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CRM Automation: How can you elevate your Customer Relationship Management?

CRM Automation How can you elevate your Customer Relationship Management

The automation of companies has become a trend of our time. The modern market has set its own rules. The perfect product alone is no longer enough. In addition to the ideal product, customers also demand impeccable service. If a good product and competitive pricing are no longer enough to gain customers’ loyalty, then a personalized experience will have to be your differentiator. The competition is very strong, so a modern entrepreneur should pay close attention to customer management. Because one thing that 2021 has taught us, is to add good service to even the best product. Thus, automation is one of the most effective ways to effortlessly streamline your business.

 

The corona pandemic has further accelerated existing trends in automation. More and more companies want to automate their processes with the help of their CRM. The trend of collecting, unifying, and transforming customer data is becoming difficult and time-consuming without a proper data integration tool, but new offerings are finally making it possible for small and medium-sized businesses to do the same. One of the biggest benefits of automation is that instead of manually filling out all the documents, your team can let the software do it automatically. This gives them the opportunity to spend their valuable time on more important things. The software is programmed to work flawlessly, saving you a lot of problems and headaches and making the choice between tradition and automation obvious. CRM is becoming more and more synonymous with automation. The global CRM market is poised to reach about $113.46 billion by the end of 2027 (Globe Newswire, 2021).

 

As CRM takes care of a significant portion of the work processes; you don’t need to hire a big team to do the job but some entrepreneurs are still afraid to introduce the innovation because they are not sure if this solution really works and will be accepted by their team. Along with the fear of change, often companies don’t have an integrated approach to contact information and use different customer relationship management solutions that don’t communicate with each other. Without data integration tools, the process becomes difficult and time-consuming. In such a situation, employees need to log into multiple systems, download multiple sets of data to create their own unified customer database.

 
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On the other hand, the organizations which have successfully adapted to the new situation, use new tools and technologies to review and adapt their sales process to close more deals. Businesses that implement CRM automation experience an average of a 451% increase in qualified leads. (Annuitas Group). The CRM system, allows them to manage all contacted data of their customers and business partners in a structured and clear manner. They can record interactions and collect all important information about them along the customer journey, the journey from lead to purchase. Also, by bringing valuable data together, they are able to better understand and respond to the needs of their customers. Without electronic support, this mass of data cannot be handled at all.

 

Software for consolidating customer data easily connects disparate silos so data can be shared across systems for trend analysis, better decision-making, and greater customer satisfaction. For instance, using chatbots for communicating with customers, automating helpdesk tickets, or using automated email workflows to assist prospects in the sales process, customer management automation is on the rise. This automation enables companies to provide quality customer service while optimizing operational costs. The tools for this are also becoming easier and cheaper, which is why this is becoming more and more interesting, even for smaller companies.

 

However, it’s very important to choose software that is suitable for your team. Many CRMs look too mysterious but work as simple appointment schedulers. Therefore, the demand for quality software is very high among both small and medium-sized businesses. Well-designed software is user-friendly, which makes it easier for managers to explain the importance of the new software to their employees. It should definitely be done, and in this case, it’s not complicated.

A fully-integrated CRM system offers significant benefits but you must look for implementation options, scalability, adaptability, business value, and, of course, best value for money. The focus must always be on the individual requirements and needs of your company and the associated tasks, but also on employees who will use the system in the future.

The Data Modelling Techniques for BI

The Data Modelling Techniques for BI

Business applications, data integration, data management, data warehousing and machine learning – they all have one common and essential component: a data model. Almost every critical business solution is based on a data model. May it be in the areas of online trading and point-of-sale, finance, product and customer management, business intelligence or IoT, without a suitable data model, business data simply has ZERO value!

 

Data models and methods for data modelling have been around since the beginning of the computer age. A data model will remain the basis for business applications for the foreseeable future. In the area of ​​data modelling, the basics of mapping complex business models are developed. In order to model data successfully, it is particularly important to understand the fundamentals and relationships between the individual topics and to reproduce them using examples. Data needs a structure, without it, it makes no sense and computers cannot process it as bits and bytes.

 

What is the business intelligence and why is it important?

 

The concept of business intelligence first appeared in the 1960s. Business intelligence, also known as BI, is a collective or generic term for the various sub-areas of business analytics, data mining, data infrastructure, data visualization and also data tools. In summary, BI analyses all the data generated by a business and makes reports, performance measures, and trends that helps management in decision making.

