A simple tip: one should begin by identifying the granular metrics used adaptively for a win-win bargain and optimize promotion as per demand. Let’s evaluate the key metrics for improving marketing strategies and measuring promotion effectiveness.

Important Metrics for Measuring Promotion Effectiveness

In order for your promotional campaigns to work as per your expectations, you need to measure the promotion effectiveness of past campaigns and resultant sales. Such insights are only available when you have certain metrics to measure; a few of which are elucidated below: –

Consider Empirical Data as Metrics for Measurement

Various factors impact the success of any promotional campaign; considering them for further evaluation can help businesses mint money with effective marketing strategies. Hence, careful granular data analysis can shed light on factors that boost or drop sales numbers. There are multiple sources from which such insights can be drawn effectively, such as: –

Tracking performance on a daily, weekly, and monthly basis to identify the change in trends early or account for ambiguous factors, like a change in weather, that can directly impact sales. Such data can help businesses take measures accordingly to ward off any hurdles during campaigns and ensure the effectiveness of promotional activities as per expectations.

Frequent visits from representatives for measuring promotional effectiveness can easily arrest out-of-stock situations at retailers’ end before they occur. Simply put, if your retailers are running out of stock during campaign weeks, your brand stands to lose potential buyers attracted by the campaigns. But reps maintaining healthy communication with the retailers are better positioned to identify such discrepancies before they occur, thereby saving your business from losing customers or brand reputation.

Measure shelf space to keep the flow of customers to your retail outlets steady. Believe it or not, extra shelf space and extra stocks are enough to draw your customers’ attention away from the competitors.

Track and Compare Competitor Market Share

A simple question – why do businesses spend dollars running promotional campaigns? 

Firstly, to spread knowledge about the availability of certain products in the market matching the ongoing demand.

Secondly, to attract potential and prospective buyers.

Thirdly, to expand the market for your products.

Unless owners have zero knowledge about the market share of their business and that of their competitors, creating effective campaigns will result in zero output; forget about measuring the promotion effectiveness. Hence, tracking market share and comparing it with that of the other players is to smoke out competition from the market.

Measure Category Growth

Category growth is as important as the company’s market share. A campaign also works by expanding the market of your products and bringing in a new category of buyers that was never your targeted demography. For instance, a brand selling skincare products does not necessarily target female buyers only. They introduce a new line of beauty products for men to draw them as prospective buyers. Category growth simply means creating demand in the market for new product lines.

Prioritize PoS Data

Businesses often focus on tracking shipment data. How many of your products are sitting in the storage or on the shelves of the retail outlets is not enough to measure promotion effectiveness. Rather, how many products are leaving the store in the hands of your buyers should be the focus area for analyzing campaign effectiveness.

Analyze Incremental Sales as Marketing ROI 

Tracking PoS data also helps understand the additional sales that occurred following promotion campaigns. Monitoring incremental sales against expected figures can help you understand how much extra business your company has made due to a promotion.

The metrics mentioned above are crucial to measuring trade promotion effectiveness only when such insights are available in real time and shared just when needed. Manual tracking and measuring of such metrics are time and labor-intensive. And, before the insights reach your hand, you have already lost a good amount of time and business to your competitors. 

Many businesses today resort to tech-based solutions to speed up the data extracting process and make strategies on real-time inputs to arrest the valuable time lost. 

Measuring Promotion Effectiveness with Machine Learning

If we consider consumer goods companies as an example, statistics suggest that over 500 billion dollars are invested by them in promotion campaigns annually. If the campaigns fail to generate the desired outcome, all the efforts, time, and labor are completely wasted. Hence, measuring trade promotion effectiveness is paramount – something we have already covered earlier on.

The main challenge arises when sufficient data to analyze the effectiveness is unavailable in real-time. This is where new-age technology solutions like AI and ML can make a striking difference.

