What are Smart Analytics and how could this solution help your business thrive?
Smart Analytics is a process of collecting and analysing data to help businesses make informed decisions about their products and services. It’s often referred to as the smart way to make decisions.
The biggest benefit of using Smart Analytics is the accuracy of the information it provides and if you have the right software, this will also offer a better understanding and a breakdown of that data and how to best interpret it to make educated decisions.
Collating accurate information and knowing how to properly take advantage of data enables businesses to gain a significant competitive advantage which will ultimately provide your customers with the best possible chance to provide the service their customers want.
What is AI?
According to IBM ”Artificial Intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.” AI programming encourages computers to understand, learn and then develop upon human intelligence. AI is used within Smart Analytics to provide more accurate data sets to better inform organisations on the next steps to take.
Below we discuss five cool ways in which Smart AI Analytics ARE currently being used ACROSS A RANGE OF INDUSTRIES:
1. Machine Learning in Banking
We’ve probably all had our own experiences with fraudulent banking as criminals are becoming even more sophisticated in their approach. There is a huge amount of data being generated in the banking sector and Smart Analytics can capture big data so that banking officials can easily study, analyse, and detect any illegal activities such as money laundering, misuse of credit cards and unusual activity.
This type of technology, referred to as Machine Learning Artificial Intelligence, helps banks to avoid loss and most importantly, keeps their customer’s money safe. AI and big data enable organisations to analyse many transactions, not just based on historical data, but also the customer’s current behavioural and predictive analytics to identify and prevent fraud as and when it appears.
The Financial Conduct Authority recently undertook a survey which found that the use of AI in financial services is accelerating with 72% of firms actively using or developing machine learning applications…a trend that is expected to more than triple in the next three years.
2. Big Data in Healthcare
Due to the recent events of the pandemic and the uncertainty of our government, healthcare is facing rising financial pressures and long waiting list times.
Analytic applications within healthcare mean that we are now able to track the treatment journey of patients. Delivering healthcare providers with smart data is helping them to work smarter rather than harder.
Inventory base reported that every missed GP appointment costs the NHS £30, and the overall scale of the loss is phenomenal: every year 9.6 million appointments are not attended by patients, equating to a cost of £288 million to their already overstretched budget.
Simon Stevens, NHS chief executive, said: “As part of the NHS Long Term Plan we are going to be using new technologies and treatments to improve patient care and save more lives. We are seeing an artificial intelligence revolution that will be a big part of our future over the next five years, with technologies that can cut the time patients wait for scan results and ease the burden on hard-working staff. We’re kicking off a global ‘call for evidence for NHS staff and technology innovators to come forward with their best ideas for how we should adjust our financial frameworks to best incentivise the use of safe and evidence-based AI and machine learning technologies across the NHS.”
There were 315,000 MRI scans and over 520,000 CT scans in March alone, according to the latest figures. That is up 20% from 260,000 MRI in the same month three years ago and CT scans were up a third from 390,000 CT.
According to Royal College of Radiologists, in mammography screening, the NHS is performing around two million breast screens for women a year in the UK, with each test result reviewed by two clinicians. Testing of AI and machine learning technology has already demonstrated its potential to ease the burden on staff and free them up for other work.
An AI system trialled at Moorfields Eye Hospital in London found their solution made the correct referral decision for over 50 eye diseases with 94% accuracy, matching the world’s best eye experts.
There is also now a Long Term Plan NHS plan in place where the institution is aiming to become the first national health system in the world to digitise its outpatient system through the use of video and online consultations and make use of AI and machine learning technologies to help clinicians interpret scans part of the NHS routine.
3. Improving the retail and e-commerce experience
Recommendations are very important for an e-commerce business, but it’s not always easy to get them right!
Predictive analytics can maximise sales from e-commerce promotions by using data from multiple sources to work out a personalised recommendation that will work for a particular customer, or a segment.
NorthEastern University recently reported on how data analytics is helping industries to make better business decisions.
“Data analytics today is allowing us for the first time to take the massive amount of data we’ve been assembling for years and use it for predictive purposes rather than in just descriptive ways,” says Thomas Goulding, professor for the Master of Professional Studies in Analytics program within Northeastern’s College of Professional Studies.
