Transforming Businesses Through Personalization

ODSC - Open Data Science
5 min readJan 7, 2021

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In the wake of COVID-19, unprecedented acceleration in digital adoption has significantly re-shaped consumer behavior. The world is experiencing a decade’s worth of digital acceleration over the last few months, leading to significant changes in the way customers interact with brands and how brands interact with their customers.

Companies are realizing the importance of enhanced customer interactions and personalization to increase engagement and revenue. In fact, based on a survey conducted by Business2Community, 74% of online shoppers get frustrated when content is not relevant to their interests. But companies that can do this effectively are realizing revenue gains and 89% of businesses have reported improved results from personalization efforts.

Done right, personalization can unlock enormous value from the customer base. Unlike traditional methods, where customers are treated in a siloed manner by different departments through disjointed efforts, personalization engages customers in a continuous, systematic manner. This can be envisioned as a flywheel — generating momentum and value for a business.

So how do you achieve personalization? That’s where ZS’s Personalize.AI (P.AI) comes into play.

What is Personalize.AI?

It is an AI-powered SaaS solution that ZS developed in the area of personalization, used to help companies drive revenue growth from personalized, member level product offer recommendations. It has an intuitive, business-friendly platform that allows users to rapidly deploy to a partial or full customer base within 3 months.

The platform is designed to enable collaboration across key stakeholders (business executives, data scientists and marketing or technology operations) in organizations that are driving personalization.

How does Personalize.AI generate tailored, customer experiences?

To unlock the value of personalization, brands need to leverage all their data assets from loyalty rewards programs, customer interactions, current campaigns, digital activity and more. However, data is just one part of solving the personalization puzzle. Creating a strong, personalized experience requires these capabilities: discover, design and deploy. Personalize.AI keeps these core themes at the forefront for optimal use.

Discovery for personalization

Discovery focuses on understanding customers, identifying activation parameters and generating recommendations across customer lifecycles. P.AI considers the below categories when delivering personalization at the discovery level:

  • Customer micro segmentation: Micro segmentation is an important exercise to understand customer personas and capture their behaviors. As customer behavior is continuously changing, businesses need a hierarchical segmentation capability to create relevant micro segments quickly. P.AI’s classification hierarchy allows users to view customer micro segments and group them based on expected behavior or business objectives. AI easily identifies which customers are better targets for the business based on past responses to campaigns or shopping patterns.
  • Automated feature engineering: Digital/E-comm marketing is evolving quickly, leading to considerable changes in data and behavior. Additionally, internal protocols are also changing due to continuous upgrades in digital platforms, marketing strategy, and new products. Similarly, competitors’ actions are affecting customer behavior which in-turn effect customer actions. Thus, an autonomous hypothesis generation and testing engine is needed. This not only helps identify factors affecting defined KPIs like churn, but also feeds into other engines of segmentation and recommendations.
  • Customer elasticity: P.AI leverages historical purchasing patterns of customers to identify elasticity. Each customer is unique, and their behavior needs to be captured on an individual basis. Understanding this can help organizations apply the right marketing efforts for reaching key business objectives.

Design for personalization

The design in personalization involves understanding customer needs and providing them with the right content, experience or offer. The below capabilities are necessary to create a platform focused on design for personalization:

  • Campaign design: P.AI has the ability to design campaigns with defined objective functions such as revenue maximization, customer engagement, profit and constraints such as budget, location, etc.
  • Campaign execution: Synchronous integration between design and discovery modules is required to attain design for personalization. Once design is complete, recommendations are adjusted for defined KPIs for selected cohorts.
  • Testing/Optimization: Using our test-control platform, quickly test campaign performance before fully deploying it. This also helps optimize content and offers for personalization.
  • Testing logs: Extract prior test logs to understand the marginal impact of parameters on defined KPIs.

Deploy for personalization

Deploy is responsible for taking input from multiple sources and generating output through APIs which can be integrated to platforms serving recommendations. This is critical to ensure last mile delivery at scale to customers.

Why should you invest in personalization?

At a time when the retail and e-commerce industry is rapidly evolving, it’s crucial to keep customers satisfied, but more importantly loyal to your brand. Personalization, driven by tailored AI, can drive up to 15% revenue growth at the customer level from increasing average order size to improving cross-sell and conversions.

According to a recent ZS survey, 70–80% of senior executives are looking at personalization to grow their business in the next 12–24 months. As you begin to think about ways to increase revenue and engage your customers in more meaningful ways, consider personalization — it could be your path to survival.

Interested in learning more about P.AI? Reach out to us.

Article by Gopi Vikranth and PKS Prakash of ZS.

Original post here.

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ODSC - Open Data Science
ODSC - Open Data Science

Written by ODSC - Open Data Science

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