Below are the applications of data analytics:
Over 2.6 billion and counting active social media users include customers and potential customers for every company out there. The race is on to create more effective marketing and social media strategies, powered by machine learning, aimed at providing enhanced customer experience to turn prospective customers into raving fans. The process of sifting through and analyzing a massive amount of data has not only become feasible, but it’s easy now. The ability to bridge the gap between execution and big data analysis has been supplemented by artificial intelligence marketing solutions.
Artificial Intelligence (AI) marketing can be defined as a method of you using artificial intelligence consonants like machine learning on available customer data to anticipate customer’s needs and expectations while significantly improving the customer’s journey. Marketers can boost their campaign performance and return on investment read a little to no extra effort in the light of big data insights provided by artificial intelligence marketing solutions. The key elements that make AI marketing as powerful are:
- Big data – A marketing company’s ability to aggregate and segment a huge dump of data with minimal manual work is referred to as Big Data. The marketer can then leverage the desired medium to ensure the appropriate message is being delivered to the target audience at the right time.
- Machine learning – Machine learning platforms enable marketers to identify trends or common occurrences and gather effective insights and responses, thereby deciphering the root cause and probability of recurring events.
- Intuitive platform – Super fast and easy to operate applications are integral to AI marketing. Artificial intelligence technology is capable of interpreting emotions and communicating like a human, allowing AI-based platforms to understand open form content like email responses and social media.
All artificial intelligence technology-based solutions are capable of extracting information from data assets to predict future trends. AI technology has made it possible to model trends that could previously be determined only retroactively. These predictive analysis models can be reliably used in decision-making and to analyze customers’ purchase behavior. The model can successfully determine when the consumer is more likely to purchase something new or reorder an old purchase. The marketing companies are now able to reverse engineer customer’s experiences and actions to create more lucrative marketing strategies. For example, FedEx and Sprint are using predictive analytics to identify customers who are at potential risk of deflecting to the competitor.
Only a decade ago, if you type in “women’s flip flops” on Nike.com, the probability of you finding what you were looking for would be next to zero. But today’s search engines are not only accurate but also much faster. This upgrade has largely been brought on by innovations like “semantic search” and “natural language processing” that enable search engines to identify links between products and provide relevant search results, recommend similar items, and auto-correct typing errors. The artificial intelligence technology and big data solutions can rapidly analyze user search patterns and identify key areas that the marketing companies should focus on.
In 2015, Google introduced the first Artificial Intelligence-based search algorithm called “RankBrain.” Following Google’s lead, other major e-commerce websites, including Amazon has incorporated big data analysis and artificial intelligence into their search engines to offer smart search experience for their customers, who can find desired products even when they don’t know exactly what they’re looking for. Even small e-commerce stores have access to Smart search technologies like “Elasticsearch.” The data-as-a-service companies like “Indix” allow companies to learn from other larger data sources to train their product search models.
Recommendation engines have quickly evolved into fan favorites and are loved by the customers just as much as the marketing companies. “Apple Music” already knows your taste in music better than your partner, and Amazon always presents you with a list of products that you might be interested in buying. This kind of discovery aide that can sift through millions of available options and hone in on an individual’s needs are proving indispensable for large companies with huge physical and digital inventories.
In 1998, Swedish computational linguist, Jussi Karlgren, explored the practice of clustering customer behaviors to predict future behaviors in his report titled “Digital bookshelves.” The same here, Amazon implemented collaborative filtering to generate recommendations for their customers. The gathering and analysis of consumer data paired with individual profile information and demographics, by the predictive analysis based systems allow the system to continually learn and adapt based on consumer activities such as likes and dislikes on the products in real-time. For example, the company “Sky” has implemented a predictive analysis based model that is capable of recommending content according to the viewer’s mode. The smart customer is looking for such an enhanced experience not only from their Music and on-demand entertainment suppliers but also from all other e-commerce websites.
