What is Data-Driven Marketing? 5 Latest Trends Today

In the digital age, when consumer behavior is difficult to determine, data driven marketing is the most optimal approach to information that businesses are currently using. Through data-driven methods, businesses can closely follow the customer journey, build suitable marketing strategies to increase revenue, and make more accurate and sound decisions.

1. What is data-driven marketing?

Data-driven marketing is a method of optimizing your business’s brand communications based on customer data. By leveraging the customer data your business possesses, you can identify their wants and needs, thereby making faster and more accurate decisions for marketing and promotional campaigns.

2. 5 Benefits of Data-Driven Decision-Making for Businesses

To evaluate the benefits businesses gain from implementing data-driven marketing, let’s first distinguish how traditional marketing differs from data-driven marketing:

As you can see, in traditional marketing, achieving goals often depends on two factors:

• Market research available at that time
• Their assumptions about the target

Notably, these are just intuition-based assessments. Therefore, to better understand data-driven marketing, let’s consider the benefits it brings to a business:

2.1 Make business decisions based on data instead of intuition

Decisions made based on data are more accurate than those based on assumptions. Analyzing data provides a clarity of information flow that you cannot get from making assumptions or relying on personal opinions. It will help you accurately evaluate current performance and map out the future development cycle of a project, thereby helping to increase the CTA rate.

2.2 Identify new threats and emerging industry trends

Analyzing weekly keyword search traffic is a regular task for marketers. Therefore, analyzing this traffic helps you identify new trends based on user interest, allowing the business to create advertising campaigns and articles that can attract user attention, increase website traffic, and plan sales more effectively.

2.3 Improve operational efficiency and save costs

By analyzing data before making decisions, businesses can avoid spending money on ineffective activities. This is because when making a data-driven decision, we can already determine whether it aligns with an upcoming trend.

2.4 Provides a stronger connection with potential customers

By using data, businesses can better connect with their potential customer base, regardless of the organization’s size.

As Tom Benton – the CEO of the Data and Marketing Association pointed out: “The amount of data from the nearly infinite combination of media, devices, and platforms allows marketers the opportunity to deliver a 1-1 customer experience at scale.”

See more: A compilation of the most effective ways for businesses to approach potential customers

2.5 Enhance long-term business performance

Producing results based on data allows you to measure and achieve specific goals. In the long run, this factor will help improve work completion performance. Departments can evaluate work performance based on being data-driven, thereby making changes more easily. Therefore, it opens up new directions and opportunities faster.

Additionally, performance enhancement is also demonstrated when employees identify problems and proceed to solve them directly through Kaizen statistical process control.

3. Steps to apply Data-Driven principles to a business’s marketing campaign

3.1. Data Collection

To be data-driven, data collection is essential. The dataset a business needs must be relevant to the problem being addressed. Additionally, a suitable dataset must meet the following criteria: Accurate, timely, and reliable.

According to research, 80% of a project’s time is spent on collecting and processing data, while the remaining 20% is for completing the project.

Common data collection methods:

• Surveys: Can be conducted on a large scale through Google Forms, Google Surveys
• Research from reports based on past data
• Through interviews, customer experiences

3.2. Data Processing and Classification

Data Processing

After successfully collecting data, the business will process it to filter out quality data. To evaluate the quality of a data source, we must consider the following factors:

• Accuracy: it reflects the correctness of the information provided by the data.

Example: The name of a VIP customer is actually Nguyen Van Dung, which has been verified and contacted by customer care staff, but in the previously collected dataset about the VIP customer group, the name was recorded as Nguyen Van Dung, which is an error.

• Completeness: answers the question “Is the data collected according to the requirements complete?”, meaning all components and elements in the data have tangible values – with no instances of “missing values” or “null values”.
• Consistency: there are no contradictions for the same data object across different datasets.

Example: In a transaction dataset, if the transaction time is formatted as day/month/year, then all other cells must also follow the day/month/year format, and month/day/year should not appear in any cell. The same applies when considering multiple transaction datasets.

• Integrity: A dataset that does not ensure Integrity is considered a dataset with missing information, missing values in observation cells, internal data that is unusable due to being skewed, modified, duplicated, or erroneous, etc.
• Relevance: the collected data must be related to the business goals and research objectives of the organization, and be useful for future strategies and initiatives. Reasonableness is reflected in whether the data sample meets the expectations of the organization or company.

Example: Does the distribution of points of sale in the Ho Chi Minh City area make sense? This is determined by analyzing customer data from this specific region.

• Timeliness: Data about an event, phenomenon, or research subject must be collected as soon as possible after it occurs. Over time, data can become inaccurate, lose its value, and no longer be suitable for use in current or future contexts.
• Validity: This relates to how data is collected and transformed, not the nature of the data itself. Data is considered valid and usable if it meets requirements for format, data type, value, and if the information it provides falls within an appropriate range, etc.