 

BI is essential when it comes to optimizing business processes and positioning yourself successfully for the future. As the goal of BI is to provide you with company data from all of your company areas, so can use it for the company’s efficiency & increase productivity and react to changes in the market. With business intelligence, you are able to identify and evaluate data and ultimately react to achieve goals.

 

Data modelling techniques – an overview

 

The following is an overview of the various data modelling techniques:

    • Flat data model: in this very simplest database model, all data is in a single two-dimensional table, consisting of columns and rows. Columns are assumed to have a similar types of values and in the row, elements are supposed to have relational value to one another.

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    • Hierarchical model: data is stored in a tree-like structure. Data is store in a root or top-level, directory that contains various other directories and files.

 

    • Network model: This model is very similar to the hierarchical model but the hierarchical tree is replaced by a graph. In this model, the records are connected to each other and their allocation takes place via a link table. In this manner, the hierarchy is maintained among the records.

 

    • Relational model: This model represents the database as a collection of relations. A relation is nothing but a table of values. A predicate collection over a fixed set of predicate variables, the possible values ​​or combinations of which are subject to restrictions.

 

    • Star schema model: A star schema is a database architecture model where one fact table references multiple dimension tables, optimized for use in a data warehouse or business intelligence.

 

    • Data Vault Model: Entries with long-term stored historical data from various data sources, which are arranged in and are related to the hub, satellite and link tables. At the core, it is a modern, agile way of designing and building efficient, effective Data Warehouses.

 

Accelerated Automation: RPA as a Service (RPAaaS)

RPA – Robotic Process Automation – is the automated processing of business processes by digital software robots. This technology beings its support in clearly structured, repetitive, and rule-based processes and tasks that are carried out by people. With RPA, companies can implement a level of precision in the processing of tasks that is often unattainable by human alone. In this robot-controlled process automation, the bots take over the roles and tasks of users and interact with other software systems. Almost half of all processes in companies can be automated with the help of RPA.

 

RPA as a Service is the easiest and fastest way to use the advantages of Robotic Process Automation (RPA). RPA as a service means companies can take advantage of the many benefits of RPA without the upfront cost, inconvenience, and maintenance of buying their own RPA technology or hiring in-house RPA developers. RPAaaS offers a selection of out-of-the-box RPA services and tools accessible through a monthly subscription in order to meet the needs of every business for quick and cost-effective intelligent automation.

Accelerated Automation RPA as a Service RPAaaS
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Every organization has different needs, timelines, and budgets, therefore, RPAaaS solves the problem of a lack of in-house development resources not only by providing the development platform but also by offering bookable, qualified RPA experts, such as, RPA business analysts, RPA developers, or RPA architects. Thanks to the model of resources that can be booked as required, load peaks can be flexibly balanced and the schedules for scaling bots can adhere. The main objective of RPAaaS is to reduce development and deployment costs and quickly and effectively meet the need for effective and reusable automation components.

 

The relocation of automation assets to the cloud opens up a way for financially constrained companies to tackle the planned business process automation or to scale existing automation approaches without having to hire qualified automation personnel and also by eliminating the need for infrastructure and maintenance.

 

Business continuity, supporting a distributed workforce or digital transformation, whatever the business goal is, with RPA as a service, a company does not need to purchase its own servers, licenses, or professional services. RPAaaS is therefore also suitable for companies that only plan to use it for a limited period of time. Because in this case, there is no point in purchasing expensive RPA technology that will no longer be used after a certain project has been completed.

Automation myths debunked: Why is Automation important for your business?

Hardly any company that strategically pursues their company growth can get around automation today. Automation enables tasks that were previously slow, manual, old-fashioned, and time-consuming to be supported with suitable software and thus run independently. As a human error can be unpredictable and happen when you least expect it, with the right technology companies’ processes are more accurate and faster. Use cases of automation are, for example, employee onboarding, analyzing reports on transactions, monitoring bookkeeping activities regularly, customer service, databases updates, sending personalized emails, perform inventory, etc.

Business processes automation can not only be used to gain efficiency. Availability of modern technology, as well as enhanced software applications, have made it easier to increase employee efficiency and you can get better results when you embrace automation. A win-win situation for companies and employees.

