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Thursday, October 4, 2018

7 examples of retail information for big data

Big Data is one of the main words of fashion. But, unlike malicious words like "marketing Omnichannel" or "growth piracy," the big date is greatly undervalued. According to IBM, 62% of retailers report that using big data is giving them a serious competitive advantage. Knowing what your client wants and when you want, can be available at your fingertips with big data; All you need are the right tools and processes to use it. Let's explore 7 innovative examples of big data customization in the retail industry to inspire us.

Macy's: the traditional department store is ahead of its time

This luxury department store has a long history of providing excellent customer service and has become a household name. Despite the inheritance established since the opening of the first store in 1858, the brand passed into the digital age as a fish in the water.

Macy's uses big data to deliver a smarter customer experience. The brand analyzes various data points, such as inventory levels and price promotions, and combines these findings with the inventory unit data of a product in a given location, as well as customer data, to determine which products are on sale at each store. This ensures that the chosen products conform to customers' shopping habits at each location.

In addition, Macy's collects customer data that range from the frequency of visits to style preferences. This data is used to customize the customer experience, offering point-of-sale incentives with loyalty prizes and promotions. This data also allows you to send direct mail to your customers to generate conversions.

The Amazon shopping recommendation mechanism.

Amazon, the heavyweight e-commerce, has dominated its recommendation mechanism, but its functionality is quite simple. The algorithm is based on a user's purchase history, the items they already have in their cart, the items they rated or liked in the past, and what other customers saw or purchased recently. In fact, it has been reported that over 35% of all Amazon sales are generated by the recommendation mechanism, a testament to the importance of product recommendations.

The main reason for the mechanism of recommendations is to address the "long tail problem": the fact that rare or obscure elements are generally not sought after and therefore do not generate revenue. By recommending long tail items to buyers, you can seriously increase the return on investment potential of slower e-commerce lists.

Kohl's

Kohl's is a brand with large data plans. This brand has recently seen a 2.4% drop in sales, along with a drop in buyer traffic, and the brand's CEO has seen close to 1,100 stores. However, in a change of heart, the brand decided to implement new technologies to optimize its shopping experience and make the stores smaller. To achieve this, he invested more than $ 2 billion in technology initiatives and big data. Leaving aside product recommendations, the brand has the mission to use Big Data primarily for the benefit of its customers, in addition to making stores more profitable.

The entire online and physical shopping experience is personalized because a visitor accesses the home page and faces deals and products on each page for customized offers that counteract the abandonment of the purchase. Kohl also uses its big data to create customized marketing campaigns that were produced with customer data in mind. The brand now plans that data science will help with marketing allocation, including external data such as macroeconomic conditions and social data, which will determine which products will be stored. This will ensure that the products leave shelves more quickly.

Mall of America navigator chatbots

IBM provided the Mall of America with a chatbot called E.L.F to help shoppers navigate the vast complex. The Mall of America is located in Bloomington, Minnesota, and is the largest commercial complex in the northern states. It is home to 520 shops, 50 restaurants, 14 cinemas, 2 hotels, an indoor theme park and a museum.

ELF. You can create personalized shopping itineraries for each customer, finding the right experience for them (depending on your needs). A chatbot is operated by a simple interface similar to a text messaging platform. ELF. It is available through the Facebook Messenger application, the browser page, or the Mall of America kiosks.

Nordstrom: merging the online and offline shopping experience

This luxury retailer has mastered the use of big data to merge shopping experiences online and offline. Nordstrom's marketing team tracks Pinterest pins to identify which products are trends and then uses those data to promote the right products in their physical stores.

More than 30% of Nordstrom's budget is spent on technology, having established the Seattle-based "Nordstrom Innovation Lab" for product development and testing. In addition, Nordstrom hosts interactive touch screens in locker rooms to allow customers to order products and view inventory online.

TopShop

TopShop has been experimenting with new technologies to implement augmented reality in their shopping experience since 2010. Flagship stores have virtual assembly rooms where customers can select clothing to see how they would look on a screen. This saves the customer time and effort to try on their own clothes.

In 2015, TopShop partnered with Twitter to analyze real-time data on the social network and identified the trends as they occurred during the London Fashion Week event, which lasted five days.

These trends were grouped into posters with Twitter hashtags, so customers who pass by can send a hashtag to their TopShop account indicating their favourite products. The fashion retailer responded with a curated collection of the best selections.

This novel use of big data ensured that TopShop knew exactly what its customers wanted to buy after the London Fashion Week.

IKEA

The Swedish interior giant IKEA presented the recognition of images and augmented reality for the first time when it showed its catalog 2013. Customers can scan the catalog with their mobile devices to highlight the products that interest them and, from this, the brand offers personalized digital content and comments to inform your purchase. The brand also used image recognition technology, with which customers can scan items from the catalog and place them virtually in their own homes to see what they would look like. Then, they can select the colours and sizes that work best in space, without having to go to the store and buy the product. This allowed catalog readers to make informed purchases, which resulted in greater customer satisfaction and fewer returned items.

