All of today’s businesses are digital, in one way or another. The benefits of cloud-based services are evident for both small-scale and larger businesses. So much so, that it has been the go-to solution for most due to its accessibility, convenience, and high availability. Most cloud computing providers also provide specific services without the need to invest in an entire platform. This makes it a practical alternative to larger and more expensive on-premise systems that can be challenging to maintain in the long run. Online businesses and eCommerce websites get the most benefit from the cloud as it provides the software and tools needed at a fraction of the cost.

There is still something to be said about old technology or what is commonly referred to as “legacy systems.” Case in point: the mainframe. Despite being a technological marvel from decades ago, it holds up until today by providing computing power that rivals, and often outperforms, today’s more modern systems. It’s not surprising to hear the concept of mainframe modernization when discussing digital transformation because the mainframe remains a staple for larger enterprises that require its power to process enormous amounts of data each day.

The banking and eCommerce industries still rely on mainframes because it’s able to process 2.5 billion transactions each day. Mainframes handle almost 90% of credit card transactions globally and are integrated into online banking and payment systems.

“Old” is Not “Obsolete”

Data is considered the lifeblood of artificial intelligence (AI), and companies are constantly looking for ways to manage data and wield its power for the benefit of both consumers and businesses. There is, however, a great source of valuable data that’s often overlooked. Mainframe systems hold data that go back decades—data that can be used to enrich what’s available today. The main issue is compatibility; most modern technologies aren’t compatible with mainframes and working with them could pose challenges along the way.

The benefits far outweigh these challenges, though, and the integration of artificial intelligence (AI) and automation has proved beneficial in many aspects of business, including inventory monitoring, data mining, and robot automation. This is why the mainframe remains relevant, with its power that enables it to run high-value business logic. Mainframe modernization will empower businesses to combine the power of the old with the innovation of the new.

The modernization of a tried-and-tested system will allow it to be integrated into newer systems, leading to long-term business benefits, including the following:

1. Modernization of legacy stacks

Many critical business applications face compatibility issues because they were written using COBOL code. Mainframe modernization integrates these applications with modern distributed applications by making them available from modern API’s.

2. Reduced costs of MIPS (million instructions per second)

The cost is one of the many reasons why companies prefer more modern solutions over mainframe systems. Modernizing mainframe systems does away with this dilemma by offloading transactions into a distributed data fabric so hardware and software licensing costs are reduced.

3. Seamless cloud integration

The mainframe and the cloud are not mutually exclusive, and believing so leads to a host of missed opportunities. Allowing them to coexist lets each system leverage the strengths of the other, helping in the creation of a private cloud environment.Mainframe systems also possess many of the cloud’s best characteristics, including large amounts of memory, huge storage capacities, and workload virtualization capabilities.

4. Creation of business value

Cognitive automation is a main feature of mainframe modernization that combines AI processes like machine learning, natural language processing, data mining, and emotion recognition to automate business processes. This also helps in improving business processes cycle times and driving quicker deployment of new applications and features.

5. Increased flexibility of systems

Integrating legacy systems with modern solutions gives them the power to be more nimble and easily scalable. Through mainframe modernization, common maintenance issues are addressed and costs are reduced while relevant system updates help manage regulatory compliance and security risks. 

The Mainframe of the Future

The cloud is undeniably one of the best digital solutions available today, but it’s a mistake to think that it’s the only feasible one. Ironically, what’s heralded as the cloud’s best and most innovative features have been present and in use for decades via mainframe systems. It won’t be too far-fetched to think that the mainframe is “the cloud before the cloud,” since it’s one of the technologies that gave way to the cloud computing known today.

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To leverage the power of real-time analytics, businesses have been rethinking their approach to the gathering and operationalizing of data. The data fabric is one of those approaches that shows great potential of becoming a mainstream solution. Data fabrics allow you to “visualize” your data in a way that data lakes don’t. Seeing your data in motion allows you to keep track of data and make it simpler to migrate them to another platform.