Many software solutions are present in the market today to address the challenge mentioned above. These platforms leverage machine learning and artificial intelligence to help businesses understand, measure and enhance promotion effectiveness. These tech-based tools offer the following features: –

Data Management: Data is the foundation for making game-changing strategies. Timely availability of granular insights can make all the difference, even while planning promotion campaigns. A tech-based software solution powered by ML capabilities accommodates data acquisition, cleansing, enriching, and harmonizing data across the supply chain. Based on such inputs, campaigns can be planned, executed, and measured for effectiveness.

Casual Modeling: Machine learning-based casual modeling understands promotion performance and identifies empirical factors silently impacting the overall effectiveness of promotions.

Simulation and Prediction Modeling: Both of these consider various ‘what-if’ scenarios to adequately understand how campaigns will perform when given a specific scenario. Companies can effectively plan the most optimal promotion to run under user-defined constraints based on the input.

Benefits of Using ML Tools in Measuring Promotion Effectiveness

Using such advanced technology aims to gain optimum visibility into factors silently influencing campaign performances. Those insights are further utilized for a recommendation of optimized promotion parameters.

The following are a handful of benefits businesses can enjoy with these tools: –

• Gaining insights into promotion performance drivers
• Evaluating promotion effectiveness and ROI
• Optimizing campaigns based on real-time data

In a nutshell, promotion effectiveness helps enterprises measure, evaluate, and fully understand the effectiveness of trade promotions. But, in the absence of real-time insights, creating optimized promotions will remain a distant dream. Organizations will spend billions of dollars on campaigns that are incapable of generating enough business and adequate returns on investment. With the help of tech-based software solutions, the hindrance of data unavailability can be aptly addressed, accurate analysis across multiple metrics, like spend ratio, and volume uplift, amongst others, can be easily managed, and creating more optimized promotions will be possible.

The post Measuring Trade Promotion Effectiveness and ROI of Promotional Campaigns using Machine Learning appeared first on Web Updates Daily.

To understand the importance of Machine Learning, the first thing is to know what we mean. Machine Learning is about what the word itself indicates, endowing a machine with the ability to learn, just as we humans do. It is true that hearing the statement that a machine can learn to manage various tasks as if it were a human being can cause a bit of fear, but let’s hope that it is only beneficial. There is no ‘reveal of the machines’ as speculated many times.

Machine Learning Applications

If the power to equip a machine with learning is so important because it is not used, you will think. Even if you don’t realize it, Machine Learning is used in many fields or for many tasks that you had never considered how they were carried out. For example, we have the case of voice assistants. When you use your voice to give a command to the device, what it does is translate those words into text. Once he understands what you mean, he does an internet search or even responds to you. This can only be done if the machine has been learning, for example, what ‘Play a YouTube video’ or ‘Tell me the time’ means.

And it is not only present in the fields of entertainment. It can also be used to create a 3D model of various medical tests and, with increasing accuracy, to be able to detect and prevent tumors, for example. Suppose the machines can recognize tumors in a still very reduced state. In that case, it may be possible to eradicate them from the body without further problems, which detecting them, thanks to the fact that the machines have learned to do so, can save many lives.

How Does It Work

What is done is to provide the machine with certain information as a base. Once that is done, the next thing is to enter data so that it can develop a task and learn from mistakes. That is to say, it acts as the human mind. The main idea would be the following: Introduced to the machine with the label that it is a cat, then different photographs such as number 2 are passed to it to learn what it is a cat. When he has enough information, you can send him a picture of any cat, and he recognizes it. On the other hand, if you send him one of the dogs, he has to detect that it is not a cat.

It is a fundamental way of explaining how it works, but enough to understand it. Things get more complex in much more complicated tasks, such as a car driving autonomously, but the basis is the same: obtain information and learn from it and its mistakes. To conclude, I have to say that even though a significant advance has been made in Machine Learning, there is still a long way to go and who knows, it may be that in the future, many the important diseases will be eradicated or the driver’s license it is only used in circuits since cars will have the ability to circulate autonomously. It is a future that may terrify some, but it is also true that it will be exciting to know to what extent life can be improved.