Product Categorization and Pricing
E-commerce businesses and marketing companies have increasingly adopted artificial intelligence in their process of categorization and tagging of the inventory. The Marketing companies are required to deal with awful data just as much, if not more than amazingly organized, clean data. This bag of positive and negative examples serves as training resources for predictive analysis based classification tools. For example, different detailers can have different descriptions for the same product, such as sneakers, basketball shoes, trainers, or Jordan’s, but the AI algorithm can identify that these are all the same products and tag them accordingly. Or if the data set is missing the primary keyword like skirts or shirts, the artificial intelligence algorithm can identify and classify the item or product as skirts or shirts based solely on the surrounding context.
We are familiar with the seasonal rate changes of the hotel rooms, but with the advent of artificial intelligence, product prices can be optimized to meet the demand with a whole new level of precision. The machine learning algorithms are being used for dynamic pricing by analyzing customer’s data patterns and making near accurate predictions of what they are willing to pay for that particular product as well as their receptiveness to special offers. This empowers businesses to target their consumers with high precision and calculated whether or not a discount is needed to confirm the sale.
Dynamic pricing also allows businesses to compare their product pricing with the market leaders and competitors and adjust their prices accordingly to pull in the sale. For example, “Airbnb” has developed its dynamic pricing system, which provides ‘Price Tips’ to the property owners to help them determine the best possible listing price for their property. The system takes into account a variety of influencing factors such as geographical location, local events, property pictures, property reviews, listing features, and most importantly, the booking timings and the market demand. The final decision of the property owner should follow or ignore the provided ‘price tips’ and the success of the listing are also monitored by the system, which will then process the results and adjust its algorithm accordingly.
Customer Targeting and Segmentation
For the marketing companies to be able to reach their customers with a high level of personalization, they are required to target increasingly granular segments. The artificial intelligence technology can draw on the existing customer data and train Machine learning algorithms against “gold standard” training sets to identify common properties and significant variables. The data segments could be as simple as location, gender, and age, or as complex as the buyer’s persona and past behavior. With AI, Dynamics Segmentation is feasible which accounts for the fact that customers’ behaviors are ever-changing, and people can take on different personas in different situations.
Sales and Marketing Forecast
One of the most straightforward artificial intelligence applications in marketing is in the development of sales and marketing forecasting models. The high volume of quantifiable data such as clicks, purchases, email responses, and time spent on webpages serve as training resources for the machine learning algorithms. Some of the leading business intelligence and production companies in the market are Sisense, Rapidminer, and Birst. Marketing companies are continuously upgrading their marketing efforts, and with the help of AI and machine learning, they can predict the success of their marketing initiatives or email campaigns. Artificial intelligence technology can analyze past sales data, economic trends as well as industrywide comparisons to predict short and long-term sales performance, and forecast sales outcomes. The sales forecasts model aid in the estimation of product demand and to help companies manage their production to optimize sales.
Programmatic Advertisement Targeting
With the introduction of artificial intelligence technology, bidding on and targeting program based advertisement has become significantly more efficient. Programmatic advertising can be defined as “the automated process of buying and selling ad inventory to an exchange which connects advertisers to publishers.” To allow real-time bidding for inventory across social media channels and mobile devices as well as television, artificial intelligence technology is used. This also goes back to predictive analysis and the ability to model data that could previously only be determined retroactively. Artificial intelligence is able to assist the best time of the day to serve a particular ad, the probability of an ad turning into sales, the receptiveness of the user, and the likelihood of engagement with the ad.
Programmatic companies can gather and analyze visiting customers’ data and behaviors to optimize real-time campaigns and to target the audience more precisely. Programmatic media buying includes the use of “demand-side platforms” (to facilitate the process of buying ad inventory on the open market) and “data management platforms” (to provide the marketing company an ability to reach their target audience). In order to empower the marketing rep to make informed decisions regarding their prospective customers, the data management platforms are designed to collect and analyze the big volume of website “cookie data.” For example, search engine marketing (SEM) advertising practiced by channels like Facebook, Twitter, and Google. To efficiently manage huge inventory of the website and application viewers, programmatic ads provide a significant edge over competitors. Google and Facebook serve as the gold standard for efficient and effective advertising and are geared to words providing a user-friendly platform that will allow non-technical marketing companies to start, run and measure their initiatives and campaigns online.