 

But despite these benefits, there are still myths surrounding automation that keep companies from getting started. Even though automated processes create positive changes, still, many companies fear high costs, difficult implementation, and staff changes – but these are just prejudices that we would like to address here and thus show that every company can benefit from automation.

 

Automation is a complicated and complex process

Hmmm, yeah. Not if it’s done right. As is often the case, good preparation is half the work. So, before starting with automation, make sure you understand what your company’s expectations are. Your decision to automate must depend on your needs, capacity to build it, and also your customers’ requirements. Specific goals can be developed using your personal business case. This step is essential so that automation succeeds and creates benefits for the company.

These requirements should also be discussed in-depth with different automation tool providers instead of falling for fancy advertising promises or the cheapest subscription. It is advised to meet with different process automation providers for not only choosing the right tool, but also to evaluate your own requirements.

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The future is automated

‍Automated processes bring a lot of advantages in operational processes, such as improved operational efficiency, long-term cost reduction, better customer service, visibility & transparency, and an increase in productivity. However, if a company only carries a small range of products, stores a manageable amount of goods, and generally only offers a small storage capacity, there is no need to implement a fully automated system. In such cases, manual or partially automated solutions that grow with you are the better options. Companies then have to weigh up whether small order quantities can be processed more efficiently in this way.

 

Automation is killing jobs

Nowadays it is constantly stated that automation is accompanied by a huge burden of unemployment. With the increased use of machines and automated processes, the fear of reducing or replacing staff increases. It’s true that automation is impacting various jobs in different sectors around the world. Due to automation, human intervention is certainly reduced in a business process. For instance, from production to planning, everything can be controlled by artificial intelligence or machine intelligence. It is easier to bring accuracy into the production process and increase overall productivity with machine intelligence. Every company wants to reduce the number of its employees as much as possible through technological improvements. But that does not mean that we are heading towards an unemployed society in years to come. As the machines are performing tasks previously done by humans, companies are busy transforming and redesigning jobs in a way that can make technological elements compatible with human capital development. The future workplace is where humans and machines will enhance each other’s strengths by working side by side.

 

Existing systems prevent the integration of new solutions

Automation doesn’t happen overnight – Companies are constantly faced with the challenge of proper integration of a set of services related to automation and ensuring that all expectations are aligned with business goals. Integrating your automation initiatives successfully is impossible without a flexible, scalable infrastructure. Therefore, on-premise infrastructure must be avoided/limited because of its limitation in terms of automation roll-out, scalability, and ease of use. For this purpose, cloud solutions are ideal as they let you get straight to work without wasting your valuable time on on-premise setup and maintenance.

 

So, now that you know that automation is here to stay, and can help you better run your business, it’s a safe bet that such automation can be trusted and utilized. By taking into account the myths discussed in this article, and learning the truth about each, you’ll be able to run your business more effectively.

IT automation – Top Technology trends to look for Business Transformation

IT automation is one of the biggest IT trend terms in recent years. This involves a wide variety of solutions that give IT specialists more freedom in their day-to-day business. “IT automation is certainly a worthwhile goal because it saves a lot of money through lower personnel costs” – Tony Iams, Managing Vice President at Gartner. Yet IT teams in small and medium-sized companies often struggle with budget constraints and a shortage of skilled workers. When the demand for IT services increases, they are therefore heavily overloaded and look for ways to increase efficiency.

The 2020 IT Operations & Strategic Priorities for IT Executives report from Kaseya shows that IT automation is a critical need for SBMs, 60% of IT executives worldwide plan to invest in IT automation in 2021. IT automation is their primary strategy for doing more with less. With IT automation being a top priority for businesses today, IT teams are now applying it to a wider range of business and IT functions. Here below technologies are particularly efficient:

IT Automation Technologies for Business Transformation

 

Robotic Process Automation (RPA)

Robotic process automation (RPA) is changing the way companies operate around the world. It is well known that with Robotic Process Automation (RPA) you can optimize many processes and save time and money. An immense number of processes are still carried out manually in companies and authorities, although this is actually not necessary. RPA tools use software bots that simulate human-computer interaction to automate routine tasks. RPA is rapidly growing thanks to the benefits it has to offer such as lower labour costs and less human error. RPA bots usually do not require any software customization or deep system integration. According to Gartner, global RPA software revenue is projected to reach $ 1.89 billion in 2021, up 19.5% from 2020. It goes without a doubt that RPA is accelerating digital transformation.

