When it comes to the talk of technological advancements, artificial intelligence will always be one of the most revolutionizing technologies that are present in many industries. Artificial Intelligence is defined as the theory and development of computer systems to be able to perform human tasks like visual perception, speech recognition, and even decision-making.

The first AI program can be traced as early as the 1950s and since then, it’s been continuously developed. Right, now we can already see how powerful AI is and how widely present it is in many industries like gambling.

Artificial Intelligence has always been heavily acquainted with gaming including gambling. This is more specifically for online gambling or online casino gaming. AI has been playing an important role in different aspects when it comes to online gambling. Here are some of the aspects of online gambling that is benefiting from Artificial Intelligence.

• Getting New Customers

Online casinos are constantly trying to find ways to get new customers. The competition is just tough because there are now so many online casino operators out there nowadays and all operators have their ways of enticing people like giving great casino bonuses.

Players would usually always prioritize to choose a casino bonus that will be beneficial for them and this is why casinos must reach people who will be interested in their offers. And so, advertising plays an important part here and casino ads are usually more visible to people with casino gaming history. This is something that operators learn by using AI-powered algorithms.

• Player Verification

Security is extremely important to casino operators and they must screen their players well. The verification process allows them to make sure that people signing up on their site are real people. For some casinos, their verification process includes face-recognition as well.

Aside from the verification process, they also use AI tech to keep track of suspicious activities. The AI tech that they use on this can identify whether suspicious activities or fraudulent behaviors are going on. This helps ensure that the personal information of their customers are players are secure.

• Smart Data

Ever wonder why it is that whenever you log in to your casino account, you always seem to be presented with games that you are likely to play? AI technology is also responsible for that. The AI algorithm determines what games you might enjoy based on the other games that you’re playing. This way, online casinos get to ensure that what they are offering to their customers appear customized.

Just remember that any AI program functions based on the data it gets. It may get data from the player’s information and activities. Whatever the AI algorithm will do is based on its programming. Once data is gathered, that’s when the algorithm functions and knows if something is suspicious or this is something that a player will find interesting.

• Virtual Reality

Virtual Reality has been the talk for a while now in the gaming and gambling sector. There are already online casino games that you can play with a VR device. Playing with VR casino games lets you experience realistic online casino gaming and this is also made possible because of AI technology.

When playing any VR games, the result of the interaction you make with any object in the virtual world is the product of AI. How your virtual environment reacts towards your actions is the AI functioning. If you are playing an online casino game against a computer, that’s also AI. It’s the computer functioning like a real player and the actions that your computer opponent take is also the result of compiling data from other players.

• Combatting Gambling Addiction

Many online casinos nowadays are very much cooperative when it comes to combatting gambling addictions in different countries as it is a very serious issue. AI helps casinos combat gambling addiction by analyzing player activities. There are AI programs that could detect problematic gambling patterns or behaviors.

Problematic behavior can be detected in an early stage and it could alert online casinos. If proven that there seems to be something problematic when it comes to the activities of a player, then the online casino could suspend the player’s account. The operator could also give options to the player when it comes to how they could help fight gambling addiction.

AI technology is continuously developed. AI programs are consistently learning and are now function more human-like. Just imagine how far technology would go because of this and this could make online casino gaming more realistic in the future. It is understandable why many are fearing the power of AI, but its benefits are countless.

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A huge change is happening in the back offices of companies over the globe. Many name it as the ascent of the robots, however, an increasingly fitting term is robotic process automation (RPA). While the association between robotics technology and RPA might be to some degree inexactly characterized, the simple fact is RPA is only an extravagant abbreviation for a software robot, or “bot” in IT vernacular. RPA delivers innovation that joins scripting with intelligence and execution. It is a mix of automated prowess that has been a genuine boon for back-office tasks.

However, for all its ability to drive profitability and diminish physical work, RPA has been met with dread. That dread of job losses and staff decreases, has an exceptionally human component, leading to unwarranted presumptions about robots. Furthermore, nowhere is that fear more noteworthy than in the data center, where highly compensated experts feel undermined by the ascent of the bots, with doubts that RPA will diminish their significance to overall activities and put data center administrators out in the city.

Organizations structure the customer base of most of the data centers, regardless of whether that is a small regional data center, a bustling colocation or the universally distributed network of huge data centers that underlie people in public cloud providers.