What is a Data Fabric?

A data fabric simplifies integration of data management and processing across on-premise, cloud, and hybrid systems. Even if data is stored in multiple locations and always in motion, business applications can access data securely through a consolidated data management platform. Data fabrics allow integrated computing components to both work independently of each other when necessary and allows for complete data visibility. Thi, in turn, helps provide actionable insights, ensures data security and integrity, and makes overall data control more efficient.

As data volume grows and the technologies that harness it evolve, the data fabric will become an indispensable tool in enabling the digital transformation of an organization. The data needs and the ways a business changes because of these needs will vary, but data management will be less complex with the help of the insights provided by a data fabric. It’s highly adaptable and an ideal solution to addressing the ever-changing computing needs of forward-thinking companies. It helps enable digital transformation by making data accessible to end-users whether data is stored on the premises, in the cloud, or within a hybrid environment.

Using in-memory computing technology, a data fabric is able to offer the following features:

• Single-point access to all data regardless of data structure and deployment platform
• A common process for all data and deployment platforms via a centralized service-level management system
• Consolidated protection for all data, including data backup, security, and disaster recovery
• Unified data management through a single framework
• Cloud mobility for fast migration between cloud platforms

From Data Fabric to Data Mesh

Larger companies mean larger volumes of data. As such, these companies resort to multiple applications to gather and process the data they need. The challenge here is making sense of all the structured and unstructured data gathered and processed in silos from disparate sources, including cloud storage, transactional databases, data warehouses, and the like. A data fabric helps stitch together current and historical data from these silos without the need to replicate all the data into another repository. Through a combination of data virtualization, management, and integration, a unified semantic data layer is created, which helps in accelerating data processing and other business intelligence processes.

A static data infrastructure can only do so much. As the data fabric becomes more dynamic, it has developed into what is referred to as a data mesh. A data mesh is a distributed data architecture that’s supported by machine learning capabilities and follows a metadata-driven approach. It distributes ownership of domain data across business units so data is owned at the domain level. These domain datasets are then made available to the different teams within an organization. A data mesh provides dynamic data pipelines, reusable services, and a centralized policy of data governance.

For end-users, the most beneficial aspect of a data mesh is its data catalog, a centralized discovery system that’s available for all users. It allows multiple teams to access a single data catalog to obtain critical information that’s indexed in this centralized repository for quick discoverability. Data consistency is also maintained across domains by allowing for interoperability and delivering standards for addressability between domain datasets.

The Business Case for Data Fabrics

High-growth firms have seen an increase in data requests in recent years, with market data, social media data, and marketing and CRM data at the top. This is followed closely by financial data, risk and compliance data, operational data, and macroeconomic data. This trend shows an increasing reliance on data and analytics to gain fundamental insights that can be used to create new strategies and business models. Companies strive to gain better business outcomes through modern technology, but most of them fail to anticipate the technical feasibility and constraints that a digital transformation entails. This is why investing in in-memory analytics solutions like a data fabric is a sound business investment—one that will more than pay off in the long run.

Today’s business applications fall under three main categories based on how they produce and consume data:

• Legacy applications that serve a single function and are often on-premises
• Data warehouses that act as a middleware repository for frequently accessed data
• Cloud-based platforms and integrations that require the most data to serve a host of use cases

The road to being a data-driven business begins with establishing an efficient and secure connection between these three layers. This is where the data fabric comes in. A data fabric is designed to provide an architecture for better data governance between interconnected systems. Whether an organization uses an on-premises database, cloud-native platform, or a hybrid analytics platform, a data fabric eliminates the need for moving data from one location to the other. This ensures that data remains intact and minimizes bottlenecks by reducing data movement to and from disk and within the network.

Conclusion

Digital transformation used to be a term for tech companies that relied on modern technology to ensure business success. This definition, however, has become the typical definition for all companies that are trying to make it in this constantly evolving data-driven landscape. Big data has become “bigger data,” and it shows no signs of slowing down.

