During the last decade, social networking sites/apps have become the most important channels of communication.Social networks such as Facebook, Twitter, and Instagram contain a considerable amount of informative data not only about social matters but also about business and marketing.

Of course, if you want to take advantage of this ever-changing space to swing the balance in your brand’s favor, you don’t have a choice unless using big data analytics.In this post, I’m going to describe how social media can be affected by big data analytics and how businesses can make the most out of it.

What is big data?

Big data is any novel technique used to analyze a massive volume of data that is so large it is impossible to process with traditional methods.Big data can handle both structured and unstructured data and help you tackle the problem of processing power. The main purpose of using big data techniques is finding overall patterns, trends, and connections between different variables.This is particularly important for studying data related to human interactions and social behaviors. This is exactly where social media can be influenced by big data, especially for marketing purposes.In fact, the huge, unstructured knowledge available on social media can’t add real value to your marketing strategy. So you need a powerful tool like big data analytics to be able to handle it.

Important data on social media

The stream of social posts, likes, mentions, shares, followers, and many other impressions can clearly prove why big data is important in social media marketing.This is now a must for businesses to collect these tones of information in real-time and analyze it to know how well their social presence is.In fact, every single positive interaction can add to their reputation and any negative feedback can put their efforts at stake.Without the shadow of a doubt, social media marketing analytics, especially for big companies, can’t be impactful without big data. Therefore, many businesses are investing masses of money on big data tools to track real-time consumer behavior across social media.To do this you will have to:

1. Collect the most related data available on social networks
2. Recognize the weight of each data on your target market
3. Convert these data into useful facts and use them in your strategies

Of course, the velocity of your data mining, the volume of the data, and also its variety is of paramount.You’ll need to use the best data technology and analytical tools if you want to leverage big data in marketing effectively.Also, it’s important to consider integrating various social networking sites/apps. Using Facebook, Instagram, Twitter, and LinkedIn will lead to better social interactions and the data from them can represent reality more exactly.

Advantages of big data for digital marketing

A lot of big and small companies are thinking about considerable budgets for big data analytics tools to get ahead of the competition.Here are 5 top benefits a big data analysis approach can bring to your social media campaign:1. Taking care of huge information sourcesAs a social user, you may need to process all data related to your niche that comes from mainstream channels.Analyzing diverse channels is not an easy task and can only be done by using artificial intelligence and big data technology.A lot of business sites allow users to sign up via Google or other main channels. So marketers can collect and analyze data about their niche customers from social networks, browser history, applications, cloud storage, etc.2. Targeting the right audienceIt’s clear that you can’t reach out to all the internet users so you have to narrow down to the most probable group of customers.Thus, social media marketing is all about identifying your target audience. Big data technology has provided marketers with access to insightful data of users’ personal information, photos, favorites, locations, and various kinds of activities.3. Predicting online behaviorsA big data approach can also be used for better decision-making based on previous trends. Data-based businesses are becoming incredibly efficient, as computers can predict the potential choices of customers.In sum, the interests and habits of people can be estimated as they’re changing based on specific overall trends.4. Managing marketing campaignsBig data techniques enable marketers to accurately track the ROI metrics of their social media campaigns.It will provide advertisers with insightful data into how effective a social media campaign has been or can be. Predictive analytical methods greatly help in predicting what products/services consumers want.Tracking consumer behaviors all over social will clear many things about the effectiveness of previous campaigns.This includes media including their engagement and reaction to online advertising. So marketers can optimize their plans for future campaigns to get a higher ROI.5. Identifying fair pricesOne of the biggest problems for marketers is to find reasonable prices for sponsored ads. A lot of different factors are affecting the prices.For example, during the COVID-19 pandemic, a lot of influencers have considerably cut their rates. So, it’s important to track the accepted costs on the internet and make arrangements accordingly.A big data analysis on social media can be helpful to clearly know what prices your competitors or niche customers are agreeing at.

Social media strategy when using big data

If you want to make sure that your big data approach will lead to a higher ROI on social media, you need to have an efficient strategy.Without a strategy, you don’t know what exactly you want from the unstructured data on social channels. Therefore, you’ll waste your time and money without generating considerable leads and boosting sales.A common social media strategy usually comprise the below steps:

• Market research
• Defining SMART goals
• Identifying the audience
• Choosing the right platforms
• Generating relevant content
• Scheduling posts and establishing a social presence
• Engaging with the community
• Influencer marketing
• Analyzing the performance

This strategy should help you achieve certain goals which usually contain below items:

• Drive traffic to your site
• Get higher conversions rates
• Build brand awareness
• Make you appear like a niche leader

Additionally, you’ll need certain metrics to analyze your performance and show you how well you’re achieving these goals.Actually, great use of a big data strategy is to help you know your social performance. You’ll be able to optimize your social techniques based on this information and make the best out of it.

Conclusion

Big data is one of the newest features of information technology and can be of great use to digital marketers. It was tried to summarize the important aspects of big data analytics in social media marketing and the benefits it can bring to your campaign. Remember to take advantage of this technology to optimize your social presence and get ahead of the marketing competition.

The post How Big Data Can Help to Analyze Social Media Performance appeared first on Artificial Intelligence.

It’s pretty safe to say that as the world of e-commerce grows and expands with the constantly changing behaviors of consumers, so does the data that is stored.

Have you ever noticed how you may do a Google search of a particular type of product and then the next time you log into your social media account, you start to see that same type of product in ads from different businesses? Conspiracy theorists like to say that “we’re being watched,” and in a sense, we kind of are being watched, but not in a “G-14 classified” kind of way.