 

Implementation of RPA

Despite the numerous advantages this technology has to offer, most businesses struggle with successful RPA implementation. The use of Robotic Process Automation (RPA) promises a high increase in process efficiency but also brings huge implementation challenges if it’s not seen as a central building block on the way to hyper-automation but just another technology. According to a study by Deloitte, many companies have found that scaling RPA is more difficult than expected. Larger RPA implementations, when the number of bots is more than 50, often take longer and are more complex and costly than companies originally expected. Particularly when introducing Robotic Process Automation, attention must be paid to a strong strategy, right expertise, and communication with employees for the realistic expectations of Robotic Process Automation, because the software robot should automate entire processes or at least make work easier through partial automation. A lack of acceptance makes the implementation of corresponding RPA projects more difficult and leads to distrust and displeasure among employees. Rather, if the employees actively participate in the development of the robot, particularities in the processing of the process can be emphasized. Positive communication also creates trust and motivates the workforce to contribute their own ideas.

 

Predictive analysis

As the name suggests predictive analysis techniques include machine learning, data mining, and predictive statistical modelling to use historical data from various operational sources to predict the likelihood of future events. It’s an emerging technology that is helping companies acquire and retain their most profitable customers and grow the customer base to improve operations. They also help companies predict future customer needs, business needs, human resource requirements, and process improvements that they should make to their operations. It’s being used by multiple companies to improve the efficiency of production and operations, reduce risk and fraud, and create better customer experiences by mapping the likely journey of customers and the expected touchpoints. It can also improve the accuracy of supply chain management, and help organizations create marketing plans with more precision, knowledge and confidence. This technology is also helping IT security experts to identify potential vulnerabilities, determine the likelihood of cyber-attacks and work on improving the company’s security structure.

 

Artificial Intelligence (AI)

AI refers to the ability of a machine to display human-like capabilities such as reasoning, problem solving, learning, planning and creativity. By using machines are programmed to think like humans and mimic their actions. The AI ​​system is fed by sensors in machines, ERP systems, customer relationship management systems and even Internet data. Alexa, Siri, Cortana – the chatbots from Amazon, Apple and Microsoft are examples of how artificial intelligence makes the interaction between humans and computers easier and more efficient. Business software manufacturers are increasingly using intelligent algorithms to make life easier for their employees and customers.

 

Hyper automation

Basically, the term hyper-automation covers the combination of different technologies. From a global perspective, the focus is still on the automation of simple and complex tasks, but the combination of different technologies such as PRA, ML, and IBPMS ensures more efficient processes. In addition, there is no need for human action. Hyper-automation contributes to higher productivity in the company. According to Gartner’s, List of Top Strategic Technology Trends for 2021 – Hyper automation is a key trend that digital strategy teams should consider.

 

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2021: Ensure Your Business Growth by Becoming Data-Driven Company

ensure Your Business Growth by Becoming Data-Driven Company

 

In 2021, government agencies and businesses will need to be able to make decisions based on current/real-time data faster and more accurately than before. Because: due to COVID-19, markets, supply chains and customer behavior have changed in recent months, only data-driven businesses are able to respond quickly and effectively in a rapidly changing world. In order to transform into a data-driven business, it’s not only important to understand the importance of data quality and governance. But it’s also key to drive a data strategy that is aligned with your business strategy. By integrating analytics into business strategies, businesses can transform data into decisions that improve lives and results.

 

A study of more than 3,500 business executives and senior IT decision-makers across the UK, France, Germany, and the Netherlands found a gap between companies using data to inform decisions during the pandemic and those who are not. The YouGov survey, commissioned by Tableau, asked executives of small, medium, and large businesses about their use of data during the pandemic, lessons learned, and confidence in implementing long-term business change. For executives in data-driven companies, a majority (80%) believe they had a key advantage during the pandemic.

 

These leaders are also deeply committed to the important role data plays in the future of their business. A large majority of 76% plan to increase investments in data literacy; especially after the long bumpy ride we have all been on since the start of 2020. Additionally, 79% are confident that they will ensure business decisions are supported by data. The results show that non-data-driven companies are slower to grasp the meaning of data in these uncertain times. Only 29% see this as a key benefit and 56% say they will reduce or stop investing in data skills. Additionally, only 36% are confident that the data will support business decisions.

