coming future, we’ll see that machine learning can recognize, alter, and improve upon their own internal architecture with minimal human intervention.