Also Read: Why Machine Learning Projects Fail- 7 Reasons that can Take Your Efforts for a Ride?

The post The Technological Revolution Of Machine Learning appeared first on Web Updates Daily.

What Platforms Does Facebook Offer

Practically anything we need regarding Machine Learning we have at our fingertips included for free. Within their AI page, we can find various frameworks and tools. On the other hand, we can also find libraries and data sets, languages, discourses, and reasoning. Within each of these sections, there are various ML and AI platforms. Some of these platforms are even revolutionary in their sector, such as PyTorch, which is a Deep Learning framework which is a Python package designed to perform numerical calculations using tensor programming. It also allows its execution in GPU to speed up calculations.

The main advantage that PyTorch offers over other platforms is that it takes into account aspects of real life. To quickly understand this can be done with an example. If you throw two billiard balls, one against the other, the logical thing is that they collide. However, things like these are not contemplated on other platforms. They are in charge of controlling the movement of the ball, verifying its trajectory, and making sure that it remains a ball. However, they cannot take into account what would happen if it collided with something. In PyTorch, this can be done, since variables can be changed after a process has started. On the other platforms, you have to predefine all the possible variables at the beginning.

Another great platform is ONNX, which is responsible for representing Deep Learning models, which allows developers to move AI models between different platforms. ONNX supports many well-known frameworks such as PyTorch, Caffe2, Microsoft Cognitive Toolkit, and has added support for CoreML, TensorFlow, Qualcomm SNPE, Nvidia’s TensorRT, and Intel’s graph.

Now that we know different frameworks, let’s get to know other sections that are also very interesting. One of the available platforms is the translator. Every time we realize that there are more platforms of translators available. However, each one has sections that others do not have and it is always good to be able to compare different platforms and choose the one that seems best in practice. In addition to this, Facebook has a platform called ParlAI, which is compatible with PyTorch and TensorFlow. This is responsible for collecting voice-to-text data and understanding it, even being able to answer questions as chatbots currently do.

To finish, it is necessary to name a tool that will be increasingly necessary for the future. We are talking about the tool dubbed Hateful Memes. This is in charge of addressing a great challenge today, such as classifying memes according to whether they really try to cause laughter, or on the other hand, simply try to be hateful to some group of people or even directed towards a single person. The big problem with this is that memes are usually a photograph accompanied by text. It is easy for us to understand that photography and text go together to give it meaning. However, a machine would interpret them separately. That is why Hateful Memes has developed a way to classify memes into 3 different types:

1- Predominantly text

2- Predominant image and text

3- Predominant image

By cataloging them in these 3 groups it is easier to understand what the meaning of each meme is. Once the machine has been able to understand the meaning that is given to that meme, it is time to classify whether it is hateful or not, thus managing to eliminate posts that are harmful to various groups of people. To conclude, we must add that Facebook is doing a great job in developing its AI and highlighting how important it is to allow other people to try its AI for free and understand how it works. In this way more people will join the development of ML and who knows, maybe a decade from now we will have ML tools capable of performing tasks exactly as a person would.

Also Read: Trends In Inbound Marketing For 2022

The post Facebook AI [Artificial Intelligence] And Machine Learning Platform appeared first on Web Updates Daily.

But don’t get to brooding. Not yet! Instead, it is more advisable to first understand the reasons why most other ML projects end up failing. Once you are aware of the possible and obvious pitfalls, you can simply go back to your project, eliminate them right away, and get your development back on track.

Also, as per VentureBeat, almost 87% of AI projects failed to make it through 2020, owing to a host of intrinsic factors. And as intelligent projects hinging on technologies like Computer Vision and Natural Language Processing cost a lot of money, failing isn’t always an option.

So before we talk about the ways to make your ML project a roaring success, let us delve right into the reasons that enhance fallibility:

Lack of Expertise

Well, the first reason doesn’t need any validation. Machine learning projects require algorithms, data procurement, high-quality annotation, and other complex aspects taken good care of. Having an inexperienced team or data analytics person take care of these intricate ML concepts and developments is waiting for a mishap to happen. Final deployment, continuous monitoring, and successful predictive testing are far more critical and require the assistance of end-to-end service providers.

