Personalization at Scale :
It's Not Simply a Marketing Concept

Personalization at scale is a bridge that connects a larger audience with your business. In simple words, it indicates targeting a broad audience. It means analyzing large volumes of user data to deliver suitable customer experiences according to their purchase history, preferences, and interests. You can find personalization at scale in several situations like video streaming services, music, or eCommerce brands with rich digital presence.

Increase customer engagement with personalized solutions
Personalization at scale (2)

Personalization at scale involves the use of dependable data like

Survey responses

Click profile data

Purchase history

On-site actions

See how personalization works for your business

Features of Our Personalization Engine

It works via the following steps:

Integrating customer data

Segmenting audiences

Developing and launching customized campaigns

How Express Analytics Can Help?

At Express Analytics, we help organizations use personalization to reach their marketing goals. We can help with :

  • Data gathering and analysis
  • Use of personalization strategies
  • Selection and use of personalization platforms
  • Tracking and improvement of personalization strategies

Lets Connect –

Why Do Companies Still Face Problems Achieving Personalization in Marketing?

Personalization at scale in marketing has become a crucial strategy for organizations looking to increase customer engagement and conversions. Despite its benefits, many businesses still face problems in implementing proper personalization:

  • Collecting data from several sources will cause data silos. Hence, without a comprehensive customer profile, it’s challenging to deliver personalized experiences.
  • Most organizations do not have dedicated teams that understand behavioral analytics, customer segmentation, and campaign strategies to make on-time decisions.
  • Many companies don’t use required tools, resources, and expertise properly to implement AI and ML models to inspect customer behavior and predict interests.
  • Issues such as outdated, irregular, or incorrect data are encountered when businesses have access to tons of customer data.

To solve these problems, companies should

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Don't simply target, connect