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<Img
img="/uploads/trends_in_cdp_2024_bb8e8dcdff.png"
alt="Trends for 2024 in Customer Data Platform"
/>

In the modern world, user data lies at the core of innovative tools rapidly advancing and benefiting businesses. Guided by established customer behavior models and real-time insights, companies make strategic decisions, outpacing their competitors.

This happens for companies equipped with the right software aiding them in achieving data management goals and enhancing the customer experience. Is there a single solution for these tasks? Yes, there is - it's a specialized Customer Data Platform (CDP).

By 2026, the CDP market [**is expected to exceed $5.7 billion**](https://www.idc.com/getdoc.jsp?containerId=prAP50581323), doubling global data volumes.

As more resources flow into customer data, the demand for privacy, neural network capabilities, flexible management processes, and automation will rise.

## Five Key Trends in CDP Development

[**Data management platforms**](https://altcraft.com/en) have been rapidly evolving, particularly in recent years. As brands aim to better understand and efficiently engage with their customers, an increasing number of companies are turning to CDP technologies for data collection, integration, and management to create unified customer profiles. Such profiles are essential for personalizing marketing campaigns and enhancing the consumer experience.

CDPs enhance data structure and organization without the need for a complete overhaul of existing customer support systems. Businesses leveraging AI or machine learning in their operations require a reliable CDP for seamless functionality.

In 2024, several prevailing trends stand out in the CDP market.

### 1. CDPs leveraging neural networks

[**Artificial intelligence**](https://altcraft.com/blog/neural-networks-in-business-marketing-and-life) is transforming every industry, and the CDP industry is no exception. This trend is expected to continue in the near future, with machine learning algorithms becoming commonplace for data analysis, scenario creation, and planning that influence marketing strategies.

For instance, neural networks will be utilized to analyze human behavior and forecast future purchasing trends. This information will then be applied in targeted marketing strategies.

The Altcraft Platform's CDP includes [**an ML module**](https://altcraft.com/blog/altcraft-platform-updates-machine-learning-new-reports-whatsapp) called “Best Send Time.” This function aims to enhance customer service quality through a self-learning algorithm analyzing buyer behavior to determine the optimal time for sending an email to a specific subscriber. This increases the likelihood of the recipient opening the email. Predictive analytics significantly boosts marketing efficiency.

<Img
img="/uploads/best_send_time_altcraft_be3c6fa00e.png"
alt="Send Time within Altcraft"
src="Best Send Time within Altcraft Platform"
/>

Improving service efficiency through ML is a recent trend. For instance, in June 2023, Figma released a major software update. Alongside various enhancements, Figma introduced an artificial intelligence plugin enabling users to tackle tasks like generating icons on demand, creating diverse interface element variations, and modifying information within them.

<Img
img="/uploads/on_demand_icon_generation_281520c433.png"
alt="On-demand icon generation"
src="Example of on-demand icon generation"
/>

<Img
img="/uploads/on_demand_interface_generation_76f204bba3.jpg"
alt="On-demand interface generation"
src="Example of on-demand interface generation"
/>

### 2. Enhanced focus on user data protection

[**Data protection laws**](https://altcraft.com/blog/customer-data-privacy-issues-in-digital-marketing) are tightening, prompting audits to identify breaches in data collection, storage, and utilization by companies.

By 2024, CDPs are expected to prioritize data protection more rigorously. Platform providers must ensure compliance with legislation, and transparency regarding the collection, storage, and use of customer data is crucial.

### 3. Real-time data processing

One of the significant advantages of CDPs is aggregating customer data from various sources to create [**a unified customer profile**](https://altcraft.com/en/features/cdp). However, this process is time-consuming, and the obtained data may lag behind real-time information.

<Img
img="/uploads/customer_profile_in_altcraft_platform_d7720fb7ee.png"
alt="Profile in Altcraft Platform"
src="Unified customer profile in Altcraft Platform"
/>

This challenge is tackled through the development of CDPs that collect and process information in real-time, enabling businesses to make quicker managerial decisions based on up-to-date data. CDPs analyzing customer behavior online, like Altcraft Platform, gather and analyze data immediately upon receipt.

