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. dairy with liquid milk, milk powder, infant formula 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.
TRACKER MODEL
|
ECOMMERCE DATA
Listing data for the top-selling 120 products on Tmall for each subcategory. |
CONSUMER DATA
Quantitative survey data compiled quarterly from 1,600 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 country.
Understand whether a category over- or under-prices or discounts by comparing average market price and discount against weighted (on sales orders) average price and discount. Filter by origin, price range and volume range.
Understand the market map of brands, comparing them by sales revenue and price premium. Categorise brands by high sellers, low sellers, premium and standard brands. Filter results by subcategory, domestic vs foreign or country.
Understand and categorise the top-selling claims on the market by sales performance and price premium. Filter by origin and subcategory and refine claims by claims category. 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.
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 and for which categories.
Understand current purchase levels and future purchase intent of product claims. Play around with target demographics to define which claims are most important and which ones show the most future potential. Use this to define your offering.
Understand which features are most compelling for consumers from brand features to product features. Filter and define a target segment to see which feature 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 how their preferences differ and define your marketing approach accordingly.
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):
TRACKER MODEL
|
ECOMMERCE DATA
Listing points
Calculated Points
Filters & Slicers |
CONSUMER DATA
Survey Points
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.
| DAIRY TRACKER
• Liquid Milk |
SKIN CARE TRACKER
• Moisturisers |
BEAUTY TRACKER
• Blemish Balm Coming soon |
Contact us today at datatracker@chinaskinny.com to learn more about how the China Dairy Tracker can optimise your performance in China.
China Dairy WhitepaperView relevant data from the dairy category and see successful marketing case studies from some of the successful dairy brands in China in this whitepaper.
