The new economy powered by machine learning would soon overcome the companies which are only making plans to capitalize and revolutionize.
Machine learning is bringing technological advancements everywhere. It basically enables the computers to develop self-learning habits and the same is achieved by utilizing the experience and data with no pre-programmed procedures.
A few of the well-known AI applications are YouTube recommendations, Netflix recommendations, and virtual personal assistants like Alexa and Siri. These features are not only confined to entertainment and e-Commerce related websites. In this day and age, Machine learning is for every organization from small scale to large scale and from ordinary processing to cutting-edge activities.
It is imperative to gain an insight into the working mechanism of machine learning skills to artificial intelligence for remaining competitive in the market.
Turning Data Overload into a Critical Mother Lode
In this modern age, it is good to have maximum information to successfully manage your business. You would have a higher competitive benefit if you know more about your niche, products, the marketplace, and your customers.
Approximately, 90 percent of worldwide data by 2017 had been released only in the last two years, and reportedly this has been released at a rate of 2.5 quintillion bytes per day. A limitless and exponentially growing stream of information is being delivered by mobile phones, web browsers, and sensors, etc. That is referred to as Big Data. Moreover, for any business setup, the major barrier is to determine which data among all information is actionable, vital, and precise and which is not.
In the Evolution of Analytics by Patrick Hall, he says that organizations are now pushed to perform an advanced search in their data to identify new and innovative methods to increase efficiency and competitiveness.
In the ‘Evolution of Analytics’ by Patrick Hall, it is revealed that an advance search is underway by the organizations in their data so that the state-of-the-art techniques could be discovered for efficiency enhancement.
The data is increasingly growing from all dimensions and the conventional computer systems cannot handle this much data keeping in view the factors like speed, time, and efficiency. It is inferred that companies are reaching a threshold, where, the amount of data they can process, examine, and execute for meaningful purposes will be specified.
According to Forrester Research, companies do not effectively make use of almost 60-73 percent of the acquired data due to strategic reasons.
Applying machine learning to automate the data analysis models is actually the answer to convert this enormous data into a pragmatic model fit for construction. By and large, 1-2 models can only be built by humans in a week; however, tens of thousands of models can be produced by machine learning in a week.
Not only the raw datasets of huge quantity are handled by machine learning, but this data is also desired by the ML. It turns out to be intelligent with the constant provision of data. The clusters, patterns, and relationships among data can be described by machine learning. Subsequently, results are forecasted and actions are recommended.
Soon, your data can be valuable for your customers with the capability of machine learning and other AI-based applications. If your organization is not data-driven at the moment, it has to be in near future.
The self-reported data is the maximum information about the customers maintained by the companies. We can also say that newsletter subscriptions, product registrations, downloads, surveys, and customer service feedback are the sources for the generation of this particular data. In addition, website visits, cookies, and search engine data tend to automatically monitor the rest of the data. However, it is significant to monitor the data of anonymous visitors. Social media is another data source of data in particular reviews, click-through, and engagements. If you know how to mine the competitors’ data, then it could also be proved as innovative and meaningful.
Human beings are likely to generate textual data most of the time. Consequently, information and thoughts of customers can be extracted through web crawling. Subsequently, you will remain ahead of your competitors with the help of this method.
With a great amount of textual data being produced, the information about what customers are thinking for a competitive edge can be sought through web crawling. Moreover, it can assess community awareness of organizational initiatives besides maintaining brand reputation.
The updated and accurate data is none other than the transactional data. According to many, almost 450 billion hits per day would be revealed by 2020 in respect of business transactions through the internet, which may entail the data automatically generated via IoT.
The magnitude of data generated and copied on annual basis will be doubled and is predicted to hit 44 zettabytes by 2020. The capacity of ordinary storage cannot compete with this modern technique for gathering and maintaining this data. Nonetheless, free modules are offered by Hadoop – an open-source Java application to be used by anyone. Extensive data sets are primarily meant for this application. It is a key tool for competitive advantage and the processing of large-scale data has become possible as a result of this Hadoop technology.
A substantial benefit can be realized by those who can successfully mine historical data. On the other hand, virtually unlimited storage is provided by the cloud at affordable rates. Moreover, smart and unique computing services are being offered by the cloud leaders such as Google Cloud, Microsoft Azure, and Amazon Web Services.
The companies which have shifted their complete IT systems to the cloud can outshine their competitors. Moreover, they have developed a system of interference by which the stage of market leadership is eventually established.
The “data lake” is a repository, where diversified data is maintained to boost machine learning”, Compared to standard data warehouses, a data lake is different since the data is accumulated here in its natural shape even if it is in structured, semi-structured, or unstructured form.