As data is gathered, regardless of platform, it will be decentralized and distributed. Without a unified data management strategy, collected data will be useless, its potential reduced to meaningless ones and zeroes. A data fabric helps navigate the complexity of data processing and management by providing a clear visualization or“big picture” view of an organization’s data in real time. There’s no need for complex codes with a data fabric’s pre-packaged connectors that allow connection to any data source without the assistance of developers and data analysts. This brings data closer to the user and the costs down to a very reasonable amount.

Understanding the architecture of data systems is vital in the digital transformation of a business, but it should be something that makes the process easier instead of harder. The integration of a data fabric in your data systems and strategy will do just that.

The post How a Low-latency Data Fabric Enables Digital Transformation appeared first on Tech Web Space.

Unfortunately, data alone can’t provide the insights or benefits businesses seek; without the means or tools to properly manage and transform data, its full potential can’t be fully realized.

In today’s data-driven business landscape, there’s no excuse to not have a solution in place for data analysis and management. The availability of modern systems and methodologies has allowed companies in different industries to harness the power of data to predict and, to a certain degree, control business outcomes.

Customer experience has a key component in keeping customers engaged and coming back for more. Customers want “instant everything” and if a company can’t provide, they will look elsewhere.

For organizations with large amounts of data stored in multiple systems, access to pertinent operational data can be a source of slowdowns and request delays. This is where a modern operational data store (ODS) comes in. By unifying the API layer, it decouples applications from systems of record to ensure always-on services. High user concurrency is no problem with an ODS, with its in-memory speeds and short application response times.

Even if your data is stored in legacy systems, an ODS will help you migrate data to the cloud, effectively being one of the drivers of your organization’s digital transformation.

How to Be a Big Player in the “Experience Economy“

In 1998, the term “experience economy” was coined to refer to a phenomenon that shows consumers spending money on experiences rather than physical products.

Experiences are linked to events, and these are more memorable than any product a customer can buy. Even the value of a product is linked to how that certain product makes a customer feel or what a product signifies. The experience economy shifts the focus from goods or services into what effects these have on the life of the people who buy them.

It’s a similar case when it comes to how consumers experience your brand. Companies , especially startups, must ensure that they provide the best customer experience possible. Aside from looking to veteran companies, startups should also consider how they manage their data to provide the best experiences. Al and machine learning can empower data analytics to quickly sort through large amounts of data and determine what’s useful to the business.

Having an ODS in place additionally helps with quick data access; by putting operational data at the forefront where users can easily access it, it helps the organization focus on the data services that are required to deliver executive operational insights rather than on the minor details of integration and migration.

A number of companies employ an API-first platform, which can pose challenges when a company begins to scale. The main challenge with this platform is unpredictable usage; because client applications are deployed to several end users, there is a tendency to subject API’s to high-load conditions.

This could lead to major performance issues, including round-trip latencies from client application requests to responses from backend systems and scalability problems with backend systems because they are not designed to serve high-concurrency repetitive requests.

Enter the ODS

An ODS can act as an organization’s “digital integration hub,” acting as intermediary for a data warehouse so that frequently accessed operational data resides closer to the user.

It also helps applications by providing a high-throughput, low-latency in-memory database in between backend systems and the API management layer. With this high-performance database, companies have the power to do the following:

• Use the in-memory database as a data cache for requests from API’s that return data to analytical systems. Processing in the database allows for quick turnaround times compared to referring back to backend applications.
• Use cached data for database requests to avoid placing too much load on systems of record and overloading backend applications.
• Reduce residual load on databases and backend applications by handling most of the requests from the database.
• Every request handled through the database using cached data reduces the load on the back-end applications and other systems of record, preventing them from becoming overloaded.

Startups looking to optimize business processes and improve outcomes can do so by maximizing what value can be obtained from modern data architectures like an ODS. By rethinking how they approach data analytics and management, imagination can bring innovation and provide added value to the business.

