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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/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/How_CD_Ps_Will_Save_Marketing_in_a_Cookie_Free_World_c0c129f31f.png"
alt="How CDPs Will Save Marketing in a Cookie-Free World"
/>

Cookies (third party cookies) have been the lifeblood of online marketing for over a decade. With the help of them, marketers tracked the actions of users on the Internet and made advertising. The company received information about the behavior, preferences and interests of the audience.

But with the advent of GDPR and CCPA, which will close access to third-level data in 2023, marketers are beginning to think more seriously about advertising in [**a privacy-first world**](customer-data-privacy-issues-in-digital-marketing).

Marketers are starting to adapt to a world without cookies and are realizing that CDPs (Customer Data Platforms) are becoming a powerful marketing tool. CDP manages, collects and stores customer data in compliance with data protection regulations.

## What a world without cookies would look like

Imagine what would change on the Internet without cookies.

### Google Topics (and others…)

Topics are the third part of Google's decision to block access to third-party cookies. The tool groups users by unique identifiers: the IP address accessed by the client's web browser. The API stores information about three to five topics with which a person constantly interacts on the Internet. For example, sports, news and pets.

Websites are then able to access these topics to produce advertisements that will possibly attract the user when they visit the site. For example, an ad for pet products appears next to sporting goods.

But that's part of the problem when it comes to new ideas for ad targeting.

In the new reality of targeting, marketers are discussing and testing other methods:

- Contextual targeting 
- Universal Identifiers
- Probabilistic
- Cohort-based targeting 

### First-party data precedence

Because of the rejection of third-party cookies, companies and marketers must make full use of first-party data. This is information that the company collects from customers and becomes the sole "owner". Such data is valuable to marketers because it provides accurate information. Another advantage is the transparency of the provenance. 

Data from customers is important in a world where privacy is a priority. When a business has complete information about the customer's journey, it regulates the use of data without any problems: it selects those to which users themselves have opened access.

With first-party data, marketers have access to a wealth of information such as consumer preferences, location, and other demographic data that can all be used to build a complete [**customer profile**](creating-a-customer-profile). Further, advertising is set up for the user and the company's marketing works. First-party data strategies are able to maintain positive and compliant customer experiences, protect personal information and enhance advertising opportunities for marketers.

### Customer Data Platform Benefit

The creators of Data Management Platforms (DMPs) and Customer Data Platforms (CDPs) were already gearing up for a world without cookies.

According to the latest research by Zeotap, 1% of marketers are ready to abandon DMP and switch to CDP. Why?  It's all about the [**first level data**](first-second-and-third-party-data) that CDP usually uses (as we now know, this is the key to success in a future without cookies). DMP works more often with third-level data.

## 6 ways CDP will solve the problem of a world without cookies

### 1. Data collection management

The gigabytes of data that companies collect across dozens of touchpoints (customer service, websites, point of sale) can reveal the needs and desires of consumers. However, collecting, managing and analyzing data from multiple sources can sometimes be a minefield.

This is where [**CDP**](/features/cdp) comes into play. The CDP is able to collect information, manage first-level client data from unrelated sources and make it usable.The platform unites information into single customer profiles, sometimes referred to as "golden records".

CDP receives this data in an unlimited number of formats: files, integration with other platforms and even web application pixels. A good CDP will pull information from just about anywhere.

### 2. Resolve identities into a single customer view 

A 360-degree customer view, often referred to as a single customer view, is a comprehensive aggregation of available user data.

This information may include contacts, user purchases, details of transactions that customers have made at business touch points (such as customer service or stores) along the customer journey. Customer data comes from different sources.This is a large amount of information that must be aggregated, cleaned and combined. It is then stored in a secure location that is accessible only to interested employees. But with cookies gone, identifying customers and attributing the right data fragments to them gets harder.

Here, Identity Resolution technologies play a decisive role in platforms. They compare, standardize and verify information. The attributes of the same individual user are linked together under a unique ID by applying a classification scheme to the data labels of your multiple sources. It enables the creation of a single view of the client that the business needs.Therefore, customer data platforms are the best software for creating a secure and complete client profile.

### 3.  Manage Consent

CDP tracks and manages the whole consent journey. This is vital to ensure that the customer's data is only handled in the way that the customer has authorized.

For example, a customer consents to the use of personal data for the company's marketing purposes via a [**mobile app**](mobile-app-as-a-marketing-channel), but might later retract that consent when engaging via email.

In unifying that customer’s data from separate channels and data sources, a CDP integrates customer consent records to work with their data. The process is called consent orchestration, which makes it easier for the user to choose.

### 4.  Enable activation in walled gardens 

In the cookieless future, marketers will still want to use the addressability options that will remain in the biggest platforms. These ‘walled gardens’ like TikTok, Facebook and Google Ads, which will take on a new role as the open web adapts to life without cookies.

But there is one thing. Walled gardens mean multiple data sets with multiple activation requirements: a very manual process which can result in delays. Paid media managers know exactly what we're talking about.

If a business needs a data-driven strategy that launches in a couple of clicks, a CDP is the way to go. With a customer data platform, a company gains access to customer segments that are activated across multiple channels with a single click.

There will be no dependence on data collection teams, and the information received will work more efficiently. CDP will help you [**identify segments**](how-and-why-to-segment-target-audience) of possible customers through behavioral data from various touchpoints. 