As organizations wake up to the power and effectiveness of the cloud, they’re setting up DevOps teams and microservices which demand real-time processing, elastic scalability, big data storage capacity and 99.99% or more reliability. To satisfy the new needs required by these business models while keeping costs aggressive, data centers need to lessen overheads while improving reliability and performance

As framework turns out to be increasingly complex and distributed, there’s an extra contention for robotic assistance. People are essentially incapable to screen and process the numerous floods of data coming into a data center without making mistakes or lessening pace and performance. Network downtime is serious enough, however, with data ruptures presently pulling in record fines, mix-ups can compromise the very presence of a data center.

As we enter this new revolution in how organizations work, it’s important that each bit of data is dealt with and utilized appropriately to improve its value. Without cost-effective storage and progressively amazing hardware, digital transformation and the new business models related to it wouldn’t be possible.

Specialists have been foreseeing for quite a while that the automation advances that are applied in processing plants worldwide would be applied to data centres later on. In all actuality, we’re quickly advancing this probability with the use of Robotic Process Automation (RPA) and machine learning in the datacenter environment.

Human mistake is by a wide margin the most critical reason for network downtime. This is trailed by hardware failures and breakdowns. With practically zero oversight of how hardware is functioning, move must be made once the downtime has just happened. The cost effect is a lot higher as the focus is detracted from different things to deal with the reason for the issue, joined with the effect of the actual network downtime. Dependability, cost and management must be fixed to give an increasingly productive data center. Automation can help accomplish this.

With the fear of bots suppressed, numerous CIOs and data center managers are considering how to best embrace RPA and where to apply the innovation. Apparently, data center tasks today are tied in with accomplishing more with less and are on the leading edge of changing wetware into processes that carry extra value to business operations. All things considered, it turns out to be evident that RPA, particularly as intelligent automation, can carry phenomenal efficiencies to operations. A valid example is data center management, where bots can be made to perform backups, spool up virtual machines on demand, move bots from near online to online frameworks, resolve issues. etc. Everything comes down to the degree of creative mind present and the capacity to distinguish tasks that lend themselves well to automation.

However, RPA is substantially more than macros or contents. RPA presents a level of intelligence that enables bots to decide, which lets them go about as an intelligent automation agent. For instance, bots can be deployed to screen network traffic and trained to make a move dependent on a threshold being accomplished. In addition, the bots can utilize pattern recognition alongside analytics to characterize the thresholds in real-time, enabling them to respond a lot quicker than any human can.

Such abilities look good for data centers that should be flexible and are under consistent security threats. Bots can be worked to recognize usage patterns, standardized traffic, CPU cycles, etc as a reason for scaling up or down. Activities that once spurred a technician into action would now be able to be mechanized. Intelligent automation has additionally demonstrated to be a decent line of barrier against malware, ransomware, and information spillage. With bots checking activity, normalized patterns of usage can be deducted and anticipated behaviors of uses, clients, and different components can be measured. When activity falls out of norms, bots can make a move utilizing either foreordained guidelines or significantly increasingly inventive responses driven by AI.

Another RPA advantage is the normalization of procedures and strategies. By evacuating the variable activities of people from a procedure, data centers can anticipate a more significant level of standardization with considerably more unsurprising results. That in itself is a help for companies driven by compliance regulations, where the upcoming strategies is a critical aspect of meeting compliance.

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Manufacturers and retailers are inundated with information from an ever-increasing number of disparate sources, but the real challenge that they face is how to ensure their big data becomes smart data.

Only if retailers and brands can gather, integrate, analyse and create actionable insight from their point of sale (PoS), customer loyalty and e-commerce data will they win shoppers’ hearts and minds and gain a competitive advantage.

In fact, the ability to turn big data into smart data has become a game changer for organisations that have chosen to embrace such a positive strategy. They know that being able to study the minutiae of individual consumer preferences and customer choices can have a real impact on their bottom line.

A data strategy has to be layered to be really effective. The first level should be the big data, the second tier the smart data and the third layer the insight that can be actioned from it.

Gaining access to an increasing amount of data does not necessarily lead to better decision-making. The key is to know what business questions to ask that your data might be able to answer. You also need the knowledge, and technology, to make sense of what can be mountains of data captured and stored in different parts of the organisation, and often in several different locations. Having sales and marketing data in silos, for example, makes it difficult to create accurate short- or long-term forecasts.

What is clear is that sales data analysis and data science do provide retailers with new insights into areas such as product availability, repeat purchases of promoted and new items, range assortment and shopper missions.

There is so much untapped potential from smart data, but organisations need to be brutally honest about whether their data is working hard enough for them. It is likely there is currently not enough investment in combining big data and analytics to strengthen the impact and ROI of their pricing, promotional and media strategies.