Subpar Data Volume and Quality

If you evaluate closely, the success of every ML project hinges on Data. Starting from data identification, aggregation, cleansing, augmentation to data labeling, the entire data-specific circle comprises 80% of the total time allocated to developing any machine learning model. 

Image idea: Role of data and how 80% of the time is due to different data-specific tasks

But then, most organizations, in an effort to launch the MVP at a lighting pace, end up compromising on data volume. The lack of diversity makes the models linear and far less intuitive than expected. Data quality also takes a hit if experienced AI training data collection services aren’t requested. 

In the end, it is all about how well Data Science is implemented, and not many ML projects can get this part right.

Erroneous Labeling

Lack of properly annotated data can kill off even the most obvious projects. While labeled data can still be bad data at times, it actually covers the skill sets of your model provided the data collection vendors have done their job to perfection.

Did you know that almost 76% of global organizations end up annotating training data in-house, which stalls some of the more promising AI projects?

Organizations can scale beyond this issue either by outsourcing labeling to experienced service providers or by maintaining adequate training standards for the in-house data analysts, annotators, and scientists.

Lack of Proper Collaboration

Did you ever realize that AI and ML projects can even fail due to intrinsic factors? Surprised, right! Well, don’t be as every organization in charge of developing Machine Learning projects have BI specialists, Data Engineers, Data Scientists, DevOPs, and other professionals working in tandem. And finally, there is the core engineering team that takes the model to production.

However, if one segment fails to interact and collaborate with the other, data quality, volume, algorithms training data sets, testing sets, and other aspects can easily get compromised.

Dated Data Strategy

While a majority of concerns show up in between or at the fag end of the project conclusion, Data Strategy or the lack thereof is an issue that needs to be taken care of right at the onset. The factors included in a data strategy are often as follows:

• Total data requirement
• Can features be extracted from datasets
• What is the mode of data access?
• Does the procured data require cleaning, and if yes, to what end?
• Has the compliance been checked and verified?
• Pairing disparate datasets together for relevance and uniformity

Image idea: Volume of data preferred for organizations

Most organizations planning to launch an ML project follow an obsolete data strategy that only talks about the sources and annotation techniques, which then results in project failure.

Absence of Efficient Leadership

It is important that the crux of the project is defined by an AI leader, who would even understand the practical implications of the product. Having a team of engineers, data scientists, and analysts handling the job is fine but if efficient leadership is missing, the project gets restricted to a mere confluence of technologies and datasets. In the end, it’s all about what difference the project makes and what problem it ends up solving, and these aspects can only be iterated by the business leader itself.

Unanticipated and Unpleasant Data Bias

To be honest, an intelligent ML model is all about automating tasks based on the data that has been fed to it. If the data collection, labeled and trained with is one-dimensional, chances of bias coming into the picture grow significantly. Unsure as to what data bias in AI and ML means? Well, here is an example.

Imagine there is a resume filtering product that picks the best candidate for the job by shortlisting 5 resumes out of 5000. However, the model ends up picking 5 male candidates when there were at least 35 highly qualified female candidates that should have been prioritized. On closer scrutiny, it is identified that the model and subsequent algorithms were fed with male-specific data sets, thereby leading to gender bias and instant elimination of female candidates.

Wrap-Up

While there can be many other reasons for an ML project to fail, these are 7 of the most common yet underrated reasons. And if you look closely, it all comes down to the procurement, usage, deployment, cleansing, annotation, implementation, and transformation of data. Also, as data forms the backbone of any machine learning campaign, it is advisable to onboard a credible end-to-end service provider to handle every aspect of the ML project with precision and accuracy.

The post Why Machine Learning Projects Fail- 7 Reasons that can Take Your Efforts for a Ride? appeared first on Web Updates Daily.

