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<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.

dannye-o-klientah-dlya-ml.png

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/audit_dannyx_014ad471e9.png"
alt="How to Maximize Your Buisness Decisions with Data Auditing"
/>

The world generates [**2.5 quintillion bytes**](https://explodingtopics.com/blog/big-data-stats) of information every day, with the Big Data market reaching $274 billion in 2022 and the analytics platform market following suit. This upward trend shows no signs of slowing down.

Data is highly valuable for businesses, serving as an inexhaustible source of inspiration for marketers and [**providing insights**](https://martech.org/analytics-audit-101-identify-issues-and-correct-them-to-ensure-the-integrity-of-your-data/) into consumer habits and expectations. Through data, companies can make informed predictions about their growth and select effective methods for outcompeting rivals.

However, processing large amounts of information is a challenge. Without quality analytics, the data collected is nothing more than meaningless numbers.

Data analysis, first and foremost, involves checking the authenticity of the information. But that's not all. The main task of the audit is to determine how the collected data can help the company solve the set tasks. Without accurate data, companies can't make informed decisions. Let's explore data auditing in more detail.

## How data auditing can benefit your business

Data can be a lifesaver for a company, as long as the sheer amount of it doesn't overwhelm management. Storing a large volume of information can be costly, and it needs to be organized and utilized correctly. This is where a data audit comes in. Data auditing can:

- **Consolidate disconnected databases from various departments.** Auditing, using cloud tools, can combine information across the entire company. This allows marketers to utilize valuable information collected by the sales department.
- **Resolve data integrity issues.** Company databases often contain duplicated, incomplete, or incorrect information, which cannot be used as a basis for business decisions. Relevant and reliable data is needed to solve business problems.
- **Comply with regulations.** To maintain order in business processes, many companies establish internal regulations for handling information, while some industries are regulated externally. Auditing helps ensure compliance with prescribed rules, avoiding fines for violations of data storage and usage deadlines.
- **Avoid data leaks.** [**Data leaks**](how-to-respond-to-data-breaches) occur when third parties gain unauthorized access to information that is not intended for them. Personal data security issues can be significant for companies, which is why they are vigilant in this area. Data auditing highlights vulnerable areas.

## Phases of data auditing

### Step 1. Planning

At this stage, it is necessary to develop a step-by-step plan for the audit and assign responsible individuals for its preparation, conduct, and presentation of results. The success of the entire «operation» depends on how well the planning is done.

### Step 2. Inspection and information gathering

Find out what information is currently stored in the company. How it is collected, how long it should be stored, and most importantly, how it is used in business processes.

### Step 3. Analysis and evaluation of data collected

Evaluate the situation for compliance with legislation and regulatory acts. Check data security and determine the procedure for employees' access to information.

### Step 4. Development of recommendations

Develop correct business processes and implement them in practice. Reject and replace non-working algorithms. Provide recommendations to employees on how to work with information. Remove excess data, set current storage periods for information that quickly becomes obsolete. Provide access to information for those employees who need it for their work.

<Img
img="/uploads/data_audit_steps_7cd0afe42d.png"
alt="Phases of data auditing"
/> 

## Who is in charge of data auditing?

Data scientists are usually the ones responsible for conducting data audits. In some cases, it may be appropriate to form a team of competent individuals to monitor information usage, make budget decisions, and set priorities. The team's work typically results in internal policies and data processing strategies that are then shared throughout the organization. This is how employees are trained to work with data within the company.

## Why is data audit automation needed?

Based on the analysis of events of the past three years, it can be confidently stated that customer information quickly becomes outdated: their behavior and desires change so rapidly and unexpectedly. Data quality auditing is necessary for modern businesses, as it provides the opportunity to make relatively accurate predictions of the near future. However, processing large amounts of information manually is practically impossible.

This is where data management automation platforms come in. They completely take over routine and labor-intensive processes such as cleaning the database of duplicates, monitoring information sources, and clearing the database of clutter. All of this is necessary, but it is not practical to do it manually.

