How Markaz.com, an e-commerce reseller platform uses user feedback to determine optimum pricing for top-selling items, thereby increasing order volumes and customer satisfaction.
Markaz is a thriving social commerce entity, empowering users to establish e-commerce businesses. The primary demographic includes stay-at-home individuals and students looking to earn additional income by selling merchandise to their immediate circles. The product assortment spans apparel, home décor, and accessories - niches within the e-commerce realm that lack fixed price standardization. Consequently, manufacturers dictate their product prices on Markaz. Sellers then add a commission on top of the manufacturer's price prior to sales, which shapes the final price for the end consumers.
Sellers voiced concerns about the manufacturers' prices being prohibitively high. They often found similar products available at lower prices on competing platforms. While manufacturers were willing to lower prices (compensated by increased order volumes), they sought benchmarks from Markaz to aid decision-making.
Markaz's optimal solution involved collecting sellers' suggestions on product pricing that they believed would align with market rates and drive more orders. However, the associated challenges were significant. Gathering extensive feedback across seller demographics for each product, analyzing their responses, and repeating this process for all top-selling products presented a complex problem.
Further complications included linguistic diversity (Urdu, English, and Roman Urdu), variable response formats (mix of numbers and strings), continuously changing product trends, and the need to adequately represent a diverse user base.
Incorporating Blitzllama's in-product surveys significantly simplified and expedited this process. The image-based in-product surveys enabled the product team to seek accurate pricing suggestions while providing product images for reference. Deploying these surveys within the Markaz app ensured that users were in the ideal context when providing feedback.
An impressive average response rate of 50% yielded thousands of responses within hours. A new survey could be launched and a statistically significant dataset could be obtained in a matter of hours.
Multilingual responses were automatically translated into English, and a GPT-powered AI facilitated the categorization of numeric and textual data. Blitzllama's integration with Mixpanel x Blitzllama cohort sync made it easy to correctly sample users and achieve an ideal user representation, reducing days of analysis to real-time insights.
Through Blitzllama's resurveying guardrails, Markaz ensured that the same users were not surveyed repeatedly, thus streamlining the iteration process and facilitating efficient data collection for pricing over 30+ products.
Optimal pricing, driven by user feedback, allowed sellers to generate 10-20% more orders each week. This set a virtuous cycle in motion: buyers purchased more items, and sellers, witnessing higher sales, invested more in buyer acquisition.The latest Net Promoter Score (NPS) survey reflected a 35% reduction in complaints about high product pricing.