The goal to be pursued is to offer you a much more rewarding and personalized experience. Would you like to know the benefits and applications of the IoT in the field of banking? Stay and read this post, and we will show you everything you need to know.

Benefits And Applications Of IoT In The Financial Sector

As we have already commented, the purpose of the IoT in the financial industry is to understand its customers’ behavior and needs better. IoT technologies work with such precision to ensure the integrity of the data, that is, that none of the data is incorrect. Therefore, we will see how this can benefit banking in various aspects of its day-to-day life.

Improved Business Efficiency

Thanks to the use and improvements of the IoT, customer service and its day-to-day functions will be optimized. We can verify this through biometric access to the banking applications themselves or the development of new payment systems in shops, such as biz. In the same way, we can see how today we can withdraw money from ATMs simply through our mobile device, without requiring our card.

Immediate Assistance

Our smartphones will notify account supervisors at financial institutions when we visit a branch, and this will go a long way in helping customers and saving time. In the same way, within the applications, we can access virtual assistants that can be robots or our supervisor, if required.

Payment Security

One of the main objectives of FinTech firms is to be able to offer greater security in payments to their clients. We have been able to verify this fact when we make online purchases; at the end of our purchase, we have to access our virtual bank to enter a code or key / digital signature.

Improved Financial Status

As we have seen throughout this article, IoT solutions allow you to improve the financial status of your customers. You can set a limit on the number of your purchases, as well as check your status and financial expense. That is, in what is their average savings and spending during the month, just as they can observe which are the sectors in which they have made the most purchases.

As we have seen throughout the article, the Internet of Things allows the banking sector to improve its productivity by offering a better and personalized service to its customers. Thus, thanks to this technology, banks will prosper and obtain better results, which are expected in the coming years.

Also Read: IoT Benefits With A Unified Communications Solution Everyone

The post IoT In The Financial Sector 2022 appeared first on Web Updates Daily.

The future of mobility depends on the connectivity between all the cars and the rest of the agents present on the road. For this reason, it is also necessary to digitize infrastructures. In the coming decades, the concept of “road,” as we understand it today, is doomed to disappear. The more than 35 million kilometers of pavement spread throughout the world will cease to be completely passive elements to become roads that help increase driving efficiency, improve safety and reduce environmental pollution.

A sample button. from 2022, all cars must incorporate intelligent speed assistants and involuntary lane change. A high level of autonomy will require an adaptation of the road infrastructure. Because, according to experts, the problem is not that there are excess carbs, but inefficient roads.

Consequently, in digitizing mobility, applications are also being found on asphalt, and it is increasingly common to speak of intelligent roads. Roads that incorporate advanced technology in terms of safety, connectivity, or even charging of electric vehicles. A series of solutions that:

• They will enable dynamic traffic management,
• Have the potential to reduce congestion and
• They will contribute to the decrease in the number of accidents, since 90%, according to the DGT, come from the human factor. 

Previously, advances were limited to physical components, such as signs or new materials derived from bitumen. Now, in addition, the potential of wireless networks and artificial intelligence is being investigated. What’s more, cross-border 5G-connected road trials are already underway across Europe. Tests that will collect key data for the development of autonomous driving.

Roads that generate energy Intelligent roads began between 2016 and 2017 when solar roads with photosensitive paint that collected sunlight were designed in France and China. And, at night, they illuminated the roads with it. Even though these projects were not successful, the highways are still the subject of study to double the space they occupy by generating electricity. And solar energy, whose future lies in cost reduction and greater architectural integration, continues to be the main focus.

This is demonstrated by the PV SUD project, a new concept for a rooftop photovoltaic system for motorways developed by the Austrian Institute of Technology.

• Energy production,
• The flexible use of the road network,
• Increasing the useful life of the pavement – protecting it from overheating and precipitation – and
• Additional protection against noise.

The idea is to scientifically study all these factors and analyze the economic viability of this formula. But, although it is not its objective, the PV SUD project is also presented as a solution to one of the problems preventing the absolute explosion of the electric vehicle: autonomy. Because, what if it were the road itself that powered the batteries of the vehicles? It seems like science fiction, but in Sweden, they are already developing a prototype of a wireless router that will allow the trucks and buses that travel it to be recharged inductively while traveling.

