Why Machine Learning Is The Future Of Loyalty Programs

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Over the last 30 years we have witnessed the very nature of marketing change. We have seen the focus shift from creative advertising, through digital marketing and mobile apps to the current focus on data-driven engagement and data-centric marketing. The future, however, will be machine learning, artificial intelligence and predictive marketing.

According to New York Times journalists, John Markoff and Steve Lohr, many of the tech industry’s biggest companies like Amazon, Google, IBM and Microsoft, are jockeying to become the go-to company for A.I. by 2020. The market for machine learning applications will reach $40 billion market research firm IDC estimates. And 60 percent of those applications will run on the platform software of just four companies — Amazon, Google, IBM and Microsoft.

In the very near future, intelligent software applications will become commonplace and machine learning will touch every industry. Today, only about one percent of all software apps have A.I. features, IDC estimates. By 2018, IDC predicts that at least 50 percent of developers will include A.I. features in what they create.

We are already seeing an epochal shift in how businesses operate and compete with the development of a new generation of companies using this type of technology and engage with customers one-to- one. These companies use data to personalise and predict what its customers are going to do next and then engage them with intelligent marketing programs.

The most disruptive companies in the world, for instance Uber, Amazon and Netflix, have one thing in common. And that is that they drive business growth and rapid global roll out using data-driven, personalised, predictive and responsive engagement. Those companies that can unlock the power and value of data are seeing higher engagement and viewership and higher revenues from customers. For example, the majority of Amazon’s and Netflix’s sales are driven off predictive marketing engines using machine learning technology.

If you’re still not convinced, maybe this example will help. In recent years, the supermarket giant Tesco dropped the amount of offers it sent out by nearly two-thirds but massively increased its revenue from conversion and ROI by using this type of technology. Tesco achieved a 675 percent growth in its bottom line, as a result of its data-driven loyalty programme. By analysing customer data, Tesco found that 80 percent of the discounts and offers utilised by customers came from 20 percent of the offers generated. This prompted a reduction of offers from 750 to 300 a year,amounting to approximately $600 million savings in promotion expenses while increasing market share.

Machine learning creates enormous efficiencies because you no longer have teams of campaign managers generating and managing hundreds of offers – many of which are actually delivering negative ROI. So instead of sending out 100 offers, you can send out 30 offers but each one of those 30 offers will have a much higher conversion. And not only is that a much better use of a business’s time and energy but it will also surprise and delight your customers when they start to receive offers that actually matter to them.

Wouldn’t you like to know what your customers are going to buy next and contact them with a product offer exactly when they are thinking about it? Then visit Quantiful’s website and get in touch.

 

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Why Customer Loyalty Programs Are A Vital Part Of Your Marketing Mix

When MasterCard sponsored Beyonce’s world tour, it rewarded its valued customers with exclusive access to backstage passes and front-row seats. Mastercard labelled this offer ‘VIP Priceless’ to tie in with MasterCard’s iconic ‘Priceless’ messaging. This campaign gained widespread recognition as one of the most innovative loyalty programs to date.

 

The success of the VIP Priceless program confirms that, if used innovatively, loyalty programs can greatly enhance the brand reputation of any company.

 

The introduction of meaningful customer loyalty programs is crucial to business growth. Brands need to offer real value, in addition to traditional rewards, to engage with their key audiences.

 

We have covered this point in a previous article, “Why customer loyalty programs are failing to engage customers”, so in this blog we’ll look at why brands need to introduce value-based loyalty programs to better engage with their core audiences.

 

Value-based loyalty programs

Only 3 percent of your newly acquired customer will ever return to buy from you again. This is an alarming statistic when you consider how much you have invested in acquiring these customers. One way to address this is to introduce value-based loyalty programs.

 

Today with the availability of so many service providers who are offering the same products/services, it is easier for a shopper to compare thousands of prices in a few clicks. If your consumers are simply thinking about the product/service you offer rather than your brand, then you will find yourself in a tough price war, where established and larger discount brands are likely to win.

 

Introducing value-based loyalty programs stops you from competing with your competitors solely on price. A value-based loyalty program enables you to surprise and delight your valued customers by rewarding them for something that previously went unnoticed.

Introducing a value-based loyalty program can help your business retain your newly acquired customers because if these customers find your service and products valuable, they will stick with you. When you reward them with offers and services they can use, it is more likely that they will develop a lasting relationship with your brand. Given the fact that customer retention costs between 4 and 30 times less than customer acquisition, customer loyalty programs can play very crucial role in today’s volatile market.

 

Customer loyalty programs can increase company growth, improve your brand image, and help you retain your customers. To learn how to use loyalty programs to enhance your marketing mix, please visit Quantiful’s website.

Pinpoint By Quantiful: Predictive Marketing For Any Business Size And Budget

Machine Learning technology delivers much deeper customer engagement. Typically, 3 to 5 percent is the average conversion rate from rules-based marketing. Machine Learning-based marketing typically delivers 10 to 20 percent in the first year and then ramps up to 30 to 50 percent over the next three to five years.