The ODS is just one way of changing the game when it comes to data management. For organizations to completely harness the power of data, a digital transformation should be considered—and the ODS is the ideal first step toward that milestone.

The post Get Your Startup Up and Running With an Operational Data Store appeared first on Tech Web Space.

This doesn’t necessarily mean business is booming for all eCommerce businesses, but it’s a taste of what’s to come as the channel becomes one of the most feasible channels in light of the current pandemic-driven landscape.

As more people flock to eCommerce websites and online service providers, website performance has become a main focus for most businesses today. Consumers want instant gratification, and they expect a website to completely load and give them what they need in two seconds or less. In fact, the probability of users abandoning a website increases by 32% if it loads a second more, and up to 90% if a page takes five seconds to load.

The bar for website loading speeds has become higher through the years, and near-instantaneous loading times have become an expectation for consumers. It not only results in satisfied customers but also puts your website or brand on a high level of professionalism in the perception of your customers.

Even search engines like Google have updated their algorithms to include website speed as a ranking factor. 

For the eCommerce industry, optimizing website speed is one of the most beneficial and cost-effective investments a business can make because it can lead to increased sales and a more efficient sales process. Even a few seconds of delay can lead to lost business and customer churn.

It may not seem like much at face value, but calculate how much your business earns per second and you’ll immediately see how valuable each second can be.

Why Caching is Important

In-memory caching is a tried-and-tested way of boosting website performance, especially in this age of instant everything. Ultimately, quick loading times are dependent on website architecture, and RAM has always played a major role in any server environment.

Using RAM is especially important in eCommerce, where websites handle a large number of transactions on a daily—or even hourly—basis. Logically, more RAM equates to a faster website that can serve a larger number of customers.

As the business grows, it’s therefore required to purchase more RAM and CPUs for the server hosting the website. This can lead to infrastructure costs that can increase exponentially, depending on the business’s rate of growth.

Obviously, this isn’t something that’s sustainable in the long-term, and there will come a time that this business model of continuously scaling up will reach a breaking point. On the other hand, customers will not wait for a slow website to load and will take their business elsewhere if they’re not satisfied with something as simple as website speed.

This is where caching comes into play; it addresses high network consumption and high CPU utilization by introducing a caching mechanism into the server that will store (cache) all new requests coming in. Caching effectively reduces data movement to and from disk and within the network so that the only requests that go to the disk are those that aren’t yet in the caching database.

The use of a caching mechanism on the server also minimizes the amount of RAM needed to boost overall performance of the website and improves the stability of web applications, leading to increased server uptime.

Without caching, online stores will suffer slowdowns because they are dependent on the chosen eCommerce platform server, which reloads data whenever a request is received, even if that request is a simple page refresh. This constant reloading of data wastes time and puts unnecessary strain on the web server that could lead to unexpected downtimes and slowdowns.

Caching a web page means reloading of data isn’t necessary, removing the load from the server and only doing a hard refresh when there’s a request for data that’s not yet in the caching database. Ultimately, what you can achieve with caching is a faster website that can serve more requests from customers without experiencing slowdowns or going offline completely.

Conclusion

Faster websites mean happier customers, and because pages load faster, customers visit more pages on the website. This helps improve website rankings on search engines and leads more people to your website, increasing the chances of conversion and transforming first-time customers into long-term patrons. To realize the value of caching, think of a request to the cache as one less request from the webserver and one less request that the server needs to compute and send. This simple process leads to a website that’s ten or even a hundred times faster than one without a caching mechanism in place.

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In-memory solutions have shown that they can provide the fastest processing times and most responsive compute capabilities compared to other solutions available today. This is why digital transformation has been something that companies have been racing to achieve in recent years.

Although in-memory computing has been around for years, there are some organizations still reluctant to adopt the technology for fear of its complexity and the risks involved in migrating to a different platform.

