The fourth industrial revolution is getting closer every day and it is inevitable that you and your company must be at the forefront if you want to survive in the market. Thanks to the use of various technologies, it is now easier and more accurate than ever to determine, for example, why customers have cancelled their subscription to a service or when it is cheaper to upsell your product and thus outperform your competition.
Artificial Intelligence (AI) is a technology that can improve the progress of businesses and the effectiveness of marketing strategies. MachineLearning is one of the AI developments that will undoubtedly play an important role in our digital marketing context in the coming years.
Machine learning is a branch of artificial intelligence that gives systems the ability to automatically learn from experience and improve without having to be explicitly or externally programmed to do so. This branch of AI focuses on developing computer programs that can access and use data to learn on their own without human intervention.
However, using Machine Learning (ML) as a marketer will make a difference to our competition as we can use it to improve the customer experience, which in turn means more sales .
So far, there is no clear evidence on how machine learning can be used even with little data. What is clear, however, is that ML is shaping the business world by accessing a wide range of data and producing analyses and reports that help various company departments gain more insights and dig deeper into specific business areas.
According to a 2018 Google study on the benefits that machine learning can bring to the implementation of a strategic plan, it was found that around 74% of respondents believe that their company's current goals could be better achieved with greater investment in machine learning and automation. However, it was also found that only half of the companies surveyed had the financial capacity to make such investments.
This is the case of a well-known global company that has chosen to implement machine learning to understand and improve the experience of its customers.
Beauty company Shiseido has been experimenting with machine learning and according to chief digital officer (CDO) Alessio Rossi, here's what they've learned:
"Since humans can't tell us exactly what they want, we predict the future intentions of our customers based on past behaviours and signals. The flip side of this branch of AI is that machine tools 'take' the work away from professionals, as they provide much more accurate and faster information."
While we can't teach you how to implement machine learning in your business in this article, we can at least present the key benefits so you'll be ready when the time comes.
These 4 key points are the ones where we see most clearly how the implementation of Machine Learning positively impacts our digital marketing strategies:
Increasing brand awareness is just one of the goals pursued by digital marketers. While building meaningful relationships with potential customers and other types of customers is part of their mission to optimise dialogue and develop and encourage interaction on online platforms, machine learning will quickly and reliably support and analyse the type of content and phrases that are most used and desired by the target audience . Similar to Netflix when it comes to "recommending" certain content to you.
Client churn is also referred to as turnover. This happens when clients cancel or deregister from the service.
For a company to grow, the number of new customers must of course be higher than the churn rate.
To do this, you need to be able to predict the churn rate in order to minimise it . To achieve this, you need to observe the behaviour of your customers. Machine learning is all about making predictions based on user behaviour so that you can act in time to prevent churn rates from increasing.
Imagine that an algorithm tells you in good time which customers are about to cancel. As soon as you know this in time, you can, for example, guarantee them a discount or offer them a particularly good service. The keyword is churn pred iction and it is not a dream of the future, but an applied practice in the field of machine learning.
When you talk to someone face to face, it is easy to understand how they feel. You can analyse facial expressions, tone of voice and body language. This way you can determine exactly what the person wants to tell you or with what intention. This can be lost in digital communication because direct contact is missing.
This is where machine learning plays a key role, as it scans social media comments, your emails and even the internet to warn you of negative content . In this way, your business can address the problem and even go as far as avoiding it in time. Similarly, machine learning can identify who the happy and satisfied customers of your services are, and this is an opportunity to turn them into ambassadors for your brand, for example by sending them a link to a review site to publicise their positive experience with your business.
Chatbots are on the rise and bring many benefits, especially if they are smart. Those that are smart use machine learning to improve their performance by using sentiment analysis to determine the mood of a customer message . This ensures that the bot always hits the right note, improving the customer experience.
Especially in combination with social media, the chatbot of the future can become particularly effective when large amounts of information about customers are collected and thus personal preferences and writing style can also be adapted. As a result, the potential customer receives better targeting and product recommendations. Basically, ML helps chatbots to further personalise the customer experience.
The advantages of machine learning are undisputed, even if they are not yet fully accessible to everyone. A free opportunity to benefit from intelligent algorithms even as a small company, in order to obtain comprehensive information about your company, your target group and your competitors, can be found in the free brand monitoring app rankingCoach FREE. So get ready for the future of online marketing and keep a close eye on the Machine Learning trend, because the advantages are obvious and the potential is far from exhausted.
Joshua Schofield is Marketing Content Manager for rankingCoach, the all-in-one online marketing app for SMEs. For the last ten years he has worked in content creation, digital marketing and corporate education. He holds a Masters in American Studies from the University of Nottingham.
Cloud Integration, iPaaS, SaaS, BPA… Ough, hard to keep track of all these terms. They are currently used frequently (and increasingly) in the context of automation, and it is sometimes difficult to make a clear distinction and distinction. We have already written blog posts on the terms iPaaS, SaaS and BPA, but we’ll take them up again here to make the difference.
But let’s start with cloud integration, because that’s the central umbrella term in which we embed all the other technologies in this blog post.
Arrange a free cloud integration consultation now
Arrange a free cloud integration consultation now
To illustrate these advantages, an example is suitable that we know well from our everyday work as an automation agency:
The central data to be used here is the data of a major customer. This can be the simplest information, such as the address. This address is required in numerous but completely different processes in the company: on the one hand, for correct invoicing in accounting. On the other hand, in the CRM system, where all the data of the large customer is also stored. But the address is also important in sales, for example, when employees go to the sales meeting on site.
Now the customer announces that the address of the company has changed after a move. This information will reach you by e-mail. There are now two options:
01. The e-mail is forwarded to all affected departments, accounting, sales, customer service, marketing… All persons open their corresponding program, CRM, accounting software, marketing tools (such as newsletter marketing) and change the data already stored there of the customer. This means that in multiple applications, different people do exactly the same thing: change one address.
02. But there is also an alternative: By connecting your applications, thus by integrizing them, the customer’s e-mail, or rather the information it contains about the address change, is automatically passed on to all affected applications: CRM, accounting, marketing, ERP. This does not require any clicks, because the cloud integration detects a trigger, i.e. address change, and thus automatically starts the process.
What sounds unimpressive in a single process becomes more effective when such a process occurs several times a day or weekly. Because there is a lot of data that is available in different applications and should always be correct. If these applications are cloud applications they are suitable for cloud integration.
But cloud integration doesn’t just happen. There are now a variety of applications that enable and implement this. Such tools usually allow us to link the relevant cloud applications on a central platform and define clear rules on when, how, where, how much data should be passed on and what happens to them.
To realize cloud integration, there are various applications and technologies that are sometimes used interchangeably.
We have made a first distinction between iPaaS and BPA here.
We explain the term SaaS in more detail here.
Cloud integration is rather an umbrella term that includes numerous technologies, such as SaaS, iPaaS and BPA, and this is also absolutely necessary. Cloud integration is a concept that is made possible by appropriate technologies.
However, all terms share the commonality that they are cloud-based and thus offer enormous potential for growth and scaling. In addition, they are often cheaper to implement and maintain because changed requirements are easy to implement.
As an independent automation agency, we implement cloud integration according to your requirements. We use a variety of SaaS tools and iPaas (strictly speaking BPA) software. Together we find individual solutions that are flexible and scalable.