These innovative uses of big data really improve the customer experience and have the potential to increase sales. You do not have to be a big player in retail to use big data. You can use it yourself to get ahead of your competitors, especially if you use a Shopify storefront. This platform is integrated with Blendo, a big data analysis plugin. Add-ons and applications can be very useful ways for you to collect and extract data from multiple sources to inform your business decisions.

Wednesday, October 3, 2018

Artificial Intelligence and fusion of augmented reality for new business solutions

In today's world of technology, artificial intelligence and augmented reality come together to create a new world of business processes.

Big Data continues to shape the corporate universe in unexpected ways, especially when artificial intelligence and augmented reality come into play. A few years ago, most experts believed that large data would only be used to handle some functions:

• Use of new data models to improve marketing.

• Improve supply chain logistics by collecting data on various points in the supply chain.

• Increased security through the use of data to improve threat detection models

Since then, several new big data applications have been discovered. One of them is augmented reality. Virtual reality exists long before the term Big Data becomes popular. However, virtual reality systems were not very sophisticated or realistic.

Big Data has helped these systems evolve. New augmented reality algorithms keep detailed information about real-world systems, so simulations are much more subtle.

Big Data creates new augmented reality systems

Earlier this year, the IEEE published a paper on the intersection of big data and augmented reality. The authors summarize the fascinating ways in which the big date is changing the field of augmented reality:

Augmented reality (RA), less popular with virtual reality (VR), is becoming popular in business applications. What started out largely as a gaming technology is expanding into software, health, education, wearables, retail and other fields. Augmented reality coupled with virtual reality has reached maturity and these technologies are responsible for innumerable modern and intriguing technological advances. Both technologies are collectively labelled as extended reality or "XR". Experts believe that the combined XR market is expanding rapidly and is currently valued at about $ 10 billion and is estimated to reach approximately $ 61 billion by the year 2022.

What is the difference between AR and VR?

For laymen, augmented reality and virtual reality may seem synonymous. However, the two technologies have several distinct characteristics, especially when it comes to their applications in business. With virtual reality, users are immersed in a totally artificial digital environment. On the contrary, augmented reality is a technology that overlays virtual content in a real environment. This means that in AR, the user sees and interacts with the real world by adding digital objects.

RA applications for the average user

For the average user, the AR can be tested on a smartphone, through an AR application or through a special phone. For example, think about Pokémon Go and how millions of people around the world have been frantically searching for small virtual creatures. This is one of the most realistic examples of AR applications for the average consumer.

Big Data and AR Enterprise Applications

Several companies are beginning to recognize the benefits of using augmented reality. A case study conducted by Cognizant highlights some of them and emphasizes the importance of using AR tools, such as Meta Vision's Microsoft Holo Lens.

For the retail industry, AR allows customers to interact or interact with brands and products without the need to leave the comfort of the interior. The AR offers a potential for retries without visiting the physical store. Consumers can try out various AR products: clothing, watches, shoes and jewellery without leaving the comfort of their homes. This only means that AR ads have the potential to be powerful tools to drive sales and increase business revenue.

To enhance the experience of buying a car, AR applications can be designed to allow potential customers to try out a virtual car in their garage. These RA applications allow customers to get an idea of the vehicle by opening the doors, looking in and walking around the vehicle in the same way they would have done on the floor of a showroom. AR can give consumers the sensation experienced by a person playing an attractive video game. This helps cultivate emotional connections to the product, increase brand awareness, and ultimately encourage the customer to make a purchase.

In a nutshell, RA can help companies connect with customers in a variety of ways, including intellectual and multisensory engagement as well as emotional connections. The good news about AR technology is that AR enterprise applications will not necessarily require separate devices to work. Instead, users can only use the devices they already have, such as smartphones and tablets. The challenge is for companies and companies to create and maintain a stable and secure AR environment in their IT network. This may require advanced IT support skills. If you are thinking about harnessing RA technologies to enhance your customers' experiences and increase your commitment to your business and brand, check out Utah IT Support for a variety of possible services to support your AR vision.

Augmented reality and Big Data are changing business in a variety of ways

Augmented reality is a growing technology that is transforming companies in many ways. This is only possible with new big data advances. As Big Data and AR continue to evolve, companies will benefit from the new technology.

Tuesday, October 2, 2018

Use of data to drive the success of marketing automation

Introduction

When studying the changes, trends and trends in marketing over the last five years, it is obvious that the trajectory points firmly towards automation, customization and efficiency. Almost any new marketing, platform or product technology emphasizes one or more of these focal points. And if you study it more closely, you'll also notice that many of the breakthroughs in marketing focus on the big data and the use of analysis.

Actually, 2014 was not so long ago. In the field of marketing, which is a field in rapid evolution, it seems that it was years ago. Then, when industry experts made great predictions of a heart in the heart of every digital marketing strategy. At that time, I had big data on one side and marketing automation on another. There was not a lot of crossing. Nowadays, they are practically inseparable. The gap has closed and is now highly dependent on each other.

The automation of big data and marketing works in a single way to provide companies with more effective ways to systematically collect and organize marketing strategies. It allows marketers to eliminate guesswork and tailor messages to make them more accurate. This saves time, improves results and generates a better commitment to the brand.

