Big changes are afoot in the marketing world, and these shifts are largely down to the power of Machine Learning (ML). Such is its impact that 97% of leaders believe the future of marketing will consist of savvy marketers working in collaboration with machine learning-based automation entities.
ML techniques are used to solve a host of diverse problems, and businesses stand to benefit a great deal as we veer towards a world of hyper-converged data, channels, content, and context. For the modern marketing team, ML is about finding pieces of predictive knowledge in the waves of structured and unstructured data and using them to your advantage.
The ability to respond quickly and accurately to changes in customer behavior is critical in today's world, and machine learning. In this article, we explore the ML technologies that are being used effectively and its potential uses across businesses.
What are the emerging machine learning technologies?
Machine learning has already made its mark in healthcare as well as fraud detection, with PayPal leveraging it to fight against money laundering. And both new and emerging machine learning technologies are set to make waves in the realms of marketing. These current innovations include:
A supercomputer that combines AI with superior analytical software, IBM Watson is a fully-optimized question answering system - and it processes at a rate of 80 teraflops (trillion floating-point operations per second).
Honing in on the world of recruitment, Spark is a platform that can recommend the best candidates for vacant roles using a sophisticated algorithm. By adding a host of requirements, characteristics, and preferences, Spark's embedded application speaks to other software and returns the best candidates to companies in order of suitability.
A machine learning solution, Kafka Apache allows brands and businesses to build real-time data streaming applications or pipelines to carry out critical functions or interpret information from a variety of sources. It’s currently used by LinkedIn to handle more than 1.4 trillion messages per day, increase efficiency and help them make critical decisions based on insightful data.
Machine Learning is not only for digital-first companies
A recent study suggests that chatbots will power 85% of customer service interactions by 2020. As the prevalence of machine learning becomes more prevalent in the business world, the likes of Netflix, amongst others, are investing heavily in ML technology with a view to increasing customer engagement.
However, this cutting-edge technology is no longer exclusive to AI researchers and digital companies like Amazon and Google. If efficiently utilized, it can have an impact on key areas of the marketing big data ecosystem in 2018.
With the astute ability to automate customer service interactions in a smart, human way, chatbots serve to increase productivity in a big way. In fact, 44% of consumers in the US already prefer chatbots to humans for customer relations - a testament to the effectiveness of ML technology for business.
By democratizing the use of key data and analytics, providing staff in the front line, as well as those in technical and marketing roles, with the necessary skills, business leaders will not only get the most from ML technology but help encourage adoption throughout the business.
20% of C-level executives are using machine learning as a core part of their business strategy - McKinsey & CoTweet This
So, how exactly can businesses use ML to increase productivity and efficiency?
Automated data visualization
90% of the world's data was generated in the last two years alone. The ability to visualize notable relationships in data not only helps businesses make better decisions, but also builds confidence. There are a number of tools that offer rich snapshots of data that can be applied to both structured and unstructured data. But, at present, visualization tools are only powerful if you can interpret the underlying data.
As machine learning develops, we expect to see more user-friendly data automated visualization platforms and widgets that are arranged and interpreted through machine learning, providing a wealth of new insights, increasing productivity in the process.
"Of the marketing technologies using ML and AI today, programmatic ad buying and improvement of ad spend allocation impress me the most." - Joe Martin, Social Analytics Lead & Evangelist at Adobe
Content Management & Analysis
One of the most critical ways to encourage brand loyalty, drive engagement and forge long-lasting consumer relationships is by sparking meaningful conversations with your target customer base. As brands and businesses aspire to engage in more valuable dialogues with their customers, machine learning will be critical in analyzing particular words, phrases, idioms, sentences and content formats that resonate with specific audience members.
Take Pinterest as brand successfully using this strategy to personalize suggestions to users. By processing 150 million image searches each month it uses ML to help source content that users will be interested in based on objects they have already pinned. It also looks at captions from pinned content and which items get pinned to the same virtual boards in order to link a particular pair of jeans to a shirt pinned alongside it, despite them looking nothing alike.
This year and beyond, machine learning will prompt a progression in lexical analysis, allowing marketers to personalize their content at a campaign as well as a personal level, to vastly improve engagement.
Incremental Machine Learning
By using large and complex sets of data, machine learning tools can develop their knowledge base and abilities on an ongoing basis, helping a business to become smarter, savvier and more informed through automation.
Currently, the way in which machine learning platforms interpret predictive data can be somewhat limited, missing chunks of key data as a result. But, as incremental machine learning progresses, solutions will be created that allows new developments and layers of data to be rolled out in real-time, improving predictive capabilities and marketing execution as a result. Enhanced accuracy without any gaping holes in data means better results and incremental machine learning will play a pivotal role in that.
As machine learning becomes more prevalent, more adopted and more widely used, brands and businesses will be able to use its power to their advantage, understanding how it can yield marketing results and accelerate growth. Rather than replace existing roles, ML will broaden them, implementing human efforts rather than hindering them.
What about machine learning & leadership?
Now machine learning is picking up serious momentum; it's the responsibility of top-level executives to steer their company in the right direction, ensuring each team understands how ML will expand and enhance their roles.
This new and exciting technology will help make businesses more productive, predictive, efficient and intelligent, but at first, it may be met with a certain level of cynicism.
By thinking about what you want these ML entities to do, the way we want them to behave, or operate and how you are going to work with them to push the business forward, you will be able to roll out a transparent strategy, helping others adapt their skills and processes accordingly.
To successfully implement ML into the workplace, digital leaders must consider three critical stages:
- Description: Collecting data in databases to look at past insights and gather a well-defined snapshot of the business's aims, goals and requirements.
- Prediction: Gathering predictive data to foresee critical future outcomes for the business. To ensure this is carried out successfully, executives must ensure the quality, clarity and organization of this data is flawless.
- Prescription: This part of the ML adoption process will take the most man-machine collaborative approach. To translate these all-encompassing streams of data and use them to understand how the business should operate within its new ecosystem, the C-suite should be directly involved in the creation and formulation of the objectives that these algorithms attempt to optimize.
We’re entering a world where people and machines will work in harmony to connect, campaign and market their products and services in a way that’s more personal, efficient and informed than ever before.
By embracing the potential of ML, getting into the right mindset and helping your team understand how it can help them do their jobs more effectively, you stand to make a real impact tomorrow and long into the future.