The CDP Altcraft Platform provides detailed analytical reports, allowing for campaign effectiveness tracking and informed decision-making. These include:

- Channel reports

- Traffic reports

- Goal reports

- Sales funnel

- Click map

- Return reports

- Undelivered message reports

- LTV (Lifetime Value) reports

- Audience growth reports

- Cohort analysis

<Img
img="/uploads/channel_report_in_altcraft_platform_9ab35b1279.png"
alt="Channel report in Altcraft Platform"
src="Example of a channel report in Altcraft Platform"
/>

<Img
img="/uploads/traffic_report_in_altcraft_b2f024a43e.png"
alt="Traffic report in Altcraft Platform"
src="Example of traffic report in Altcraft Platform"
/>

### 4. Demand for multichannel capabilities will continue to rise

Today, consumers engage with companies across multiple channels, including email, social media, and [**messengers**](https://altcraft.com/en/features/messengers). Consequently, to meet customer expectations, companies must ensure consistent interaction across all touchpoints.

This anticipates a surge in demand for CDPs with multichannel capabilities, such as [**the omnichannel CDP Altcraft Platform**](https://altcraft.com/en/features/omni-channel-marketing). It aggregates and integrates customer data from various channels to develop personalized marketing strategies and enhance customer service quality.

<Img
img="/uploads/channels_of_interaction_with_customers_81cf2f76ae.png"
alt="Channels of interaction with customers"
src="Channels of interaction with customers on Altcraft Platform"
/>

### 5. Priority on integrating CDP with various marketing services

In recent years, marketing automation platforms have rapidly advanced, aiding companies in [**automating marketing operations**](https://altcraft.com/blog/what-is-marketing-automation-and-how-it-affects-business-development) and personalizing strategies, with customer data utilization being a primary focus.

As a result, by 2024, integration between CDP and various marketing platforms will become inevitable. This will enable companies to leverage all customer data for developing more [**personalized**](https://altcraft.com/glossary/personalization-what-it-is-and-why-personalized-marketing-matters) and effective marketing campaigns.

<Img
img="/uploads/integration_with_other_services_ca6fa86a6d.png"
alt="Integration with other services"
src="Integration of CDP Altcraft Platform with other services"
/>

## Conclusion

Are you harnessing the benefits of customer data? Most modern CDPs come equipped with robust [**analytical systems**](https://altcraft.com/en/features/analytics) capable of storing, enriching, and analyzing data for profit generation. Unfortunately, many users are unaware of these capabilities. Currently, 59% of marketers primarily use CDPs as simple data collection and storage tools.

It's time to think bigger: CDPs are crucial in creating a unified data ecosystem, extracting insights, and implementing data-driven strategies.

Primary source: [**Lytics**](https://www.lytics.com/blog/privacy-agility-and-ai-emerging-cdp-trends-for-2024/)

*products of Meta, a recognised extremist organisation in the Russian Federation

CDPs evolve for better marketing strategies. Trends: AI-driven analysis, data protection, real-time processing, multichannel capabilities and more. 

medium_trends-in-cdp-2024.png

small_trends-in-cdp-2024.png

thumbnail_trends-in-cdp-2024.png

trends-in-cdp-2024.png

In this article about what trends in the field of CDP should be paid attention to in 2024.

Trends for 2024 in Customer Data Platform Development

Key Trends in CDP Development in 2024

<Img
img="/uploads/etika_nejrosetej_c5ddc95460.png"
alt="Ethical Challenges with Neural Networks"
/>

Discussions about neural networks are ongoing, with new AI-based programs and applications constantly being released. For marketing professionals, keeping up with these trends is challenging, and it's even harder to choose among the multitude of applications to find those truly worth using.

