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Definition, cycle and application examples

The term marketing data science you may have heard or often heard from time to time. Especially for those of you struggling in the world of digital marketing strategy to improve customer journeys.

Since the era of digital transformation or digitization of businesses has become popular in various industry niches over the past decades, large digital data collections (big data) have become a determining factor for the success of businesses. business processes. This is no exception in the application of marketing strategies where data science influences marketing or promotion techniques that marketers previously only traditionally did and can now be done digitally (digital marketing).

However, it is possible that the application of marketing data science in digital marketing campaigns is a strategy of marketing operations in the management of complex business processes. So what exactly is the concept of data science in marketing as a form of business growth today?

Understanding Marketing Data Science

The definition of marketing data science refers to the notion of data science itself. According to the Hop.Online website, data science is a scientific approach that involves a range of areas of expertise, such as mathematics, statistics and computers, to simplify the process of analyzing data and create a holistic, holistic view of raw data.

And in the field of marketing, data science is useful to assist the process of analysis, collection (data mining), review (data cleaning), to present data related to the needs of marketing campaigns. For example, data on potential customers (leads), marketing performance, customer reach and impressions, customer satisfaction, and even sales mix.

Initially, it was enough to analyze the data using simple BI (business intelligence) tools. However, as the function and role of marketing data science is beginning to be considered by many industries, many companies are opting to integrate machine learning, AI (artificial intelligence) and data visualization. in their marketing strategy as a system.

With this data science approach, marketers can better understand customers (customer recognition) and their behavior (consumer behavior). The goal is to provide the best customer experience while increasing customer lifetime value (CLV).

In addition, companies can improve business performance and automation of marketing processes (marketing automation) as well as ease of decision-making (decision making). In this way, marketers will avoid mistakes in their marketing planning (marketing plan).

5 Data Science Marketing Cycles

5 Data Science Marketing Cycles

In fact, the data science lifecycle in marketing refers to the five main phases of the data science lifecycle. Here is the explanation.

1. Objective definition

First, you need to define your marketing goals and set priorities. For example, what problems do you want to solve with a marketing strategy or what challenges customers will face when it comes to marketing and selling a product or service.

During this first phase, you should start with the initial data collection, tidy up the data (data disposition), and identify data quality issues (data management). During the process, various interesting subsets may emerge that help you formulate a better hypothesis.

2. Data preparation

In the data preparation phase, you need marketing analytics tools which are a separate area of ​​the data warehouse. Next, be sure to explore, preprocess (data preprocessing), and refine the data (data enrichment) before moving on to the next phase.

3. Building models

Modeling phase refers to the process of selecting the most appropriate data model and collecting a group of data into a train and test pool (data ingestion).

During this creation stage, you must determine the techniques and methods you want to use to establish relationships between variables or data (business process modeling). You can take advantage of machine learning techniques, such as feature selection, principal component analysis, and clustering algorithms.

4. Deployment

In this phase, your business goal is to create a marketing mechanism that will help you deliver your model to customers or other systems. Depending on your type of project, the deployment steps might mean getting the model output or scaling it in the cloud user base the biggest.

5. Monitoring (Monitoring)

Whatever your ultimate goal, remember that you need to continuously monitor and evaluate your workflow. This step will allow you to quickly identify regression and business stability issues.

Examples of the application of data science in marketing

Although many companies have used marketing data science, it is possible that there are still many start-up companies, such as startups or SMEs/UMKM, who are simply navigating this approach.

To help you better understand how data science can work for your digital strategy, here are some examples of data science applications in marketing that are most commonly used by businesses.

1. Customer segmentation

One of the most important benefits of data science in digital marketing is customer segmentation. In short, customer segmentation is the division of the target market or potential customers (prospects) of a particular market share into distinct groups (niche marketing).

This data science cluster will simplify the process and help marketers create specific strategies for each segment and convert customers. It can be based on different characteristics of the customer (buyer persona), for example demographics, behavior, wants (demand), problems (customer pain points) or bargaining power of customers.

For example, if you plan to launch a product targeting millennials in Java, you need to create an effective targeting strategy for each consumer (STP marketing).

2. Customer Sentiment Analysis

Basically, it is very important for you to create a positive reputation for your business or your brand (brand reputation). Well, one way to ensure your business is in control of its reputation is to leverage customer emotions (emotional marketing) through sentiment analysis.

Sentiment analysis is a text classification technique that allows you to understand how customers feel about your brand (brand experience), product or service. This involves sorting out the sentiment behind data such as social media conversations, customer feedback, reviews, customer satisfaction surveys, and customer service conversations (proactive customer service).

3. Marketing Funnel

Examples of the application of data science in marketing

In the past, marketing campaigns focused on brand awareness, customer acquisition, and brand activation. Through the use of marketing data science, it is now much easier for your business to gain revenue, customer loyalty and sponsorship (referral marketing).

You can use this marketing data science approach to:

4. Lead targeting and lead scoring

When it comes to digital marketing for the SaaS (Software-as-a-Service) industry, having quality leads is an important first step to building customer loyalty.

Using marketing data science, marketers can predict which promotional strategies will grab the most attention from customers. This will encourage companies to qualitatively qualify leads, also known as lead scoring. Additionally, a data science-based marketing approach can help you assess customer value based on the characteristics of customer segments and their behavior.

5. Predictive analysis

Predictive analytics brings together data mining and machine learning models to predict the likelihood of certain future events that may affect your customers or business (business forecasting).

This analytical technique uses historical and current data that allows data scientists to identify marketing trends and predict the likelihood that customers will take certain actions, such as canceling their subscription to a product or service.

Additionally, marketers can also distribute marketing content to the right audience, determine the effectiveness of digital advertising campaigns before they are released, and effectively plan cross-sell or up-sell products (sales plan).

6. Pricing strategy

Data science models excel at integrating new information and detecting trends and emerging demands (demand planning). This opens up exciting opportunities for SaaS companies that want to define an effective customer-oriented pricing strategy for their products and services (customer-centric).

Overall, data science can benefit pricing strategies by providing you with valuable insights into the elasticity of demand (demand management). This is how customers will react to different prices and the best price for your business based on its goals.

7. Business Channel Optimization (Channel Optimization)

And finally, Marketing Data Science also offers access to compiled datasets through a number of different marketing channels or media mixes, such as websites, social media, YouTube, and email marketing. .

By analyzing prospects’ online interactions within the group, data science helps marketers build connections, create journeys, and identify missed opportunities across the channels most popular among your target audience.