This Deja Vu type of shopping experience is no coincidence but more so one of the results of big data in e-commerce marketing. And big data isn’t something that’s just pulled from your web browser histories either. Big data is collected from things like

• Abandoned shopping carts
• Road cameras
• Geolocation services
• Social media activity
• Transaction receipts

Typically, big data is divided into three categories: transactional, social, and machine data.

Transactional Data is exactly what it says it is. Any types of online transactions you’ve made, big data analyzes the behaviors you have before making that transaction, leaving companies to use that information to improve your buying experience.

Social Data stems from your online social activity. “Likes,” “Loves,” “Comments,” and “Tweets” are actions that provide e-commerce companies with insight into certain interests and behaviors of their ideal customers. Even video uploads and “number of views” deeply influence e-commerce marketing analytics.

Machine Data is information that is collected from various types of equipment and sensors that keep track of various patterns and behaviors of users.

E-commerce businesses are leveraging off of big data analytics as a way to have a better understanding of their customers and their shopping behaviors. The data that they collect and analyze allows them to “custom-fit” all their marketing efforts to the specific preferences of their customers, train staff on tailored customer service tactics to meet their customers’ needs, and the creation of products their customers actually want to buy.

To be an e-commerce business owner trying to keep up with the ever-changing trends and behaviors of their customers is a job all in itself. That reason alone is why so many e-commerce business owners turn to sites like Shopify where they can go to the marketplace to hire industry experts that have expertise specifically for e-commerce marketing.

So, to answer the question of how is big data impacting e-commerce marketing in 2020? The answer would be that it’s impacting e-commerce marketing in a major way. According to businesswire.com, global big data analytics in retail was reported at $3.45 billion in 2018… By the end of 2024, the market is expected to reach 10.94 billion.

Because of the boom in e-commerce and the increase in online shopping, retailers are using big data as effective efforts to stay competitive and relevant in their market.

Let’s dive deeper into just how big data is fostering a major impact on e-commerce marketing.

Big Data’s Impact on E-Commerce Marketing in 2020

A More Personalized Shopping Experience

E-commerce businesses are leaps and bounds above brick-and-mortar stores when it comes to creating a more personalized shopping experience for customers. Sure, having a greeter at the front of a store is nice, but all too often, they can be a bit of a nuisance at times.

E-commerce businesses have a plethora of predictive analytic tools to give them a better idea of current and future behaviors of customers. Everything from tracking clicks per page to tracking the length of time between a visitor’s journey from your home page and checkout, big data is providing several different sources of data.

Data scientists are doing even more research now to find new sources of data to provide customers with a more personalized shopping experience. New on the horizon, text analytics is one of the newest sources of data being utilized in lots of businesses.

Everything from analyzing the texts in comments on business websites to reviews on third-party sites, this is a source of data that can take information that customers are saying and apply it as an improvement measure to give customers what they really want.

Increase in Cross-Border Sales

People all over the world love to shop, and big data has made it possible for e-commerce businesses to be more accommodating to neighboring countries. Based on the products you’re selling, big data can tell you where your products are competitive or unique, and you can start selling in that market.

Just make sure that you localize the products you’re selling for those geographic locations. That means having language options, making sure your prices and sizes are converted, and that you’ve set the shipping costs properly.

Impeccable Customer Service

Happy customers are your key source in turning customers into loyal customers, and the way to keep customers happy is through impeccable customer service. Big data helps e-commerce businesses provide great customer service in detecting your brand’s perception on social media and customer satisfaction levels (if you offer customers to rate you). In fact, big data can tell you exactly when a customers’ perception of your business shifted!

E-commerce businesses can take this information and use it to make appropriate changes to their site and even use it to train employees on how to interact with customers, whether it’s through chat box interactions or simply making the checkout process more convenient.

Personalized Marketing

Long gone are the days where you can get away with emails that say things like “Dear Valued Customer”… especially when people are loyal customers to a particular online store. We are in the era of sophisticated shoppers with high expectations, and when your customers are sophisticated, that means that your marketing efforts need to get sophisticated as well.

Big data has transformed digital marketing in ways to make the customer feel like a particular store really cares about their taste, interests, and preferences. For example, if you have a customer that only buys purses from your site, you may want to send her emails when new inventories or collections of purses arrive. Sending her an email about shoes might make her delete the email altogether.

The post A YEAR OF CHANGE: HOW IS BIG DATA IMPACTING E-COMMERCE MARKETING IN 2020? appeared first on Artificial Intelligence.

Digital marketing relies on leveraging insights from the copious amounts of data that gets created every time a customer interacts with a digital asset. Algorithms optimise various factors and data points that influence digital marketing success.

In 2020, we anticipate a significant uptick in the mainstreaming of AI and machine learning use cases in digital marketing across several areas. 

Search will get very smart

In the past year, online search has had several AI and machine learning developments. Google is leading the pack with exciting applications in information retrieval. For example, Google’s BERT  technology can process a word in the context of all the other terms in a sentence, rather than one-by-one in order. BERT also enables anyone to train their own state-of-the-art question answering system. 

AI-driven personalisation of messaging 

Several adtech companies have been focussing on using AI and machine learning to find the right audience to write better ads than humans, and to increase conversion rates and engagement with the target audience. There are also several AI-led developments in the area of creating dynamic ads and landing pages to personalise marketing messages on the fly. 

AI has an application in content creation in terms of determining the logic of personalisation as also curating content specific to an individual, using techniques such as natural language generation (NLG).

Use of machine learning in campaign operations 

Platforms such as Google and Facebook have been at the forefront of AI/ML applications in marketing. Starting from smart bidding and smart campaigns to auto-generated ads, Google is making it easy for advertisers.

