Case Study

How MBX created double-digit profit uplift on Amazon with Luca

Tanvi Surti
Jun 8, 2023


MBX® Official Website | Beauty Made Easy, Inspired By You – Memebox

MBX (formerly Memebox) is a venture-backed beauty and personal care startup headquartered in San Francisco.

The company makes all of its products in Korea, leveraging the latest K-beauty innovations in the formula, packaging, and speed-to-market.

Its brands include Kaja, I Dew Care, Pony Effect, I’m Meme, and Nooni and, in the United States, it sells its goods via Ulta, Sephora, Amazon and DTC.‍

The Challenge

Lynn Chim, MBX’s US Amazon business lead, needed to improve margins in the face of rising COGS and ad costs.

This meant trying to increase prices without hurting revenue. Lynn realized that this tradeoff was a tricky needle to thread – make too aggressive a price increase and you churn customers deteriorating both bottom and topline, make too conservative a price increase and you end up leaving profit on the table.

She approached Luca to help measure customer elasticities and recommend price changes to meet her goals.

Luca’s Approach

Each business has unique goals and requirements. In the case of MBX, they had distinct P&L goals for each brand as well as requirements around sell-through-rate and competitive positioning for certain hero SKUs.

“We were aiming to push some of our products to achieve #1 in the best seller category. While it is important for us to maintain and increase our profitability over time, we also have to make sure that we do not compromise on the sales volume by unit. We needed a price plan that could hit two birds with one stone!” - Lynn Chim

To improve margins while respecting all of MBX’s requirements,, Luca’s AI-powered pricing engine broke up the problem into four distinct parts –

  1. Generate Elasticities: First, Luca’s Price Elasticity model  looked at years of historical order and conversion data to calculate elasticities for each SKU, or where data was sparse, clusters of similar SKUs in MBX’s portfolio. This provided a starting point to identify where there was headroom for price increases and where there wasn’t.
  2. Apply Optimization Logic: After price elasticities were calculated, Luca’s Optimization Model processed MBX’s business goals and constraints to identify the right price for each SKU. In Lynn’s case there were inventory considerations on one sub-category and heavy competitor pressures on others which the model had to account for in the price plan.
  3. Operator Input: With pricing recommendations available in the Luca dashboard, Lynn was able to make informed decisions about what recommendations to accept and reject.
  4. Experiment, and Attribute: It is easy to mistake a bad price change for a good one when, especially in the absence of clean user-level A/B testing (which is hard enough to do in retail, and impossible to do on Amazon). Luca’s Attribution Engine employs a number of experimentation methods, such as diff-and-diff and synthetic control, to isolate the performance of each price change withoute nvironmental factors such as promotions or seasonality. This gave Lynn impact numbers she could trust.

The Results

At the end of the observation period, MBX had not only hit its profitability goals with a 19% profit uplift but also observed top-line growth as well with 4.4% revenue uplift.

If you’re interested in  revamping your pricing strategy on any channel you sell on, book a call with us to chat about how Luca can help!


Tanvi Surti
Tanvi is the CEO and Co-Founder at Luca. Before Luca, Tanvi spent a decade building product teams at Uber and Microsoft. At Uber, she led the pricing team that created ~$1B in margin improvements on the ridesharing business, and now gets to help retailers solve the same problem, at scale.

Ready to learn more?

Let's chat about bringing pricing intelligence into your process.