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<Img
img="/uploads/marketing_trends_with_data_analysis_and_machine_learning_0e6843230d.png"
alt="Forecasting Marketing Trends with ML"
/>

The volumes of information in the world are enormous, and trends in digital marketing change at a staggering pace. Just as marketers finish studying the market and digital environment, they find themselves having to start their research anew.

It's crucial to think about the future in advance: how consumer behavior, fashion, and promotional tools will change. The first option is to "guess" and rely on vision, intuition, and experience, making marketing management unpredictable.

The second path is [**to use a data-driven approach**](https://altcraft.com/blog/data-driven-marketing) that provides accurate forecasts. For example, choosing data analysis in marketing through machine learning to "predict" trends.

The article will explore why forecasting marketing trends is important, the role of it, and how machine learning works in this context.

## Why forecast marketing trends

Audience preferences change rapidly, so marketers need to "catch the wave" before customer interests shift. This applies to both global trends and local changes. Lagging brands won't gain anything if they miss a trend. Knowing in advance what will be relevant allows for preparation. Then the company's marketing strategy can adapt to changes.

Forecasting trends is a path to precise planning and risk reduction. Marketing analysis will indicate where to direct resources and how to gain benefits. Trend forecasting is useful not only for creating advertising campaigns but also for the brand's products themselves. This approach simplifies promotion because the product already has an advantage. For example, a popular color, features, and the right values that meet society's needs.

"Looking" into the future provides insights into which creative marketing campaigns and promotion formats yield results. There's still time to come up with new ideas and explore new tools and communication channels.

Tracking trends in marketing is crucial for businesses to avoid becoming outdated. The competition for attention continues constantly, and those who can adapt to changes and offer something new emerge victorious.

## Data analysis for marketing

When marketing is based on data analysis, forecasting becomes more accurate, and marketing becomes more manageable, reducing guesswork. Predictive analytics is a reliable way to plan marketing actions and advertising budgets.

A sufficient amount of information about past campaigns will also indicate weaknesses and guide efforts to work on mistakes. Every marketer should conduct data analysis before launching advertising, sending out campaigns, or creating a content plan to properly [**segment the audience**](https://altcraft.com/blog/how-and-why-to-segment-target-audience).

This way, the development of the marketing strategy will be tailored to a specific user group, enhancing [**personalization**](https://altcraft.com/glossary/personalization-what-it-is-and-why-personalized-marketing-matters), ultimately leading to greater predictability of results.

## Machine learning for marketing

The volume of data is growing, and a human cannot manually process such vast information. The problem is solved by [**marketing automation**](https://altcraft.com/blog/what-is-marketing-automation-and-how-it-affects-business-development), which includes machine learning — a method of artificial intelligence. Robots collect and analyze large datasets, and machine learning transforms the information into something useful for marketing purposes. Marketers begin to:

- **Properly understand and interpret the gathered information.** The application of machine learning eliminates human factors and subjective assessments. Information about orders, purchases, feedback, and preferences is not lost and is considered in data analysis. <p>
</p>
- **Explore the customer in 360 degrees.** AI technologies can process terabytes of internal (customer database, email campaign results) and external (social media comments, likes, reposts, etc.) data to identify patterns. The result of such analysis is precise personalization of offerings. <p>
</p>
- **Predict trends.** Machine learning in marketing is essential for identifying future trends in customer behavior and user preferences. This approach is valuable for refining products, services, and content.

Predicting trends through machine learning differs from trend watching, which is the method of finding trends here and now because ML forecasts trends are based on historical data and "looks" into the future.

## How machine learning predicts trends

To apply machine learning in business, researchers go through the following stages:

1. Gather information. Sources include internal databases from CDP, data from external third-party resources, and so on. <p>
</p>
2. Cleanse and enhance the information. It is crucial to minimize errors and collect comprehensive data to enable the AI to learn correctly. This stage involves adjusting the data to the required format. <p>
</p>
3. Determine which machine learning model to use for forecasting.

<Table>
| Learning model | What it means | What kind of forecasting is useful for |
| ------ | ------ | ------ |
| With a trainer | A person provides the system with data labeled according to specific rules. | When results are limited in number; if categorization of data is needed. |
| Without a trainer | The algorithm retrieves the data without partitioning. | To predict future purchases, and to clean up information for the next steps. |
| Involving trainers in part | A small fraction of the data is labeled, but most of it is unlabeled. | When you need to process documents where there is a lot of information. |
</Table>

4. Initiate model training and monitor its success: check the results it produces. If the forecasting is successful, the model is potentially used for other predictions with new data.

## Examples of trend forecasting

Since 2018, the [**Pyaterochka**](https://www.retail.ru/news/x5-primenit-mashinnoe-obuchenie-v-marketinge/) retail chain has been using machine learning to customize special offers and promotions in the future. The system relies on customer reactions and sales volumes, with the data anonymized. As a result, the company achieves a personalized approach.

[**Walmart**](https://www.clickz.com/5-businesses-using-ai-to-predict-the-future-and-profit/112336/) predicts the likelihood of customers purchasing a product and personalizes product offerings on the website. Additionally, predictive analytics manages delivery times and other processes to ensure customers receive their products on time.