Fortunately, it’s not that complicated to implement in-memory technology to your systems, especially if you’re using instances of Amazon Elastic Compute Cloud (AWS EC2). The benefits of in-memory computing will significantly boost the performance of Amazon Web Services (AWS) workloads while also reducing costs related to cloud resources used.

What is AWS EC2?

Elastic Compute Cloud or EC2 is an Amazon Web Service that provides organizations complete control over their computing resources and the reliability of running their systems on a proven environment. Designed to make it easier for developers to scale computing for the web, EC2 comes with a compute capacity that can be resized according to your needs and a simple web interface that allows for obtaining and configuring capacity with relative ease.

What makes AWS EC2 an ideal alternative is its deep compute platform that’s unmatched by other alternatives in the market today. It provides users the choice of operating system, processor, storage, networking, and purchase model so you can create a system that’s tailored to fit your system requirements.

For those looking to integrate in-memory computing into their AWS ecosystem, Amazon Machine Images (AMI) is a viable solution because it comes with AWS-ElastiCache and its variety of specialized file and database solutions. AMI allows developers to code in-memory capabilities into applications and provide them a reasonable approach to harnessing the potential value of in-memory computing.

The challenge here is that DevOps teams and AWS system administrators are usually not allowed to alter the code of the applications they manage, regardless of whether or not they are responsible for AWS budgets and management of EC2 workload goals.

This is where AMI comes in. To help developers deliver on the promise of the value of in-memory capabilities, they provision AMI’s that can optimize all instances of EC2 with in-memory capabilities for their managed workloads. Developers usually look for pre-configured AMI’s that ensure data persistence and cache consistency when building in-memory AMI’s customized for specific application performance requirements and availability targets.

They may also need to use the operating system’s kernel, the AWS hypervisor kernel, and other AWS resources in the coding of custom caching utilities into in-memory AMI solutions.

Pre-configured vs. Custom AMI’S

Whether you choose to build your own custom in-memory AMI or go to the AWS Marketplace for an in-memory EC2‐optimized AMI, processing speed isn’t the be-all and end-all goal. There are some essential factors that you need to consider:

• Data persistence. If you require data to persist beyond the RAM cache, ensure that your AMI uses EBS-SSD. These automatically replicate themselves within their AWS Availability Zones to provide failover and protect stored data from component failure. Top-tier in-memory AMI’s will also maximize write-concurrency by leveraging the algorithms and hybrid caching options of an EBS-SSD.
• Consistency. There are different consistency considerations when it comes to distributed workloads, and these are vital in minimizing the chances of cached data being inconsistent with its counterpart in the data persistence layer. It’s important to note that there will always be small differences due to latency; however, a good in-memory solution will minimize this issue to a degree that it won’t affect scalability, availability, and performance.
• Single tenant caching. In a virtualized environment, contentions for RAM can overshadow the benefits of in-memory solutions if not addressed immediately. Such contentions should be mitigated at the hypervisor level. To make this possible, a portion of RAM should be dedicated to the deployed in-memory solution.
• Simplicity. Arguably the most important consideration when choosing an in-memory computing solution, reducing the complexity of deployment ensures that the chosen solution’s value is immediately realized across the organization and its spectrum of IT needs, including applications, databases, and infrastructure services.

If you want to choose the path of least resistance, choosing a pre-configured in-memory AMI might be the best option, as it’s already optimized with all the necessary hypervisor and operating system utilities for EC2 instances and is likely the least expensive.

They also make use of other AWS components to provide an efficient in-memory computing platform for EC2 workloads. If, on the other hand, you have unique requirements, building your own solution is best. It may be more expensive and complex, but the investment will be worth it if it addresses concerns and provides use cases specific to your business.

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Fortunately, in-memory data grids are up to the challenge, providing a cost-effective and minimally intrusive scaling solution. An in-memory data grid or IMDG is the key to an organization’s digital transformation, with its high speed and availability without the need to replace existing systems. Operating within a computer cluster, it uses the combined memory and processing power of available computers within the network and distributes the dataset across the cluster nodes to provide faster and more efficient data processing.

