We’ve been more than used to Artificial Intelligence popping up in sci-fi films, novels and scaring the hell out of us for decades. Warnings about our use of new tech in the likes of WestWorld and Black Mirror aren’t subtle and you’d be forgiven for thinking our robot uprising is just around the corner.
AI is more than a reality now, it’s changing the way we live work and play. With more and more customer data being collected and processed, predictive analytics allows brands to generate insight and take action.
And companies are using AI and machine learning to optimize business efficiency – clever computers are learning about customer behaviours on a deeper level to help businesses deliver better products and services. Tech can recognise patterns in data much quicker than humans, so to get the most out of big data, AI is becoming a valuable tool.
Dashboards and reporting are probably the most common use of predictive analytics within businesses.
How else is AI impacting the web, changing the way we shop and becoming a key player in numerous industries?
One of the easiest ways to show someone how they’re interacting with AI-inspired tech is to get them to think about a few simple tasks they carry out online.
Sitting down on the sofa with some ice-cream and Netflix is a way of life and when you’re browsing through which true-crime documentary to binge watch next, the recommendations that pop up are all based on predictive analytics. A clustering algorithm is used to continually check and improve suggestions for users by analysing their behaviour and what they watch.
Like most other examples we’ll look at, AI is being used to win, retain, and serve customers in a more useful way.
This is similar to product recommendations when customers are looking to buy – a tool like IBM Watson helps businesses to match up user data with third-party data to suggest products and advice. Users can be retargeted with relevant ads everywhere they go online.
Online frauds and data breaches are increasingly common, so when banks and security companies use AI to analyse usage patterns for cards and transactions, this can help to spot online fraud when it occurs. Spotting point-of-sale (POS) fraud effectively is a way of positioning a business to customers as robust and secure.
Predictive analytics can really benefit security analytics, especially people working on large-scale data protection for big companies. There are lots more occurrences of high profile data breaches over the last few years e.g Uber, as fraudsters become more sophisticated.
When AI can analyse unstructured data too, it helps to minimise the ways that fraudsters can hack into companies, without stronger authentication measures.
Online fraud is only going to get more serious and sophisticated so using AI and predictive analytics to protect consumers is vital.
Clearly, businesses want to price products at a level that appeals to customers but also makes a profit and achieving this balance can be difficult.
Predictive analytics can help with price optimisation by analysing data that provides info about pricing and sales trends. AI enabled tech can help to match this up with inventory levels too.
Pricing processes can be simplified with AI by providing key insight into how revenue and units are likely to change if the price is altered, as well as the types of prices that will maximise revenue.
Deep learning gives businesses even more detailed insight into user behaviours. Delivering ads to users is a form of recommendation, just in a different context to suggested products etc. For many industries, adverts provide a key source of revenue and a way to get in front of more users.
It’s possible for businesses to run more cost-effective advertising that gives them the best ROI. Algorithms can optimise ad bidding and find the best cost per acquisition ads for businesses.
Incredibly detailed targeting can be organised too. AI and machine learning can create different ads for specific users to increase the likelihood that they’ll click on an advert. For example, there can be hundreds of variations of the same advert that are created, with different pictures colour schemes and texts based on the individual user and their customer behaviour and demographics. Customer data can form an identification model for finding new prospects as well.
We’ve seen this happen over the last couple of years with political advertising. Detailed micro-targeting has been used to target voters, but the fact this is unregulated does rings alarm bells for many. The IPA has called for a suspension of micro-targeting ads in political campaigns, due to the covert, secretive nature of ads that do not have to be listed for public display like other political advertising. It creates a culture that lacks openness and can be vulnerable to abuse.
Development in neural networks has led to lots of progress being made in language and speech recognition tools over the last 5 years. What does what you’re saying or writing mean? This is used by search engines to try and align what a user is asking for with the most relevant and appropriate search results.
Brands are using AI and machine learning to take the unstructured information they’ve gathered from customer interactions etc to turn it into actionable insight that they can use to improve a user experience.
This helps to personalise experiences for customers based on their data and behaviours – it could be using different images, recommendations or text based on info about the user.
Being able to allocate adequate resources to your customer service provision is really important if you want to retain customers and improve engagement.
If a company can analyse thousands of interactions across platforms then they’ll be able to work out how people are contacting them, what problem they usually have and also how to personalise their customer experience.
The demographics of users that a business sells to might not be interested in telephone-based customer service and are more likely to engage with chatbot-based tech or social media platforms – this can help businesses to build and provide more relevant service.
Chatbots analyse natural language and a brand’s datasets in order to respond to requests and allow customers to take action. This can end up being a more seamless and effective customer service format; because it’s quick and waiting time is minimized. Getting in touch with a business through chatbots or social media might end up being the main and most cost-effective way for a business to operate its customer service offering.
Planning resources is a big part of maintaining a productive and efficient business and making sure time isn’t spent on interactions and questions that can be solved in a better way with a better service is important. Predictive analytics using data generated from CRMs can give more clarity.
Inbound communication can be analysed and filtered too, so the appropriate action is taken based on past customer correspondence and behaviour.
Data, analytics, and digitisation bring lots of opportunities to use AI and automation to improve business efficiency. Start-ups that invest in new tech will be able to adapt more easily as the business landscape continues to change at a rapid pace.
We’ve only scratched the surface because it’s difficult to quantify just how many AI applications are being used by marketers, businesses and customers.
Spoiler alert: it’s probably a lot.
Predictive analytics tools and techniques are redefining how we buy, do business and they’re here to stay.
Bow down to your robot overlords!
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