 

“This year has accelerated change for businesses and ushered in a fully digital world faster than anyone could ever have imagined. Data is at the heart of this digital world,” said Tony Hammond, Vice President Strategy and Growth EMEA. at Tableau. “In this age of data, our research shows that data-driven companies see clear benefits and are more confident about the future of their business. As a result, they really rely on the power of their data. Companies that haven’t woken up to it run the risk of falling behind. But businesses big and small can rest assured that it’s not too late to harness the power of data – the time is now.”

 

When asked how it helps to be data-driven during the pandemic, company leaders recognized several benefits. At the top of the list are: more effective communication with employees and customers (42%), the ability to make strategic business decisions faster (40%) and improved collaboration between teams for decision making and problem-solving (36%).

 
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“We started building data skills in our company in 2013, and due to the pandemic, we have definitely benefited from these functions,” explains Dr. Dirk Holbach, Senior Vice President and CSCO Laundry & Home Care at Henkel, one of the world’s leading consumer goods and industrial companies. “For example, within a few days, we were able to record all of our personal protective equipment controls so that each facility can see how we are equipped in this regard so that our business can continue to operate. I am confident that we will take some good lessons with us in the future, especially when it comes to working together. “

 

For all respondents, the key takeaways from the pandemic are: the need to be more agile (30%), prioritize and implement projects faster (26%), and access to more accurate, up-to-date, and cleaner data (25%). Jay Kotecha, the data scientist at full grocery brand Huel, said of his data strategy: “Our data-driven strategy helps the company respond to consumer behavior and enables us to pivot and react faster and more clearly. It’s about empowering the entire organization through data. Employees examine data from across the company and turn it into insights we can act on, whether it’s sales projections, sales effectiveness, or marketing spend. “

 

Across Europe, the results show that just over half (56%) of business leaders consider their companies to be data-driven, while one in three (38%) think they do not. These results indicate a clear way for organizations to leverage data to support business resilience and decision making during this time. German companies are taking the lead with 62% as their business is data-driven, while the UK lags behind with just 46%.

 

The promise of digital change is based on the ability to harness the power of technology to grow your business, open up new markets, and acquire new customers. It also means that you need to understand all of the data (the digital exhaust – the trail of data left behind by browsing the Internet) that new customer experiences create. A data governance strategy as well as information and data quality management as an integral component of management systems significantly supports the achievement of the organization’s goals, ensures compliance conformity, increases throughput, and supports organizations in the transformation to a data-oriented culture. Organizations thus secure their competitiveness and can expand this further through increased data intelligence.

 

Sources:

Data-driven companies are more resilient and confident.

Data-driven businesses vastly more optimistic – research

How can Digital Marketing benefit from Artificial Intelligence?

AI in Digital Marketing xorlogics

 

AI is now more accessible than ever with it’s potential to change business forever. With its positive impact, and being a lot more affordable than before, both big and small companies are able to benefit from the insights and automation options it provides. With the integration of AI brands can not only leverage customer’s data but also anticipate their customer’s next move, understand sales cycles better, correlate their strategies for converting prospects into paying customers and improve the overall customer’s journey with machine learning efforts.

 

As a subset of AI, machine learning involves the analysis of historical data from various business interactions with customers / prospects (for example: when and what was the last time the customer ordered?). As ML has the ability to analyse extremely large sets of data, its being used in the digital marketing departments around the globe to identify sales patterns and increase success factors. Algorithms for ML generate insights via predictive analytics, based on these insights, marketers can either take actions individually or automate AI to do the job. For example, they can automate emails that are aimed at re-targeting your audience, giving you a better chance of a higher ROI.

 

ML is also playing a major role in SEO. Even tough SEO algorithms change across major search platforms, with AL and ML tools, the insights from searchable content may become more relevant than specific keywords in the search process. In order to maintain a high-ranking place on search engine result pages, consider the quality of your content rather than simply the keywords included. By doing so, you’ll be ahead of the game when it comes to future-forward content creation and SEO.

Marketers can also benefit from ML tools to analyse what type of content, keywords, and phrases are most relevant to your desired audience. Once they have the key insight, they can optimize their dialogue and develop engagement across multiple online platforms, to drive brand awareness and create meaningful relationships with leads, prospects, and customers alike

 

The key difference between modern AI-based and traditional outbound marketing strategies is the integration of contextual data, i.e. information that results from the interaction with the customer is not stored in labour-intensive and time-consuming data warehouses but is available in real time and can be used for decision-making. With these customer profiles and interests recorded in parallel to the interaction, the previously defined decision strategies are fed in order to achieve high planning security for high, profitable conversion rates.