Platforms dealing with content and advertising also utilize machine learning. For example, on [**VKontakte**](https://vk.com/@smm_pub-neiroseti-za-kulisami-vk-kak-socset-primenyaet-algoritmy), algorithms are essential for optimizing advertising campaigns, particularly for suggesting an audience similar to the advertiser's customers. In other words, the algorithm predicts who else might be interested in the brand's offer.

[**IBM**](https://www.ibm.com/products/weather-company-advertising-accelerator)'s service generates personalized creatives for advertising campaigns and adapts variations for different displays to appeal to users and display correctly. User engagement data is used in this process, and brands like Chevrolet and Mastercard adopt this approach.

[**Netflix**](https://www.argoid.ai/blog/netflix-ai#:~:text=Machine%20learning%20technologies%20are%20at,their%20rating%20and%20watching%20history.)'s recommendation algorithm is based on machine learning, considering user preferences and other factors. Content views, time, ratings, and other factors are taken into account. The [**Rive Gauche**](http://www.mskit.ru/news/n207949/) company also achieves personalization through machine learning tools, predicting customer behavior based on data and creating campaigns that [**enhance loyalty**](https://altcraft.com/blog/customer-loyalty) and sales.

## Challenges of machine learning and data analysis

**Dependency on data quality.** Predictions about the future are based on existing information. If data is insufficient, incomplete, or incorrect, there is no way to correct it in real-time.

**Limited coverage of offline business.** Machine learning may not fully capture user actions in the real world, especially in offline scenarios. While tracking all orders, user actions, and reactions to content is feasible in the digital realm, it becomes more complex in offline stores. However, AI can still be applied to some extent.

**Need for significant data volumes.** Machine learning requires substantial datasets, often in the thousands, and insufficient sample sizes may lead to unreliable results.

**Cost.** The task of forecasting requires a trained specialist, and resources are also spent on the system itself, configuration, and integrations. Implementing ML is feasible only for businesses with a sufficient budget for these purposes.

## The future of data analysis in marketing

Marketing is undergoing a transformation, integrating technologies as the average user engages in [**20-500 interactions with a brand**](https://www.invoca.com/blog/state-of-data-driven-marketing-update-your-strategy) before making a purchase. This generates vast amounts of information that need to be tracked and processed accurately. Additionally, users expect a personalized approach and recommendations for suitable products ([**91% of consumers**](https://www.wunderkind.co/blog/article/smarterhq-wunderkind-audiences/)). Without IT tools, marketers cannot handle such tasks, so the use of machine learning, artificial intelligence, [**neural networks**](https://altcraft.com/blog/neural-networks-in-business-marketing-and-life), and Big Data will continue to be implemented in marketing practices.

## Summary

It is crucial for brands to stay on trend, tracking global trends, and anticipating customer behavior for budget planning, creating advertising campaigns, and generating ideas. Data analysis becomes a reliable way to "see" into the future, eliminating guesswork.

To predict trends, machine learning algorithms are employed, providing a comprehensive view of the customer. They guide what offers to make to encourage user purchases.

For trend prediction through ML, information is collected and processed (large datasets), a model is selected, and the algorithm's performance is launched and monitored.

Machine learning is a reliable method for prediction but comes at a cost. Significant data volumes are required for ML to function properly. There are also limitations for predictions in offline business. Marketing is already intertwined with IT and will not thrive in the future without AI, ML, Big Data, and other information processing methods.


Brands must track trends with data analysis, using machine learning for customer behavior prediction and future-proofing marketing with AI and Big Data.

marketing-trends-with-data-analysis-and-machine-learning.png

What is trend forecasting in marketing and how to use ML effectively.

How to Predict Marketing Trends via Data Analysis & ML

Forecasting Marketing Trends with Data Analysis and Machine Learning

<Video
url="https://www.youtube.com/embed/SFc7HTAp1uI?si=Y5YOjepnxvqd-R8G"
/>


In 2023, it is challenging to envision a news feed without the concept of "machine learning." It is anticipated that the market for this technology will reach 225 billion dollars by 2030. For comparison, the forecast for 2023 predicted [**growth to 26 billion dollars**](https://www.fortunebusinessinsights.com/machine-learning-market-102226).


In other words, machine learning is here to stay for the long term. The technology will continue to be integrated into various aspects of human life: predicting, classifying, generating objects. Therefore, it is essential not to fall behind and understand what machine learning is, how ML emerged, and what methods and techniques it employs. We will cover these topics in our article.


## Essence of machine learning


Machine learning is a branch of [**artificial intelligence**](https://altcraft.com/blog/neural-networks-in-business-marketing-and-life) that simulates human thinking processes. Unlike traditional software that follows a rigid sequence of actions, machine learning involves continuous "thinking," mimicking the way the human brain operates.


Machine learning entails making predictions based on vast amounts of data, where algorithms discover patterns. The concept is closely related to neural networks, which are part of one of the types of machine learning and operate through deep learning.