Certainly, neural networks are not a panacea. They cannot accomplish anything without human input. The key issue here is ethical considerations. Neural networks are almost incapable of generating content that could be immediately applied across any industry without concerns. There should always be symbiotic relations between human activity and technology: AI suggests ideas, and humans edit and refine them.

To train a neural network, an enormous dataset is required. The more data, the more sophisticated the AI model will be. However, access to information can be hindered by issues related to confidentiality and intellectual property rights. Ethical questions stand out distinctly. They will be explored within the scope of this article.

## Human or Machine?

Today, virtually no online business operates without content generated by neural networks. This technology significantly eases the work of specialists: they can enjoy the creative process rather than deal with routine tasks. However, every time [**we use AI**](https://altcraft.com/blog/neural-networks-in-business-marketing-and-life), the following questions come to mind.


### 1. Who owns the content or code created by a neural network?

If you use AI to write code or blog posts, who owns the copyrights? Is it you? Or the provider of the neural network? Legislation currently lacks a clear answer regarding the ownership of intellectual property results obtained through neural networks.


### 2. Do you want to assist your competitors?

If you create content using a neural network, the data you upload will be used to train other AI models. This pertains to situations where competitors might be interested in your company's data, such as closed-source code under NDA or a marketing strategy that surpasses theirs.


### 3. How to deal with the risk of legal action for using content created by AI?

There have been cases where people were sued for using content generated by neural networks. Even companies like Google have faced similar issues.


### 4. What if there's no longer a need for manually created content in the future?

Neural networks can generate content optimized for search engines. Will this lead to a decrease in the value of 'human' content? Or will generated and manually created content organically complement each other?


### 5. Isn't there a risk in giving confidential and corporate information to neural networks?

Artificial intelligence tools need data for training and operation. What data is needed? How will they be used? What are the confidentiality issues when transferring data to artificial intelligence?


### 6. Who is responsible for incorrect information?

AI tools and [**chatbots**](https://altcraft.com/blog/what-distinguishes-chatgpt-bard-and-bing-in-the-world-of-marketing) are not flawless. They make mistakes. What happens when neural networks generate inaccurate content? Who is responsible for this?

Artificial intelligence tools largely depend on the data they are trained on to create the final text. What goes into the input data will reflect in the output. Sometimes, AI can learn biases and stereotypical thinking.

<Img
img="/uploads/stereotypical_AI_thinking_performed_by_chat_GPT_e4a1b0136a.jpg"
alt="Stereotypical AI thinking performed by ChatGPT"
src="Stereotypes performed by Artificial Intelligence"
/>

### 7. Can AI replace humans in the workplace?

Will neural networks replace marketers when they can create 10 times more content than humans using only 10% of the resources? How will the decision-making process change for humans? What will happen to human rights?


### 8. How will the implementation of AI affect the cost of content?

If AI can create content indistinguishable from human-generated content, will the cost of content decrease? People might not want to pay for material if they can get it for free from AI.

## The problems with AI are becoming evident

Generative neural networks are not always accurate. But the main issue arises when sensitive information about a company is uploaded to AI; the AI developer gains access to that information and can freely utilize it.

Therefore, confidential data should not be handed over to neural networks without the owner's permission. When training AI models, it's crucial to ensure that the information used for training was obtained legally and doesn't violate any laws or regulations. Most publicly available generative AI services (such as ChatGPT) do not disclose the datasets they were trained on. As a result, it's unclear who actually owns the data.


<Img
img="/uploads/problems_with_AI_generated_content_2e4c4a5759.jpg"
alt="Problems with AI-generated content"
/>


The situation is exacerbated by the inability to verify the objectivity of the results, and AI bias is a real problem. Robots 'pick up' stereotypes from training data. They can convincingly lie. Therefore, it's unwise to trust content generated by AI as the sole source of information, especially when it comes to scientific research.

