17 min lesen
September 28, 2023
Modern marketing is very demanding and fast-paced. The multiplication of communication channels and the frequency of consumer contacts have made it more difficult to recognize each individual’s interests. It is generally accepted that without customized experiences, it is impossible to capture consumers’ attention. To focus on the user, data is needed. Information intelligence, a term referring to tactics where artificial intelligence is applied to content to personalize visitors’ online experiences, helps in this regard. Content intelligence is divided into two levels: The first level automatically identifies content with tags by converting speech into text, recognizing images, and conducting semantic analyses. On the second level, machine learning algorithms track the content published on front-end channels to collect data about the users who interact with this content. In summary, the tags describing the viewed topics are the same as the tags describing users’ interests. The resulting dataset is continuously fed into your CRM system, providing a wealth of real-time information about the user’s journey, allowing you to fine-tune your marketing automation.
Let’s first define how MA systems work: They are programmed by humans who teach them to respond to specific data inputs with corresponding outputs or actions (e.g., sending an email when a shopping cart is abandoned).
This is where machine learning comes into play: It is no longer humans who define the rules for linking products to specific types of people. Instead, the platform itself collects as much data as possible from users, including via social media, so that machine learning can analyse this data and learn the degree of a person’s affinity for our e-commerce products, adjusting accordingly. If a customer abandons a purchase, the machine automatically selects the most successful lead generation and nurturing plan to retain the customer.
These new MA functions can predict user behaviour and, thanks to data aggregation and self-learning, immediately respond with personalized solutions. You receive predictive data: You can estimate a person’s affinity for your products (e.g., 80%) as well as for products not sold through your sales platform. We can capture people’s desires in real time and determine the level of interest a group of people has in a product or service by linking MA with social activities and machine learning, just as we do.
Machine learning continuously learns from data, including past data, to increase the efficiency of its actions. It is no longer a static analysis but a refined analysis. The results are highly practical: increased loyalty, engagement, and conversion rates.
Today’s technological advances make it possible to reach a large audience through a variety of online and physical channels. But this is not necessarily an advantage. Without the right tools, it is practically impossible to determine how and where to focus your marketing efforts. Automation makes marketing much easier and less demanding. By using machine learning technologies in conjunction with your marketing solutions, you can precisely understand which customers respond to which types of approaches—such as emails, free e-books, Facebook ads, and so on. This allows you to provide your customers with a tailored experience.
One-to-One Matching is a common method for solving the matching problem. Suppose you are a retailer trying to determine how much foot traffic your physical stores have received as a result of your digital marketing efforts. You compare a user from the campaign group with a user from the control group using the one-to-one method. Both users must have similar demographic or other characteristics. The control group is then used to create a timeline to determine which differences between the groups led to a positive or negative change in habits in the campaign group.
This approach has several advantages but also some drawbacks. First, there is a risk of errors due to missing datasets. The more characteristics are added, the more complex it becomes. A predictive machine learning model can be used as a more effective alternative to one-to-one matching. It allows you to combine all your customer data to achieve a specific outcome – in this case, the likelihood that a customer will visit your store on a given day.
Your predictive model tells you how many visits a customer is likely to make in the future. It also shows whether your advertising campaigns have led to an increase in visits. By automating your marketing attribution efforts, you can track and collect data on your customers’ entire online behaviour, allowing you to see which marketing campaigns have led to revenue growth. This way, you gain actionable insights and can select your marketing campaigns based on data. As a result, you can have more conversations, increase your revenue, and generate high-quality leads.
You must constantly improve your marketing activities to achieve the desired results. This is called marketing optimisation. Every one of your marketing strategies needs to be optimized. Additionally, you must ensure that these methods align with your overall strategy. For many marketing managers, optimisation is a challenging task. To get visitors to click on your CTA button, you need to choose the right colour. To ensure that your customers read your email campaign, you need to find the perfect subject line. These are just some of the challenges marketers face.
A common method for marketing optimization is A/B testing. With this method, you must conduct two separate experiments over a specific period. The successful variant is then used to optimize your campaigns. However, A/B testing has a weakness. It is called regret and refers to the amount of money you lose each time you experiment with a suboptimal variant.