Visual Search and Image Recognition
Leaps and bounds of the advancements in artificial intelligence-based image recognition and analysis technology have resulted in uncanny visual search functionalities. With the introduction of technology like Google Lens and platforms like Pinterest, people can now find results that are visually similar to one another using the visual search functionality. The visual search works in the same way as traditional text-based searches that display results on a similar topic. Major retailers and marketing companies are increasingly using the visual search to offer an enhanced and more engaging customer experience. Visual search can be used to improve merchandising and provide product recommendations based on the style of the product instead of the consumer’s past behavior or purchases.
Major investments have been made by Target and Asos in the visual search technology development for their e-commerce website. In 2017, Target announced a partnership with interest that allows integration of Pinterest’s visual search application called “Pinterest lens” into Target’s mobile application. As a result, shoppers can take a picture of products that they would like to purchase while they are out and about and find similar items on Target’s e-commerce site. Similarly, the visual search application launched by Asos called “Asos’ Style Match” allows shoppers to snap a photo or upload an image on the Asos website or application and search their product catalog for similar items. These tools attract shoppers to retailers for items that they might come across in a magazine or while out and about by helping them to shop for the ideal product even if they do not know what the product is.
Image recognition has tremendously helped marketing companies to gain an edge on social media by allowing them to find a variety of uses of their brand logos and products in keeping up with the visual trends. This phenomenon is also called “visual social listening” and allows companies to identify and understand where and how customers are interacting with their brand, logo, and product even when the company is not referred directly by its name.
With the increasing availability of healthcare data, big data analysis has brought on a paradigm shift to healthcare. The primary focus of big data analytics in the healthcare industry is the analysis of relationships between patient outcomes and the treatment or prevention technique used. Big data analysis driven Artificial Intelligence programs have successfully been developed for patient diagnostics, treatment protocol generation, drug development, as well as patient monitoring and care. The powerful AI techniques can sift through a massive amount of clinical data and help unlock clinically relevant information to assist in decision making.
Some medical specialties with increasing big data analysis based AI research and applications are:
- Radiology – The ability of AI to interpret imaging results supplements the clinician’s ability to detect changes in an image that can easily be missed by the human eye. An AI algorithm recent created at Stanford University can detect specific sites in the lungs of the pneumonia patients.
- Electronic Health Records – The need for digital health records to optimize the information spread and access requires fast and accurate logging of all health-related data in the systems. A human is prone to errors and may be affected by cognitive overload and burnout. This process has been successfully automated by AI. The use of Predictive models on the electronic health records data allowed the prediction of individualized treatment response with 70-72% accuracy at baseline.
- Imaging – Ongoing AI research is helping doctors in evaluating the outcome of corrective jaw surgery as well as in assessing the cleft palate therapy to predict facial attractiveness.
Big data analysis, in coordination with Artificial intelligence, is increasingly running in the background of entertainment sources from video games to movies and serving us a richer, engaging, and more realistic experience. Entertainment providers such as Netflix and Hulu are using big data analysis to provide users personalized recommendations derived from individual user’s historical activity and behavior. Computer graphics and digital media content producers have been leveraging big data analysis based tools to enhance the pace and efficiency of their production processes. Movie companies are increasingly using machine learning algorithms in the development of film trailers and advertisements as well as pre-and post-production processes. For example, big data analysis and an artificial intelligence-powered tool called “RivetAI” allows producers to automate and excellently read the processes of movie script breakdown, storyboard as well as budgeting, scheduling, and generation of shot-list. Certain time-consuming tasks carried out during the post-production of the movies such as synchronization and assembly of the movie clips can be easily automated using artificial intelligence.