While, the data can identify things without any hassle, yet it is sometimes hard to perform innovative analyses due to its rigidity. Because of structured data, it is likely to obtain answers to specific questions in an efficient way, but it may also fail to cope with the possible impending issues.
The unstructured data is enabled to respond to new questions with flexibility and this credit goes to “Data Lake”. As a result of this cutting-edge technology, an individual can observe high accuracy in performing these analyses and responding completely to supplementary problems. Therefore, the raw material is supplied to realize competitive benefits.
You have Data with You; What is Next?
With a huge amount of data, we can train the machine learning algorithms based on experience. Afterward, they come across the new data to find the patterns. At this stage, they learn from their exposure to new things besides generating the predictive models in their personal capacity.
The programs should be developed in a way that they could perform the analysis of large-scale data to find the historical patterns. Moreover, the programs should be sufficiently intelligent to adjust to new data and upcoming patterns besides investigating the effect of predictive frameworks.
In this day and age, supervised learning and unsupervised learning are the two extensively adopted machine learning techniques for most industries.
Concerning supervised learning, the data containing labels like names, dates, and financial strings are needed. With respect to relational databases, businesses generally use this sort of data. The websites, human input, or gadgets maintained with the point of sale terminals tend to generate the structured data. If you know a specific output, or, desire to identify new data points matching a certain target value, then supervised learning algorithms prove to be helpful. Moreover, this is vital for functions, for example, predictive maintenance, recommendation engines, and inventory control, etc.
The data, which has no historical labels, which is unstructured and cannot be adjusted appropriately into a database, are usually required by unsupervised learning. This kind of data encompasses document text, customer transactions, social media, graphics, and other content. Almost 80-90 percent of the data found in organizations is unstructured. The unsupervised learning algorithm has to make its assumptions to realize what is being sought. The same is happened by searching for clusters and hidden patterns. This particular type is imperative for hardware fault diagnostics, market segmentation, and fraud detection, etc. You are also enabled to identify unknown patterns in the data just because of unsupervised learning.
Whether a project implements supervised or unsupervised machine learning or a blend of both, five ways are there through which any business can realize benefits to attain a competitive gain.
Automate Business Processes
To automate back-office processes, machine learning can be instantly integrated by the industries. These procedures are mainly rules-based and high-volume functions, which might be driven by a “lights out” plan. Resultantly, employees get sufficient time to think and achieve the strategic objectives of the company.
The human workforce executes certain transactions, out of which almost 80 percent are consumed by these simple functions. As a consequence, the support costs are escalated with no value addition to the customers and the company per se.
Numerous everyday tasks are outsourced by most of the companies to computer systems. AI enhances the number of automatically matched invoices and this is done by tracking the current processes and by looking for different instances. Organizations are thus enabled to restrain the amount of work delegated to service centers. Accordingly, the finance staff is also enabled to focus on strategic responsibilities.
Lights-Out-IT
In 2017, Tata Consultancy Services conducted a survey on 835 companies and found that the biggest adopter of AI was none other than IT. This domain not only successfully monitored the hackers and their moves in the data center, but AI was also used by IT to deliver smart solutions to employees involving tech support, task automation. Moreover, a technology from certified vendors was also ensured. After the survey, it was found that IT departments of nearly 34%-44% of companies were implementing AI technology.
Hands-Off HR
Skills acquisition and human resource are fields where AI can play a great role in reducing the workload, enhancing efficiency, and avoiding prejudice. The following are the repetitive HR tasks that can be efficiently handled by the chatbots and other intelligent tools of machine learning, for instance:
- Selecting job applicants from thousands of CVs.
- Planning interviews, conducting reviews, and other group meetings.
- Refining office-based workflows.
- Determining and supporting employee interactions.
- Inviting and communicating with top talent.
- Answering the questions regarding office procedures, company policies, and basic conflict resolution, etc.
With the application of predictive analysis, a lot of time can be saved through a well-accepted machine learning technology. The main goal is to avoid wastage of time in hiring. Consequently, the process turns out to be consistent and precise.
Human productivity is also increased through machine learning. For example, tracking hundreds of news items, auto-generated weekly reports, and reporting social media mentions, etc. These tools help to monitor data besides forecasting results. Moreover, corporate groups are enabled to line up product development and marketing efforts as a result of ML.
Ears-On Analytics
Data from brand new and massive sources of information can be extracted by machine learning, which was never accessed by human resources.
Other progressive uses of machine learning can enhance the efficiency of the company through the creation of new products and enhancement of reliability of existing products and all this is possible because of machine learning.
To review social media and to create new products for delivering online solutions, machine learning algorithms are successfully implemented by the company IKEA, which is an expert in furniture development.