“Through the use of mathematical modelling and data analytics, data can now tell me something I wouldn’t otherwise have been able to learn. As a result, because of analytics, we can make informed business decisions today that simply were not possible ten years ago.”
It makes the challenge easier by using machine learning to understand a consumer’s behaviour, including their purchase history and the performance of different products on the site. This helps to determine the most relevant recommendations with a higher probability of generating a sale.
It’s the same with promotions. Predictive analytics identifies those promotions that have worked best in the past and then offers them in real time based on the consumer’s browsing pattern. Clever right?
Take a look at our recent blog post on why retailers are choosing video tech to help guide business decisions.
4. Managing market prices and purchase journeys
Predictive analytics looks at pricing trends together with sales information to determine the right prices at the right times to maximise sales from e-commerce while boosting profits. If you’ve ever browsed Amazon and saved a product in your basket, you’ll notice how often the price of that product changes and you’ll most likely receive notification reminders that it’s sitting in your basket, or that the price has dropped.
Pricing is managed using a predictive model that looks at historical data for products, sales, customers, competitor pricing and product pricing trends. Based on this model, the price for a product and customer can be predicted at any given time. Online giant Amazon is a huge user of predictive pricing and clearly, it’s working for them!
Intelligence Node recently reported that Amazon is the leading e-retailer in the United States, with net sales amounting to close to 386 billion U.S. dollars in 2020 and it’s down to their super intelligent software. For instance, their algorithmic repricing is the most sophisticated form of repricing tool available in the market. This type of repricing tool uses self-learning algorithms to reprice products. It doesn’t just lower prices or match prices with competitors but considers multiple factors while determining a winning price for a product – which could often be above the lowest price but still win the buy box. Although a great solution, algorithmic repricing tools are expensive and would make sense only for established sellers with a large inventory on Amazon.
5. Predicting Customer Interactions
The utilisation of smart analytics tools is enabling organisations to track what their customers may be interested in purchasing in the future. In the past, the customer journey was guided by historical factors to predict future decisions. With the use of predictive analytics, organisations can view real-time behaviour to anticipate what action a customer is likely to take.
This allows retailers to determine the most relevant recommendations with a higher probability of generating a sale. From targeted advertisements to an email campaign with specific promotions, businesses can better personalise the customer experience with smart analytics.
Forbes recently wrote an article that outlines how various companies are using AI to push sales. The popular motorcycle company relies on predictive. Harley Davidson uses an AI program called Albert to identify potential high-value customers ready to make a purchase. A sales representative can then contact the customers directly and walk them through the sales process to find the perfect motorcycle. It’s a win for both sides: customers get personalised service now they’re ready to make a purchase, and the company can focus on serious customers.
According to the Times, Netflix is also using big data to drive success! Big data helps Netflix decide which programs will be of interest to you and the recommendation system influences 80% of the content we watch on Netflix. The company even gave away a $1 million prize in 2009 to the group who came up with the best algorithm for predicting how customers would like a movie based on previous ratings. The algorithms help Netflix save $1 billion a year in value from customer retention.
Final Thoughts
It’s exciting to imagine how Smart Analytics will evolve over the coming years and how detrimental this will be to some organisations and public services who are not jumping quickly enough onto the bandwagon.
Data breaches have made enhanced security a critical goal for organisations, leading them on the path to incorporating big data analytics applications. As smart analytics continues to permeate our everyday activities, there has been a significant shift of focus to finding real value in its use.
The significance of big data lies in how an organisation is operating once the data has been collected and not just how much data is being collected. Data needs to be used as insight, something at Facit we refer to as See Further; we allow employees to see more deeply into the data so informed decisions can be made as a result.
By employing data analytics solutions, businesses can analyse heaps of data easily to efficiently unearth actionable methods to improve performance
These big data solutions are empowering businesses. It’s a game-changer for sure! Check out our video or document compliance page to find out more about how our own artificial intelligence solutions can protect your business, or learn about our smart analytics solutions, smart count, smart queue and smart zone, technologies that will help enable you to make data-backed operational decisions to improve organisations environment.