Therefore, if in the past you created audiences and thought: “I wish I could combine data from our CRM and website”, now you have found the answer.

### 5. Activation across the open web through Universal IDs

In the future, CDP will become the main tool for dealing with addressability on the open web through Universal IDs.

Universal IDs are shared, persistent identifiers that link users across the digital marketing ecosystem. They are needed to ensure the future of identification and addressability when third-party cookies are deprecated in 2023.

The customer data platform will be able to unify the disparate data collected by the business and create a hashed unique universal user identifier. This identifier is transmitted over CDP communication channels and compared with information from data sources (websites or apps).

If identifiers from different channels match in parameters, then they are assigned as a single universal ID for one user. Then you can launch ads for target consumer segments.

### 6. Provided Opted-In Audiences To Publishers

CDP tracks brand touchpoints and marketing preferences so that a business is only advertised to an interested audience when it reaches different segments on the open web.

**Here are benefits of CDP for marketing:**

- Advertising works according to the law. Each opt-out request is automatically tracked and counted across all channels.
- The accuracy of the audience selection, because data is selected from several sources within the business.

In this post, we talk about why CDP is a powerful marketing tool. We also share 6 options for how platforms will solve the problem of a cookie-free world.

How CDPs Will Save Marketing in a Cookie-Free World.png

As marketers begin to adapt to a cookie-free world, they are quickly learning that CDPs are becoming a powerful marketing tool.

How CDPs Will Help Businesses in a World Without Cookies

How CDPs Will Save Marketing in a Cookie-Free World

<Img
img="/uploads/customer_profile_bd139d94c0.jpg"
alt="How to create a customer profile"
/> 

Whether selling through traditional avenues or eCommerce platforms, marketers must first understand which individuals are most likely to purchase their products and services. Creating an accurate customer profile of who, why, where, and how is the critical starting point for an enterprise to promote its brand.

Social media marketing, for example, offers a fabulous opportunity to develop a targeted presence economically. However, a company must understand where their most likely clients spend their time, how they communicate, their likes and dislikes, and problems they hope to solve.

## What is a Customer Profile?

A customer profile describes your prospective and current customers’ buying behaviors, demographics, interests, everyday activities, and pain points that influence their purchasing decisions. By creating and adjusting their consumer profiles, marketers can adapt their strategies and campaigns to focus on similar customers.

Building from your existing data, you should begin to create a clear image of the attributes and behaviors of prospective customers that will guide your future marketing strategies.

### How Does a Customer Profile Differ from a Buyer Persona?

A Buyer Persona is a generally fictionalized version of the group of buyers that you might hope to target, often developed before your products have reached the market.

On the other hand, a customer profile is a [**data-driven**](data-driven-marketing) analysis of past successful conversions, identifying the common psychographic characteristics that drove their decisions to buy. These are the customer profiling characteristics that influence how and where your product advertising and brand-building should focus.

## Advantages of an Effective Customer Profile

Your customer profile should be specific. Boiling down your profile to a demographic that targets billions of people leaves you with virtually no direction. More detailed criteria that focus on the primary target allows you to:

- Identify prospects that are most likely to convert

- Reduce marketing and acquisition costs per conversion

- Focus better on individual buyers for bot sales and continuing support

- Generate more fact-based rather than speculative decisions

- Define eCommerce and [**social media strategies**](self-marketing-how-to-promote-yourself-on-social-media) that take your message where your most likely customers are hanging out.

## Building Your Customer Profile

Accumulating sufficient data from existing customers to fine-tune a compelling customer profile is the main challenge. Here are some steps to help create a profile that works:

1. Review specific details with your Sales Team to find some common characteristics they may have noted in successful sales situations. How did those customers arrive at the point of buying your products?

2. Survey past clients. Specify common characteristics from sales data, customer reviews, and [**feedback**](how-to-use-customer-feedback-to-increase-sales). Determine distinct age groups, gender, and means of contact. Were they buying from a desktop or mobile device, for example?

3. Identify which online forums are most important for your customer base.

4. Who are your customers’ prominent [**influencers**](what-is-influencer-marketing-how-to-build-strategy)?

5. Where was the purchase originated? Hopefully, you can identify by city, state, country, and continent to identify trends.

6. Investigate your industry and competitors, seeking details about the specific places and methods where their marketing efforts are concentrated.

## What’s Next with Customer Data Profiling

With more resources than ever to develop a brand and create a lasting presence, effective enterprises know that customer data profiling is an essential tool for success. While businesses would simply purchase “broadside” advertising through TV, radio, newspapers, and magazines in the past, marketers today can create very specific content in online locations frequented by individuals who are most likely to purchase your products or services.

Companies that are not fully aware of the characteristics and habits of their most likely customers are destined to flounder as the competition moves ahead. Take the time to unearth the data and [**tools**](/en) that will help to define your customers, and create your strategy to meet the demand precisely where it exists.


By creating and adjusting customer profiles, marketers can adapt their strategies and campaigns to focus on similar customers.

customer profile.jpg

Accumulating sufficient data from existing customers to fine-tune a compelling customer profile is the main challenge. Here are some steps to help create a profile that works.

What Is an Accurate Customer Profile and How to Create It

Understanding Your Target Customer: Creating a Customer Profile

uzum-case.png

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

bnb-bank-kejs-v-menyu.png

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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