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Marketing Data Collection in 2024: Use Cases & Best Practices

Data has a big role to play in modern marketing. In 2021, $52 billion worth of marketing data was used globally. However, practices of data collection and usage are changing. In the current business environment, marketers who do not invest enough in data collection planning and are transparent about data collection practices can have some complications.

Marketers need to stay up to date on data collection trends to ensure the smooth flow of data into the different marketing operations. 

This article explores what data collection means for marketing – specifically online use cases of data in marketing – and some best practices for marketers to consider. 

What does data collection mean for marketing?

Fundamentally, marketing data collection is simply the collection of data from all marketing efforts, campaigns, and projects. However, this is easier said than done since it requires marketers to gather all marketing data into a unified location, which involves organization and standardization of the data, in addition to the collection. The types of marketing data that is collected by businesses can be categorized in the following ways:

1. Personal data

This category includes social security data, gender data, IP address, browser cookies, and device data.

2. Behavioral data

This includes data such as buying behavior, purchase history, and online navigational data (mouse movement of the customer while on the website).

3. Engagement data

This data includes how consumers interact with the business website, mobile apps, text messages, social media pages, emails, etc.

4. Attitudinal data

This type of data includes customer satisfaction data, the product’s desirability, the customer’s purchasing criteria, etc.

How is data used in marketing?

According to McKinsey, companies using data to optimize their marketing and sales operations are 23 times more likely to acquire customers and 6 times more likely to retain them. Consequently, these businesses are also 19 times more likely to be profitable. This section will explain some use cases of data collection in marketing.

1. Training AI models

AI is revolutionizing the field of marketing. The global market for AI-enabled marketing solutions was valued at around $8.2 billion in 2021 and is projected to grow to $23.2 billion by 2027. There is an abundance of applications of AI in marketing, with a few of them being the following 

  • To use data to create more personalized campaigns for customers
  • To measure the ROI on marketing campaigns and initiatives
  • To forecast results from marketing campaigns

However, the level of accuracy of an AI/ML model for marketing is determined by the quality and size of its dataset.

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project needs.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

Get Data Collection Whitepaper

2. Improving the customer experience

One of the most important reasons for data collection in marketing is to improve the customer experience. Many companies rely on collecting data to better understand how the customers interact, behave with, and purchase from the brand in order to improve their services.

For instance, companies can collect customer engagement data to improve the website and make it more user-friendly.

3. Optimizing content

Content is one of the most critical assets in online marketing. This includes product information, product description, and any material that your customer might read during their interaction with the website/brand.

Data collection can help adjust the content that is added to your website by making it more aligned with your customers’ needs. For instance, if customers like reading information about product usage, marketers can add individual product usage instructions to every product. They can also outsource or crowdsource content creation for their website to enable multi-language content creation and content diversity.

This is how it works:

4. Target marketing

Data collection can also fuel marketing analytics tools to improve your target marketing strategy. For instance, AI/ML tools can identify which leads have potential and eliminate leads with no prospects.

What are some best practices for using data in marketing?

This section will highlight some best practices that marketers can follow to ensure smooth data flow in their marketing operation.

Collecting data transparently

Many countries now have policies restricting companies from collecting customer data “unknowingly” and encouraging them to make customer data collection and usage more transparent.

Brands should inform the customer while collecting their data or give them the option to opt-in/opt-out. Companies can also provide incentives to customers for sharing data that can lead to a mutually beneficial situation. For instance, a customer loyalty program can offer incentives to the customer, such as discounts in exchange for their data. 

To learn more, check out this article on data privacy law.

Optimize collection methods

There is a high chance that the customers will skip the request for surveys or other data collection methods that pop up while surfing the company’s website. To overcome this, marketers need to make their data collection efforts more personal and engaging.

Companies can also use customer intelligence or VOC (voice of customer) tools to understand customer behavior better.

Click here to learn more about customer intelligence and VOC.

Collect beyond quantitative data

Another best practice while collecting data to improve marketing operations is to collect data beyond the black and white. Marketers now need to understand the facts behind the numerical data they collect in the form of customer engagement data, customer satisfaction scores and conversion rates, etc.

Marketers can also search for patterns of words or phrases in customer communications or use sentiment analysis to understand the emotions behind customer interactions.

Further reading

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Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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