According to Capgemini research, 3 in 4 organizations implementing marketing machine learning into their business increase sales of new products and services by over 10%. Nowadays, marketing and machine learning slowly become inseparable, enhancing one another. No wonder that currently, there are numerous machine learning uses in the marketing sector. In this article, we will examine machine learning algorithms for marketing.
According to Forrester’s Global State of AI Online Survey, AI and machine learning contribute to companies’ growth in many essential fields. The surveyed organizations said that ML improves their:
- The customer experience (57%)
- Products and services enhancement (44%)
- New business models (43%)
- Development of new products and services (41%)
- Customer satisfaction (33%)
You don’t have to be a data scientist to assess the value of the improvements mentioned above. Machine learning is genuinely transforming marketing. It enhances marketing analysis and customer experience and helps in designing new products and services. That’s why, within the following years, it will be a sector’s standard. We may expect that every decent marketing company will have machine learning programs and applications up in their sleeve.
Before that happens, we ought to examine some of the possible applications and analyze what machine learning can do for marketing.
Machine Learning In Marketing Analytics
Machine learning in marketing analytics is currently a scorching topic and for a reason. It’s a discipline that helps you to analyze your marketing campaigns, assess their efficiency, and see possible improvements. The same applies to your website. One of the most advanced tools for that is Google Analytics (GA), and its machine learning algorithm called Analytics Intelligence.
According to Google, Analytics Intelligence is a set of features that use machine learning to help you better understand and act on your data. Its support is threefold. Analytics Intelligence:
- Answers the questions you ask regarding your marketing campaigns and gives you instant, accurate directions.
- Analyzes your data and continuously look for changes or opportunities you should be aware of.
- Models conversion probability.
This last feature is particularly exciting. Conversion probability is an incredibly important factor, that answers the question “What are the odds that given customer will make a purchase?”. It’s a coefficient calculated for each user separately. How does it happen? The Google ML algorithm evaluates transactions, and the resulting probability of conversion is expressed as an average score of 1-100 for all users during the date range (typically one month), with 1 being the least probable and 100 being the most probable. Based on that, you can easily estimate the probability of increasing or maintaining sales in the coming period.
As it is a machine learning tool, the more data Analytics Intelligence obtains, the more accurate it becomes. But that does not refer solely to your account. Google has one of the most extensive datasets of customers’ web activity, so the more marketers implement GA Analytics Intelligence into their campaigns, the more effective and accurate it is.
Machine Learning Algorithms For Marketing
Now, let’s see some more machine learning algorithms for marketing. There are four other fundamental ways in which machine learning can bring a significant shift to marketing. They revolve around the aforementioned applications and concentrate on increasing sales and customer retention.
There are at least two essential ways to enhance customer experience with aid from machine learning. First, machine learning applications can offer personalized product recommendations to help the customer find what they want. Imagine an eCommerce website with thousands of products. Based on customers’ behavior and activity on your website, a machine learning algorithm can establish a given customer’s interests and preferences. Thanks to that, the same algorithm can recommend a product that matches their requirements.
For instance, it can be done with the usage of the TensorFlow and Google Cloud Platform. TensorFlow is a machine learning algorithm with an API for training and predictions that resembles common machine learning tools. You can use it (along with GCP) to build a real-time recommendation system for your eCommerce website.
Another critical application is related with direct communication. Chatbots are the next milestone in customer service, and machine learning can improve them outstandingly! According to a recent study, conducted in Poland by the consulting company PwC, most customers perceive high-quality service as fast, problem-solving, and convenient. Customers estimate that these needs are best met by the channels in which the client has direct contact with the customer service. Internet chats were indicated by 77% of surveyed customers. And chatbots are a perfect way to make this channel available 24-7.
How can machine learning improve chatbot? With the database of previous interactions, it is possible to develop chatbot’s own consciousness of the text. This, in turn, can be used to “teach” ML algorithm how to “talk” with human customers and respond to various queries and problems. There is still a lot to accomplish, but chatbots get better and better every year, so we can expect that, within the coming years, they will become self-sufficient.
PRODUCTS AND SERVICES DEVELOPMENT
Machine learning algorithms can help in the development of new products and services, much more accurately tailored to the modern market’s needs. How so? The answer – surveys! Machine learning algorithms can conduct thousands of surveys worldwide and “talk” to potential customers.
Moreover, they will do it much faster than any human surveyor. As a result, the company possesses a vital data from hundreds of surveys. All you have to do next is to analyze this data (again, with the machine learning assistance!) in order to deliver the perfect product or service demanded by the market.
The same solution can be applied with ease to deliver various products (or various versions of the same product) to different markets. For instance, let’s say that surveys in the United States indicated that Americans’ perfect car has a four-wheel drive, and at least 250 HP engine, while in Europe, hybrid vehicles are in demand. With this knowledge, a car manufacturer can easily design a future bestseller either for North America and Europe.
Although at first, it doesn’t sound like much, you should also pay attention to this application. Nowadays, content optimization is one of the crucial aspects of SEO, the discipline that focuses on increasing your visibility in Google’s organic search. According to Backlinko.com the highest SERP (Search Engine Result Page) results have the highest CTR (Click Through Rate, simply put–a chance that someone is going to click your link), and the #1 result in Google gets over 30% of all clicks.
This, naturally, translates into the rapid growth of your company. That’s why every SEO specialist tries to get their websites as high as possible in the SERP. How can marketing machine learning be helpful here? The marketing machine learning algorithm can help you to estimate which types of content, questions, and headlines are most probable to become popular among your target audience. If they get lots of clicks, it contributes to raising your position in Google.
Moreover, it is vital to mention A/B tests. They allow marketing professionals to try out various options (not necessarily two, but usually there’s version A and version B), gather the results and determine which version works best. In turn, this means that the company can focus on creating a content highly demanded by the target audience, ergo–clickable. Companies can use feedback provided by the machine learning applications to write more targeted (selling) content.
Additionally, ML algorithms can analyze websites of your competition and their marketing activities. This allows you to understand their tactics and compare them with yours. Eventually, you are able to adjust your campaigns and advertisements to make them more efficient.
The days when marketing was done by hand are far behind in the past. Today, almost every marketing activity can be automated. Just think of:
- Google Ads
- Facebook Ads
- E-mail marketing
- Discount systems
And many more. All of them can be automated. On many occasions, this happens with the support provided by marketing machine learning. Take the example of SALESmanago Copernicus. It’s an AI-based tool to automate the vast majority of your marketing activities. This system analyzes transaction and behavioral data to prepare product recommendations for your customers.
Its analysis covers not only the behavior of a given customer but also links between them and other users who purchased the same product. The algorithm can predict what products the buyer may be interested in, thanks to data based on previous purchases made by customers with similar interests. Copernicus remembers and analyzes not only the behavior of monitored contacts (logged in) but also anonymous people visiting the website so that both groups receive personalized content on the website.
To conclude, we can definitely state that machine learning in marketing is the next milestone and significant game-changer. With the support of marketing machine learning algorithms, your company promotes efficiently and provides much better customer service. And these two factors are essential when it comes to growth and development.
However, you don’t have to build this entire machine learning structure from scratch. This is the point where we come in! Addepto serves either as a tools and solutions supplier and a counselor who helps you in obtaining the best possible results of adopting machine learning and artificial intelligence into your business.
We are always keen to talk about your challenges and ideas. Give us a call or send us an e-mail and let’s chat! Go straight to the contact section.
  https://www.forbes.com/sites/louiscolumbus/2018/02/25/10-ways-machine-learning-is-revolutionizing-marketing/#36f320b85bb6
 PwC, 2016, Customer in the Digital World