Identifying issues with AI is not difficult. Content created solely by neural networks stands out significantly:

- Long, complex paragraphs with repetitive content 

- Clumsy clichés, awkward structure, and syntactically long sentences 

- Abundant use of adjectives 

- Facts based on subjective opinion.

<Gallery>
<GalleryItem
original="/uploads/ethical_problems_with_neural_networks_504eeff9b9.png"
thumbnail="/uploads/ethical_problems_with_neural_networks_504eeff9b9.png"
/>
<GalleryItem
original="/uploads/what_problems_of_ehtics_with_AI_50e97b3737.png"
thumbnail="/uploads/what_problems_of_ehtics_with_AI_50e97b3737.png"
/>
</Gallery>

## Ethical rules for using artificial intelligence

Using generative AI is recommended with a license from the company. This is important in terms of responsibility and ownership rights. However, it's crucial not to directly copy the content or code of a neural network. Always edit, modify, and supplement material to make it unique.

- [**Data protection**](https://altcraft.com/blog/how-to-respond-to-data-breaches) is the most important aspect. The accuracy of information generated by neural networks is questionable, and it should not be blindly trusted, especially in the case of rapidly changing topics. 
- The output of a neural network should not be copied verbatim. Generating content is good, but always remember the need to refine the output for truly fresh ideas. 
- Artificial intelligence is excellent for surface-level exploration and idea testing. It should not be used to prove one's ideas. 
- Most information is considered 'sensitive' content. Any information entered into a neural network may be accessible to competitors, current and potential clients. 
- Do not share personal data with AI tools like ChatGPT. The same applies to customers' personal data (names, email addresses, phone numbers, and other personally identifiable information).

## Using neural networks: pros and cons

You decide which ethical principles you want to adhere to and what standards to apply in your company. However, relying solely on a neural network to create content isn’t the solution.

Gather as much information as possible on the topics you're interested in. Review videos, documents, blogs, drafts, etc., and compile them to create a substantial library of information. Then, write a portion of the content yourself and let artificial intelligence refine the rest. Maintain a consistent writing style and incorporate relevant examples and keywords.

### Examples of misuse of neural network tools

- Generating content for marketing emails using personal licenses. 

- Inputting a company's financial data into an AI algorithm. 

- Generating code using neural networks and using it without making any modifications.

### Examples of effective use of AI tools

- Using corporate licenses to generate content for email campaigns; testing and meticulously editing content before publication. 

- Engaging neural networks to improve algorithms without handing over the code to AI directly. 

- Generating ideas for new marketing campaigns and analyzing how they could benefit the business.

## Everyone knows that your content is written by a neural network

Let's set aside ethical issues related to AI, confidentiality, and cybersecurity. One of the biggest problems arising from the improper use of AI is that text generated by neural networks is relatively easy to identify.

### Why is it a problem?

Let's say you use AI to automate all your "cold" emails. Outlook by Microsoft and Gmail by Google are the two largest email service providers. [**To protect their clients from spam**](https://altcraft.com/blog/how-to-avoid-spam-folder-in-gmail), they flag emails generated by neural networks as spam. The capability to identify such messages has improved. So even if your crafted message looks impressive, it will be rendered useless if it doesn't land in the potential client's mailbox.

While it might seem like just an email, phishing attacks based on artificial intelligence are increasingly common. To safeguard their clients, major tech companies are blocking content entirely created by neural networks.

People might lose interest in your email if they suspect its content was entirely crafted by a neural network. Expanding the volume of text at the expense of quality will likely cost both you and your company.

However, using AI and machine learning algorithms isn’t a crime. Not all content needs to be generated by artificial intelligence. It’s more practical to use AI for inspiration, planning, and headline creation, but it shouldn’t entirely replace human effort.

Ongoing neural network advancements pose challenges in marketing. Ethical considerations and human-AI collaboration remain vital.

medium_ethics-neural-networks.png

small_ethics-neural-networks.png

thumbnail_ethics-neural-networks.png

ethics-neural-networks.png

Today we suggest to consider aspects of ethical problems in working with neural networks.