Smart bidding options such as TROAS, TCPA, and others use advanced machine learning algorithms to train on data at a vast scale to make accurate predictions about how different bid amounts might impact conversion or conversion value and assist advertisers in optimising without getting into too many details. 

Google factors in a wide range of contextual signals (through search data) to predict user behaviour and to influence auction time bidding as per the goal set by advertisers. Facebook has also incorporated machine learning across campaign planning and execution, as also in ad placements and ad delivery.

Similarly, on the organic search side, machine learning-based product ALPS reverse engineers Google’s ranking algorithm, and is able to accurately quantify ranking drivers, provide precise recommendations for changes, and predicts the impact of SEO actions before they are implemented.

Similar technology to drive improved ad copy testing in digital marketing exists. These help in evaluating ad copies and landing pages on various parameters like relevancy, use of action promoters/inhibitors, urgency inducers, page layout, load times, etc., to gauge the impact on ad relevance, expected CTR, and landing page experience. 

Future trends 

Many retailers are also testing AI and VR/AR technologies together to make the user experience personalised to an individual.

Other areas of impact include voice search. We will increasingly see ads about things which we just said or talked about, but haven’t searched for yet. Similarly, image search is also being used by many brands for their consumers to match patterns and identify products using image search. 

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We might still be decades away from the superhuman artificial intelligence (AI), like sentient HAL 9000 from 2001: A Space Odyssey, but our fear of robots having a mind of their own and acting at their own (free) will and using it against humankind is nonetheless present. Even some of the greatest minds of our time, such as Elon Musk and Stephen Hawking have been talking about this possibility.

On a more down-to-earth and practical level, artificial intelligence has already sneaked into our lives. We’ve grown so accustomed to some of the best AI apps,such as Cortana, Alexa or Siri, that we already think of them as our trusted companions that help us run our everyday tasks easily and smoothly.

However, while a catastrophic sci-fi movie scenario is not a thing we should be worried about (at least not at the moment) there are some risks related to AI implementation which are far more tangible and possible.

Read on to find out more about some real-life benefits and risks of AI implementation.

Benefits of Artificial Intelligence

By now, all of the industries have opened their doors to the various advancements AI brings. Here are some of the most prominent usages we’re witnessing and will be seeing more of in the years to come, in the digital marketing, healthcare, and finance industry.

AI in Digital Marketing

If you’ve recently used a chat to reach customer service, chances are high you’ve been talking to a chatbot, maybe even without realizing this fact. It may come as a surprise that 40% of customers are fine with both options, as long as they get their issues solved.

Chatbots embody many benefits AI brings to businesses, and are a great example of how it may improve a sensitive and time-consuming matter such as customer service.

Some of the crucial points where you can see the advantages of AI-powered chatbots are:

• Availability. Thanks to AI, customer service can now be there 24/7/365 for customers, so that they can get answers to their questions outside of regular business hours.
• Scalability. As numerous queries can be managed at the same time, customer service can expand to adjust to the needs of customers, without having to hire more employees.
• Fewer errors. AI-powered chatbots can now successfully solve up to 80% of standard customer queries, without human-related errors.
• Personalized interaction. As they monitor and collect customer data, they can use it at any moment to tailor the communication and hyper-personalized it, in a way that can only be imagined by their human counterparts.

Besides chatbots, AI can benefit digital marketing in many different ways, as it can be used to automate many different tasks, such as email and paid ads campaigns. It can also help marketers create more precise buyer personas, predict customers’ behavior and give sales forecasts, help with content creation, etc. These benefits to the e-commerce industry can hardly be measured, as businesses can now always be there for their online customers, assisting them in making their purchasing decisions and helping them navigate their customer journey.

AI in Healthcare

Another noticeable way AI benefits our lives is through its usage in healthcare.

We’ve recently witnessed a win of trained AI over human experts, as AI outperformed six radiologists in reading mammograms and recognizing breast cancer. Images can now be analyzed in a few seconds by the computer algorithm, so the use of AI can significantly improve the speed of diagnosis.

Except in radiology, AI is widely used in digital consultations, on platforms such as Buoy or Isabel symptom checkers, offering remote medical assistance, and suggesting how to see a professional based on their location.

AI in Finance

The advantages of AI have been recognized early by the finance and banking sectors, and the technology is now implemented in the ways beneficial for both parties.

One of the best examples of how beneficial AI in this industry can be, is Erica, a virtual employee of the National Bank of America. Erica has by now served over 7 million customers and managed over 50 million of their requests, helping them with their transactions and budgeting, tracking their spending habits and giving useful advice.

Risks of Artificial Intelligence

As for the potential actual risks of AI nowadays, the one that seems to bring the most concerns is job loss, which in some industries seem inevitable.

AI-powered employees have quite a few advantages when compared to their human colleagues. As they have no personal and emotional responses they’re never exhausted, bored or distracted, not to mention that they are more productive and efficient. Furthermore, their capacity to make errors is significantly reduced.

Such qualities of AI are the most likely to cause layoffs where a lot of tasks can be automated, such as the trucking, food service and retail industry, leading to millions of unemployed and an even higher income inequality.

Another rising concern has been an invasion of privacy. This has already taken place in China, where AI-powered technologies are used for the purposes of mass surveillance, impacting the so-called social credit system.