The reality is that a competitive advantage is being missed because a retailer will not have an accurate picture of what is the right price point for a particular product. They may also be unable to create and deliver the most effective promotional campaigns.

Asking the right questions

Data provides an understanding of shopper behaviour, so a manufacturer or retailer must ask the right questions to obtain a clear picture of how changes in behaviour might affect their own business now and in the future. For example, what data does it need today and how might this data be used to deliver the ROI it requires? What data has already been collated and is ready to analyse, and what important data has yet to be gathered?

With so much data available every organisation needs to know exactly what business issue it is trying to solve.

For a retailer, is the aim to improve overall sales and margins only?

With the growth in the omnishopper, smart data can reveal which channels particular shopper segments tend to use and when. This becomes increasingly important as consumers’ expectations continue to rise, and the choices provided by multiple channels can help shoppers to change their behaviours.

Mobile has become more important to the whole shopping experience and smart data analysis is helping retailers and brands create a seamless experience for consumers. Indeed, being able to turn big data into smart data can help to find ways to remove the friction points and barriers that make an offline or online retail experience frustrating.

This can boost loyalty, which is created not so much by low prices these days, but by other factors such as customer service and transparency.

Making smart thinking the norm

As mentioned, there are obvious business benefits from mixing data with analytics to help adapt to changing shopper behaviour. This is certainly true when it comes to digital and the amount of data now being collected from high-speed applications and from ads viewed on mobile devices, for instance.

Retailers and manufacturers need to be able to analyse and assess people’s digital footprint to discover whether the sales achieved by a particular campaign justified the marketing spend. Being able to identify what has been purchased at a point of sale after an advertising campaign is the best option for a brand to define its ROI.

Technology has a role to play in helping organisations extract and analyse data more effectively and assist with new product development and range optimisation. For example, retailers are realising the benefits of automatically loading, integrating and augmenting data from their stores to create an up-to-date single view of their customers.

The process involves analysing everything from point of sale, CRM and loyalty card data, as well as inventory and consumer panel information. When these data sets are coupled with analytics for price, promotions and assortment analysis, retailers get a better understanding of how they and their suppliers can best target their own shoppers.

Imagine the business opportunities from using trip mission segmentation where the analysis tells you why customers are visiting stores and reveals the clusters of different shopping missions that need to be fulfilled across the entire store portfolio.

Better data analysis certainly means more effective targeting when spending money on promotions. Smart data will reveal what is working best in which store in which region or country and with which consumer segment.

In some areas households might be more sensitive to promotions than in other places and smart data allows brands and retailers to be sensitive to these differences and react accordingly.

If a campaign is going to be cost effective, a manufacturer also needs to be able to create and deliver the most effective promotional and media campaigns. They only achieve this with a better analysis of customers’ individual preferences and buying habits.

Collaboration between retailer and manufacturer

This means manufacturers must work harder to fight their corner and if their own data is not as detailed as that held by the store owners they should consider collaborating with research companies that can help them to plug any of their data gaps.  The findings can then be shared with stores to demonstrate a product’s value to a particular category.

Manufacturers can discover what is unique about a particular product from the shopper’s point of view. Every product must have some level of ‘uniqueness’ to convince people to buy it over and above another brand.

Hot sauces is a good example because the consumer is faced with so much choice today. What makes shoppers chose one variety of hot sauce over another? What product characteristics are the most relevant for the different shopper segments?

A retailer might already stock three Indian flavour sauces, for example, and is unlikely to want to sell another one. However, by using predictive analytics, manufacturers can analyse the key attributes, such as size, packaging, flavour, brand name and price, and combine key competitor analysis to pinpoint what is more likely to sell in different geographies and stores. They can then work out how incremental a product will be before taking it to market.

  Changing technology requires smart approach to data

Technology is developing at an incredible speed and smart data has become a powerful weapon to ensure technology is used as effectively as possible to connect with consumers.

Technology is not only helping to gather data at the point of sale and through in-store activity such as sampling, it is also assisting retailers in planning their stores of the future.

Smart data fuels innovation because it reveals to marketers what works and what does not.

Manufacturers can dive deep into the key attributes of their products, and demonstrate through analysis the value generated for the specific product and the category. The value proposition can ultimately help to persuade retailers to keep a product on the shelves.

Smart data might reveal issues with packaging, merchandising, pack size or problems around category management, price or a promotion that can be solved with an innovative solution. It can also reveal gaps in the market that could be exploited for competitive advantag

In today’s world data is knowledge and knowledge is power, and having access to data and being able to analyse it quickly to make commercial gains is absolutely crucial. Unfortunately, many organisations have only scratched the surface of what is possible.

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