Deconstruct's partnership with Mason proved fruitful, with Mason contributing to approximately 10-15% of the monthly revenue. The positive collaboration, ease of working together, and proactive support from Mason's team prompted Harsha to recommend the platform to other D2C brands.

Deconstruct, founded by IIT Kharagpur alumna Malini Adapureddy, is a science-based skincare brand disrupting the Indian skincare space. Deconstruct offers facial skincare and body care products.  Launched in 2021, Deconstruct prioritizes transparency, offering 14 evidence-based formulations for skincare. Boasting a 50% repeat purchase rate and an annual run rate of $2 million, the brand focuses on consumer education and plans to expand globally.

Optimizing customer experience is critical for sustained growth of any brand. This case study sheds light into how Deconstruct, a specialized skincare brand, overcame challenges of repeat purchases and website conversion rates through the implementation of Mason's AI shopping engine.

Meet Harsha, the D2C Lead at Deconstruct

Harsha Sinha, Deconstruct's D2C Lead, spearheads the website, marketing, and technical aspects of the brand. With a dedicated team covering development, marketing, and customer experience, Harsha aims to enhance key metrics such as sessions, conversion rates, average order value (AOV), and customer lifetime value (LTV).

The Challenge: Elevating Repeat Purchases

Deconstruct noticed a common challenge – a substantial percentage of their website traffic was first-time visitors. Harsha's objective was to boost the repeat purchase rate and foster brand loyalty by tailoring offers based on individual user behaviour.

We worked with a CRO agency before. And then Mason came along. we saw that a lot of people were coming on the website, but they were abandoning the Carts. Which is why the CVR, the Conversion Rate was kind of not as high as the number of sessions that we were getting. I mean, it was not proportional, the increase in the sessions and increase in the CVR.
Harsha Sinha - Marketing Manager @Deconstruct    

Leveraging Mason's AI Shopping Engine

To address the personalization challenge, Deconstruct turned to Mason's AI shopping engine. By leveraging Mason's capabilities, the brand sought to create tailored offers, improve conversion rates, and establish a loyal customer base.

Implementation and Initial Observations

Post-integration of Mason's AI shopping engine in August, Deconstruct concentrated on refining the conversion rate by minimizing abandoned carts. Initial findings showed a significant drop in abandoned carts, increased conversion rates for Mason-driven orders, and an uptick in AOV.

What I have noticed is that as we started working with Mason since August. And if I just looked at the data, the conversion rate for orders that came through Mason has been much higher than the overall CVR. The AOV has been much higher.
Harsha Sinha - Marketing Manager @Deconstruct

Key Features Utilized by Deconstruct

Abandoned Cart Plugin:
Mason's abandoned cart plugin enabled Deconstruct to directly engage users on the website, resulting in a noteworthy 9% CVR, surpassing the overall website CVR.

Product Bundles:
Deconstruct effectively enabled product bundling, encouraging users to make additional purchases with discounts, thereby invreasing AOV.

Bulk Buy Feature:
The bulk buy feature incentivized customers to purchase more of the same product, contributing to increased revenue.

Earlier we were missing to capture on the website saying that, hey, here's a 5% off. This is what you checked last time. Get another discount here and all of that. And then with the timer, that's where Mason has helped us. And it's there in the results to see.
Harsha Sinha - Marketing Manager @Deconstruct  

Results and Impact Metrics

- Conversion rate for Mason-driven orders consistently outperformed the overall website CVR.

- AOV increased significantly due to Mason’s strategic bundling and upselling.

- Abandoned cart rates had a substantial decrease, resulting in improved overall conversion metrics.

Deconstruct's partnership with Mason proved fruitful, with Mason contributing to approximately 8-10% of the monthly revenue. The positive collaboration, ease of working together, and proactive support from Mason's team prompted Harsha to recommend the platform to other D2C brands.

Conclusion

In India’s competitive D2C space, the success story of Deconstruct showcases the value of leveraging advanced AI shopping engines like Mason's to optimize critical metrics, enhance customer experiences, and drive sustainable business growth. As the partnership evolves, Deconstruct remains committed to staying at the forefront of personalized skincare solutions and plans to expand in the international market.

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