According to various studies, around a third of cars will be autonomous by 2030. The next ten years will be crucial in determining the future of roads. The development of the autonomous vehicle industry and the electrification of the fleet seem unstoppable trends. Efforts are now focused on developing active and digital infrastructures that could reduce road travel by allowing cars to travel faster than today in a safe, efficient and sustainable way.

Also Read: Artificial Intelligence Is Changing The Medical Sector For The Better

The post 5G Technology And Photovoltaic Roofs Proposals To Create The Road Of The Future appeared first on Web Updates Daily.

Applications Of Artificial Intelligence In Customer Service

Artificial Intelligence is being used in many sectors and has really interesting applications in areas such as caring for the environment or industrial development, among others. But, as we mentioned before, the help of AI in the Customer Service division is invaluable. These are some of the most useful applications in this field:

• Analysis Of Customer Reactions: One of the functions of Artificial Intelligence is based on the analysis of user interactions. Thus, for example, if the person, in a phone call attended by AI, says words such as supervisor, dissatisfaction, or any other that indicates their dissatisfaction, the system will alert the person in charge of the corresponding department, who could listen to the call, and intervene. if necessary. The main advantage of this functionality is that the analysis is carried out in real-time, so the problem can be solved on the spot, improving the customer experience.
• Detection Of Work Peaks: The use of Artificial Intelligence does not replace humane treatment, but rather complements it. Although more and more users choose to use machines for certain services, such as ATMs, take-out, or tolls, for example, when solving problems or dealing with more sensitive issues, we all continue to prefer humane treatment. For this reason, the detection of work peaks, coming from the analysis of the calls received in a call center, will facilitate the work of the agents and allow the coordinators a much more effective use of resources, which will have a direct impact on better service.
• Direct Interactions With Clients: Interaction with clients is one of the most used functionalities of Artificial Intelligence. Through it, users get answers to frequently asked questions, without the need for an agent to be available. This not only reduces call queues and waits considerably, but also frees workers to take calls that require human intervention.
• Get Ahead Of The User’s Wishes: Prediction is the future of AI and a lot of financial and human resources are being invested to perfect this quality. Artificial Intelligence is called like this, because in many aspects it simulates a human brain, obviously saving distances, but like this one, it is capable of learning through experience

This is especially useful in certain circumstances, and although it may sound very complicated, it is not that complicated. Thus, for example, in a routing system for calls or frequently asked questions, the machine learns from the responses of customers, offering more and more valid options. In the same way, it can be used for other purposes. Imagine that a supermarket customer regularly consults the offers at the end of the month, the system detects it and the brochure could be sent to him by mail, on the 25th, for example, anticipating his action.

Advantages Of Using Artificial Intelligence In Customer Service

In addition to all the functionalities of Artificial Intelligence, the use of this through the virtual switchboard offers us a series of additional benefits. These are the three most interesting for the company:

• Permanent Service: One of the main advantages of using Artificial Intelligence is that it satisfies the customer’s need for immediacy. On the one hand, it can serve a large number of users at the same time, and in addition, the service will operate 24 hours a day, 365 days a year, without the need for the employer to make a large investment.
• Greater Efficiency: Another great benefit of AI is that it increases efficiency, since it allows agents to dedicate themselves to the really important tasks, giving them more time and fewer users to attend to, so those that are derived, from the automatic system, will be treated in better conditions.
• Great Data Analysis Capacity: The data analysis of every call attended by Artificial Intelligence in real-time is very useful. This will allow us to learn about our target audience and the buying habits, concerns, and concerns of our customers.
• Also Read: What Applications Does Artificial Intelligence Have

The post Cloud Switchboards – Artificial Intelligence For Cloud Switchboards appeared first on Web Updates Daily.