**Machine learning algorithms are used to create services that:**


- **Recommend products, services, and content,** including based on user actions. For example, online cinemas suggest movies and series based on viewing history. <p>
</p>
- **Forecast the future:** possible trends, sales volumes, patient illnesses in medical clinics based on their medical history, events, and more. Machine learning models determine credit ratings in banks and predict the behavior of customers who may struggle to repay a loan. <p>
</p>
- **Recognize objects in images and videos, understand speech, and analyze text.** This speeds up routine processes, simplifies human work, and even enhances security. For instance, recognizing vehicle license plates through surveillance cameras adds an extra layer of control.

## How machine learning works

1. **Defining criteria for selection and data collection** involves gathering vast amounts of information. <p>
</p>
2. **Data preparation** involves labeling the data, which is crucial for ML algorithms to recognize the desired elements. Currently, labeling is mostly done by specialists and is less frequently automated, making this process time-consuming. <p>
</p>
3. **Data verification and pattern identification** identifies errors in the data to correct them and make the next stage more precise by searching for patterns. <p>
</p>
4. **Model selection and training commencement.** The algorithm processes the data and produces results. <p>
</p>
5. **Obtaining and evaluating algorithm performance.** At this stage, errors are corrected, and algorithms may be adjusted for further operations.

<Img
img="/uploads/functioning_of_machine_learning_773a210191.png"
alt="Functioning of machine learning"
/>


## History of machine learning

### 20th Century

The first instances of machine learning date back to the mid-20th century. Scientists Walter Pitts and Warren McCulloch successfully mimicked a neuron in 1943. There is also the notable secret project by the U.S. Army in 1946, aimed at creating tables to improve shooting accuracy through software.

The flourishing era of [**machine learning**](https://altcraft.com/blog/customer-data-for-machine-learning) began in the 1950s, marked by the appearance of Checkers-playing, a program by Joseph Weizenbaum, Frank Rosenblatt, and Arthur Samuel that could play checkers.

During the same period, the Mark I Perceptron, a neural network model simulating human brain function, was introduced by Rosenblatt. In the late 1950s, the SNARC, a neural network capable of handling complex tasks, was developed by American scientist Marvin Minsky.

The term "machine learning" was officially coined in 1959 at a conference at Dartmouth College (USA).

In the 1960s, the prototype of the first virtual assistant was launched – ELIZA, a system that simulated a conversation with a psychotherapist. In the same decade, an algorithm capable of recognizing and classifying data was invented. Towards the end of the 1960s, Bernard Widrow and Sebastian Trun developed the backpropagation algorithm, a significant advancement in improving neural network performance.

In the 1980s, scientists once again employed technologies for gaming, this time in chess. Young researchers from Carnegie Mellon University devised the ChipTest system. Based on this development, the supercomputer Deep Blue emerged in the late 1990s, defeating the renowned chess player Garry Kasparov. This showdown between man and machine showcased the power of machine learning in predicting robot actions.


### 21st Century

In our time, the computing power has increased, and data volumes have grown, propelling machine learning (ML) into a new stage of development. In the 2000s, the concept of "deep learning" emerged. The early 2010s marked an era of discovering new projects related to neural networks. Notably, Google entered the race, and by 2012, a Google X Lab algorithm had learned to recognize cats in images and videos. Google also introduced the Google Prediction API, a service for analytics and machine learning.

Amazon, Microsoft, Facebook, and other giants were not left behind; they developed their platforms where machine learning methods were applied. Mark Zuckerberg's company, in particular, introduced the DeepFace technology, which achieved high-precision facial recognition.

In the 2020s, the role of machine learning continues to grow. Technologies are already operational in the fields of finance, healthcare, industry, and transportation, gradually integrating into people's daily lives.


## Machine learning techniques

<Table>
| Method | Description |
| ------ | ------ |
| Supervised learning | The model is trained on already prepared data (labeled) and ready answers. The algorithm has to choose the answer to the hypothesis: right or wrong, and the human has to control the result. This is a relatively simple way of ML that is suitable for data classification. It is learning with a teacher that helps to distinguish between objects. |
| Unsupervised learning | For training, information without markup is taken. The algorithm itself must understand the patterns and features that distinguish objects. Suitable for prediction and for automated cleaning of input data, recognizing and understanding human language. |
| Semi-supervised learning | Here marked and unmarked data are used. The rest of the partitioning is performed by the algorithm itself according to the specified parameters. Such training is useful for processing long files (e.g., longreads). |
| Reinforcement learning | The method is based on a point system that the model receives depending on the actions. It is similar to a reward system in a game, where the player gets a bonus for a correct action. |
</Table>


## Machine learning methods

### Naive Bayes classifier

The method is named after Bayes' theorem. Its foundation lies in determining the class of an object based on its features. Considered one of the simplest methods, it is used for categorization, object recognition in images, and identifying [**spam emails**](https://altcraft.com/blog/how-to-send-millions-of-emails-and-avoid-spam-filters).

<Img
img="/uploads/naive_bayes_classifier_in_ml_3fba99f3e8.png"
alt="Naive Bayes classifier in ML"
/>

### Decision Trees

In this method, the model identifies the relationship between events and objects, as well as the consequences of their interactions. Visually, this scheme can be depicted as "branching trees," representing different development scenarios depending on the choices made. It is similar to algorithmic tests with "yes" or "no" answers, leading to a result. The method's advantage lies in its systematic approach, but it is not applicable everywhere.

