How to get started with Machine Learning

Machine Learning can bring many benefits to your business but how do you get started with implementing Machine Learning within your business?

Define your business objectives:

In order to help you define your business problems and objectives we begin by working with you to understand your current marketing processes, data and marketing automation situation. With that we also seek to understand how improving personalisation can help your business.

 Our first step is to work with you on a single data set to understand from the data any insights and opportunities around that problem. This stage is based on developing a simple Machine Learning model and identifying where Machine Learning could fit into and augment your existing data and marketing automation processes.

 Our second step is to prove the power of Machine Learning analytics by using dynamic modelling. Dynamic modelling is a model capable of learning from responses and unlocking insights from that based on the opportunity that we have identified.

Rapid Test and Learn Approach:

Following on from here, we design a rapid test and learn approach investigating a number of hypotheses to validate and observe conversion rates.

Predictive Modelling:

The next step is to connect into your existing systems and build a predictive model showing how your existing customers and potential customers will respond to your offers in the future.

Operationalise the model:

We then clip onto your existing CRM and CMS systems to trial and publish offers and train the model. Machine Learning uses algorithms to interrogate and learn from the data to prove out the hypotheses. Once proved, decisions around where and how to scale the model within the business can be made. We then add additional data sets, scale out and integrate the model within your existing platforms.

Outcome Modelling:

Machine Learning doesn’t work on a predefined target segment model. It works on an outcome model meaning that it looks for the customers with the highest conversion likelihood at the lowest possible cost. It goes into the database to find your highest converting customers, ranks them and selects the best marketing channel and offer that has the highest chance of success for your individual customers. We then use Machine Learning system generated targeting to create personalised offers/services to your highest converting customers

Continual Learning:

The response data and Feedback Loop is crucial to Machine Learning-driven predictive marketing. We capture each individual’s response to the marketing campaign, be it positive or negative, in order to further refine the targeting of your customers. As the process repeats, our Machine Learning solutions continually learn about your customers and how to better target them in order to maximise the number of successful conversions.

Choose Quantiful: Predictive marketing for any business size and budget

http://www.quantiful.co.nz

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Why do Marketers need Machine Learning?

Today’s marketers need Machine Learning because Marketing Automation, which uses traditional rules or segment based modelling and tools, isn’t working very well.

Typically, the average conversion rate from rules-based marketing is 3 to 5 percent.

The reason for these low conversion rates is that most Marketing Automation systems put your customers into ‘persona buckets’ or into predefined segments – and then keep them there.

However, in real word, customers rarely conform to these predefined assumptions. And if these assumptions are wrong then you will never be able to engage with your customer effectively.

Most Marketing Automation systems are also non-responsive, meaning if a customer rejects or ignores the offer, the same offer or type of offer keeps getting sent. But the problem with segment driven, Marketing Automation systems is not just the low conversion rates they deliver.

Rules-based Marketing Automation systems are actually fuelling consumer frustration and increasingly creating brand damage.Because as more and more organisations put Marketing Automation into their business with rules that are unresponsive or dumb, it is creating more and more clutter in customers’ inboxes. And this results in more and more customers disengaging from brands and hitting the ‘delete’, ‘dump’ and ‘unsubscribe’ buttons.

Instead, businesses should be able to send personalised offers and messages that predict and respond to your customers’ behaviour so that if customers don’t like an offer or don’t respond to it, then they don’t get it again.

These offers need 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.

Machine Learning is based on learning about your customers through the response data and knowing whether an offer was taken up, a reward was redeemed or a communication read.

The feedback loop allows the Machine Learning algorithms to learn whether what was served to the customer was right or not and continuously refine and optimise a business’ engagement with a customer.

And even if that offer was not right that is still useful feedback that needs to be fed back into the Machine Learning engine so that you don’t serve that same offer again.

This Machine Learning also 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.

Businesses of the future need to build intelligent engagement programs that use Machine Learning capabilities to unlock the power of their data and improve ongoing engagement.

Choose Quantiful: Predictive marketing for any business size and budget

http://www.quantiful.co.nz

What is Machine Learning?

What Is Machine Learning?

 There is a lot of talk about Machine Learning but what exactly is it? Machine Learning is a method of data analysis that automates analytical model building to classify an event or predict an outcome. The model is self-learning and improves itself by observing the newer data thus increasing the effectiveness of the model.

Machine learning allows computers to find hidden insights without being explicitly programmed. There are different ways of applying Machine learning. Some of the Key learning styles used for developing machine learning algorithms include Supervised Learning, Unsupervised Learning, Semi-supervised Learning, Neural Networks and Deep Learning.

 What Are The Benefits Of Using ML?

Machine Learning allows you to manage large and small data sizes. In recent years, the volume of data and variation in data quality has posed the need for more automated techniques to analyse data. As such, Machine learning can process both small and large amounts of data at a much faster rate.

 Machine Learning also allows you to manage the increasing need for accuracy of insights within your business. Machine Learning techniques identify linear and non-linear patterns in data and are able to give a higher level of accuracy to predictions.

 In addition, Machine Learning allows you to manage the need for self-learning capability. Models based on traditional techniques are static and unable to adapt to changing patterns in data. Machine Learning based models self-learn and emerge when necessary, with no compromise to precision.

Lastly, Machine Learning allows you to deal with low event rates. Situations with low event rates like credit default are hard to model by traditional techniques.

 Machine Learning Delivers Better Results

Machine Learning techniques are extremely powerful and give much better results for most analytical problems.

 Applying Machine Learning To Your Business:

Machine Learning techniques work with your own and external data sets including CRM, transactional, response data, marketing performance and any other important data sources. It uses algorithms that interrogate large data sets to look for patterns of behaviour down to n=1.

It then uses that manipulated data to predict what that customer is most likely to do in response to the engagement activity being generated. The critical piece is that Machine Learning learns from the data and the customer behaviour. And the beauty is that it’s all automated and the algorithms adapt to the behaviour of the customer.

Choose Quantiful: Predictive marketing for any business size and budget

http://www.quantiful.co.nz

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.