Automating data auditing is beneficial for the company. It significantly frees up employees' time that they can spend on more critical and creative tasks. Moreover, the results obtained are more accurate and reliable than if the database were processed by a human. Robots do not make mistakes, do not get distracted, do not tire, and their vision does not «blur». Therefore, in this regard, they work much better than a human.

## How to choose a data audit automation tool

Platforms designed specifically for collecting and storing marketing data can help you with this task. Artificial intelligence has made significant advancements in this area, enabling precise targeting of marketing campaigns and reaching interested audiences.

Smart platforms identify new visitors using JavaScript tags and track their IDs, gradually building detailed profiles that include behavioral characteristics, demographic data, and purchase history. The data is not mixed up between users, processed accurately, and subjected to analysis.

When selecting a platform, it is important to look for the ability to [**segment users into target audiences**](how-and-why-to-segment-target-audience) in real-time. Businesses find it easier to succeed in the market when they have accumulated enough useful information. The key is to ensure that this information is relevant to improving current business processes within the company.

In this post , we will explain what a data quality audit is, how it can help your marketing, and how to conduct a data audit.

audit dannyx.png

For those who want to know what a data audit is and how to conduct it.

How to Maximize Your Buisness Decisions with Data Auditing

Data Quality Audit: How It Can Benefit Your Marketing

<Img
img="/uploads/chto_takoe_tranzakcionnye_dannye_a27ed47d1a.jpg"
alt="How to use transactional data to grow your business"
/> 

There are many sources of information that can be used to study the target audience and customer behavior. For example, you can track the actions of visitors on the company's website — any data collected will be very valuable. It is better for a business to build marketing campaigns based on the analytics of the accumulated information, because this way the creatives will hit the target.

Transaction information is a type of data that is easy to collect, yet very valuable. In this post, we will explain what transactional data is and how to use it to improve the effectiveness of your marketing.

## What is transactional data

**Transactional data** is any information relating to customer transactions. A transaction in an online store is defined as an "event" during which the user's web browser and the company's website exchange data. What is the difference between transactional data and other data types? It contains the exact date and time of purchase.

<Img
img="/uploads/transactional_email_from_amazon_5a6e29cb70.png"
alt="Amazon transactional email"
/> 

In addition to the date and time of payment, managers get other valuable information: the product or set of products that attracted the customer, the type of payment, the frequency of purchases and the promo codes used. It is also possible to analyze user behavior before and after a purchase.

## How to use transaction data

It is not enough just to gather data — you need to use it in the right way. The value of this information is that it provides businesses with a comprehensive view of customers' online behavior. The company tracks clicks and knows why users drop off the site without making a purchase or [**abandon their shopping carts**](how-to-reduce-cart-abandonment-rate). Analytics gives tips on where business growth points are and how to increase customer loyalty.

The undeniable advantage of transaction data is that it is reliable because it comes from first-party sources — directly from prospects and leads.

## Examples of using different types of transactional data

### Location info

For offline business, information about the "hot spots" of potential buyers has a huge weight. It is taken into account when opening new outlets. But when it comes to online stores, location is just as important. Geography of customers is useful in product planning. Logistics should be thought out more carefully in cases with seasonal goods. For example, you can move goods from regions with low demand to where demand is higher.

### Payment types

The payment type is an important piece of transactional data. Firstly, it is useful for businesses to calculate how much each transaction costs, and what it includes: a commission from a bank or another intermediary. Ultimately, all this affects the profit of the organization.

You need to add all the payment methods demanded by customers in order to grow in sales. The more comfortable the payment process is for customers, the more they are attracted to the company, and the more likely they will make repeat purchases.

<Img
img="/uploads/mango_payment_methods_3811e2c819.png"
alt="Mango payment methods"
/>

Payment information is also used to quickly troubleshoot technical issues. If a company is used to tracking transactional data, it will see and respond to errors in card reading and delays in payment processing in time. All this together will reduce the number of abandoned carts and unsatisfied customers.

