Experience Best-In-Class Digital Asset Management

Digital Asset Management thumbnail

Digital Asset Management

Manage content planning, creation, and distribution all in one place to get on-brand, personalized customer experiences to market faster.

Productivity Management thumbnail

Productivity Management

Improve productivity and collaboration, increase visibility, and align strategies with business objectives in real time to move your brand forward.

Plan & Spend thumbnail

Plan & Spend

Track, measure, and optimize your marketing spend and performance with planning, budgeting, and expense management tools all in one place.

Marketplace thumbnail


Explore partner apps, connectors, tools, and templates that expand the capabilities of the Aprimo platform.


By Industry

Financial Services

By team

By Company Size



The Definitive Guide to Content Operations

Everything you need to know about content ops and how it helps drive personalized CX at scale.

Discover Aprimo's Content360°

Get a single view into strategy, planning, execution, review, and delivery across all teams and locations in real time.

Marketing Cheat Codes Podcast

Join industry luminaries and DAM experts on how they beat Marketing: the game that never ends. New episodes every other week!

About Us

Back to glossary

Artificial neural network

What is an Artificial Neural Network?

An artificial neural network (ANN) is a machine-learning model inspired by the human brain’s neural structure. It consists of interconnected nodes, or “neurons,” organized into layers that process and analyze data to solve complex problems. ANNs are commonly used in various domains, including marketing, to model patterns, make predictions, and automate decision-making processes.

Use Cases:
Artificial neural networks find numerous applications in marketing, enhancing decision-making, personalization, and campaign optimization. Here are some examples:

  1. Customer Segmentation: ANNs can analyze customer data and segment the audience based on various characteristics, allowing marketers to tailor their messages and offers to specific groups.
  2. Predictive Analytics: ANNs can forecast customer behavior, such as predicting which products a customer is likely to purchase or when they might churn from a subscription service.
  3. Recommendation Systems: Many online retailers and streaming services use ANNs to recommend products, movies, or music based on a user’s past preferences and behavior.
  4. Sentiment Analysis: ANNs can assess social media and customer feedback to gauge public sentiment about a brand, product, or campaign.
  5. Image and Video Recognition: In visual marketing, ANNs can identify and tag objects, people, or scenes within images and videos, facilitating better content curation and optimization.

Frequently Asked Questions

How do artificial neural networks learn?

ANNs learn by adjusting the weights and biases of their connections through a process known as backpropagation. This involves comparing the network’s predictions to the actual outcomes and updating the model to minimize errors.

Are ANNs the same as deep learning?

Deep learning is a subset of machine learning, and deep neural networks (DNNs) are a specific type of artificial neural network with multiple hidden layers. While all DNNs are ANNs, not all ANNs are deep learning models.

What types of data are suitable for ANNs in marketing?

ANNs are versatile and can handle various types of data, including customer demographics, purchase history, online behavior, text data, images, and more. The choice depends on the specific marketing task and the nature of the data available.

Do I need extensive programming skills to implement ANNs in marketing?

Implementing ANNs often requires programming skills, but there are user-friendly platforms and libraries that make it more accessible. Additionally, some marketing tools offer ANN-based features with a user-friendly interface.

What are the limitations of using ANNs in marketing?

ANNs require large amounts of data for training and can be computationally intensive. They can also be challenging to interpret, making it difficult to explain their decisions. Overfitting, where the model learns noise in the data, is another common challenge that marketers need to address.


About UsCareersPress and MediaContactTrustGlossary
Privacy PolicyTerms of Service

© 2023, Aprimo All Rights Reserved.