For some time now, we’ve been hearing a lot about Machine Learning and AI, but what is it actually? And is it actually of any relevance to your day-to-day work life? We break into the details and explore the advantages and considerations before taking it on…
What is Machine Learning and AI?
Whilst most of today’s media portray Artificial Intelligence (AI) as the creation of robots that will take over the world, in practice AI is a powerful tool that can help to complete tasks ranging from the simplest of problems to complex problems that no human could ever solve.
Essentially a subset of AI, Machine Learning, is the development and creation of algorithms that take historical data and use mathematical smarts to make predictions about the data, without the need for direct human intervention.
What does that mean for you?
To cut through the noise in today’s competitive landscape, organisations need data to really provide valuable insight and informed strategies to move forward. But big data, or mass amounts of data, is of no use to anyone. So where do you start?
Enter Machine Learning. Machine Learning can be harnessed to sift through data by the tonne and manipulated to provide valuable insight into the behaviours of your customer base. These insights can then be used to inform data-driven decisions, on go-to-market strategies, real-time audience targeting and endless more applications. We see live examples of successful organisations applying these models and reaping the rewards.
Four ways you can use Machine Learning in your marketing strategy today
There are a variety of common scenarios and challenges where Machine Learning models can be applied and used as a solution. Here we look at four (4) different examples:
Product Recommendations
With borderless economies and today’s digital era, your customers are spoilt with choice and options, making the purchase journey more competitive and complicated. This is where you can leverage Machine Learning models to create product recommendations.
Product recommendations on what your customers may enjoy using or purchasing will assist in enticing your customers and leading them further down the purchase funnel. In addition, this model will enable you to optimise the timing and content of marketing to increase the likelihood of engagement from a better personalised experience.
Example
Amazon leverages a product recommendation model to increase the likelihood of purchase from their customers. Through using large sets of customer behaviour data and data about the products being sold, Amazon is able to select the optimal product to promote to a customer. A large proportion of what Amazon sells can be attributed to this algorithm, with Mckinsey reporting that 35% of what consumers purchase on Amazon come from the product recommendation algorithm (Source).
Churn Rate Modelling
Acquiring a new customer can cost as much as five times as much as to retain one. So, it is a common challenge, especially for B2B or SaaS tech organisations, where the customer churn rate is on an incline or is already at an alarming rate – which directly impacts revenue.
Machine Learning can help in this instance by identifying the likelihood of individual customers or a segment of the customers leaving the customer base.
This will help you plan and prepare to take actions to attempt to reduce the chance of them leaving. Reaching out to these customers through email, push notification or other methods can help to retain future customers and revenue.
Example
Prominent ridesharing companies are some of the biggest adopters of a churn rate model. These services rely on both a constant flow of drivers and passengers. Ridesharing companies will use a multitude of different data sources to try to predict the likelihood that a customer or driver will cease being a regular user of their service. To combat the potential loss of users, the rideshare companies will send promotions and push notifications to reduce the risk of losing these particular users.
Customer Segmentation
Combining demographics, product preferences and purchase behaviours – there are endless groups of customer segments that you can create. Often many organisations have the challenge of understanding which segments drive the most impact and identifying new segments for consideration. This is where you can leverage clustering machine learning algorithms.
These algorithms are the perfect assistant that will create customer segments. By creating customer segments, you are able to market in a targeted way and drive better outcomes – increasing the likeliness of engagement, reducing customer acquisition costs whilst retaining or increasing your revenue.
Example
Kellogg’s have proven to be a great example of how to use machine learning to optimise marketing content. Kellogg’s has used 33 million participant’s data from its Family Rewards loyalty program to create more targeted marketing for its customers. They used geographical data as specific as the postcode of customers to provide YouTube ads that are targeted based on their location. In Australia, Kellogg’s promoted the Nutri-Grain brand based on customer behaviours, which resulted in 120% increase in effectiveness and a 25% decrease in costs (Source).
Lead Scoring
It’s that age old sales question, which prospect lead do we prioritise first? Many organisations have come a long way from going through the rolodex, onto lead scoring, where there is an automated process to assign a value to a lead, based on their likelihood to become a sale. However, the vast majority of organisations that use lead scoring will simply ‘eye-ball’ the values rather than analyse the factors that cause a lead to become a sale.
Using machine learning models will more accurately predict the likelihood of a lead to convert into a sale. A well implemented machine learning lead scoring model will lead to an increase in efficiency within your sales team, a decrease in lost opportunities and a higher conversion of leads to sales.
Example
US Bank has been utilising AI in order to create a lead scoring model that has optimised their sales pipeline. Their lead scoring model also grants the company factors which lead to the customer having their particular lead score. Their lead scoring model has proven to be successful as their conversion rate increased from 4.9% to 15.2%, likely due to their sales teams ability to focus on leads that are more likely to convert to sales (Source).
Considerations to take before you dive into the world of machine learning.
We know there are convincing use cases to start leveraging Machine Learning into your organisation, so it’s important to consider these before you dive in feet first. Below we share our three top considerations:
Machine learning needs high quality data
When deciding whether a machine learning model is right for your company you first need to look into the data that you have access to. Machine learning is best implemented with a large volume of high-quality data, and you will run into issues if you have ‘dirty’ data. Whilst there are techniques to create machine learning algorithms with limited amounts of data it is still recommended that you have a large pool of data to allow your model to learn from.
Implementation and support
Machine learning is a complex topic with a multitude of possibilities for errors. Implementation of these models takes a team of people with understanding of data and the machine learning topics and without the right team from the start you are setting up for failure. Some consideration also needs to be made into the upkeep and support of these models, errors happen and the organisation needs to have a plan to get things working again after these hiccups.
Keep it simple
Whilst you can create a machine learning algorithm to solve a wide range of problems, you should always ask yourself should I create a machine learning model. As mentioned above, machine learning models are complex systems that take significant investment to implement and support. If there are simpler solutions that get similar results you should really ask yourself “should we keep it simple?”.
This article was written by Thomas Drum and was originally found here: Machine Learning and Artificial Intelligence in Digital Marketing (ideascience.com)
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