Artificial Intelligence (AI) possibilities, the Internet of Things (IoT), and the new information and communication technologies are extensive. Among the trends that do not require a tremendous cost for companies are chatbots, digitization, cloud computing, and robotic process automation (RPA). As for chatbots and autoresponders, any SME should be aware that e-commerce.

Has grown exponentially in the last year and, with it, competition between product and service providers. Customer service is, more than ever, a differentiating feature of each business, and the consumer increasingly demands an immediate response at any time. The use of chatbots (which work thanks to Artificial Intelligence) increases productivity – streamlining the sales process – and offers the image of an automated and innovative company.

But automation should not only focus on the external customer. Many platforms leave behind contracts, records, invoices, and all kinds of physical documents and transform them into digital format, which allows them to be stored, centralized, and worked with them with tremendous flexibility and security. 

Some of the countless electronic signature solutions available for any budget. Not forgetting storage accounts in the cloud –cloud computing- such as Microsoft Azure, Dropbox, OneDrive, and many other providers specialized in online accounting software, human resources, finance, etc., which allow access to documents and manage them from anywhere.

One step further is robotic process automation (RPA), an evolution of using AI software or machine learning to handle many repetitive tasks, such as those run by finance departments. Eliminating manual processes allows working with data in a more secure, intelligent, and efficient way, allowing employees to dedicate their time to other tasks. SMEs can start this process, for example, by automating invoices, collections, and payments.

Advantages Of Automation

In addition to increasing productivity and efficiency, using these business process automation techniques brings excellent benefits to SMEs. It allows a better adaptation to the demands and fluctuations of the market, optimizes labour costs, increases the return on investment of marketing strategies -automating email marketing campaigns and using automatic analysis of consumer trends-; minimize human errors.

Facilitates teleworking; improves collaboration – primarily through online platforms and cloud computing; and increases the consumer and employee experience, among many. But automation doesn’t happen overnight. It is necessary to scale little by little, starting with the most routine processes, marking a calendar and following a specific order.

From Open Banking To APIs

After Open Banking –which allows SMEs to manage their money intelligently, connecting with third-party services that collect their bank details and automate payments and collections-, the trend that is gaining weight in the field of finance is to automate their services thanks to the implementation of APIs (application programming interface).

It is an interface that allows you to synchronize, link, and connect the database of service with any application. In other words, it establishes a bridge that guarantees the exchange of data in a secure way and without involving third parties. The integration into the SME system of these API Products allows the automation of many processes that will make it possible to reduce possible errors derived from manual intervention, reduce management times, and streamline processes.

The post Smart Future – Process Automation In SMEs appeared first on Web Updates Daily.

Competitive Advantage

In this sense, part of the large companies already included Artificial Intelligence in their race for innovation. Despite this, and according to the latest annual report from the Boston Consulting Group, few companies have harnessed the full potential of AI.

Taking advantage of all the advantages it offers will make a significant difference compared to the rest of the competitors in your sector. Following the BCG report data, nine out of ten executives agree that AI is a business opportunity for companies. It is the technological revolution of the moment. Investing in a system that promises to be the future is a long-term investment that is likely to bring numerous benefits to your business over time.

The Best Services

There are many production processes and services that, thanks to AI, make their quality much better. An example of this can be seen thanks to machine learning. In e-commerce or VOD platforms, they constantly use it when recommending their products. Machine learning improves bid to reach your customers and is one of the most beneficial online shops. The products in your catalogue will automatically adjust to the consumption patterns of your customers.

Optimizing Your Business

If we continue with the previous example, in addition to helping to improve customer-facing services, Artificial Intelligence optimizes many processes. Knowing perfectly what your customers demand will help you obtain a global vision of their consumption patterns, which you can geographically segment and establish areas of interest. In this way, each commercial action you implement will be much more effective, thanks to AI. You will get more data on what the public wants, and this, today, is the best route to achieve success as an entrepreneur.

The post AI – Why Is It Important To Invest In Artificial Intelligence? appeared first on Web Updates Daily.

Why Do You Need Marketing Workflows?