<Img
img="/uploads/decision_trees_in_ml_f32278a671.png"
alt="Decision Trees in ML"
/>


### Logistic regression

A statistical method that has found application in machine learning. The logistic function determines the relationship between one or more variables. Objects are categorized into two classes based on specific values in the range of 0-1.

Determining credit potential, predicting sales success, advertising campaigns, and other probabilistic events are identified through the logistic regression method.

<Img
img="/uploads/logistic_regression_in_ml_b1142fb219.png"
alt="Logistic regression in ML"
/>

### Support vector machines

Incorporating several algorithms, support vector machines classify objects in a hyperplane separated by vectors. The system's task is to find the most appropriate positioning of the line in the plane for optimal classification.

This method solves complex problems and can determine factors like whether there is a man or woman in a photo, what ads to show a user on a website, and so on.

<Img
img="/uploads/support_vector_machines_in_ml_618826ad84.png"
alt="Support vector machines in ML"
/>

### Linear regression

The essence of this method is to relate one variable (N) to another variable or several variables. Conceptually, on a plane, there are data points for which one needs to find the most suitable line that connects them.

<Img
img="/uploads/linear_regression_in_ml_8fe2d66c32.png"
alt="Linear regression in ML"
/>

This algorithm easily predicts trends and determines what information is missing in simple linear sequences. For example, what needs to be added to the sequence 10, 20, 30, 40, 50…

### Assembly methods

An ensemble of methods where various classifiers are generated and data is divided based on averaging or voting principles. This approach reduces the probability of errors. Assemblies include:

- **Bagging:** Basic models are trained in parallel with the collection of more complex classifiers.

<Img
img="/uploads/bagging_in_ml_e74a5f22b8.png"
alt="Bagging in ML"
/>

- **Boosting:** Strong models are created using weak ones. Training occurs on previous classifiers to improve and correct errors for subsequent ones.

<Img
img="/uploads/boosting_in_machine_learning_367667ed4d.png"
alt="Boosting in machine learning"
/>

### K-Means clustering

One of the clustering methods in machine learning. The algorithm utilizes a random number of clusters K. Each has its center (point), from which the distance to each data point in the groups is calculated. Then the center of each cluster is listed, vectors again partition data points into clusters. This process continues until no further changes occur.

Clustering is useful for biological research, IT technologies, and sociology.

<Img
img="/uploads/k_means_clustering_a983f099a5.png"
alt="K-Means clustering"
/>

### Generative adversarial learning

This method is based on the operation of neural networks that, in a way, compete with each other (hence the name). The algorithm involves discriminator and generator networks. The discriminator attempts to classify incoming data and correlate it with categories, while the generator, conversely, aims to choose images that fit the categories. The generator creates objects, and the discriminator checks their authenticity. This algorithm is capable of generating images of non-existent people that resemble real ones.

<Img
img="/uploads/generative_adversarial_machine_learning_3bf8f109d1.png"
alt="Generative adversarial machine learning"
/>

## Summary

Machine Learning is a form of artificial intelligence that simulates the activity of the human brain. The initial machine learning algorithms emerged in the 1940s and 1950s, but significant growth was achieved in the 21st century, and they are now utilized in various aspects of human life.

ML methods are classified based on human involvement. There is supervised, unsupervised, semi-supervised, and reinforcement machine learning.

Additionally, methods are categorized by the algorithm's operation. This includes Bayesian classifier, decision trees, logistic regression, assembly methods (bagging, boosting), K-means clustering, generative adversarial learning, among others.

**Product of the company Meta, prohibited on the territory of the Russian Federation.*


ML methods are categorized based on human involvement, comprising supervised, unsupervised, semi-supervised, and reinforcement learning.

machine-learning.png

About what machine learning is, how ML appeared, what methods and methods it uses.

Machine Learning: Essence, History, Methods & Techniques

Methods and Techniques of Machine Learning

<Video
url="https://www.youtube.com/embed/dmc-FcSRKgk?si=hkyvSz2raSWiRuDM"
/>

Not long ago, voice commands from a person to a computer were scenes from science fiction movies. Today, [**over 50% of people use voice search to find information on the internet**](https://www.demandsage.com/voice-search-statistics/#:~:text=Voice%20Search%20Statistics%20(Top%20Picks,searches%20take%20place%20every%20month.) almost daily. Voice search offers a convenient way to quickly and easily access information in various aspects of life, such as stores, services, weather, current news, and more, especially when users are driving or have their hands full.


Despite voice input, text remains the foundation of communication, as the system converts spoken language into a textual format to search for answers on the internet and provide relevant results. Voice search opens up new opportunities for marketing and promoting a company. In this article, we will delve into voice search, its advantages, and its integration into businesses.