It is believed that machine learning efficiently investigates the historical sensor, logistics, failure data from devices, and machinery. Later, the prediction of the model may suggest preventive maintenance. It also tends to do away with transportation-related issues or even identify the impending real-time issues.
The full potential of machine learning is yet to be discovered by many businesses. The possible benefits are far more than a higher level of productivity and faster supply chains.
Marketing
Prior to the introduction of the digital age, marketing professionals were the ones to welcome and accept new and emerging technologies. For digital marketers, AI and Machine learning could be well-known tools. With the growth of data, this evolution will occur to a large extent in the years to come, and new models will materialize. In addition, marketers will develop new plans for maximum benefits. AI performs this task in an efficient way to find the perfect message to send to the right individual at the right time.
The type of vague marketing can be restrained by machine learning. With the implementation of behavioral data, marketers can concentrate on their audiences to convert shoppers to regular customers.
To boost advertising and marketing, given below are a few techniques implemented by companies, both online and offline.
- Require prognosis and sales projection
- Customer churn risk modeling
- Pointed cross-sell and bundling
- Personalized and product ranking
- Augmented reality
- Refining lead sourcing
- Optimized-message targeting
- Micro-segmentation
- Text-to-video creation
- Customer facilitating web data
To smartly invest in the market budget, modern techniques have been developed by machine learning and other key technologies.
Massive amounts of data can be checked in real-time just because of these technologies. Today, the most critical basics for any online business are to control big data and obtain actionable perceptions.
Smarter Segmentation
Personalization and segmentation of lists have been key drivers since the era of direct mail. Most Google natives joined the pay-per-click companies and the AI-marketing tools remained present to assist the clients. Expertise in these skills is required with an open eye and mind to gain insight into new machine learning methods.
Customer journey is one thing, which is important with respect to machine learning. With the help of algorithms, high-value customers can be differentiated from their counterparts. Accordingly, businesses come to know how to strengthen their sales. Clusters of the best customers sharing shocking attributes can be discovered through the ML. You can desire to review those customers that buy at a fixed point of time in a year besides identifying the factors affecting their purchasing conduct.
Foregoing in view, decision making becomes easy for recommending the products to customer groups and it can be done through promotions and personalized offers.
Lead Sourcing and Scoring
With conventional methods, the new leads could either require purchasing lists or waste plenty of time in searching and scraping contract information from company portals.
The original data collection of lead generation can be facilitated through machines. Nonetheless, AI can easily analyze emails, phone calls, and social posts to discover patterns and reveal the right option.
A thorough understanding of customers and prospects is required by the right marketing and it will help in nurturing the business environment. This collection from acquainted databases is automated by machine learning. In addition, the algorithms access the external world to examine, gather, foresee, and learn.
These AI applications are present in the market in the shape of products. The digital marketing experts don’t need to be technically expert. However, executives and marketers should be aware of the key features of machine learning to gain proficiencies before making a huge investment in high-value resources. The marketers ought to know the things where humans are good at and the things better left for machines.
Pay-Per-Click Savings
The area where machines do well is bidding. It requires choosing a strategy defined by humans and changing bids subject to the cost per acquisition or expected return on ad spending. Engaging a third-party vendor or doing it manually can be costly.
Bidding is one of the ideal applications of machine learning since it relies on pattern recognition and statistics. Depending on the keyword stats and earlier behaviors, machines can easily envisage how an ad might be used.
Dynamic Pricing
Supporting elasticity in online pricing is another significant application of AI for marketing. Contrary to human pricing models, there can be a shift in AI-driven dynamic pricing, which can be on the basis of the level of interest of shoppers, competitors’ pricing, and previous interactions via earlier marketing.
This can be challenging since enormous data is needed to understand consumer behavior with respect to pricing, service delivery, and another allied state of affairs. However, airline services and ride-share are the companies, which have successfully developed dynamic price optimization techniques for boosting revenues.
The waste-wasted budget and wasted labor are eliminated by machine learning regardless of its deployment in your marketing technology stack. You can maximize marketing with trivial capital because of machine learning techniques. You may seek AI-driven opportunities through which you may earn more with the same human resources.
For machine learning’s role in marketing, this is only the beginning of the bell curve.
Transforming Customer Experience
Implementing the applications that examine and improve the customer experience is one of the ways through which machine learning can boost the competitive gains.
Regarding the connected economy, real-time and highly personalized interactions are expected. In this day and age, almost every company is making use of digital technologies to stay competitive besides collecting and releasing data to make the events beneficial.
Churn Modeling
Machine learning has a number of techniques that can create a difference for your customers. Churn modeling is a technique, which is not only good for marketing but also beneficial for product enhancement and customer support.