Important Ethical Aspects of Neural Networks Today

Ethical Challenges in Working with Neural Networks

<Img
img="/uploads/Blog_1_320fd9f520.png"
alt="Why You Need Data Modeling"
/>

Business cannot exist without data. Companies plan, analyze and solve their problems using data. Information is collected from everywhere at each stage of the organization's activities. Modeling is needed to make the most of the collected data.

## What is data modeling

Data modeling is a method of compiling and structuring information. You don't need special technical skills to do data modeling. All you need is logic and understanding of business processes.

## What are the benefits of modeling

Here are some of the challenges data modeling can help you solve in business:

- Organizes business processes;
- Provides storage, systematization of information and make it easier to find the information you need;
- Sorts extra data and fills in gaps.

Proper organization of data ultimately leads to effective management decisions.

## How to model data

Start by building a conceptual model, then create a logical model based on it, and finally a physical one. 

### Conceptual data modeling

This is a visual representation of business processes and the relationship between them. This stage does not require software; it is enough to draw a model by hand.

### Logical data modeling

You create a conceptual model in the first step, and then you can convert it into a logical data model. To do this, add key and non-key attributes to the entities. And also you can show the relationships between primary and foreign keys. The result is an independent model that is compatible with all databases.

### Physical data modeling

The entities are transformed into tables; the attributes are transformed into columns. The names of the table and columns within it are adapted for compatibility with databases.

For ordinary users, the physical model is difficult to understand. Use the logical concept for dialogue with users, and use the physical one for in-depth analysis.

## Data Modeling Approaches

### Hierarchical data model

The simplest model was presented by IBM in 1996. In the approach, much attention is paid to the hierarchy based on the principle of a tree structure. The «parent» entity is located at the top, «children» goes down from it.

[**Information Management System**](https://www.ibm.com/ibm/history/ibm100/us/en/icons/ibmims/) is great for taking inventory. In a clothing store, at the top of the model is «clothing», its «children» can be «male» and «female». The decomposition separates women's wardrobe items (tops, dresses, skirts) from men's wardrobe items (trousers, shirts, jackets).

<Img
img="/uploads/hierarchical_data_model_a6083d81ed.png"
alt="Hierarchical data model"
/>

### Relational data model

This model was invented by Edgar Codd in 1970. Edgar, an IBM employee and computer scientist, based the model on an intuitive way to create tables. The data is organized in columns and rows, each cell is in its place. This method is still used effectively in the field of enterprise computing. Specialists use this method in the field of enterprise computing. Using relational models, it is convenient to track the number of goods in stock, store information about customers and conduct transactions in e-commerce.

### ER data models

This model stands for «entity-relationship». Within the model, entities are connected using relationships.

For example, when a user places an order in an online store, the order is assigned a unique code. This is an entity. Further, many events can happen with this order: the client can add or remove items, change the quantity, or even make a return. All these actions are the relationships to the entity.

### OODM

The Object Oriented Data Model (OODM) maps any complex scenarios. A closer look highlights the obvious and not so obvious business problems. OODM has proven to be great for funnel analysis. A potential client acts as an object with its own attributes (name, email, phone, etc.). As long as the lead has not [**turned into a client**](MQL-and-SQL), there are two objects: a «potential client» and a «client» itself. The «client» has all the same attributes as the «potential client», but with additions: ID, addresses and payment details.

OODM is designed to build relationships around a real-world object. When specialists build such a model, they rely not only on logic and mathematics, but also on the actual situation.

### Dimensional data model

When there is too much information, it is divided into data of the first, second and subsequent orders. In such a structure, it is very difficult to quickly find the required solution. Dimensional models are used to optimize databases and make it easier to find the information you need.

<Img
img="/uploads/dimesion_data_model_fa8c1d26c3.png"
alt="Dimensional data model"
/>

It is recommended to use OLAP (online analytical processing) for trend analysis, sales forecasting, financial reporting, etc.