## What is voice search and why businesses need this tool


Voice search is an automatic speech recognition technology that translates spoken language into text and searches for answers on the internet. Voice-activated search engines are powered by [**artificial intelligence**](https://altcraft.com/blog/what-distinguishes-chatgpt-bard-and-bing-in-the-world-of-marketing) and are a prospective channel for promotion as they take into account SEO optimization rules and provide results that the system deems relevant.


Speech-to-text input is facilitated through special software known as voice assistants such as Google Assistant, Microsoft Cortana, Apple's Siri, Alexa, Bixby, and other systems. To activate the program, you simply need to say a specific phrase followed by your request, for example, "Okay, Google, what's the weather in London?" Voice search works on Android, Apple devices, PCs, and various other systems.


<Img
img="/uploads/voice_assistants_bf4af40d0f.png"
alt="Voice assistants"
/>

The ability to provide verbal commands to search engines is essential for brands looking to expand their audience. It's also another way to engage in primary marketing communications and promote through voice marketing, which includes [**podcasts**](https://altcraft.com/blog/podcast-marketing) and voice-based social platforms like Clubhouse.


## Advantages of voice search


**Voice search on the internet is more detailed** than text-based search. People tend to use more extended and natural language in their queries, allowing search engines to provide better results. Many people find it easier to speak a few words rather than write them down, resulting in more natural and conversational queries.


**Enabling and using voice search is straightforward**, leading to a growing number of users activating this feature. It is predicted that the voice assistant market [**will grow to $56 billion**](https://www.meticulousresearch.com/pressrelease/280/speech-and-voice-recognition-market-2030) by 2030.


**Opportunities for SEO optimization**, which is an essential part of marketing and promotion channels. Detailed queries encourage an extension and clarification of the semantic core, making words and phrases more accurately respond to user queries. The closer the information matches the user's query, the better the indexing.


**User-friendliness.** Voice typing can be done without having to hold the device, which is particularly convenient while driving or when your hands are occupied. It's easier to ask by voice rather than stopping and typing text hastily or on the go.


**Enhanced accessibility for visually impaired individuals.** Users don't need to adjust keyboard settings to a larger font size as voice assistants provide spoken responses to user queries. This helps integrate individuals with disabilities into the digital world.


**Continuous technological advancement.** Voice search is here to stay and continually evolves. It has already become an integral part of everyday life through smart speakers, which control lighting, play music, answer user questions, and provide entertainment. Developers continue to improve speech recognition algorithms, such as Google's Hummingbird, which aims to understand non-standard queries, unclear speech, and complex formulations. As voice search capabilities expand, businesses need to adapt to this trend.


## Which businesses need voice search


Voice typing is particularly important for businesses where users need information promptly:


**Brick-and-mortar stores and services.** When people urgently need to buy something (pet food, medicine, car parts, etc.) or access services (manicures, dental appointments, dry cleaning), a voice assistant can expedite the search process. Users can quickly obtain information about addresses and contact details, sometimes without the need to visit a website, as they want the product or service promptly.


**Cafés and restaurants.** This is especially relevant in tourist areas where proximity plays a crucial role. Users typically prefer establishments nearby when they want to eat quickly.


**Delivery services.** Speed is of the essence when it comes to delivery. People want their items promptly, so they search for nearby companies that can quickly deliver restaurant meals, groceries, packages, and more.


**Taxi services.** Ordering a taxi usually only requires a contact number rather than a detailed website exploration. Being at the top of search results after a user employs voice input is advantageous for these services.


**E-commerce.** Voice assistants can add items to the cart, place orders, and even recommend additional products based on the user's purchase history (as seen with Amazon's Alexa). [**Personalization**](https://altcraft.com/glossary/personalization-what-it-is-and-why-personalized-marketing-matters) and speed appeal to users who save time while shopping online.


**Information portals.** Voice typing is convenient for quick data searches, news, and answers to questions. Web resources, including news outlets and content-oriented blogs, benefit when they optimize their content for voice search.
## How to integrate voice search into your company's marketing


### Use long keywords and complete phrases


In voice search, people often speak in extended phrases, such as questions they would ask in a conversation. For example, "*Where can I buy cat food in London?*" instead of the more typical "*buy cat food London*" used in regular text searches. Therefore, it's essential to include complete phrases in your content, even if they are low-frequency. Don't forget to incorporate conversational words that frequently appear in queries.


### Add a FAQ section


A section with specific concise answers will simplify the search and increase the chances of ranking higher in search results. Additionally, there's an opportunity to appear in Google's answer box, where a short text from your website and a link to it will be displayed.

<Img
img="/uploads/image1_1223cea23c.png"
alt="FAQ section as a promotional tool"
src="FAQ section increases the chance of getting to the top of search results"
/>


### Adapt content for mobile devices


[**According to Exploding Topics**](https://explodingtopics.com/blog/mobile-internet-traffic), 92% of people access the internet from mobile devices. It's logical to assume that users also prefer to use voice assistants from their smartphones. Therefore, the website should open conveniently and maintain the same functionality as the desktop version.


### Add your company's addresses to all possible platforms


Create business listings on Google, and all relevant directories. This is crucial for local search, enabling users to find services near their location. Make sure to include contact information and a link to your website in these listings.