A workflow is a series of steps to follow to manage a project or launch a marketing campaign. The workflow defines the sequence of actions that are carried out to carry out the task. Using workflows helps us organize the various tasks of inbound marketing: reviews, approvals, results from monitoring, priority changes. To see it more clearly, we are going to review very common workflows in day-to-day marketing.

How To Automate Your Inbound Marketing Workflows With AI

The Unstructured Data Problem

Automating processes is difficult because many workflows require human intervention. The reason for this is that they use what is known as “unstructured data.” The unstructured data can not quickly be introduced into a database as it does not fit specific formats or sequences. By not having an easily identifiable structure, they are more challenging to search, manage and analyze.

For example, a list of songs, singers, and music genres might be considered structured data, but the songs themselves would be unstructured. Other prevalent examples in inbound marketing would be emails, presentations, texts, or images. Working with unstructured data is a challenge for automating inbound marketing since it cannot be integrated into existing information systems. Therefore, traditional automation solutions cannot process it.

To overcome this obstacle, the answer is to incorporate machine learning into unstructured processes, to add a cognitive element that helps make sense of the information. This is done using technologies such as:

• Computer Vision: This technology identifies, classifies, and processes images or objects in pictures and videos. For example, it can be used to identify diseases in photos, classify PDF attachments or organize the inventory of an online store.
• Natural Language Processing: It is used to understand, categorize or prioritize texts. For example, it can create chatbots, prioritize customer service requests, or do sentiment analysis.
• Optical Character Recognition: This technology is capable of recognizing printed or handwritten characters within digital images of documents. It is used to extract information.
• Sound Processing: It is used to identify, classify and process audio files or ambient sounds. For example, we can use it to tag music, search for songs, or implement voice controls. Therefore, these technologies help us create and train machine learning models that enable us to automatically decipher our unstructured data and incorporate it into our processes.
• Also Read: The Circle Of Digital Marketing

How To Incorporate Cognitive Automation Solutions Into Your Workflows

Depending on your company’s situation, there are various solutions to incorporate cognitive automation and be able to interpret unstructured data. It is not necessary to rethink the entire structure of your company from top to bottom, but you can look for the tool that suits you. These are six options:

• Self-Service Tools: Self-service workflow cognitive automation platforms empower you to manage multi-phase processes that use unstructured data. These are low-code solutions that allow you to create personalized workflows with machine learning algorithms through an easy-to-use interface. Compared to other automation solutions, self-service tools are affordable. With its ease of use, this allows you to do experiments and implement the results quickly.
• Machine Learning Solutions: These platforms allow you to create machine learning models from scratch with little or no programming knowledge. They are handy for giving structure to unstructured data, but they do not have workflow creation tools, so they do not serve to automate processes from start to finish.
• Automation Solutions With Specialized Artificial Intelligence: These tools are focused on a specific sector. This means that they can manage complex processes with unstructured data, but their application area is limited. Another aspect to consider is that they tend to have high prices due to the high cost of preparing the data necessary to train the algorithms.
• Smart RPA: Classic robotic process automation (RPA) has some limitations when it comes to processing unstructured data. But as new machine learning technologies are developed, traditional RPA solutions are incorporating artificial intelligence capabilities to improve process automation. This type of solution is especially suitable for medium and large companies that have specialized RPA developers.
• AI Consulting: Large companies that have to automate complex processes with unstructured data can turn to specialized AI consultancies to find the best solution. Typically, these consultants develop bespoke innovative automation solutions or suggest vendors that are best suited to specific customer needs.
• Experts In Artificial Intelligence On Staff: Finally, the most complex cases’ definitive solution is to incorporate intelligent workflow automation within the company’s workforce. Companies with large IT departments are likely to have the resources to manage intelligent automation “in-house,” perhaps by hiring additional programmers or reassigning roles within the team. This allows you to design complex and personalized solutions, but the problem is finding and hiring skilled enough people for it.

The post How To Design Workflows In Marketing Based On AI And Machine Learning appeared first on Web Updates Daily.