The system tracks users’ behavior everywhere – it has access to their social media profiles, their financial reports, health records etc. Data collected this way, including jaywalking and failing to correctly sort personal waste can now negatively influence the credit score while donating blood or volunteering can increase it. Negative credit can, for example, ban you from buying plane tickets, or enrolling your kids in certain schools.
Finally, the possibility of using AI capacities for military purposes shouldn’t be neglected, as the idea of having this kind of power concentrated in the hands of any of the world leaders, seems like a genuine threat to the world as we know it.

And while we think about all the benefits and the risks artificial intelligence brings, let’s not forget one crucial point – AI doesn’t set its own goals. The power it has is the power we delegate it to achieve the things we are trying to accomplish, meaning that we’re responsible for both its benefits and its risks.

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For a provider of loyalty programs across some of the biggest brands in the world, it’s pretty important Aimia gets personalisation and customer engagement right.

Which is why the leader in loyalty marketing, services, technology, and data platforms recently turned to artificial intelligence (AI), to better utilise its vast amount of data.

Director of insights and analytics at Aimia, Scott Pete, told CMO one of the challenges it faces is it’s dealing with a lot of different types of industries including CPG, retail, travel and hospitality. The customer base, the way they interact, and purchasing behaviours vary tremendously, he said.

“And the data sets aren’t the same. They vary from hundreds of thousands of customers to smaller programs, tens of millions in the larger ones. So, what we needed to do is to improve our ability to understand the engagement levels in those audiences. But with the volumes of data that we’re dealing with, it can get a little daunting if you use traditional query tools and that type of thing,” Pete explained.

“We got into machine learning [ML] over the last six or so years, and we’ve been moving more and more into the ML side. And then we came across Driverless AI about a year ago and decided to see if that might help us improve our time to market and accuracy of the models, which is which is how we started.”

As Aimia was already using H2O elsewhere in the business, it decided to run a trial of Driverless AI, which uses automation to accomplish key machine learning tasks quickly by offering automatic feature engineering, model validation, model tuning, model selection and deployment and machine learning interpretation.

“Our development environment is Python, and the H2O open source project product was much, much faster. It allowed us to fit our models in about 25-30 per cent of the time it would take us using other methods,” Pete said. 

“When we tried Driverless, we were actually initially a little sceptical with the result because it seemed maybe a little too good, too easy and too fast. But we found Driverless was actually even more capable than the open source we were previously using. And we did evaluate other products before we made our purchase, but H2O came out on top.”

Since the trials, Aimia has developed multiple data models, including predictive churn, smart journeys, and fraud detection, with plans for more into the future.

“We’ve done a few predictive churn optimisations and either starting from scratch with a brand new model from H2O or taking existing models, and basically looking for ways to optimise miles and so we have had really good result with that,” he said. “The other one that really got our attention was fraud detection. And in some cases, some of the clients we deal with do have some incidents of fraud, and it’s difficult to detect because it’s really a very small percentage of the audience. And we found AI was a perfect tool for looking for a tiny signal in the data.

“The other application we are really excited about smart journey diagnostics. This allows us to look at the engagement levels in the customer base, and better understand and classify where each member is in their engagement, and also to predict where they’re likely to go next. We can actually quantify for our clients where the real opportunity is and where the risks are. We had really good results so far.”

In terms of measurement, Aimia is looking to accuracy to assess the results it is getting from AI, as well as speed and costs savings for clients.

“Accuracy is one of the top two, and our time to market. Time to insights is really an even better way to say it, because one of the one of the things marketers struggle with is trying to get insights out of their data, so that they can better prioritise marketing initiatives and marketing strategies,” Pete said. “And that’s something that we’re finding a lot of success with.

“Ultimately, once we get those insights, the accuracy in being able to predict a certain percentage of the audience likely to churn, showing a retention problem for example, is invaluable.”

As an example, one case study Aimia did recently broke the audience down into 20 different groups. The team was able to score all the members and then organise them into 20 different groups and identify the top six to eight groups where it could get the low hanging fruit and most impact from retention efforts.

“This saved the client about 700 per cent in campaign costs, because they weren’t spending money on offers in campaign communications unnecessarily,” Pete said.

Now the AI is working well across certain models, Aimia hopes to take it further over the next 12 months with a model it calls ‘next best experience’.

“Some of the research we’re doing right now we’re actually working on a model called ‘next best experience’. Marketers are familiar with next best action or recommendation engines, this model is going to take elements of those types of models and it’s going to take it to another level,” Pete said. 

“So if you if you think about our ability to better understand the audience and what engagement level they’re in, then do some behavioural segmentation, and some value segmentation so we can understand the potential value of those customers. Then ,we are better able to target our one-to-one communications and really achieve real personalisation.

“That is something we’re really excited about. Because what will happen as we achieve this is we will deliver a better user experience for the end customer. And we believe that will help deepen the relationship and improve the loyalty as well.

“I think the expectation of what we can do with technology is rapidly changing, as companies start to see the time is right to implement AI.”

The post Aimia turns to artificial intelligence to lift its data game appeared first on Artificial Intelligence.

The Digital Single strategy of the European Commission sets out to “open up digital opportunities for people and business and enhance Europe’s position as a world leader in the digital economy.”(1) This article will briefly examine a part of that by looking at the work of the Robotics and Artificial Intelligence (Unit A.1).

We know that the Unit sets out to push forward the development of a competitive industry in robotics and artificial intelligence (AI) throughout Europe. Certainly, this includes industrial and service robots plus the growing field of autonomous systems from drones and driverless vehicles to computing and cognitive vision.

Also, the Unit encourages the wide uptake and best use of robotics and AI in all societal and industrial fields. The Unit takes responsibility for the European Commission’s participation in the contractual PPP on Robotics and for the implementation and development of the relevant strategic industrial agenda.