The factor, such as Big data analysis is swiftly done through machine learning, which is humanly not possible. Marketing histories, customer purchasing behavior, and website can be discovered through algorithms.
Subsequently, the risk models can be identified with an objective to find out how the odds of churning are affected by these interventions. Through churn modeling, live customer support teams with the best problem resolution paths can be provided by the risk analysis.
Customer Support
A paper was circulated in Chatbots Magazine in 2017, where it was reported by nearly 55% of UK consumers that finding a quick and effective response to questions is the significant factor while considering the customer experience.
Companies ought to promptly respond to customer queries entailing the data associated with the previous interactions, customer journey, and other issues. This turns out to be a reality due to machine learning.
The low-cost speech analytic tools and recording has also brought comfort to the companies and customer service interactions. Instead of just directing callers through prompts, they will be organized by the speech analytics and the complete dynamics of your responses are also reviewed.
Chatbots
Widespread fame is being witnessed in automated customer support through AI-driven Chatbots. The solution for providing customers with a quick webchat service is none other than intelligent digital agents since the age of 75% of all consumers is between 18 and 25.
Moreover, consumers can easily collaborate with Chatbots due to the thousands of AI-based customer support. Usually, consumers interact with technology through smart devices, voice commands, and automated call centers. To improve customer service inquiries (if applicable), the speech-enabled Chatbots are driven by ML.
McKinsey Global Institute report came to an appearance in 2018, where, due to deep learning analysis, it has become possible for systems to review customer’s emotional tone. If the customer has an undesirable response, the call can be automatically forwarded to telephone operators and human managers.
Social Media Monitoring
A key step for companies to remain competitive is enhancing the potential of machine learning to track social media. When social media gossip is mixed with other transactions and customer demographic, product marketing gets boosted and it can increase individualist product recommendations.
As per the McKinsey report, the rate of sales conversions can be doubled by the “Next product to buy” recommendations emphasizing individual customers. The presence of quality customer support can be ensured through social media monitoring.
Achieving all this valuable feedback can be difficult for even a large-scale business with the development of global chatter on Twitter and Facebook. The process of establishing the statements of customers about you gets refined because of machine learning coupled with a linguistic algorithm.
To realize a competitive gain, a great way to apply AI is basically social media. Likewise, you can apply AI to social media to monitor the declaration of your company and to acquire market share from competitors. Because of a variety of other options, there is drop-in customer loyalty, especially when someone gets attracted by these options.
Becoming One with Machines
The companies in competitive sectors must instantly adopt ML and should not go for a “wait and see” approach with respect to machine learning. However, remaining oblivious of the competitors possessing robots is not an issue for B3C. Challenges in the technology curve are encountered by almost every company.
According to primary evidence, a business case needs to be achieved, and that companies come to know that the value addition within core functions and operations would be realized with the implementation of AI. Similarly, a higher profit margin is attained by the early adopters of AI and the performance gap with other firms will be anticipated to increase.
Apart from that, a considerable challenge for many small and big companies is the cold start challenge with AI. Due to rapid technological advancement, companies fail to identify the key areas that must be focused on. The business example of machine learning has enticed most of the industries. Louis Columbus publicized a report in Forbes in 2017, where 84 percent of the executives were of the view that they managed to stay ahead of their competitors just due to AI acceptance.
You come across a number of options when you begin to integrate machine learning into your competitive assembly. According to some experts, the next phase in the evolution of analytics would be represented by machine learning. The first key step is to consult with professional data scientists in many cases.
The companies are collaborating with varsities with an aim to pick the recent graduates specialized from advanced degree programs in data and analytics. However, the organizations should also look for internal talent to be used for implementation purposes.
It would be an intelligent decision to upgrade your staff in AI and machine learning technologies. The internal beginning is a safer investment for new AI-based companies. Prior to having the data infrastructure and data accessibility in organizations, a common mistake is to induct a number of machine learning professionals who get heavy salaries.
This is really a big challenge, and a low return on investment is the thing which is eventually happened leading to bad engineering.
According to a report published by Gartner, AI would be required by one in five workers engaged in routine tasks for accomplishing the job by 2022, which is believed to be a good thing.
Time and again, it has been acknowledged that with the digitization of office workflows and simple transactions, employees could get sufficient time to concentrate on other areas with their full potential resulting in value addition for the organization.
Besides acquiring new skills to compete with the market demands and to become proficient in ML and AI, the organizations focusing on automation will certainly realize the benefits and a competitive gain. A single word for these skills can be humanness.
Will ML provide relief to the pains? It is yes according to the experts since certain tools are offered by ML for monitoring your organization, products, processes, competitors, and customers, etc. Because of instant access to on-demand insights, businesses get an edge required to survive in the market.
