Brands can now make timely and qualified decisions based on accurate and up-to- date data and trends from China’s fast-evolving markets, thanks to China Skinny’s interactive dashboards.
Our reports are fully interactive and each page is designed around a key visualisation tile containing the core data (pricing, consumption etc.) accompanied by secondary visualisations which act as filters and slicers, allowing the user to delve into the data and tailor the experience to answer specific marketing questions.
Each tracker focuses on a single, key product category and delves into 10-12 of the top product subcategories i.e. lipstick, foundation, primer, eye shadow, etc. See a list of trackers and subcategories here. The data model is built using two key data sources:
China Skinny Trackers combine the cleanest big ecommerce data available with consumer research to capture insights that big data can’t provide.
Listing data for the top-selling 120 products on Tmall for each subcategory.
Quantitative survey data compiled quarterly from 800 consumers.
To see a breakdown of data points, click/tap here.
Get an instant understanding of the market as a whole; which categories are selling the most, the top performing brands, domestic vs foreign market share and top performing foreign countries.
Understand the category by monthly performance with sales revenue, average market price and weighted average price. Delve into price bracket performance by analysing product sales across the price spectrum by drilling down through different sized price bins or adjusting the axis range. Filter results by subcategory, domestic vs foreign or specific country.
Find out whether categories are over- or under-pricing and over- or under-discounting. This is calculated by comparing the average market price or discount against the weighted (on sales orders) average price or discount. Results can be filtered by origin and a variety of price brackets.
Understand the market map of brands (top 20), compared by revenue performance and price premium. Categorise brands by high sellers, low sellers, premium and standard brands. Filter results by product category, domestic or foreign brands or country of origin.
Understand and categorise the top-selling claims on the market by sales performance and price premium, as well as which claims’ market share outperforms market presence. Filter by origin and subcategory, refine claims by claim category and adjust axes for clearer view or to remove outliers. Use to define your basket of claims.
Understand the pricing and discounting performance of claims, which ones tend to over-price/discount and which ones under-price/discount and how to set your price for a particular claim. Filter by category, domestic/foreign and claim category to take a deep dive into claims pricing.
Understand current purchase levels and future purchase intent of product categories. Play around with target demographics to define which ones have the most current and future potential for the various categories.
Understand current purchase levels and future purchase intent of product functions. Play around with target demographics to define which claims are most important and which ones show the most future potential. Use this to refine your offering per target segment.
Understand which features are most compelling for consumers from brand features to product features. Filter and define a target segment to see which features are most important to them. Use this to define secondary marketing messaging.
Understand which channels are used predominantly for research and which ones for purchasing. Define a target segment to see which kind of channel/marketing strategy would resonate best with them.
While each tracker is unique based on the available information and structure of the category, below is a breakdown of common data points (non-exhaustive list):
Filters & Slicers
Filters & Slicers
Any combination of these can be selected to accurately identify the target segment.
Ecommerce data in China is very unreliable, even on trusted platforms such as Tmall. This is partly due to information gaps, inconsistent listings and a propensity to fudge listing info to the benefit of the store/brand. As a result, the data requires a lot of cleaning before it can be considered accurate. For this reason, China Skinny takes the top 120 listings per product subcategory so that the data can be manually combed for issues. The top 120 listing on Tmall represents the top 2 pages of listings and accounts for 65-95% of sales depending on the size of the subcategory. Below is a list of the most common data issues China Skinny manually fixes.
KEY ISSUES WE RECTIFY:
• Brands listed as sub-brand or vice versa. Listings are not separated into brand and sub-brand and given there is no standard, some are listed as the umbrella brand and some the sub-brand. This has clear influence on understanding of market share and brand performance.
• Store brand superseding product brand. Third party ecommerce stores can often list their brand, as opposed to the product brand.
• Pricing issues. Often products accidentally list the wrong price, resulting is several outliers which skew the data.
• Brand origin. Brand origins are commonly wrong, either because a domestic brand feigns to be foreign, a brand is produced in a variety of locations or the source ingredient’s origin is used instead, skewing market share and understanding of domestic v. foreign performance.
• Liquid Milk
|SKIN CARE TRACKER
• Blemish Balm
Contact us today at email@example.com to learn more about how the China Skincare Tracker can optimise your performance in China.
View relevant data from the skincare category and see successful marketing case studies from some of the successful skincare brands in China in this whitepaper.