As well as managing RD&I projects within Horizon 2020, the UNIT keeps up-to-date with legal and ethical issues in the field of robots and autonomous systems, for example, aspects related to the impact of automation and robotics on jobs and work environment, as well as liability and safety.

Currently, Juha Heikkilä is Head of Unit at Robotics and Artificial Intelligence (Unit A.1) (2). Juha joined the European Commission in 1998 and today works in their (3) Directorate-General for Communications Networks, Content and Technology (DG CONNECT). This is the department “responsible to develop a digital single market to generate smart, sustainable and inclusive growth in Europe.” (4)

In July 2019, we find out that the European Commission launched a call to develop a vibrant European Network of AI Excellence Centres. Under the Horizon 2020 Work Programme 2018-2020, proposals for this can be submitted up to 13th November 2019. Europe has important potential to lead technological advancements in AI, with a strong research infrastructure and world-class community of scientists at their disposal. It is, therefore, crucial that the crème de la crème research teams in Europe collaborate to combat significant technological and scientific challenges.

The Commission is looking ahead at a long-term effort to unify the European AI community and make the region an AI powerhouse. To achieve this, they believe that two actions are needed:

1. Research and Innovation Action to mobilise the best researchers into networks of excellence centres to reach a critical mass on important AI topics.
2. Coordination and Support Action to enable exchange between the selected projects, as well as other relevant initiatives.

These aforementioned actions should create synergies with the industrial sector and, “foster an ecosystem of R&D resources, expertise and infrastructure (in areas such as HPC, robotics equipment, IoT infrastructure).” (5)

In other news, a significant piece of robotics news from the European Commission came in late 2018 when they awarded €66,000,000 to robotics projects that will help digitise companies throughout the European Union (EU). As part of the Digitising European Industry Call of Horizon 2020, the EU’s research and innovation programme, one coordination support action and four projects have been awarded. We read more about this on the European Commission’s website.

“They will all help small and medium-sized enterprises (SMEs) adopt new technologies in the robotics and artificial intelligence area. Nearly half of the money dedicated to these Digital Innovation Hubs (DIHs) projects will be dispatched to local companies by involving them in mini-projects or experiments.”

The four awarded projects are:

1. DIH^2: A network of 26 DIHs to reach no less than 170 DIHs.
2. DIH-HERO: A broad pan-European network of DIHs specialising in healthcare robotics will be established.
3. TRINITY: The aim here is to create a network of multidisciplinary DIHs consisting of research centres, university groups companies that cover a wide array of topics.
4. RIMA: This sets out to establish a network of 13 DIHs on robotics to facilitate the uptake of maintenance and inspection and maintenance technologies. (6)

The topics briefly discussed are just a few examples of the excellent work of the Robotics and Artificial Intelligence (Unit A.1) within the European Commission and we look forward to future developments in this most exciting area of work. It’s a really important aspect of the Digital Single Market but it’s not the only part of it.

The post The Digital Single Market: A focus on robotics and artificial intelligence appeared first on Artificial Intelligence.

our business operates under a rock on an isolated island in the South Pacific. So you know artificial intelligence (AI) is the big thing today. AI assists consumers in their day-to-day activities with or without their knowledge. Consider services like voice assistance, online shopping, content streaming, and even texting.

Machine learning (ML) is an application of AI. It gives machines the ability to automatically learn and improve from experience. And this happens without the need for previous programming. This makes AI and ML services very lucrative. And many reputable certifications and learning materials exist to get you started.

Over 50 years have passed since the term “Machine Learning” was first coined. And today businesses big and small get crack at the technology. Early adopters of AI reap appreciable results. Benefits include increased efficiency, cost reduction, revenue growth and more.

And yet, less than 30% of businesses and organizations incorporate AI in their business model. You’ll find many potential upsides of the AI revolution. So don’t let your business fall behind.

Machine Learning Benefits

Start to appreciate just how big a boon machine learning can be for your business. Check out three key benefits of ML for you to chew over.

Improves User Experience

It doesn’t matter whether you run an e-commerce store, a SaaS startup, or a design/marketing agency. User experience (UX) remains one of the most crucial factors to success. Do visitors are finding it difficult to navigate your virtual property? They won’t hesitate for an instant to leave and head elsewhere.

Netflix found customers give up after searching for a movie for more than 90 seconds. So the company uses machine learning to improve search results. And Netflix averts user frustration and customer churn. The company also saves $1 billion a year in potential lost revenue.

Amazon uses machine learning to provide highly relevant product recommendations based on the user’s purchase history, ultimately resulting in a pleasant and personalized online shopping experience.

So, basically, while users are browsing your website, you can use machine learning to provide them with personalized purchase recommendations based on their browsing history and previous orders. Furthermore, you can also offer discounts and special offers to returning customers to build loyalty and positive word of mouth.

Apart from personalization, machine learning also serves as the most efficient way to build chatbots – which are becoming more of a necessity for serious businesses than a mere good-to-have feature. Why? More than 53% of customers prefer to use online chat when they need assistance over calling.

Having a chatbot on your website can assist your customer service team by being the first responder to your customer’s inquiries. Chatbots can also help your customer service team by pointing users to various parts of your website, saving time and allowing your team to deal with more important tasks required to move users further down the conversion funnel.

Chatbots built using machine learning are far superior to rule-based ones, and serve to enrich the user experience and make a lasting good impression.

Automates Routine Tasks

For small businesses, every hour saved is a big win. Streamlining operations to make them more time-efficient is a big challenge for businesses.

Automating mundane tasks is the first step to streamlining your business operations. It would free up a lot of time for you and your team to focus on core, productive tasks instead of routine drudgery.