 

Artificial intelligence opens up possibilities in marketing domain far beyond currently available functions. PwC estimates that business could save $2 trillion globally by applying intelligent automation to many activities that were previously processed by humans and making employees more productive. In addition to that, AI, robotics and other forms of smart automation will bring great economic benefits and contribute up to $15 trillion to global GDP by 2030.

 

With AI integrated marketing, business can forecast customer behaviour and run data-based campaigns to have remarkable results. It helps them to save a lot of money on marketing and sales efforts by bringing them valuable leads. Not only business can achieve valuable leads and turn them into customers but they can also maintain a good relation and provide a better service to these customers by introducing a 24/7 customer service with the help of AI equipped chatbots. These chatbots are able to handle customer enquiries and provide customer support on time and appropriately, based on the needs of customers. Business are creating value through transforming customer journeys by providing immediate response to consumer’s queries or issues. According to McKinney’s study, 75% of customer demand NOW service within 5 minutes of online contact. If business can beat this time, they can convert a ‘visitor’ into a ‘paying’ customer.

Here below are some interesting statistics of AI in marketing:

 

  • According to research from Callcredit, 5 hours and 36 minutes is the amount of time that the average marketing professional spends collating data and getting it ready for presentation. An AI integrated marketing dashboard can do the collating in minutes and give you more time for reporting process. Statwolf
  • 97% of leaders believe that the future of marketing lies in the ways that digital marketers work alongside machine-learning based tools. QuanticMind
  • By 2020, 30% of companies worldwide will be using AI in at least one of their sales processes. Gartner
  • By 2020, 85% of customer interactions will be handled without a human. Gartner
  • For 61% of marketers AI is the most important aspect of their data strategy. MeMSQL
  • 80% of business and tech leaders say AI already boosts productivity. Narrative Science
  • When AI is present, 49% of consumers are willing to shop more frequently while 34% will spend more money. PointSource
  • Large businesses with more than 100,000 employees are most likely to have an AI strategy – but only 50% of them currently have one. MIT Sloan Management Review
  • Netflix is saving $1 billion per year by using machine learning to make personalised recommendations- Artelliq
  • 44% of consumers don’t even realise they’re already using AI-powered technology platforms. Pega
  • 45% of end users prefer chatbots as the primary mode of communication for customer service inquiries. Grand View Research

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The ways that ML is being used in digital marketing practices helps organizations to expand their understanding of their target consumers and how they can optimize their interactions with them.  So, when developing appropriate AI applications for marketing purpose, the collaboration between software developers and domain expert’s must not be neglected. The ultimate decision-making competence when it comes to the all-important question of which next best actions are to be implemented in order to achieve the greatest possible marketing success usually belongs to the domain experts. You should have the experience and expertise to incorporate the right NBAs into the self-learning models for decision-making strategies.

 

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RPA – Robotics Process Automation Trends and Statistics for 2020

RPA Robotic Process Automation XORLOGICS

 

This year, many companies found their business growth through the smart interaction between human resources and supporting software robots as the direction is set towards digitalization and automation. The combination of RPA with artificial intelligence (AI) and machine learning (ML) is playing a huge role in the global economic environment.

According to a new report by Grand View Research, Inc. the global robotic process automation market size is expected to reach USD 10.7 billion by 2027, expanding at a CAGR of 33.6% from 2020 to 2027. In addition to that, in Gartner Top 10 Strategic Technology Trends for 2020, hyper-automation, autonomous things and AI security were at the top 3.

 

RPA is a type of IT solution that allows organizations to automate many of their tasks through the use of specialized software programs. Many business executives believe that RPA enables their companies to automate structured tasks that take just a few work steps and repeat themselves frequently – like transferring data from one IT system to another. RPA can relieve employees of tasks that consume a lot of time but do little to add value and are prone to errors – keyword typing errors. It also helps to automate routine jobs on the computer and processes to increase efficiency, improve service and save costs.

 

One should not think that RPA is similar to BPM. RPA only simulates human data entry and helps with simple and recurring tasks, to save time because the software does things in parallel that an employee can only work through one after the other. Such an application needs simple rules with few exceptions – and the biggest advantage is that, unlike humans, it runs around the clock. Unlike BPM which stands for describing, contolling, modeling, and optimizing the processes that are present in an organization.

