article thumbnail

AI in commerce: Essential use cases for B2B and B2C

IBM Big Data Hub

Key takeaways By implementing effective solutions for AI in commerce, brands can create seamless, personalized buying experiences that increase customer loyalty, customer engagement, retention and share of wallet across B2B and B2C channels. This includes trust in the data, the security, the brand and the people behind the AI.

B2B 67
article thumbnail

Use a Data Strategy to Make Your Startup Profitable

Smart Data Collective

Big data is no longer a luxury for businesses. In the information, there are companies with big data strategies and those that fall behind. Big data and business intelligence are essential. However, the success of a big data strategy relies on its implementation. Longer buying cycles, more risk, and larger transactions.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Diligent enhances customer governance with automated data-driven insights using Amazon QuickSight

AWS Big Data

Diligent is the global leader in modern governance, providing software as a service (SaaS) services across governance, risk, compliance, and audit, helping companies meet their environmental, social, and governance (ESG) commitments. With the Diligent Platform, organizations can bring their most critical data into one centralized place.

article thumbnail

How Big Data and AI are Revolutionizing Payments

Smart Data Collective

Data has become an essential asset for companies everywhere. By interpreting and analyzing the data, organizations can understand and predict trends, improve security and make data-driven decisions. In this post, we’ll explore how organizations can leverage big data and AI instruments to improve their ROI.

Big Data 130
article thumbnail

Data is essential: Building an effective generative AI marketing strategy

IBM Big Data Hub

According to the IBM survey, when CMOs were asked what they thought the primary challenges were in adopting generative AI, they listed three top concerns: managing the complexity of implementation, building the data set and brand and intellectual property (IP) risk. The journey starts with sound data.

Marketing 116
article thumbnail

Practical Skills for The AI Product Manager

O'Reilly on Data

Feature Development and Data Management: This phase focuses on the inputs to a machine learning product; defining the features in the data that are relevant, and building the data pipelines that fuel the machine learning engine powering the product. Consumer Companies Versus B2B Companies.

article thumbnail

The 4 pillars of the Zscaler Zero Trust Exchange: Customers share their successes

CIO Business Intelligence

The attack surface now extends to home offices, cloud applications, and public clouds, and there is an ever-increasing risk of lateral threat movement within highly interconnected hub-and-spoke networks protected by castle-and-moat security models.