## How to build an efficient data model

1. **Set a clear, measurable goal.** The goal should work for the result. Imagine what effect you want to achieve through model building.
2. **Strive for simplicity.** This advice applies to both the initial stage of working with the model and subsequent ones. At the very beginning, do not try to make the model complex and confusing. Get maximum clarity. In the future, you will fill it with the necessary information without making it difficult to understand.
3. **Organize your data based on facts, dimensions, filters, and order.** Let's say a business owner has over a hundred stores. The task is to find the point that is the most effective in terms of sales of product N over the past year. "Fact" is the sum of sales of each specific store. "Dimensions" are the product N that the owner is interested in, and the location of the store. "Filter" sets the time interval (year), and "order" determines the Top 10 stores according to the specified parameters.
4. **Avoid mistakes.** This is especially important when it comes to a large-scale project. If you skip a mistake to the next stage, there is a chance that at the very end of the project you will have to return to the roots. To avoid such situations, double-check.

## Data modeling tools

There is a wide range of data modeling tools on the market. In this post, we list a few of them. When choosing a tool, rely on the needs of your own business and pay attention to the following points:

- How clear is the organization of work with large amounts of data?
- Is there a visualization functionality?
- Can this tool streamline data mapping through the enterprise?

Here are some options:

- [**Erwin Data Modeler**](https://www.erwin.com/products/erwin-data-modeler/) is a tool from Quest. Suitable for designing and visualizing data and compiling detailed reports. 
- [**Enterprise Architect**](https://sparxsystems.com/) does a great job of visualizing business processes. 
- [**ER/Studio**](https://www.idera.com/products/er-studio/enterprise-data-modeling) is a service for working with complex data; you can build dimensional and relational models in it.
- [**Altcraft Platform**](/en) is a marketing automation platform that combines data from various sources into a single profile. Analytics works in real time, visualization is achieved through charts and reports. The advantage of the tool is the speed of processing large amounts of information.

<Img
img="/uploads/summary_report_d793cf8f9d.png"
alt="Data modeling in Altcraft Platform"
src="Data modeling in Altcraft Platform"
/>

In this post, we talk about what data modeling is, why it is important, and what tools to choose for data modeling.

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Most of your data will come from your business processes and software system. Modeling is used to maximize value from the data.

Data Modeling: What It Is and What Tools to Choose

Why You Need Data Modeling

<Img 
img="/uploads/data_science_9a8b2aae50.jpg"
alt="Data Science"
/>
 
In a second, Google processes [**over 99 thousand**](https://www.internetlivestats.com/one-second/#google-band) search queries, and thousands of photos are posted on Instagram. The amount of data on the Internet is skyrocketing. Being a resource for business and marketing, data is everywhere. Data science allows one to process and analyse data. It also helps to deal with business issues in retail, industry, banking and other fields. 

When data science first emerged in the 70s of the last century, it studied the data life cycle. In the 2010s, computers could process a wealth of information. At that time, data science grew in importance and began to be included in the programmes of the world's universities.
 
Nowadays, data science helps business and drives innovation. In this piece, you will learn what data science is and how to apply it in business and marketing. 

## What is Data Science

<Note>
Data science is a field of knowledge that studies and analyses data and looks for patterns to make decisions in practice. 
</Note>

Actually, data science is not exactly a science. Data is not a subject of data science, but a multidisciplinary resource (business analytics, mathematics, statistics, programming, strategic planning, system analysis) for gaining knowledge. Data science is closely associated with [**artificial intelligence**](https://www.britannica.com/technology/artificial-intelligence), [**machine learning**](https://www.sas.com/ru_ru/insights/analytics/machine-learning.html), [**big data**](https://www.oracle.com/big-data/what-is-big-data/) and [**deep learning**](https://www.ibm.com/cloud/learn/deep-learning).

