Men’s Fashion Outlet Powerlook Witnesses A 302% Uptick In Unique Conversions With WebEngage

WebEngage has been instrumental to us in retaining users. With the platform’s advanced Web Personalization capabilities, we can now deliver a personalized website experience to each of our customers based on their unique interests and preferences. This has not only improved the overall customer experience on our website but has also resulted in higher engagement rates and increased conversions.

Heena Pawar

Head of E-Commerce Business

WebEngage has been instrumental to us in retaining users. With the platform’s advanced Web Personalization capabilities, we can now deliver a personalized website experience to each of our customers based on their unique interests and preferences. This has not only improved the overall customer experience on our website but has also resulted in higher engagement rates and increased conversions.

Heena Pawar

Head of E-Commerce Business

302%

Uptick In Unique Conversions

1745%

Uplift in Unique Conversions With A Control Group for the Recommendation Journey

4 Lakh

Impressions On M-Site

About Powerlook

Powerlook is one of India’s fastest-growing e-commerce brands which offers a unique range of men’s casual wear, and aims to give India a brand that is high-street, fashionable, and has nuances of the west.
Founded by Amar Pawar and Raghavendra Pawar, Powerlook is a family-owned business that started its first retail shop in March 2010. They’ve been successfully carrying out e-commerce operations since 2018. Their USP lies in offering their customers the very best in terms of design and comfort, while at the same time ensuring that their products are available at affordable rates.

With business progressing at a great pace, the team has ambitious plans to take Powerlook to the next level by foraying into international markets.

Key Features Used:

→ Website Personalization – In-line
An in-line campaign (Web Personalization) enables you to create personalized experiences for your website for each user based on different custom events and user attributes.

Recommendation and Catalog
Recommendation: Personalize your communication with recommendations based on users’ actions or events. For example, if a user purchases shoes, you can use our recommendation engine to suggest other products, like a pair of socks, that the user might be interested in purchasing.
Catalog: This helps you keep all your product information up-to-date and send relevant, personalized communications. For example, you can fetch the updated price information for a product from an uploaded Catalog and ensure you never send stale or incorrect data in your messages.

→ Web Push & Email
Create multiple user segments that suit your business needs using personal & user behavior data. Choose from an exhaustive studio of 100+ pre-designed templates to create visually enticing Web Push Notifications — no coding required.

Powerlook & WebEngage – A Collaborative Effort
The Powerlook team began with their primary goal: improving user retention rate.

Before onboarding WebEngage, they had no marketing automation platform to enable them to have all their user data in one place (CDP), run omnichannel journeys, and bring retention-based engagement to the fore. That’s what we helped them solve for.

Company’s Objectives and Challenges

Objectives:

  • Give users a personalized & dynamic website experience based on past purchases, products viewed & products added to the cart. (Solved with Web Personalization)
  • Recommend products based on user purchase behavior (Solved with the Recommendation and Catalog engine)
  • Improving retention rate and driving users to repeat purchases

Challenges:

The D2C industry, especially retail and apparel, can bring ambiguity to user purchase intent. This is because the industry functions on users’ wants, not needs. And the Powerlook team struggled the same initially. Hence, their challenges to solve for were:

  • High drop-offs at an initial stage due to confusion in figuring out intent
  • Lack of user-specific recommendations based on their style (understood with user behavior)

Results?

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