So, tedious tasks such as creating appointment reminders, responding to email, and sending invoices can all be automated effectively using machine learning.

Facilitates Smart Marketing

Marketing has gone fully digital. As discussed, website personalization and chatbots play a big role in determining your business’s user experience.

Now, when you consider the bigger picture of digital marketing for establishing your brand value, many constituents come into play.

For example, email marketing is still huge — it has one of the highest return-on-investment (ROI) at $38 for every $1 spent. Automating email marketing using tools like MailChimp is a great way to make AI work for you. Such automation allows you to create emails get sent automatically when triggered by a subscriber’s activity, inactivity, or a specific date like birthday or anniversary.

Essentially, if a subscriber takes a certain action, a machine-learned response is triggered, and so on down the line until the customer (hopefully) converts.

Next, you can also use machine learning to monitor and manage your business’s social media profiles quite effectively.

Machine learning actually plays a momentous role in modern digital marketing campaigns, from sentiment analysis and split testing to customer retargeting and predicting customer churn. Read this fantastic post to learn more about how machine learning is revamping digital marketing.

Final Words

Of course, implementing machine learning for your business is not exactly going to be a walk in the park. Sure, there are plenty of plug-and-play solutions to website personalization, automation, chatbots, and email marketing. But you’ll either need to hire a bunch of developers or learn how to do it yourself if you truly want to develop a customized solution tailored perfectly for your brand.

In the end, whether you like it or not, AI is here to shake things up in the business world, and it’s better to hop on board sooner rather than later.

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As we enter into the next revolutionary age, the age of artificial intelligence (AI), it’s no surprise fear often guides the mainstream narrative. Fear of massive job loss and millions unemployed as AI and robots are implemented on a global scale. But as the CEO of Mondo, a niche tech and digital marketing staffing agency, I envision this future as one of major job creation and opportunity. This future, however, is only possible if we work together to guide AI and robotics innovation responsibly throughout all industries.

Take A Note From History

To see proof of why AI won’t take all our jobs, you only need to look at history. Predictions of doomsdays for the workforce are common during ages of innovation, like the Industrial Revolution. Although these predictions are common, they’re also unsubstantiated. Whenever there is a major advancement in how we work, there is a fear that all the jobs will go away. While there is always an impact, the tail-end of the Industrial Revolution, for example, led to the assembly line; the workforce remains stable throughout these changes because new jobs are created as a result.

Automation is a perfect example of recent tech innovation that led to more jobs, rather than fewer. I find this to be especially true in the digital marketing space as companies began (and continue) to hire technology-specific experts to help them best utilize the automation tools they’ve integrated. The digital marketing roles they are hiring for, the roles my company recruits for, were nonexistent a few years ago and are thus a direct result of the innovation of automation for business purposes.

A recent study surveying 992 companies in a variety of sectors and countries with revenues of $500 million that are implementing AI as either a pilot project or “at scale” by technology consulting group Capgemini, supports the belief that the integration of AI will create jobs, rather than destroy them. 83% of respondents in the survey reported new jobs were created as a result of AI implementation. Additionally, 63% reported AI had not destroyed jobs in their organization. These findings support the recent analysis by Gartner that AI will create 2.3 million jobs in 2020 while eliminating 1.8 million.

It also comes down to basic economics. If companies replace all of their employees with AI or robots and a large part of the workforce becomes unemployed, there will be no consumers left to purchase the products these companies produce.

It’s Not Humans Vs. Machines, It’s Humans And Machines

To drive job creation rather than job loss through the adoption of AI, the responsibility will fall on innovators and industry leaders to open new windows of opportunity by training workers for new and emerging skill sets that will grow in demand as AI becomes more widely adopted. Society as a whole also needs to adjust the way we define and stereotype the near, AI-driven future. We need to change the perception of the future from humans vs. machines to humans and machines working together to accomplish a task.

A recent McKinsey study found that few occupations are fully automatable, but 60% of all occupations have at least 30% technically automatable activities. Alphabet’s Executive Chairman Eric Schmidt cited the study at the 2017 Viva Tech conference in Paris when he contended that the problem with AI wouldn’t be massive job loss but instead would be the creation of new jobs with AI-driven components that can’t be filled, adding to the tech talent gap in the U.S. as demand for tech-based skill sets continues to outpace available talent.

The key to prepping the current workforce for this is AI augmentation, arguably the greatest AI benefit and job creator. AI augmentation is the combination of human and AI, where the two work together to supplement each other and make up for what the other lacks. As populations are getting older, the total number of people working has gone down, which means the key to continued economic success is boosting productivity through AI augmentation.

We need to look at AI as an opportunity to find ways to do our jobs more efficiently and effectively, without replacing the human component. Instead, AI can free the individual up to focus on tasks they do better than an AI system.

The only potential job disruption on a major scale resulting from AI will be a lack of qualified talent skilled in collaborating with AI components, tools or systems due to a lack of training and adequate educational opportunities available now. To prevent the doomsday AI fears keeping us all up at night, we need to start investing in and providing opportunities to our current employees now so they can begin developing the AI-driven skills we’ll rely on later.

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Insurance has become an extremely competitive market! While there are several players, customers are increasingly becoming aware about what they need. They are now seeking a more individualized experience and if you cannot provide it, rest assured they will find another company that does.

So, how do companies find ways to address the ever-increasing customer needs?
While the adoption of new technologies might have been slower than desired initially, the Indian insurance sector is certainly awakening to its benefits now. Several insurers are now deploying these processes to understand their customers better and for product innovation.
Of all these news processes, perhaps artificial intelligence and machine learning are proving to be the most potent!

