Mini-series: Artificial intelligence and the power and potential of machine learning – Part 2
This is the second instalment in Mediative’s educational mini-series on ‘Artificial Intelligence and the Power and Potential of Machine Learning’. The previous post looked at the differences between artificial intelligence, machine learning, and deep learning.
In this post, we look at how can artificial intelligence, machine learning, and deep learning be leveraged to benefit businesses.
Part 2: How can artificial intelligence, machine learning, and deep learning be leveraged to benefit businesses?
Many businesses understand that AI and ML is the way of the future, and AI is already helping marketers in large organizations make better use of the huge amounts of data available, driving efficiencies in their organizations, increasing sales, and allowing marketers to learn more about their customers. While investment and interest in AI remains high (McKinsey & Company estimated that companies worldwide invested between $26 billion and $39 billion in artificial intelligence in 2016, and a PwC report estimated this to hit $70 billion by 2020) and the technology is showing real promise, there is still a significant amount of confusion in the marketplace, and large-scale adoption is happening more slowly.
According to McKinsey’s paper, only 20% of firms whose C-suite executives were aware of AI had actually adopted at least one of its technologies at scale or in a core part of their business, with 40% still contemplating its use. Another survey found that 24% of CEOs were already using AI, and 21% said AI was a priority, but the rest said they were only aware of or evaluating AI.
“[Pragmatic AI] might not have the wow factor of a robot butler, a talking toaster or even deep learning technology, but it nevertheless manages to cut through the AI hype to provide meaningful impacts for businesses in the here and now.” (Source)
With that said, companies in all sorts of industries from banking to healthcare are experimenting with artificial intelligence and have ambitious plans for AI systems. eMarketer reported on the benefits of implementing AI, and the top benefit, with 79% of senior executive worldwide citing it, was “bringing new insights and better data analysis.” (1)
We can already see some of the operational benefits that businesses can expect to reap from the technology in the future, from an easy way to integrate AI into current operations, to more complex, company-wide integrations:
Sales and Marketing:
- Hyper-personalization in marketing can be taken to a whole other level with machine learning, delivering the right message on the right channel at the right time. Better customer segmentation leads to a deeper understanding of actual purchasing behaviour. AI provides organizations with the ability to automate scoring and predict more accurately which customers in their database are most likely to purchase a product/service. By using AI to automate segmentation and communication, interactions with customer will be more personal and effective, helping businesses acquire and retain more customers. As a result marketers will see improved response rates, reduced costs, and ultimately, more revenue.
- AI can be used to create more effective dynamic landing pages and websites. A 2017 Real-Time Personalization Survey by Evergage notes that 33% of marketers surveyed use AI to deliver personalized web experiences.
- AI can be used in dynamic email creation, based on previous website interactions, interest of similar visitors, Previous interactions with branded emails, as saved wish lists etc. Using AI, predictions can be made about what happens when customers open an email, and the journey they are likely to take based on previous experiences.
- AI can help generate business content. Gartner predicted in 2015 that 20% of business content will be authored by machines by 2018. ”Data-based and analytical information can be turned into natural language writing using these emerging tools. Business content, such as shareholder reports, legal documents, market reports, press releases, articles and white papers, are all candidates for automated writing tools.” Natural language generation (NLG) tools are used to produce this content – Wordsmith is a great example that “turns data into insightful narratives, and Forbes uses Quill for its earning reports. These tools are great for turning numerical data into factual reports, but not so great at giving a POV or an opinion (or anything requiring creativity and the “human touch” – as is so often an integral component of marketing content).
Customer service and support:
AI can help scale support services with chat bots, trained to answer simple or common queries that take up a significant amount of human time. A PwC study reported that every year, $62 billion is lost through poor customer service. The same study reported that 67% of business executives see the potential of AI to automate processes and increase efficiency, and 70% agree that AI has the potential to enable humans to concentrate on meaningful work. Further, it is reported that 27% of consumers weren’t sure if their last customer service interaction was with a human or a chat-bot. At Mediative, we see one step further. Chatbots can definitely help a lean team do more work, and free up experts to do more strategy. But machine-managed user interactions can also see trends emerge and mine customer feedback for valuable data on the customer experience. Instead of waiting for the time and budget for focus groups, new software releases and inaccurate surveys, we can see what customers like and don’t like about products. This valuable feedback can then be baked in to new products, upgrades and rewards.
ML solutions will aid the shop-floor managers to make quick and accurate decisions on the fly without the intervention of C-Level executives.
Machine learning can be used to spot cancers and other diseases better than humans by processing more information and spotting more patterns.
Customers can try on clothes in a virtual dressing room. AI can be used to cross-sell and upsell with hyper-personalized product recommendations and suggestions.
ML can be used to accurately predict malware threats. Machine learning algorithms can look for patterns in how data in the cloud is accessed, and report anomalies that could predict security breaches.
Machine learning algorithms are getting closer to predicting and executing trades at high speeds and high volume.
And these are just to name a few.
“Most marketing firms agree or strongly agree that AI will reinvent the retail industry (88%) and dramatically change what their companies do (81%)” (Source)
The use of AI will make the day-to-day optimization process much more precise and scalable and will allow marketers to focus on the elements that humans are better at – like creating an ad that connects emotionally with a consumer. But it’s important to remember that AI is not only benefiting businesses – there are also huge individual gains to be had in daily lives. Just take voice recognition for example – the ability to talk to an IoT device and give commands can be invaluable to an elderly, or disabled person. The possibilities for individuals really are endless.
A PwC survey asked people where they saw AI replacing humans in the next 5 years, and the results were as follows:
63% of consumers believe AI will help solve complex problems that plague modern societies and 59% believe it will help people live more fulfilling lives. On the other hand, only 46% believe AI will harm people by taking away jobs and 23% believe it will have serious, negative implications. (Source)
Next week, for the third instalment in this educational mini-series on ‘Artificial Intelligence and the Power and Potential of Machine Learning’, we look at what marketers should be doing today to harness the power of AI, ML and DL.
- Artificial Intelligence for Marketers 2018: Finding the Value Beyond the Hype, eMarketer, October 2017