*For example, if someone experiences a flat tire late at night and uses voice search to find "24-hour tire repair near N district," having your business listed with the specified address and operating hours could lead the search engine to recommend your service. This approach can significantly improve your company's visibility in local search results for queries containing the term "nearby."*


### Improve website usability and content structure


Standard SEO optimization criteria also work to enhance the effectiveness of voice search. If the website is user-friendly, loads quickly, and functions correctly, it is more likely to rank higher in search results.


## Summary


Voice search is an alternative to traditional text input, where technology converts spoken language into textual queries, providing quick access to information and enabling more comprehensive questions. Voice search is applicable on both PCs and mobile devices, and its ease of setup and use makes it a promising tool. Businesses should consider leveraging voice search for promoting their products and services as it offers a cost-effective way of generating leads, especially for cafes, restaurants, taxi services, and delivery companies.


To ensure users can find their business through voice assistants, companies should optimize website content with long-tail phrases reflecting common user queries, provide sections with straightforward answers to common questions, and ensure mobile compatibility. Creating business listings with company addresses on mapping services is also crucial.

Voice search converts spoken language into text queries, benefiting businesses, especially in food services and delivery industries.

golosovoj_poisk_95f6218ce1.png

Voice Search as a Digital Marketing Tool for Business

Voice Search as a Digital Marketing Tool: Integrating It into Your Business

<Img
img="/uploads/dannye_o_klientah_dlya_ml_64932f15a2.png"
alt="Machine Learning: How to Use Data Infrastructure for It"
/>

Machine learning (ML) is becoming increasingly important, and those who quickly grasp the functionality of ML will gain an advantage over their competitors. Many companies of varying sizes use purchased technologies to develop marketing strategies, but not all market players accurately assess the suitability of their databases for ML.

Progress does not stand still: companies will be able to derive real benefits from machine learning technology if they learn to collect high-quality source data. The quality of input information directly affects the results of ML.

## How to unlock the full potential of machine learning

To ensure that machine learning models function effectively, it is crucial to load them with a large amount of data. The more comprehensive the information you provide, the more productive the results from machine learning will be. Incorrect or contradictory data can lead to inaccurate predictions by the models.

The accuracy of predictions is important for making informed managerial decisions. "Good data" reduces the risk of errors.

Machine learning algorithms rely on the information they are provided to continue learning and improving. Information is also necessary for adapting the model to new conditions and predicting events in current realities.

## Terms and definitions in machine learning 

**Machine learning algorithms:** These are code elements designed to explore and analyze incoming data. Algorithms can be appropriately referred to as the "brain" of machine learning.

**Machine learning model:** This is a file that is trained to recognize correlations and patterns. Models are trained based on sets of data using a specified algorithm.

**The learning process in machine learning:** This is an ongoing process of adjusting the model based on incoming information. Training concludes when the model creator is confident in the accuracy of the predictions.

*Facial recognition is a widely used function of machine learning. Models are trained on thousands of photographs for an extended period of time.*

**Training on big data:** This is a branch of machine learning that specializes in processing large volumes of information. Without Big Data, achieving reliable results is challenging.

## Stages of machine learning

Let's suppose ML scenarios may seem standard, but the application of this technology improves the customer experience and enhances [**personalization**](https://altcraft.com/glossary/personalization-what-it-is-and-why-personalized-marketing-matters). Additionally, it improves audience segmentation, enables customer churn prediction, and generates quality analytics. The more data you have, the more accurate the forecasts. Therefore, companies need to choose a reliable customer data management platform to accumulate sufficient information. We recommend [**CDP Altcraft Platform**](https://altcraft.com/en).

<Img
img="/uploads/image1_05d977bfae.png"
alt="Customer digital profile on Altcraft CDP"
src=""
/>

**The first stage** of machine learning is the processing of incoming information. It concludes with obtaining a refined dataset. In this initial stage, it is important to identify relevant sources of information. Then, tools are applied to quickly process, validate, and cleanse large volumes of data.

Otherwise, this stage can be called 'data cleaning,' which takes up a significant amount of time and effort. If the input information is in the wrong format or lacks proper context, the training will be incomplete, and the model will not produce accurate results.

**The second stage** is the processing of test datasets. It is at this step that the time and resources invested in the first stage begin to pay off. Machine learning algorithms come into play, and the information from the previous stage becomes the 'test set.' The sets are constantly changing as the process is iterative. Data analysts carefully monitor the model's response to new tests throughout the process and adjust the accuracy of predictions.

**The third stage** essentially involves testing the machine learning model in real-time. It begins when the model demonstrates reliability in the previous phase.

## The route of data

The data journey involves a chain of data transformations, including the collection and processing of input information using machine learning algorithms to enable the model to make predictions and decisions.

### Step 1: Data collection

Information is collected from various sources such as [**databases**](https://altcraft.com/glossary/everything-about-the-customer-database), pixels, platforms, social networks, etc. It is important to have relevant and reliable information about the problem that the ML model is intended to solve.

Preparing customer data for machine learning is not an easy task. This question becomes particularly acute when multiple sources of information are involved, both external and internal.

For companies, input information includes user activity on the web, data on purchases made, interactions with customer service, and monitoring of customer activity in mobile applications.