Modernizing Applications With In-memory Data Grids

In-memory data grids run specialized software on computers within the network and is inserted between the application and data layers. It moves data on disk into RAM to allow for data processing without the need to repeatedly read and write data from disk-based storage. By doing this, IMDG does away with the usual bottlenecks caused by constantly accessing data from disk.

The four pillars of successful digital transformation ensure that an organization’s systems and applications are set up so that it can handle the complex and heavy workloads of the future. IMDG helps businesses by providing the power needed to achieve these four pillars.

Unparalleled speed.

By storing data in RAM, IMDG provides applications quick and easy access to data across the computer cluster. The platform also makes use of a feature known as “persistent store,” which allows data to reside both on disk and in RAM. By allowing this, frequently accessed data can be accessed quicker through RAM while the rest of the data resides on disk. It also keeps data-optimized so that the amount of data can exceed the amount of RAM.

Easy and flexible scalability.

What makes IMDG a cost-effective solution is the way it’s designed to support horizontal scalability. Traditional computing platforms usually scale vertically, making it a very expensive endeavor to process large amounts of data at scale. The requirement to acquire more powerful hardware and software through time is also an unsustainable model in the long term. With IMDG, scaling a system can be as simple as adding a new node to the cluster. Depending on business needs, nodes can be added or removed dynamically so resources can be allocated elsewhere.

Data security.

An IMDG provides layers of security to protect data because it is a distributed system. Most implementations provide Transport Layer Security (TLS) to secure communication between members of the cluster, “process security” to allow the system to check any new process attempting to join the cluster, data access auditing integrated into Corporate Information Security (CIS) systems, and entry- or row-level security checks to help make sound data-entry access decisions.

Reliability.

Because the IMDG is a distributed platform that allows for elastic scalability or the dynamic addition and subtraction of nodes, reliability is largely dependent on its fault-finding capabilities. IMDG is able to quickly analyze state changes in data and send out alerts so they can be addressed immediately. Data replication is done to one or several nodes so it remains available even if a node is down. IMDG can also be configured for wide-area network replication so data can be copied and accessed seamlessly across data centers. If a data center becomes unavailable, applications can simply access data from another data center.

Why Choose an In-memory Data Grid?

In-memory data grids offer a multitude of features that help in the processing and analysis of large amounts of data. In today’s data-driven world, this is a vital business solution. An IMDG helps manage fast-moving data up to more than 100 times faster compared to disk-based solutions. This helps transform complex data into actionable insights that contribute to smart decision making. Your business needs an IMDG if:

• real-time data and responsiveness is required;
• you need to scale performance of existing applications to support increasing volume;
• you require a flexible architecture that can be deployed on-premises, in the cloud, or in a hybrid environment;
• your existing applications would benefit from a distributed data layer; or
• you’re standardizing your computing platform to reduce the number of technologies in your company infrastructure.

Today’s business landscape demands real-time, near-instant results from processed data, and the amount of data grows exponentially over time. This requires a sustainable, highly available, and easily scalable solution that simplifies application development without compromising on performance.

In-memory data grids ensure low latency and high throughput through the use of RAM instead of disk, and it minimizes data movement to reduce bottlenecks usually caused by disk-based storage. As the cost of RAM continues to decrease, the IMDG becomes more cost-effective and viable as a long-term solution.

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Experts believe that some of the most revolutionary uses of NLP will center on its applications in the field of marketing. As marketing relies heavily on words to convey messages, this is not surprising. In fact, large innovative marketing companies are already relying on NLP techniques. Here are six ways NLP can boost marketing. 

1. Sentiment analysis

Sentiment analysis is one of the capabilities of NLP that’s widely used by marketers and it has many interesting applications. It is sufficiently advanced to be able to give insights into how people feel about a brand. 

