Machine Learning – We have entered the era in which marketers are inundated with data on consumer preferences. Theoretically, the data should make it easier to segment and create relevant content. Not always. Generally, the more data added to marketer’s workflows, the more time needed to understand and process it.
What Is Machine Learning? Why Do Modern Brands Need to Catch Up?
Machine learning is a subset of artificial intelligence (AI). This technology enables computers to analyse and interpret data so outcomes can be predicted accurately without being explicitly programmed. When data is entered into algorithms, they will theoretically learn in order to become more accurate and work better.
Machine learning (ML) is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Recommendation engines are common use cases for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance.
Why Is Machine Learning Important?
Machine learning is important because it gives organisations trends in customer behaviours and business operational patterns. It also supports new product development. Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning an integral part of their operations. Machine learning has become a significant competitive differentiator for many companies.
What Are Types of Machine Learning?
Classical machine learning is often categorised by how algorithms learn to predict more accurately. There are four basic approaches:
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforced Learning
Types of algorithm data scientists choose depends on types of data they want to predict.
1. Supervised Learning In this type of machine learning, data scientists supply algorithms with labelled training data, and define the variables they want the algorithms to assess for correlations. Both input and output of the algorithms are specified.
2. Unsupervised Learning This type of machine learning involves algorithms that train on unlabelled data. The algorithms scan through data sets looking for meaningful connections. The data that algorithms train on as well as predictions or recommendations they output are predetermined.
3. Semi-Supervised Learning This machine learning approach is a mix between the two preceding types. Data scientists may feed algorithms mostly labelled training data, but the models are free to explore the data on its own and develop its own understanding of the data set.
4. Reinforced Learning Data scientists usually use reinforced learning to teach machine to complete multi-step processes with clearly defined rules. Data scientists programme algorithms to complete tasks, and give positive or negative cues as it works out how to complete a task. But mostly, the algorithms decide on its own which steps to take.
Who Uses Machine Learning? What Is It Used For?
Today, machine learning is employed in a wide range of applications. One of the most notable examples of machine learning in action is the recommendation engine driving Facebook’s news feed.
Facebook uses machine learning to personalise delivery of each member’s feed. If a member frequently stops to read a particular group’s posts, the recommendation engine starts showing more of that group’s activities earlier in the feed. Behind the scenes, the engine is trying to reinforce known patterns in the member’s online behaviour. If members change patterns and do not read posts from that group in coming weeks, the news feed will adjust accordingly. In addition to recommendation engines, other uses for machine learning include:
1. Customer Relationship Management CRM software can use machine learning models to analyse e-mails and notify sales teams to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.
2. Business Intelligence BI and analytics providers use machine learning in their software to identify potentially important data points, patterns of data points, and irregularities.
3. Human Resource Information Systems HRIS systems can use machine learning models to filter applications and identify the best candidates for vacancies.
4. Self-Driving Cars Machine learning algorithms can help semi-autonomous cars recognise partially visible objects and alert drivers.
5. Virtual Assistants Smart assistants usually combine supervised and unsupervised machine learning models to interpret natural speech and supply context. If marketers expect to create more meaningful campaigns with target groupd and boost engagement, integration of machine learning can be a tool to reveal hidden patterns and actionable tactics hidden in the overwhelming amount of big data.
THese are a few ways brands can use machine learning to boost their campaigns.
Unveil trends
In 2017, ice-cream giant Ben & Jerry’s launched a range of breakfast-flavoured ice-cream – Fruit Loot, Frozen Flakes, and Cocoa Loco. They all use ‘cereal milk’. The new line was the result of using machine learning to mine unstructured data. The company discovered that artificial intelligence and machine learning allowed the insight division to listen to what was being talked about in the public sphere. For example, at least 50 songs within the public domain mentioned ‘ice-cream for breakfast’ at one point. Discovering the popularity of this phrase across various platforms revealed how machine learning could uncover emerging trends. Machine learning is capable of deciphering social and cultural chatters to inspire fresh product and content ideas that directly respond to consumer’s preferences.
Target the right influencers
Ben & Jerry’s is not the only brand leveraging the power of machine learning. Japanese automobile Mazda employed IBM Watson to choose influencers to work with for its launch of the new CX-5 at the SXSW 2017 festival in Austin, Texas. Searching various social media posts for indicators that aligned with brand values, such as artistic interests, extraversion and excitement, the machine learning tool recommended influencers who would best connect with festival fans. Those brand ambassadors later rode around the city in the vehicle and posted about their experiences on Instagram, Twitter, and Facebook. A targeted campaign, #MazdaSXSW, combined artificial intelligence with influencer marketing to reach and engage with niche audiences, as well as promote brand credibility.
Analyse campaigns
Of course, while examples above show how machine learning taps into brand’s customer bases more effectively, it is important not to overlook the real cost-efficiency of such intelligent marketing campaigns. In the past few years, cosmetics retail giant Sephora has presented a formidable e-mail marketing strategy, embracing predictive modelling to ‘send customised streams of e-mail with product recommendations based on purchase patterns from this “inner circle [of loyal consumers]”’. Predictive modelling is the process of creating, testing, and validating a model to best predict an outcome’s likelihood. The data-centric tactic led to 70% productivity increase for Sephora, as well as a 5 times reduction in campaign analysis time. This is done with no measurable increases in spending.