The reason for these lower conversion rates is that most companies still use rules-based, segmented marketing, where a customer is put into a ‘persona bucket’ or into a predefined segment.

But in this new generation of marketing that is not going to work anymore. The problem with segment driven, marketing automation is not just low conversion rates. Rules-based marketing automation systems are actually fuelling consumer frustration and increasingly creating brand damage. And as more and more organisations put marketing automation into the business with rules that are dumb, it creates more and more clutter in peoples’ inboxes. In short, customers are disengaging from brands and hitting the ‘delete’, ‘dump’ and ‘unsubscribe’ buttons.

Instead, businesses have to be able to send personalised offers and messages; send offers that predict a customer’s behaviour and be responsive so that if customers don’t like an offer or don’t respond to it, then they don’t get it again. And finally it has to be real time and contextualised, meaning that if a customer is in a particular place then show them offers that reflect where they are and make those offers easily and instantly redeemable.

Businesses of the future need to build intelligent engagement programs that use a machine learning capability which will enable it unlock the power of data. Quantiful’s Pinpoint allows you to do this.

Machine Learning creates enormous efficiencies because you no longer have teams of campaign managers generating and managing hundreds of offers – many of which are actually delivering negative ROI. So instead of sending out 100 offers, you can send out 30 offers but each one of those 30 offers will have a much higher conversion. And not only is that a much better use of a business’s time and energy but it will also surprise and delight your customers when they start to receive offers that actually matter to them.

Wouldn’t you like to know what your customers are going to buy next and contact them with a product offer exactly when they are thinking about it? Then visit Quantiful’s website and get in touch.

Understanding Machine Learning

Trust Quantiful for state of the art precision marketing, machine learning and hyper-personalistion services; designed to offer results.

Machine learning is a form of artificial intelligence that empowers computers with the ability to learn without being explicitly programmed. Machine learning, in other words, can be defined as a subfield of computer science that focuses on the development of computer programs which can actually teach themselves to change, grow and modify when exposed to new sets of data.

Machine learning is related to data mining. Both of these systems carefully search through massive pools of data to find strategic patterns. However, the difference is machine learning not only detect patterns but it then modifies and adjusts program actions. As such, it self-teaches to evolve and mature when exposed to data.

Facebook’s News Feeds, for example, leverage machine learning to personalise each of its member’s feeds. So if you stop scrolling in order to read, comment or like a specific friend’s posts, Facebook News Feed will show you more of that particular friend’s activity in your feed.

Facebook can do this because the software behind its News Feeds simply uses statistical analysis and predictive analytics to detect patterns in your browsing data and then utilises the findings of these patterns to populate your News Feed. If you stop reading or liking that friend’s posts, this new behaviour or pattern will be included in the data set and your News Feed will be updated accordingly.

Another example is Jaguar Land Rover New cars built by Jaguar Land Rover have around 60 on board computers that produce a massive 1.5 GB of data daily across over20,000 specialised and defined metrics. Engineers at the company leverage machine learning to analyse the data and understand how customers actually behave with the vehicle. Machine learning helps engineers design better vehicles which are tailored according to their customers’ personalised requirements. Also, and perhaps more importantly, by utilising and harnessing the true power of the data,designers can predict potential safety issues with their cars.

Why Is Machine Learning So Important Today?

Machine learning has become popular due to a range of factors such as growing volumes and varieties of data, affordable computational processing and more secured data storage. Because of this, it is now possible to securely and automatically produce models that can analyse bigger and more complex data and deliver faster, precise and accurate results.

To learn more about how machine learning, data science and hyper personalization can help your business, please visit Quantiful‘s website.

What is social analytics?

Social analytics means the collection and analysis of data, and statistics about how customers interface with an organization online.

Over the last few decades, social analytics have become one of the most crucial forms of informed business intelligence, which is leveraged to gather customer data, predict their behaviours and respond to their actions.

In our everyday life, whenever we browse an online shopping store, use a credit card to buy a product, or share special discounted offers from our favourite mobile brand on our social networks, we are continually throwing out hints of intelligence. These hints are goldmine of information for brands who want to learn about us, our behaviours and patterns.

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With every single click that we make online, specific data about our online activities are being collected and it is now very rare to find any website that does not collect user data in one way or the other. Some websites use a specific social analytics and customer activities monitoring tool, while others use various tools to do the job.

In its most basic form, website owners use a generic and popular social analytics tool, such as Google Analytics, to capture, analyse, decipher and use data. Some of the data gathered from these tools include unique website visits, most viewed pages, search terms used to find the website and the physical location of the visitors. There are many other advanced set of data which can be gathered using basic social analytics tools. The core purpose in gathering the information is to understand how to make the website a better experience for your customers.

Other than Google analytics, there are many other social analytics tools which offer better reporting features. The data gathered from these tools can help a company better understand its audience, their behaviours and activities. The data helps organisations measure their return on investment (ROI) on their social media strategies, and to then how to plan for the future use of social media to generate profit.

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To find out more about social analytics and customer engagement, please visit Quantiful.