<Img 
img="/uploads/data_science_i_tekhnologii_e7a204d00e.jpg"
alt="Data science and data technology"
src="Data science and data technology"
/>
 
Data science receives data from the Internet, smartphones and other smart devices, special sensors. Other sources are corporate log files, archives, transactions history. Software is the primary tool for data scientists. It processes, structures, analyses data and visualises it in a format that is understandable to the end user.
 
Data science looks into the future — it gives forecasts based on data. It predicts demand for goods, user behaviour in the application, disease progression, climate disasters and so on. 

## How does Data Science Work

Data science processes data for analysis: organises, aggregates and arranges it for non-professional users. Data scientists extract information from arrays of processed data to find patterns, build hypotheses and model a picture of the future. 
 
Stages of data science:

<Table>
| **Stage** | **Description** |
| ------ | ------ |
| **Perspective** | Setting a goal and a problem to be solved. What do we want from data? | 
| **Data collection and processing** | Searching and extracting raw data from available sources. |
| **Data preparation** | Data storage and cleaning. Blank fields from databases and irrelevant information are removed. Raw data is transformed into a form that can be analysed further. |
| **Data processing and modelling** | Clustering, modelling and classification, searching for patterns. This stage reveals what happened in the past (descriptive analytics) and which scenario is possible in the future (predictive analytics). Methods of statistics, intelligent data analysis and machine learning "look" into the future. At this stage, modelling is applied — «what if» and «if... then what». |
| **Data analysis** | The following types of analysis are used: regression, exploratory, confirmatory, predictive, confirmatory qualitative, etc. |
| **Data formatting** | The data is presented to the end user as reports and visualisation: charts, graphs and so on. |
| **Decision-making** | Business decisions are made based on the data. |
</Table>
 
## Why does Business Need Data Science

Data science answers specific business questions by testing hypotheses and ideas. Data science:

- takes into account the amount of information that a person cannot process to make decisions;
- excludes subjective opinion in decision-making.

An unbiased answer to a business question protects from errors and misconceptions. The main thing is not the authority of the head, but facts. 
 
**Data science in business also:**

- Studies the [**target audience**](creating-a-customer-profile). A company that knows the customer perfectly well offers the right products and outperforms its competitors. 
- Forecasts trends (insights for new services and products). 

## How Data Science is Applied in Business

### Industry 

Data science forecasts demand and helps to optimise supply chains. It also reduces the risks of surplus goods when demand is low and shortages when orders are large. On the basis of data, production efficiently allocates resources, controls expenses and revenues and finds vulnerabilities — the causes of defects. Predictive analytics show when equipment will fail due to wear and tear and indicate when to carry out repairs to prevent this. 
 
### Logistics and transport

Optimisation of supply chains: reduction of delivery time, search for optimal routes and reduction of operating costs. As in industry, review of data forecasts demand for services of transport companies. Data science optimises warehouse space and reduces the risk of errors in storing and moving goods. 

In transport, data science forecasts the volume of passenger traffic and vehicle occupancy and ensures safety — predicts breakdowns and accidents. 

*Example. Russian Railways has implemented data science technologies to predict dangerous failures of railway facilities. The algorithms simulate situations with more than 50 factors, assess the condition of railway tracks, the technical condition of trains and indicate areas for urgent repairs to avoid accidents and train wrecks.*
 
### Retail 

In retail, data science provides:

- forecasts of demand for product groups;
- stock control;
- insight into trends and audience needs based on social media data and information about competitors;
- custom-tailored offers: which product to offer as an alternative or for additional sale;
- price forecasts.
 
### Mobile operators 

Telecom sets a task for data science to personalise services and products. Data highlights what is important for the customer, how to optimise service packages. Mobile operators also use data to determine where it is better to place their sales points and cell towers.

Telecommunication companies have their own data science departments that create digital solutions not only for internal business needs. 