 

Here below are the statists and trends of RPA that shows it’s worth:

 

    • RPA as an industry is growing exponentially– the global robotic process automation market size is expected to reach USD 10.7 billion by 2027, expanding at a CAGR of 33.6% from 2020 to 2027 – Grand View Research

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    • The RPA industry will grow from $250 million in 2016 to $2.9 billion in 2021. This is one industry that is growing at a lightning speed. It was already worth $1.7 billion in 2018 – Forrester

 

    • RPA will achieve “near universal adoption” in the next 5 years – Deloitte

 

    • By 2025, the market for collaborative robotics is expected to reach $12 billion – MarketsAndMarkets

 

    • By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes. Gartner

 

    • The RPA fast adoption is helping business to reduce the operational costs and enhance overall customer satisfaction, improve transparency and visibility for service functions and reduce of manual efforts – Reportlinker

 

    • 11,214 results This is the number of open positions produced by a recent search for “robotic process automation”on LinkedIn’s jobs site. Titles vary within this growing IT jobs category, but “RPA developer” (and variations of the same) is an increasingly common one – reflecting the need for IT pros who can build the bots that enable organizations to offload repetitive, time-consuming tasks – LinkedIn

 

    • RPA deals with the application of advanced technologies including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans – Gartner

 

    • RPA is offering a lot of benefits to the business by giving access to collaborative intelligence where humans and technology works side by side so that they can perform their roles optimally. As employees don’t need to perform repetitive tedious tasks, they can be educated to work with automation tools and learn the latest business and marketplace information through machine learning – Xorlogics

 

    • The market for RPA in Healthcare is driven by the increasing demand to automate claims and process management. RPA vendors are focusing on developing best-in-class intelligent process automation bots – Research and Markets

 

Demand Forecast Powered by Machine Learning

Demand Forecast Powered by Machine Learning

 

The business landscape is rapidly becoming more global. Largely due to improvements in communicationsand increasing globalization which are dramatically impacting the way business is managed. No area of a business is more affected by the trend of a global business environment than the supply chain. Supply chain logistic, known as the backbone of global trade, is a network of many partners involved such as customer, dealers, manufactures,transportation, external warehouse,suppliersand inventory. Sometimes a delivery comes along with delay, sometimes there is something wrong in a package, delivered article is different to ordered article and sometimes a shipment is lost. This is annoying for all sides. It costs time, energy, money and sometimes even the customer. Challenges for decision-makers in supply chain management are growing due to the widely networked supply chains and the constant change in the environment of companies.

 

In fact, many companies are facing hurdles in their existing business processes and technologies that aren’t flexible enough to deal with “large and global” business environments. Therefore, areas such as manufacturing, distribution, sourcing of materials, invoicing and returns are impacted by the increased integration of a global customer and supplier base.

Supply Chain specialist must deals with long-term planning in terms of location, make-or-buy decisions, supply relationships, capacity dimensioning, logistics strategy and general tasks along with cost optimization in structuring of the logistics and production processes. Hence, in order to initiate the demand forecasting, it’s highly recommended to understand the workflow of machine learning modeling. This offers a data-driven roadmap on how to optimize the development process.

 

Operational inefficencies in SCM often lead to potential revenue losses, increasing costs, and poor customer service, ultimately diminishing profits. With the help of AI, machine learning techniques are able to forecast the right number of products or services to be purchased during a defined time period. In this case, a software system can learn from data for improved analysis. Only good data produces good results!

Data interpretation is a vital part of supply chain management and demand forecast as it’s used to improve your ability to estimate future sales, reduce shortages and overstock. Once the data is interpreted correctly, both in national and international trade results in having the right products at the right time in the right number at the right place.
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So, for demand forecasts that are generated by self-learning algorithms require data that is closely related to sales. However, in order for machine learning to achieve a high quality of forecasting, a certain amount of quality data is required. The result of ML process depends solely on the quality and quantity of data provided.

To ensure that the data is up to date, the input data should not be older than 5 years. Data selection can be a special hurdle before using machine learning methods, because it can be very time-consuming. In connection with the data quality, it must be ensured that there are only a few missing values of the data records in the input data, otherwise the machine learning model may generate incorrect results. Data preparation is necessary for successful implementation and definitely pays off later. If the data record does not have sufficient data quality, it must be prepared through an intensive process and carry sufficient information for qualitative algorithms and for a good forecasting performance.