As Oktopost explains, "Big Data is no longer a way to improve marketing automation; It is the only way to make it work effectively. However, B2B marketers are automating tasks without taking into account the buyer's behaviour and preferences, they are automating inefficiencies.”

Use of data to drive marketing automation

"Studies show that 60% of occupations can save 30% of their time with automation, which leads to more time for innovation and growth of the company," says ONTRAPORT. "The key to a successful automation strategy is discretion in determining what to automate and what not to."

As your company seeks to follow the right marketing automation processes and solutions, you will need to be aware of where you are drawing the line. In addition to determining what needs to be automated, be careful and not automate.

Here are some thoughts on the subject:

1. Capture the correct data.

The correct data can not be used. This means that a documented attack plan is needed on how to collect the data and what metrics it will look for. More specifically, you need a method to extract your data and select the important pieces in the middle of the irrelevant details. Just like trying to find a needle in a haystack, this is difficult without a plan.

"With the omnipresence of analytical tools like Google Analytics, the problem of data creation, but of its accessibility," writes expert Louis-Philippe Mathieu. "Identifying data that will be relevant to your automation processes can be useful in these places in the right place and build powerful processes with minimal effort."

2. Use automation to maintain communication.

The data have to do with 53 percent of marketers find with continuous and personalized communication with customers is now useful to create a moderate and significant revenue impact for their business.

Because manual communication with each client is highly inefficient and unrealistic, it can be used to automate these long-lasting relationships. When data is strategically leveraged to send automated messages that are triggered by certain predefined factors, it can be a greater loss, greater revenue and more involvement in marketing content. Ultimately, all this paves the way for future sales.

3. Implement triggers in real time.

With marketing, you do not always have multiple opportunities to reach your audience. If you want to increase your chances of seeing meaningful results, you should try to use the data to automate triggers in real time as clients to action on the function of their individual experiences.

The marketing of automation without customization is ineffective. In the event that the users of different users connect to your brand and then use the time to create triggering responses that are statistically coming from the user's database.

For example, if the user visits your site for the first time and chooses in the email form, send them to a right email. If the user also visits one of their social media profiles within the next 24 hours, they have their CRM platform activated that sends a message. The staff was very friendly and helpful.

Big Data simplifies monitoring of children in an era of new security concerns

Introduction

The "strange danger" hysteria of the late 1900s did more harm than good. Subsequent data found that children were hundreds of times more likely to be abducted by a family member than by a stranger. Unfortunately, this blinds people to the real issues facing children in the 21st century. The good news is that parents finally become more aware of the problems. Several child monitoring companies have used Big Data to develop new solutions to help parents keep their children safe.

Child supervision becomes more popular as larger data enhances tracking capabilities

Parents expressed growing concerns about the safety of their children. Although most parents are not as paranoid as abductees as they used to be, they are constantly afraid of the well-being of their children. They are especially concerned about the types of content their children are exposed to online and with the people with whom they are related.

Parents are turning to more technologically sophisticated child monitoring strategies. According to a survey conducted by the Pew Research Center, 16% of parents use tracking tools to track their children's location and content they access online.

There are several ways in which Big Data helps parents control their children's activity more easily.

Need more data that can be stored in the long-term

Monitoring solutions for older children had much more limited data storage capabilities. This meant that it was almost impossible to store exhaustive data about your online behavior patterns, text messages, or the places you visited. They could only see small snapshots of their activities, such as a list of visited sites.

Because the newer tools rely more on big data, they can store much more detailed records. This gives parents a much clearer understanding of their children's activities, both online and offline.

Big data expanded the contextual understanding of parental control tools

When parents controlled their children's activity in the 1990s, they relied on obsolete tools like Net Nanny. The problem is that these tools were very hard hit or confusing. In general, they worked using one of two controls:

• They would correspond to domains that children tried to access online against a blacklist of inappropriate sites.

• They would activate a censoring algorithm if the user tried to access the site with certain restricted words.

There were important limitations of both approaches. The problem with the first approach was that the list of sites with inappropriate material grew exponentially every year. It was impossible for tools like Net Nanny to continue and add them to the blacklist. Even in 2013, 80% of parental controls did not block all offensive sites.

The problem with the other approach was that it often caused positive failures. There were perfectly legitimate reasons for the sites to have certain phrases that might seem controversial without any important context. A parental control solution blocked a page in breast cancer research.

Big Data helped reduce false positives and false negatives that were common to other parental control solutions. Modern parental control solutions have deep learning capabilities that allow them to understand what types of content are controversial and unsuitable for children. They get better at censoring adult content without the need for a blacklist.

Use predictive analytics to identify possible concerns that may not appear in digital records

Child monitoring applications have a lot of information. You can see what messages your child sent on social networks and what places they visited. However, you cannot always tell the whole story. A free phone tracker can help parents see that their child was in Parking A on the other side of town at 11 pm Friday night. However, he will not tell them who they were visiting.

New monitoring solutions capable of assessing possible risks, based on the activities of the child. They can look closely at the data to see if they are visiting areas that neighborhood children frequent for drugs.