Information overload
New data sources like third-party databases, social media activity, internet of things, and more are providing a steady stream of information. Machine learning is helping insurers create opportunities by computing this vast stream of data for hidden insights.
Using complex algorithms, insurers can find patterns in the available data to establish predictive and descriptive data models. However, the most important point to note here is that machine learning can make sense of dissimilar datasets – whether partly structured, unstructured or structured.
An uninterrupted flow of information from multiple sources – click stream, social media interaction, audio clips and more – is processed real time, enabling the systems to incrementally adapt and self-create a continuous learning curve.

Customer is king!
Given that insurance vastly requires customer interface, machine learning has helped companies to stay on their toes. They can quickly and effectively recognise dynamic customer needs to provide them with simple and intuitive experiences. The analytics can predict the future trends and behaviour patterns of the customers.
Using advanced machine learning techniques to analyze the behavioural and socioeconomic activities of a prospective customer, some insurance companies are successfully deriving precise and individualized product recommendations.
In fact, many companies are working towards creating chat bots to enrich customer interaction and looking at automating the issuance process.

Merits of machine learning
While many machine learning algorithms are not completely novel, applying complex mathematical calculations to big data – over and over, faster and faster – automatically is a fairly new. As discussed above in detail, new technologies will only better the way insurers conduct business and serve their customers.
So, here are the key merits of machine learning to show why machine learning is crucial:

a) Superiority in advisory
Virtual advisors based on machine learning algorithms could potentially change insurance advisory by eliminating human bias and improve trust, thereby boosting customers’ confidence. While India may not yet be completely ready for the machines to take over, insurers can certainly use these virtual advisors for tackling routine tasks and allow their consulting team to handle the more constructive and socially interactive challenges.
b) Enhanced operational efficiency
Machine learning can help insurers become more efficient operationally by reducing turnaround time and improving productivity.
c) Digital Marketing automation:
Insurers can automate and optimize digital marketing expenses by implementing machine learning algorithms in multiple areas including identification of optimum bidding price for each keyword, re-targeting as per propensity to purchase, and nurturing leads through right content at the right time using the right medium.
d) Dynamic pricing:
Given the enormous data at one’s disposal, insurers can create a dynamic pricing model as per the socio-economic factors of a prospective customer. For instance, by analyzing data from wearable devices that monitor physical activity and heart rate, policies can be recommended with discounts.
e) Claims management and underwriting
Considering machine learning depends heavily on a repository of data, it will help create a deeper understanding of a customer’s risk profile. This will make the underwriting process much more effective and accurate. It will also significantly reduce the turnaround time for processing of claims, and help identify any fraudulent incidences on a case to case basis. Both these factors are crucial is ensuring loss prevention.

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This autumn or post-monsoon, depending on which part of the world you live in, the movie Blade Runner will see a return after 35 years. If you remember the first movie, the artificial intelligence character, or the “replicant”, the antagonist, played by Rutger Hauer, saves the protagonist, played by Harrison Ford, from dying. After that, the protagonist witnesses the AI character’s programmes terminate themselves.

Ford then says: “I don’t know why he saved my life. Maybe in those last moments he loved life more than he ever had before. Not just his life, anybody’s life, my life. All he’d wanted were the same answers the rest of us want. Where did I come from? Where am I going? How long have I got? All I could do was sit there and watch him die.”

That’s AI for you in a nutshell when things become sentient. Computer programmers call it “singularity”, where self-learning software would become aware with every cycle of learning and ultimately surpass human intelligence.

Rolf Bulander, Chairman of Mobility Services, Bosch GmbH, says “New technologies such as AI and ML have already ushered in the era of making networks intelligent and are changing business models.” He adds that in Germany they have already created an ethics committee to understand the man-and-machine relationship in case of automated driving.

“Fundamental questions are being raised today; who does the software save first, a child or the parent. The setting up of the committee clearly signals that human intelligence and machine intelligence should work together,” Rolf says.

Before we have robots taking over the world, serious work is being done in every industry to get tasks done by software and remove human intervention altogether.

Machines are not sentient yet, but we are already in the realm of “Narrow AI”, where mundane tasks such as customer queries and data generationwill not need humans anymore.

The world of Narrow AI

Starting from healthcare and banking to automotive and media, business models are changing thanks to Narrow AI. The languages, mostly Python and even Java, going into AI and ML are all built out of India, which is why the country is aligning its goals to the world’s quest for a global network of artificial intelligence.

The terms ML and AI are used interchangeably these days. But what are they really?

NVIDIA, the $4.5 billion GPU company, explains in a blog that most of the work in AI today can be termed “Narrow AI”, where certain tasks are better performed by technology. So before we have a sentient being (like in Blade Runner), we are already in the era of “Narrow AI”. The bot example was one of them because they use millions of data points that are processed quickly and present immediate action.

Machine Learning, on the other hand, is termed as an approach to AI. It is nothing but algorithms that make sense of data to determine or predict something, thanks to the processing power of chips today. Before Machine Learning comes something known as Deep Learning where engineers try to understand the functions of the brain and how neurons work. So while programming, engineers look at how a machine can function like neurons firing in the brain to perform tasks.

Layak Singh, Co-founder of Artivatic, says: “Engineers today can make a machine understand text, video and images. But every engine needs to be trained to understand patterns and recognise the context.”  Artivatic is building a system for banks to understand credit history of customers and reduce loan defaults.