The process is further complicated by new regulatory acts (such as [**GDPR**](https://gdpr-info.eu/)) that require companies to obtain prior consent for the use of customers' personal data. Without the users' consent to use their information for machine learning, it cannot be used.

### Step 2: Data standardization

Collected data is useless without standardization: it is converted into a format that can be processed by machine learning algorithms, typically CSV or JSON. It is important for the data to be consistent. Duplicate or outdated information is removed, and missing values are compensated for. Data analysts spend a significant amount of time cleaning and harmonizing "dirty" data.

Event specification involves real-time quality checks on incoming data sets. When a new event occurs, the verification is performed. This ensures the cleanliness and alignment of the information for machine learning purposes.

## Why do we need data infrastructure?

Reliable information is crucial not only for machine learning operations but primarily as a foundation for making informed, [**data-driven management decisions**](https://altcraft.com/blog/data-driven-marketing). Decisions that are not based on data have a high probability of resulting in financial losses.

Customer information is collected and analyzed to improve products according to consumer needs and expectations. In addition to the product itself, channels of promotion and communication with customers are adjusted based on the gathered insights.

A strong information foundation leads businesses to growth and enables the automation of routine tasks. The freed-up time allows employees to focus on developing conceptual solutions. The result is increased workforce efficiency, savings in the marketing budget, and growing company profits.

Importantly, without proper data collection, organization, and management, companies will not be able to comply with [**GDPR**](https://gdpr-info.eu/) and [**CCPA**](https://oag.ca.gov/privacy/ccpa) requirements.

Handle your data intelligently with the help of [**CDP Altcraft Platform**](https://altcraft.com/en). The platform also includes an ML module called Optimal Sending Time. It allows you to determine the best time to send emails, facilitating better customer engagement. This can significantly enhance user involvement with your messages.

Machine learning uses algorithms and data to enable computers to learn and make informed decisions, enhancing business operations and customer experience.

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Discover the power of machine learning: transform your business with data-driven insights and predictive capabilities.

Machine Learning: How to Use Data Infrastructure for It

How to Use Customer Data for Machine Learning

<Img
img="/uploads/benefits_of_chatbots_for_businesses_776h517_2dddff90e2.jpg"
alt="Rule-based chatbot"
/>

The digital age has led to high customer expectations. From eCommerce, healthcare, marketing, or finance, instant communication is essential in every business. Today, chatbots provide businesses in different industries with the capability to keep customers engaged and satisfied.

## What Is a Chatbot?

You've likely encountered this already. Have you gone to a website, and a little chat window pops up with a generic profile picture and name to go along with it? That's a chatbot, usually. Sometimes it's a real person, but that's extremely rare owing to logistics. Generally, you're encountering a chatbot.

<Note>
A chatbot is a software application that uses Artificial Intelligence (AI) and Natural Language Processing (NLP) to process and understand human intentions and provide guidance to desired outcomes. It works as a virtual assistant to enhance your customer experience.
</Note>

Basically, chatbots for marketing often leverage existing data to understand submitted queries. They also analyze appropriate answers based on their training period. And with machine learning technology, they learn context and improve their efficiency.

## How Do Chatbots Work?

Chatbots use advanced machine learning and NLP algorithms to learn from existing data. Typically, NLP is the ability for computers to process and understand human speech and respond in human language. It's designed to make the interactions mimic communication between two people.

### Types of Chatbots

- **Rule-based Chatbots**

Rule-based chatbots require human input to choose predefined options. And since the selections are limited, these chatbots tend to be the slowest in terms of customer service.

<Img
img="/uploads/rule_based_chatbots_7936fbde73.gif"
alt="Rule-based chatbot"
/>

- **Intellectually Independent Chatbots**

Intellectually independent chatbots rely on machine learning to understand users' inputs and queries. These bots are highly trainable, and you can train them to process certain phrases and keywords that require responses. Independently, the bots will also learn complex queries and generate appropriate responses.

<Img
img="/uploads/Bo_A_Erica_AI_17c0aecd89.jpg"
alt="AI chatbot"
/>

Let's take a closer look at the difference between rule-based chatbots and AI chatbots.

<Table>
| **Rule-based chatbots** | **AI chatbots** |
| ------ | ------ |
| Powered by keywords | Programmed with NLP and Machine Learning |
| Can’t capture typos | Employ NLP to understand customer inputs |
| Navigation-focused and incapable of learning from interactions | Use machine learning to get “smarter” over time |
| The interactions feel robotic rather than conversational | Engage naturally in human-like personalized conversations |
| Need to update rules in order to optimize the bot performance | It will continuously improve without much further assistance |
| The implementation process is faster and less complicated | The implementation process is more complicated and longer |
| Difficult to scale but works well if you have a very small scope | Highly scalable due to a broader range of decision-making skills |
</Table>

Utilizing rules-based chatbots and AI chatbots can facilitate a variety of outcomes which provide clear paths to sales avenues for customers. When considering what chatbots are used for, look into infrastructural needs. Artificial intelligence chatbots may be a little bit too ambitious for your operation. Sometimes you just need something that generates preset alternatives for clientele. At other times, you need AI capable of having a conversation. It all depends on the needs of your business overall. 