 

The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Therefore, ML fed with qualitative data can generate precise forecasts and thus ensure a secure basis for planning. The resulting benefits, such as reducing inventory levels and simultaneously optimizing the ability to deliver, also improve the operating result. ML uses learning algorithms to recognize patterns and regularities in data and is able to adapt automatically and independently through feedback and thus react to changes.

 

Compared to traditional demand forecasting methods, machine learning not only accelerates data processing speed but provides a more accurate forecast, automates forecast updates based on the recent data in order to create a robust system.

AI and Automation impact on medium-sized Companies and Organizations

 
The Business Benefits from AI and ML
  • Massive Data Consumption from Unlimited Sources
  • Rapid Analysis Prediction and Processing
  • Simplifies Time-Intensive Documentation in Data Entry
  • Handling Repetitive Jobs
  • Reduction of Error
  • Improves Precision of Financial Rules and Models
  • Easy Spam Detection
  • Interpret Past Customer Behaviours
  • Better Customer Segmentation and Accurate Lifetime Value Prediction
  • Recommending the Right Product
  • Digital Assistance and 24/7 Availability

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Hyper Automation and Artificial Intelligence (AI) are seeping into every aspect of business, from operations to production and from product development to business processes. Processes, tasks and role models – everything is changing with this revolutionizing in the commercial sector. This evolving area of Artificial intelligence (AI) and machine learning (ML) is the future for all businesses, and it’s already affecting the way we live and work today. In fact, research firm Markets and Markets estimates that the machine learning market will grow from $1.41 billion in 2017 to $8.81 billion by 2022!

 

The new digital technologies are having different impacts on jobs in the short and longer term. While automation may displace workers within a sector in the short term, studies are demonstrating that overall employment is growing when looking at a longer period comparing results from different sectors and the World Economic Forum has found that, by 2022, algorithms and other transformative technology could create more jobs – as many as 58 million. Because despite automation and artificial intelligence, people will continue to be important in the future.

 

Artificial intelligence opens up possibilities far beyond currently available functions. PwC estimates that business could save $2 trillion globally by applying intelligent automation to many activities that were previously processed by humans and making employees more productive.

 

Like business of all sizes, SMB are also flooded with data gathered from different sources such as, customer data, supplier data, market data, operations data and many more. AI can quickly pull out insights from these databases and extract valuable data. Managers can get valuable insights on business process from workflow management and/or make sales predictions easily by using data from ERP systems. As AI is able to pull out insights from data easier than human workforce and run the routine tasks to add value to existing business, company can use its human resources to solve potential problems, spend time on creative tasks and boost business ability to innovate.

 

SMBs can also benefit from AI to identify, understand and better target their customers. Business can forecast customer behaviour and run data-based campaigns to have remarkable results with the use of big data and artificial intelligence. It helps them to save a lot of money on marketing and sales efforts by bringing them valuable leads. Not only business can achieve valuable leads and turn them into customers but they can also maintain a good relation and provide a better service to these customers by introducing a 24/7 customer service with the help of AI equipped chatbots. These chatbots are able to handle customer enquiries and provide customer support on time and appropriately, based on the needs of customers.

 

Not only in customer service, artificial intelligence is demonstration it’s capacities in accounting, finance, auditing and tax controlling, fraud detection as well. Instead of recurring routines, data analysis, process control and optimization by automating procedural tasks would be on the agenda of SME by allowing employees to make even more valuable contribution to corporate management. As AI can decipher data and draw actionable insights easily from complicated situations and eliminate/reduce errors, it’s totally transforming and changing the way accounting and tax firms used human intelligence to perform same tasks. With an accurate integration of AI empowered workforce, accountants and bookkeepers can build a value-based business, attract more customers and increase revenue by spending less time that they were used to spend.

 

Customer service and accounting are not the only 2 departments getting aids from AI, sales and marketing are also getting their potential customer’s pipeline faster than ever before by finding interesting leads in their databases as contact information is available for the market you want to target.
By engaging each potential lead in a very personalized way, sales person can turn the lead into a paying customer and recommend the right product with the help of gathered data on the customer.

 

Sources:

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