Marketing and Advertising
A machine learning algorithm developed as a result of big data analysis can be easily trained with texts, stills, and video segments as data sources. It can then extract objects and concepts from these sources and recommend efficient marketing and advertising solutions. For example, a tool called “Luban” was developed by Alibaba that can create banners at lightning speed in comparison to a human designer. In 2016, for the Chinese online shopping extravaganza called “Singles Day,” Luban generated a hundred and 17 million banner designs at a speed of 8000 banner designs per second. The “20th Century Fox” collaborated with IBM to use their AI system “Watson” for the creation of the trailer of their horror movie “Morgan.” To learn the appropriate “moments” or clips that should appear in a standard horror movie trailer, Watson was trained to classify and analyze input “moments” from audio-visual and other composition elements from over a hundred horror movies. This training resulted in the creation of a six-minute movie trailer by Watson in a mere 24 hours, which would have taken human professional weeks to produce.
With the use of Machine learning, computer vision technology, natural language processing, and predictive analytics, the marketing process can be accelerated exponentially through an AI marketing platform. For example, the artificial intelligence-based marketing platform developed by Albert Intelligence Marketing can generate autonomous campaign management strategies, create custom solutions and perform audience targeting. The company reported a 183% improvement in customer transaction rate and over 600% higher conversation efficiency credited to the use of their AI-based platform.
In March 2016, the artificial intelligence-based creative director called “AI-CD ß” was launched by McCann Erickson Japan as the first robotic creative director ever developed. “AI-CD ß” was given training on select elements of various TV shows and the winners from the past 10 years of All Japan Radio and Television CM festival. With the use of data mining capabilities, “AI-CD ß” can extract ideas and themes fulfilling every client’s individual campaign needs.
There are several cities throughout the world that are working on predictive analysis so that they can predict the areas of the town where there is more likely to be a big surge for the crime that is there. This is done with the help of some data from the past and even data on the geography of the area.
This is actually something that a few cities in America have been able to use, including Chicago. Although we can imagine that it is impossible to use this to catch every crime that is out there, the data that is available from using this is going to make it easier for police officers to be present in the right areas at the right times to help reduce the rates of crime in some of those areas. And in the future, you will find that when we use data analysis in this kind of manner in the big cities has helped to make these cities and these areas a lot safer, and the risks would not have to put their lives at risk as much as before.
The world of transportation is able to work with data analysis, as well. A few years ago, when plans were being made at the London Olympics, there was a need during this event to handle more than 18 million journeys that were made by fans into the city of London. Moreover, it was something that we were able to sort out well.
How was this feat achieved for all of these people? The train operators and the TFL operators worked with data analytics to make sure that all those journeys went as smoothly as possible. These groups were able to go through and input data from the events that happened around that time and then used this as a way to forecast how many people would travel to it. This plan went so well that all of the spectators and the athletes could be moved to and from the right places in a timely manner the whole event.
Risk and Fraud Detection
This was one of the original uses of data analysis and was often used in the field of finance. There are many organizations that had a bad experience with debt, and they were ready to make some changes to this. Because they had a hold on the data that was collected each time that the customer came in for a loan, they were able to work with this process in order to not lose as much money in the process.
This allowed the banks and other financial institutions to dive and conquer some of the data from the profiles they could use from those customers. When the bank or financial institution is able to utilize their customers they are working with, the costs that had come up recently, and some of the other information that is important for these tools, they will make some better decisions about who to loan out money to, reducing their risks overall. This helps them to offer better rates to their customers.
In addition to helping these financial institutions make sure that they can hand out loans to customers who are more likely to pay them back, you will find that this can be used in order to help cut down on the risks of fraud as well. This can cost the bank billions of dollars a year and can be expensive to work with. When the bank can use all of the data that they have for helping discover transactions that are fraudulent and making it easier for their customers to keep money in their account, and make sure that the bank is not going to lose money in the process as well.
Logistics of Deliveries
There are no limitations when it comes to what we are able to do with our data analysis, and we will find that it works well when it comes to logistics and deliveries. There are several companies that focus on logistics, which will work with this data analysis, including UPS, FedEx, and DHL. They will use data in order to improve how efficient their operations are all about.
From applications of analytics of the data, it is possible for these companies who use it to find the best and most efficient routes to use when shipping items, the ones that will ensure the items will be delivered on time, and so much more. This helps the item to get things through in no time and keeps costs down to a minimum as well. Along with this, the information that the companies are able to gather through their GPS can give them more opportunities in the future to use data science and data analytics.