## Benefits of Chatbots for Businesses

**1. They Give Your Business a Friendly Face**

Chatbots can help your business create a friendly and relatable face for better customer interactions. For your audience, the chatbot becomes the first [**touchpoint**](marketing-attribution-models) and can be more personal than communication through the phone or mail.

Chatbot personality can have a significant influence on user experience, and it plays an important role in customer perceptions. To develop the appropriate chatbot personality for your company, you have to consider factors like brand identity and audience preferences.

To get started, consider the phrases and words that you use when marketing to your audience. The chatbot persona should mirror this language. And while you can’t appeal to everyone, making a persona that’s consistent with your brand voice can address audience preferences.

**2. Constant Availability**

Unlike traditional customer service, chatbots for websites are always available 24/7. It provides support to your customers on weekends and at late hours. Interestingly, AI chatbots are not overwhelmed by a multitude of requests. By design, it can process multiple requests at the same time and maintain accuracy.

Whenever there's a query that a chatbot can't handle, a customer can always forward the question to a human respondent.

Outside the normal business hours, customers can leave a message for customer service to attend to the next business day. But within business hours, the complicated queries can be handled by human employees.

**3. Instant Response to a Customer**

By providing real-time interaction, you can ensure instant service to your customers. The studies confirm that businesses can react three times faster than when using human customer service. That's one of the reasons why chatbots help businesses increase customer satisfaction by [**24%**](https://www.intercom.com/blog/the-state-of-chatbots/).

**4. Gathering Customer Data And Provide Accurate Marketing Insights**

Customer service chatbots can qualify relevant business leads by collecting relevant data that can be used by your sales teams for sales conversions. Ideally, chatbots can direct customers towards desired products based on their queries.

And since we're in the customer age, your customers expect personalized responses to their queries. Chatbots are designed to tap into customer history and generate the most appropriate responses to questions. Most importantly, the data helps your marketing team to send [**personalized messages**](/features/personalization) and offers.

**5. Chatbots Can Increase Your Sales**

If your business can offer the appropriate information and offers to potential customers, your [**conversion**](conversion-funnel-stages-and-optimization) rates can increase significantly. Chatbots for business can provide answers to website visitors throughout the customer journey. Most importantly, it will advise and guide customers when selecting products. According to a recent survey, businesses that use chatbots increased their sales by [**67%**](https://www.intercom.com/blog/the-state-of-chatbots/). 

## Social Media Chatbots

Generally, a website chatbot is a great tool for improving customer service and increasing sales. But you need not only rely on website options. In recent years, chatbots in messengers and social networks have been increasingly implemented by companies from a wide variety of industries. The relevance of the solution is confirmed by the fact that a very large and active audience is concentrated on [**social media**](self-marketing-how-to-promote-yourself-on-social-media). And that audience is your potential consumers.

Let's consider social media like [**Telegram**](updates-telegram-bot-facebook-ads-manager), as an example. Text and image files can be sent through this platform, and "bots", as they've come to be known, are popping up all over the place. You might send bulk messages to Telegram subscribers using chatbots. Varying buttons can be put in images, text can be added, you can have images in your messages, and basically design them just as you like. When the time comes, you can schedule it to be sent as fits your needs; this can be narrowed down to the specific time on the specific day when you need that message sent. After sending, stats can be explored to determine effectiveness. It's a great way to determine how effective your outreach is.

## Final Words

Artificial intelligence has ushered in limitless capabilities for businesses. And chatbots have proved to be an incredible invention for any business. Chatbots offer continuous customer support and recommendations to help you faster. While chatbot technology is useful for automation, it's prudent to balance chatbot automation with the human touch.

A chatbot is a software application that uses AI and NLP to process and understand human intentions and provide guidance to desired outcomes.

benefits of chatbots for businesses 776х517.jpg

Unlike traditional customer service, chatbots for websites are always available 24/7. It provides support to your customers on weekends and at late hours. 

AI and Rule-Based Chatbots | How to Use It for Your Business

The Benefits of Chatbots for Businesses

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Reduced push notification opt-out rates, increased OR, saved working hours

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The open rate for automatic Viber messages is around 60%, and MAU growth for the year is +19%

All user information is systematically collected in a unified customer data platform

Distributing messages across various communication channels: email, SMS, push notifications, messengers

Promo codes, bonuses, and points for building long-term customer relationships

You can create and edit any HTML template, as well as save your own custom blocks

Personalized distribution of messages to clients through communication channels

Conducting experiments and optimizing marketing strategies

Tracking the effectiveness of marketing campaigns to make informed decisions

Connecting external systems to the platform and synchronizing data with company databases

Creating questionnaires, surveys, and feedback forms of any level of complexity

The ability to communicate with your clients anytime and anywhere

Increasing user awareness of your company's offers

Customer Data Platform

Omnichannel Marketing

Loyalty programs

HTML Email Builder

Personalized mailings

A/B/n Testing

Real-time analytics

Integration and WebHooks

Forms and Surveys

Messenger Communication

Pop-Up Windows

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