Marketers conducting A/B tests go through an exploration and an evaluation phase. The first refers to the time spent experimenting or discovering a profitable marketing strategy, while the second refers to the time spent implementing that strategy. Suppose, in your experiment, Variant B is the better option. Consequently, testing Variant A during the research phase costs you money.
Marketers often apply a technique known as “Multi-Arm Bandits” to avoid regret. With this strategy, all campaigns run simultaneously, but AI dynamically directs users to the campaign that proves to be the most successful over time. Unlike A/B tests, multi-arm bandits promote better average returns by dynamically allocating traffic in proportion to the performance of a specific ad. Aggressive exploitation is the term for this type of behaviour. For example, if your first ad outperforms the others, your algorithm directs visitors to that ad based on its performance.
However, when it comes to determining the best results, a multi-arm bandit model is not necessarily better than A/B tests. In the long run, a successful campaign does not always have to be the best one.
Many marketers today use a more complex technique known as collaborative filtering. Collaborative filtering uses similar behaviour as a filter to define or predict a customer’s purchasing behaviour. For example, if two people share a certain characteristic, it is possible that they prefer the same product targeted at that characteristic. Although the concept of collaborative filtering is simple, it is not flawless. Not all shared characteristics are predictive of future purchasing decisions.
For this reason, more advanced marketing teams turn to sequential prediction using deep learning. In sequence forecasting, data is derived from the sequence of actions a potential customer takes on a website or portal. This sequence of behaviours is analysed along with other characteristics to determine a predictive action that may or may not lead to a successful purchase. In other words, deep learning sequence prediction helps you better understand what a customer will do next.
Machine learning has its limitations, such as unacceptably high costs for data collection, computing power, and expertise. Nevertheless, machine learning is a fascinating area of artificial intelligence that marketers should consider and adapt to. We should assume that machine learning will become increasingly economical in the coming years as technology and professional skills continue to evolve. Machine learning will become a commodity in the future market, and competition will shift away from monopoly positions.
Machine learning and artificial intelligence are often used synonymously, although they are not the same. Machine learning is a type of AI that derives conclusions from data. Marketing technology platforms are becoming more efficient, allowing companies to analyse large amounts of data. Machine learning uses a combination of data, research, and software to make predictions based on patterns in the data. These patterns are difficult for humans to recognize. For example, these patterns can predict when a customer will leave a store or what they will purchase next. However, machine learning is only as good as the person responsible for the data. Machine learning may seem like a technological trend, but it is gradually becoming one of the most popular marketing automation technologies. The reason for this is that these two technologies are inseparable, and one significantly enhances the other. With machine learning, companies can increase their revenue while improving the customer experience.
Marketing is increasingly becoming a data-driven discipline. Marketers rely on this data and try to minimize uncertainty as much as possible. Here are some ways machine learning can help eliminate guesswork from your marketing efforts.
Customer Revenue. Machine learning is increasingly being used by marketers to understand and predict problems before they arise. One of the most serious problems machine learning can help with is customer churn. The number of customers who terminate their relationship with a company is known as customer churn. The percentage of customers leaving the company within a given period is used to calculate this rate. This is a crucial issue for businesses because if the churn rate is too high, the company cannot grow. The churn rate is also a good indicator of how satisfied customers are with a product or service. Fortunately, there are machine learning algorithms that can predict customer churn before it happens. By analysing a variety of factors, companies receive the information they need to reduce customer attrition.
Lead Generation. How confident are you in your lead-scoring abilities? The survey cited below shows that most marketing experts are not confident in this area. Lead scoring is notoriously difficult to assess, but also very important. Your lead generation methods will improve as you enhance your lead-scoring abilities. This, of course, will lead to new customers. This formula considers a variety of criteria, including website visits, email open rates, social media activities, and much more. Machine learning can help qualify leads and create more accurate customer profiles.
In today’s industry, being able to predict customer behaviour, innovate, and provide a unique experience is crucial. Here are three ways machine learning can help enhance your marketing automation in this regard.