Many businesses are going to work with the applications of data analytics in order to have better interactions with their customers. Companies can do a lot about their customers, often with some customer surveys. For example, many insurance companies are going to use this by sending out customer surveys after they interact with their handler. The insurance company is then able to use which of their services are good, that the customers like, and which ones they would like to work on to see some improvements.
There are many demographics that a business is able to work with and it is possible that these are going to need many diverse methods of communication, including email, phone, websites, and in-person interactions. Taking some of the analysis that they can get with the demographics of their customers and the feedback that comes in, it will ensure that these insurance companies can offer the right products to these customers, and it depends one hundred percent on the proven insights and customer behavior as well.
The healthcare industry has been able to see many benefits from data analysis. There are many methods, but we are going to look at one of the main challenges that hospitals are going to face. Moreover, this is that they need to cope with cost pressures when they want to treat as many patients as possible while still getting high-quality care to the patients. This makes the doctors and other staff fall behind in some of their work on occasion, and it is hard to keep up with the demand.
You will find that the data we can use here has risen so much, and it allows the hospital to optimize and then track the treatment of their patient. It is also a good way to track the patient flow and how the different equipment in the hospital is being used. In fact, this is so powerful that it is estimated that using this data analytics could provide a 1 percent efficiency gain, and could result in more than $ 63 billion in worldwide healthcare services. Think of what that could mean to you and those around you.
Doctors are going to work with data analysis in order to provide them with a way to help their patients a bit more. They can use this to make some diagnoses and understand what is going on with their patients in a timely and more efficient manner. This can allow doctors to provide their customers with a better experience and better care while ensuring that they can keep up with everything they need to do.
Data analytics and some of their applications are a good way to help optimize the buying experience for a traveler. This can be true through a variety of options, including data analysis of mobile sources, websites, or social media. The reason for this is because the desires and the preferences of the customer can be obtained from all of these sources, which makes companies start to sell out their products thanks to the correlation of all the recent browsing on the site and any of the currency sells to help purchase conversions. They are able to utilize all of this to offer some customized packages and offers. The applications of data analytics can also help to deliver some personalized travel recommendations, and it often depends on the outcome that the company is able to get from their data on social media.
Travel can benefit other ways when it comes to working with the data analysis. When hotels are trying to fill up, they can work with data analysis to figure out which advertisements they would like to offer to their customers. Moreover, they may try to utilize this to help figure out which nights, and which customers, will fill up or show up. Pretty much all of the different parts of the travel world can benefit when it comes to working with data analysis.
Outside of just using it to help with some searching another, there is another area where we are able to see data analytics happen regularly, and this is digital advertisements. From some of the banners that are found on several websites to the digital billboards that you may be used to seeing in some of the bigger and larger cities, but all of these will be controlled thanks to the algorithms of our data along the way.
This is a good reason why digital advertisements are more likely to get a higher CTR than the conventional methods that advertisers used to rely on a lot more. The targets are going to work more on the past behaviors of the users, and this can make for some good predictions in the future.
The importance that we see with the applications of data analytics is not something that we can overemphasize because it is going to be used in pretty much any and all of the areas of our life to ensure we have things go a bit easier than before. It is easier to see now, more than ever, how having data is such an important thing because it helps us to make some of the best decisions without any issues. However, if we don’t have that data or we are not able to get through it because it is a mess and doo many points to look at, then our decisions are going to be based on something else. Data analysis ensures that our decisions are well thought out, that they make sense, and that they will work for our needs.
You may also find that when we inefficiently handle our data, it could lead to a number of problems. For example, it could lead to some of the departments that are found in a larger company so that we have a better idea of how we can use the data and the insights that we are able to find in the process, which could make it so that the data you have is not able to be used to its full potential. Moreover, if this gets too bad, then it is possible that the data will not serve any purpose at all.
However, you will find that as data is more accessible and available than ever before, and therefore more people, it is no longer just something that the data analysts and the data scientists are able to handle and no one else. Proper use of this data is important, but everyone is able to go out there and find the data they want. Moreover, this trend is likely to continue long into the future as well.