### Discounts and promotional codes

This is probably one of the most useful types of transactional data. There is a certain category of people who make purchases only on favorable terms. The analysis of such sales statistics brings the most unexpected insights, on which effective advertising campaigns are built.

You may find that a 10% discount on products has no effect on customer demand, but when a free shipping promotion is running, sales increase. It happens that the same discount, presented with different wording, is perceived by consumers in completely different ways. Let’s compare: “Buy one item and get the second one at half price” and “25% off the entire range of products.”

As for promotional codes, their validity period is limited and the time frame of the offer is precisely known, so you can analyze sales for this period and draw conclusions about the effectiveness of [**using promotional codes in marketing campaigns**](how-to-use-promo-codes-for-business).

<Img
img="/uploads/Screcasper_promo_code_email_b37f087575.png"
alt="Promo code in an email from Casper"
/>

It is important to always compare different types of transactional data with each other. Consider both the location and the payment method, but do not dig into the calculations. Use only the data that you need at the moment.

### User behavior before and after purchase

Everything we talked about above applies directly to the purchase. Time, date and type of purchase are undoubtedly very important transactional data, some of which is captured by the sales platform. Data on customer behavior before and after the purchase is considered to be indirect, but it cannot be classified as secondary. By studying this information, you can answer the following questions:

* How many times did the user view the item before purchasing?
* What products did the customer compare the product with?
* Did they read product reviews?
* How long has it taken the customer to buy the product since they saw the product for the first time? What [**pushed them**](5-marketing-techniques-to-push-customers-to-buy)?

Perhaps the company sent out promotional codes at the time of purchase, and this spurred the consumer to purchase. Answering these questions and analyzing patterns will help you better understand the goals and motives of your target audience.
The deeper you understand customer habits, the more effective your marketing campaigns will be.

### Transactional data makes sales easier

All obstacles to sales must be removed. By tracking transactional data, the company gains insight into the types of payments that often fail, as well as the people who started checkout but didn't complete it. Perhaps there was no convenient option in the list of payment methods, or maybe another problem “scared away” the person.

Profit grows if the company aims to facilitate the sales process in such a way that the client does not encounter any obstacles. This means minimizing the number of clicks, forms to fill out, and eliminating all system errors. Otherwise, the sale will not happen, and the business will miss the customer.

<Img
img="/uploads/mango_checkout_process_0d4a34aa0b.png"
alt="Checkout process on the Mango's website"
/>

## Increase customer loyalty using transactional data 

Transactional data helps personalize offers. Customer loyalty is extremely important for any business, and every opportunity should be taken to maintain it. All information about transactions, even anonymous ones, is useful for these purposes. The company can tailor promotions to the needs of a specific audience if it understands the needs of customers.

Take a closer look at the statistics of purchases of a particular type of products and determine the time that is best for your offer. Let's get back to seasonal items. To free up space in the warehouse for the new collection, you can come up with a promotional offer for the old one. Customers become more active, and you will spend less on moving the old collection to a location with high seasonal demand.

## Marketing automation platforms and transactional data

Companies accumulate terabytes of data every day. It is quite difficult to process such a volume. Information is an asset, and there should be just enough of it to solve a specific business task. The same rule applies to transactional data. When there is a lot of information coming in, companies don't know what to do with it. And even if they know, there is often not enough capacity to process such a volume.

This is where modern marketing automation platforms like the [**CDP Altcraft Platform**](https://altcraft.com) come in. With the right tools, transactional data works for your business.

Transaction information is a type of data that is easy to collect, yet very valuable. Read the post to learn what transactional data is and how to use it.

chto-takoe-tranzakcionnye-dannye.jpg

For those who are wondering what is the use of transactional data for companies.

How to Use Transactional Data to Grow Your Business

How to Leverage Transactional Data

<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.

Блог (1).png

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. 

data-science.jpg

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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