Startups are clearly playing a major role in innovating faster than corporates, which has led to several curious partnerships. SAP India has invested in Niki.ai, a bot that improves the ordering experience. Then there’s Ractrack.AI, where a bot improves customer engagement and provides insights; it functions as a virtual communications assistant to convert the customer into a client. Racetrack is helping companies turn leads into meaningful engagements by using AI. Another startup, LUCEP, converts all potential queries into leads with their AI engine.

Bobby Reddy, of Indus Ventures, is the Co-founder of Housingman.com, which is building a platform that uses machine learning and artificial intelligence.  “The objective is to generate insights from data and simplify customer interaction with a business and also convert them into leads,” he says.

Indian startups saw $4 billion in risk capital being deployed across 1,040 angel and VC/PE deals between January and December 2016. According to YourStory Research, disclosed funding announcements have shown a decreased value of 55 percent from the same period last year (2015) and a decrease of 20 percent from 2014. About $9 billion in VC/PE capital had been invested in 2015. The number of deals in 2016, however, has increased by 3 percent over the last year. On an average, four startup deals were announced every weekday throughout 2016. VCs predict that going forward machine learning and AI would be key themes to invest in.

The era of voice and vision

Billions of dollars have been pouring into AI across the world.

Leaders like Elon Musk, the founder of Tesla, have said this will create a global apocalypse and should be “regulated”. He expressed this fear because Russian President Vladimir Putin said the race to own AI assets will determine the ruler of the world. Musk also expresses that a system of such intelligence could “start a global war”.

One must really watch Westworld or read Issac Asimov to understand the merits and consequences of AI.

Atul Jalan, founder of Manthan, says, “We are in a great era of change because human behaviour will be out-matched by superior digital technologies. AI will eventually change everything. But humans can do better things than mundane tasks.”

In Manthan’s offices, engineers and marketing teams are jointly working together to build a voice-based AI suite called “Maya”, based on Amazon’s Alexa platform. Maya, when called upon, becomes the virtual assistant to the CEO and dynamically pulls out company sales information across regions. She can even tell what categories did not sell.

“Over time the platform can prescribe what should be done and this will challenge the entire C-Suite of leaders,” Atul says.

The impact of AI is best described in a PwC report on The Future of Work: A Journey Towards 2022 . This predicts that a student protest will erupt globally in 2020 as a consequence of the non-availability of jobs. This prognosis is based on universities not being able to keep pace with sudden changes in economy and technologies such as automation.

Amid the realignment of jobs and the accompanying chaos, companies will push the boundaries of innovation in AI. It will all be about agile IT, sensors, and data, which will change the entire Indian work scenario. Coding jobs will be scarce as all platforms will be available and automated. IT and its allied services will focus on implementing products such as SAP, Microsoft and Oracle on platforms in cloud environments.

Vishal Sikka, the outgoing CEO of Infosys, said the future of Indian IT was not in services, but in software. He was prescient as all global clients want IT to be enabled to business outcomes and building agile applications.

This year, Infosys launched Nia, its AI platform. Nia worked as a PoC in a bank which generated data volume of 596 million trade transactions on a 100-node cluster in the AWS stack. The case was peculiar and it involved pulling data rapidly to avoid fines in reporting. Usually the message insertion rate into a platform is slow; it takes more than 10 minutes. However, Nia achieved it at 130,000 records per second or 18.22 MB per second.

The report execution for 30,000 trade records was done in 35 seconds, with corresponding 120,000 trade line items, including end-to-end processing. If the bank reports delays of more than 15 minutes, it attracts a penalty by the regulator. With the Nia platform, end-to-end processing and reporting just took 35 seconds instead of 10-15 minutes. The platform helped the bank avoid non-compliance and related penalties.

Again, all this is just “Narrow AI”.

Undoubtedly Nandan Nilekani, who ushered in the Aadhar database in this country, will understand the use of AI in processing data. It has a bright future as an investment opportunity, provided CIOs take the plunge in using AI in their companies to reap benefits.

The future: reducing human error

According to Gartner, by 2018 Indian CIOs are expected to spend around a third of their IT budgets on the digital economy. Analytics, cloud services, mobility and digitalisation/digital marketing are the top four spending priorities, as per a survey of CIOs in India. Globally, spending on digitalisation is expected to jump to 28 percent of IT budgets by 2018 from 19 percent in early 2017.

No wonder Google’s AlphaGo, IBM Watson and Bosch Centre for Artificial Intelligence are approaching this subject with so much fervour. Their platforms will power homes, cars and industry.

Bosch is already working with Daimler to launch an autonomous car in the early part of the next decade. It is training a whole range of cars to use computer vision, LIDAR, Radar and Ultrasound to figure out driving autonomously. Nearly 3,000 engineers from Bosch, in Stuttgart and Bengaluru, have one thing in common: they are putting their brains – figuratively speaking – into a super brain. This “brain” can crunch 30 trillion data points per second and will process data three times faster than a human brain can. This “brain”,  powered by AI, has no reason to feel guilty about anything because it is designed to not make mistakes.

In one year since its alliance with Daimler, Bosch has seen revenues of €1 billion from the sale of these sensors with an order book value of €3.5 billion. This is significant because with Elon Musk’s Tesla paving the way for a world where software in the car will determine the travel experience, a vehicle is no longer going to be a single entity. It is going to understand everything, be it the driving methods or shopping habits of a person.

Dirk Hoheisel, member of the board of management, Robert Bosch GmbH, says, “Only AI will make this possible and reduce human error.”

He adds that an AI-led car has superior reflexes compared to human beings. It can interpret if there is a ball or a child on the road.

“We have to improve our sensors to monitor surroundings and think like humans, but only faster,” Dirk says.

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