*Example. MTS offers customers a service for developing tourism that analyses 5,000+ metrics for audience segmentation.*

### Banking

In banks, data scientists examine data to determine the credit capacity of customers. Loans not repaid on time are a risk for banks. Data science forecasts how the customer will behave depending on the market situation, assesses the reliability of the borrower. Data helps banks to personalise services. Algorithms identify the user's interests by activity and offer services that customers themselves were not looking for. 

An important task for data science is to search for the actions of scammers. In the case of large transactions, the system may suspend the transaction and ask for confirmation from the customer. Atypical purchases, several accounts with similar data will be under suspicion. 

### Real estate 

In this field, data research is not limited to offering suitable property options to the buyer. Data science analyses the documentation of objects: buildings, landing plots. Data forecasts the demand for real estate, the price in a given area, and investors decide which properties are worth investing in. 

Data speeds up rental accommodation in services. *Example. Airbnb [**analyses**](https://medium.com/airbnb-engineering/location-relevance-at-airbnb-12c004247b07#.vtj3t52mm) information from users: options they viewed and which ones they eventually chose. Data science forecasts the likelihood of booking in certain areas of the city.*

<Img 
img="/uploads/karta_s_dannymi_veroyatnosti_223d2938c9.jpg"
alt="A map with the probability of booking"
src="A map with the probability of booking in San Francisco according to Airbnb data"
/>
 
## How Data Science is Used in Marketing

### Optimisation of platforms 

Data science will show how users behave on different Internet platforms: websites, applications, social networks. Based on the data, the company understands how to simplify the purchase process and which elements to add to make the product more appealing to the customer. Which features to use to improve service. Data science indicates successful and ineffective content on the company's resources: which materials are read to the end and which are just scrolled through.
 
### Audience research and personalisation

Data science studies customers in detail: interests, preferred communication channels, online and offline behaviour. Knowledge of the audience enhances the personalisation of offers: the company knows where the customer responds best to ads, which ads they respond to, how to organise [**cross-sales and additional sales**](marketing-automation-for-insurance-companies). 
 
*Example. [**Facebook**](https://data-flair.training/blogs/data-science-at-facebook/) stores a huge amount of user data. It guarantees the accuracy of targeted advertising. Everyone on Facebook can set up what ads they want to see and remove topics that are not interesting.* 

Data science helps marketers to segment the audience in detail by dozens of parameters in a targeted manner. *For example, to offer a product when it is relevant. If you know when your customer runs out of a product (pet food, household chemicals, car coolant and so on), then advertising the product at the right moment is a chance for success.* 
 
### Analytics 

Data science helps marketing to get real-time analytics and quickly adapt to new situations (it changes strategy). It speeds up planning of future campaigns. Data-driven forecasts will answer how pricing will work and what price to choose to maintain and increase sales. 
 
### Budget optimisation

Accurate analytics and predictability of consumer behaviour save the company money. Ads are good for the company's budget when shown on time and on the right platform. Data science helps marketers to understand what content is interesting to the audience so that they do not waste resources on creating unnecessary one. There is always data to forecast future campaigns and optimise current ones. 
 
### Customer loyalty and retention

Attracting a new customer is more expensive than keeping an existing one. Data science shows when the customer starts being less active or stops communicating with the company at all. When the company knows the customer lifecycle, it sends personalised offers or useful content in time [**for retention**](customer-trust). 

## Conclusion

Data science examines data and answers business and marketing questions to test hypotheses, ideas and launch new products and services. 
 
In business, data science is a tool for objective decision-making instead of intuitive actions or subjective experience. Data science analyses information about the audience to create new services and products. In marketing, data science is about optimising platforms and budgets, personalising offers. It is a tool for analytics. 
 
Data scientists work in industry, logistics, banking, telecommunications and other fields where it is possible to collect and analyse data and then apply it for business purposes.

Nowadays, data science helps business and drives innovation. In this piece, you will learn what data science is and how to apply it in business and marketing. 

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Data science allows one to process and analyse data. It also helps to deal with business issues in retail, industry, banking and other fields. 

Data Science: How to Use It in Marketing and Business

Data Science in Marketing and Business

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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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