Data Classification:

Data sources within an organization can be divided into the following data types:

• Joinable Data: This is data in a format that can be combined with other business data when necessary.
• Sharable Data: Within an organization, departments must share necessary data sources with each other, which can be called a data-sharing culture within the business. For example: The sales department shares customer information so the customer service department can perform their post-sale duties.
• Queryable Data: There must be appropriate tools to query and break down data. All reports and analyses require filtering, grouping, and aggregating data to reduce large amounts of raw data into a smaller set that helps us understand what is happening in a business. From there, employees can identify trends or understand differences between customer segments.

3.3. Data Reporting

Data reporting is performed after the business has processed and classified the data. Data reports will indicate which data set is suitable for the marketing campaign the business is currently running. To report data effectively, employees must grasp a few tips:

• Clearly understand the analysis objectives and tasks, as well as the required data output.
• Analyze and link data: Marketing data should be as focused and concise as possible, because an overly large data set will make the analysis and decision-making process difficult.
• Be familiar with common chart types in Data-driven marketing: Bar charts, pie charts, etc.

3.4. Data Analysis

Through the data analysis process, the problems an organization is facing will emerge. These problems will be specifically presented through analysis reports. However, because they are just numbers, they will not tell you why the problem occurred or what you need to do to fix it.

Improve sales efficiency with a strategy to enhance customer retention Read more details:

4. 5 Latest Data-Driven Marketing Trends in 2022

4.1. Data Integration for Targeted Advertising

• Essence:

This involves taking your offline customer data and transferring it to an online environment. From there, you can analyze and use it for your marketing needs.

• Growth Prediction:

This is a crucial factor in creating relevant targeted ads, enabling memorable 1:1 experiences for everyone.

• Application Case:

This strategy goes hand-in-hand with omnichannel marketing, helping companies personalize their marketing activities to connect with people across various devices.

4.2. Omnichannel Marketing Everywhere

In this age of scientific and technological advancement, people are using as many devices as possible—from websites and social media to other methods—to search and shop.

Essence: Omnichannel marketing helps businesses create a seamless experience across all these channels. This allows brands to establish a strong presence wherever their target customers are, whether online or offline.

Growth Prediction: Omnichannel marketing will help businesses update product information and answer customer inquiries quickly; online ordering and home delivery will become swift and efficient.

4.3. Enhanced Identity Resolution for a Holistic User Profile

• Essence:

When implementing this trend, you will need to cross-reference multiple data points, then analyze everything related to that marketing channel as well as others. Identity resolution solutions help businesses get a holistic view of all their customers. 

• Growth Prediction:

It helps businesses review channels and better understand user behavior, preferences, and consumer engagement levels. 

4.4. Data Quality

Essence: As datafication of marketing campaigns becomes more common, the demand for clean, accurate data also increases. 

Poor data can cause inaccuracies in the data analysis process, misleading the analyst and leading to flawed decisions. 

4.5. SEO Content Marketing

The advent of data-driven marketing requires companies to rethink their keyword strategies for SEO campaigns. Now and in the future, you will need to use tools to improve your website’s ranking on search engines. 

These tools will help businesses discover: 

• Keyword search trends
• Average monthly keyword search volume

All these factors help you make better decisions about keyword selection. 

5. Case Study: Applying Data-Driven Principles to Business Operations

Progressive—the largest car insurance company in the US—launched a mobile app, but it was initially only used to introduce the insurance products the company offered.

However, when they looked at the collected data and studied consumer behavior on the app, they discovered that most users were interested in purchasing insurance directly within the app. Consequently, a “purchase” feature was added, helping the company increase its profit by over 2 billion USD that year.

6.  1OFFICE CRM Solution – Analyzing Data to Optimize Sales Strategy

Using and analyzing data with software is one of the activities that helps businesses optimize data, processing it quickly. By using 1Office’s CRM, you will get: 

• Contact Management: Organize your customer data, the most basic step being to place it in a centralized database. Typically, a basic plan starts with limits on the number of records and users, and basic filtering.
• Determine the work and project completion rates for each sales department, enabling you to make new, appropriate decisions.
• Deals: Allows you to enter data such as value, contacts, status, and person in charge.
• Automation: Some basic automation features you need include tools to track customer interactions, schedule email follow-ups, log representative activities, and sync records.
• Basic Integration: At a minimum, the CRM software must allow you to attach files, sync with popular email clients like Gmail and Outlook, import/export standard file formats, and access popular social networks for brand mentions and public business profiles.



This article provides a detailed overview of Data driven and Data driven marketing. It also identifies the benefits and explains why businesses favor data-driven approaches in the current digital era. If you are still wondering whether to use CRM management software, consider CRM customer management software – 1Office, which helps store and leverage data with high security and is suitable for businesses of all sizes. 

To register for a trial of our CRM customer management software and see how it helps you effectively manage customer information and data, please leave your phone number. A 1Office expert will contact you for a consultation today.

For more information:

Hotline: 083 483 8888

Fanpage: https://www.facebook.com/1officevn/

Youtube: https://www.youtube.com/channel/UCeTIRNqxaTwk0_kcTw6SxmA