For instance, imagine you are talking to a friend about a new laptop you bought. NLP is able to extract the data from what you’re discussing and your sentiment (satisfied, good, bad, etc). A positive or negative sentiment implied in a social media post can help marketers to target people in a more effective way. 

Sentiment and volume of mentions can result in actionable analytics if the data is accompanied by demographic information and calibrated on expected reach. This can lead to solutions like more targeted marketing campaigns and social segmentation.

Someone who tweets that a friend is driving a new Suzuki Swift and he is seriously thinking about buying one is expressing a propensity to purchase. When marketers are able to identify ‘propensity’ signals, this can lead to social prospecting solutions. Social prospecting solutions need NLP capabilities to sift out any passing mentions of a brand and focus on real intent to purchase.

NLP capabilities are going beyond being able to just match keywords and are now able to take into account the context of a mention. State-of-the-art NLP systems are able to extract the handles of people who have shown an expression of interest in a purchase or a brand on a social media platform.

Spark NLP is open-source and offers state-of-the-art NLP libraries and full APIs in Scala, Python and Java. Natural language processing examples include sentiment analysis, entity recognition, automated image processing and much more.

2. Voice search

When it comes to digital services, there’s a barrier to access for people who can’t type or aren’t comfortable typing. Voice search has become increasingly popular and it is estimated that over half of online searches will use voice in the next year or two. This makes voice an important element for marketers going forward. 

Speech recognition is an NLP technology you use every time you ask Siri or other virtual assistants a question. Your spoken words are converted into data a computer is able to understand. 

Natural language generation is the technology you use every time Siri answers your question, and it involves outputting information as a human language. Semantic search means you can ask a natural question without having to formulate it in a specific, unnatural way. 

3. Chatbots

Most websites today have a pop-up chat box on the home page and these chatbots will continue to be an important aspect of digital marketing in 2020. 

NLP can improve the performance of chatbots and customer experience improves as a result of their usability. They can also increase conversions and sales when combined with targeting and marketing psychology. 

Surveys show that Chatbots will be used for 85% of customer service by 2020. Some of the top benefits of chatbots are 24-hour service, instant response to inquiries, and answers to simple questions. Of course, most chatbots are not yet able to respond to more complex queries but they can pass customers on to those who are able to help. 

Retailer Asos reported a 300% increase in orders when using a fashion chatbot they call Enki. 

4. Automated summarization

Going back to the amount of text data faced by marketers every day, information overload is a real obstacle. With automated summarization, it is possible to create short, accurate summaries of longer text documents, thus reducing reading time. 

Companies who produce long-form content such as e-books and whitepapers might be able to leverage automated summarization to break down content and make it shareable on social media. Reuse of existing content in this way saves time. 

Automated summarization of multiple documents can be a powerful tool to gain insight into current trends. 

Automatic summarization can also be an ally for marketers when they want to produce a video script incorporating research from many sources. 

5. Market intelligence

The core of market intelligence is using many sources of information to create a broad picture of customers, competition, the existing market, and growth potential for new products or services. Sources of raw data include surveys, social media, sales logs etc. 

Say, for instance, someone is planning a shopping spree to buy a luxury watch. NLP techniques are able to analyze topics and keywords and segment a user into a specific category. Using this knowledge allows web content to be personalized to the person’s interests, increasing the likelihood of conversion and purchase. 

Personalized and conversational marketing are two trends that are increasingly popular and NLP helps marketers to provide this. As this can be done at scale, it can help marketers to quickly come up with strategies. 

Conclusion

Most marketers are excited about the possibilities of NLP. We are moving into an era when marketing will increasingly rely on leveraging insights from a largely unexploited source of unstructured data. This will enable them to react in real-time to customers and be more proactive with their marketing strategies. 

NLP-powered tools are evolving and providing practical ways to make use of big data in a sustainable and scalable way. Marketers can’t afford to ignore APL if they want to remain competitive and take advantage of the new opportunities it presents. 

The post 5 Ways NLP Can Boost Your Marketing in 2020 appeared first on Tech Web Space.