Omnilytics Support Centre
All the topics, resources, and contact options you need for Omnilytics.
Getting Started with Omnilytics
Welcome to Omnilytics
Omnilytics is a data intelligence company that helps fashion and beauty brands understand real-time market data to make buying and stocking decisions in the most effective and efficient manner. We are here to help you become an expert in applying data analytics and market intelligence to grow your business.
In this documentation, you will learn how to start using Omnilytics to extract insightful data, and we will address any frequently asked questions, provide helpful recommendations and best practices to follow as you work.
Before you start
Here are some helpful things to know before you start:
- If you run into any trouble or have questions that is not answered in this documentation, please do reach out to your dedicated Client Success Manager who will be more than happy to help. Alternatively, you can reach out to our support team via the in-dashboard chat or email us at firstname.lastname@example.org. Likewise, if you have any recommendations or feedback, please do let us know so we can continuously improve our services.
Setting up your account
Dashboard registration begins with an invitation email sent to your inbox. Note that the registration link in the email expires within 72 hours of link generation so do register before then.
In the event the registration link is no longer valid due to late registration, please reach out to your Client Success Manager or email@example.com to request for a new link.
Display currency preference
Upon registering, you will be prompted to select your preferred display currency on the dashboard. This selection can be changed at a later point by clicking on Account to access Dashboard Settings.
The Omnilytics dashboard currently supports the following display currencies:
- Australian Dollar (AUD)
- Chinese Yuan (CNY)
- Euro (EUR)
- Great Britain Pound (GBP)
- Hong Kong Dollar (HKD)
- Indonesian Rupiah (IDR)
- Indian Rupee (INR)
- Malaysian Ringgit (MYR)
- Singaporean Dollar (SGD)
- South Korean Won (KRW)
- Taiwanese Dollar (TWD)
- Thai Baht (THB)
- Vietnamese Dong (VND)
What is the currency conversion rate used by Omnilytics?
All currencies on the dashboard are converted as per the market exchange rate supplied by currencylayer.
The default size displayed on the dashboard follows United Kingdom measurements. To change the display size, click on Account to access Dashboard Settings.
The Omnilytics dashboard currently supports the following sizes:
- Australia (AU)
- European (EU)
- Italian (IT)
- International (INT)
- United Kingdom (UK)
- United States/Canada (US)
How are sizes converted from one country’s measure to another?
As sizes and measurements may differ according to retailer, the tables below serve as a guide for converting sizes based on various countries’ size systems.
Definition of filters
Filters are used to single out dimensions and/or measures to focus on in your analysis. The filters available on the Omnilytics dashboard are defined as below.
Markets: Markets refer to countries where the retailer or brand sell and deliver their products to. For example, Zalora sells and delivers to Singapore, Hong Kong and Malaysia. Therefore, Zalora is a brand option under the three markets – Singapore, Hong Kong and Malaysia.
Retailers: Retailers refer to retail websites in which the products are sold. For example, Zalora is the retailer for the retail website zalora.sg.
Brands: Brands refer to product lines that may be sold on their own or other retail websites such as marketplaces or multi-label retail websites. For example, Mango is a brand on the multi-label retail website zalora.sg. See ‘How to filter by brand (video)’.
Categories: Categories refer to the classification of products by style, type, length and cut. For example, dress (category) and midi dress (subcategory).
Colours: Colours refer to the classification of colours and shades of products as defined by ISCC-NBS. For example, red (colour) and vivid purplish red (shade).
Materials: Materials refer to the matter that the products are made from. For example, cotton, polyester and chiffon.
Keyword: Keyword refers to a particular word or phrase extracted from the product title and product description. For example, “muscle” is a keyword in a product that is titled “Ribbed Muscle Tank Top”
Sizes: Size refers to the dimension and fit of the product. For example, UK10, EU38 and S. See also ‘Size preference’.
Gender & Kids: Gender & Kids refer to product categorisation by demographic attributes. For example, men, women and kids.
Price & Status: Price refers to the retail price of the product, which can be full priced or discounted. Status refers to the state associated with the product such as new in, replenished, in-stock or out-of-stock. See ‘What is the Price & Stock Status filter and how to use it?’ for a list of definitions.
What is the difference between a retailer and a brand?
Retailer refers to the retail website in which the products are sold. Brand refers to a product line that may be sold on its own or other retail websites such as marketplaces or multi-label retail website.
Below is a table of examples to illustrate the differences between the two.
|zalora.sg||Mango||Mango (brand) is sold on zalora.sg (retailer)|
|mango.com||Mango||Mango (brand) is sold on mango.com (retailer)|
|asos.com||Topshop||Topshop (brand) is sold on asos.com (retailer)|
|topshop.com||Topshop||Topshop (brand) is sold on topshop.com (retailer)|
How to compare brands under a retailer? (video)
How to find different subcategories? (video)
The Price & Stock status filter is used to single out products most relevant to your analysis using the following attributes – new in, replenished, full price, discounted, in-stock or out-of-stock.
Each subfilter is defined as below. To illustrate the examples, the hypothetical timeframe of Jan 1, 2018 – Mar 31, 2018 will be used.
New In: New in refers to products that are newly added to the retail website within the timeframe selected.
For example, any products that were first seen on the retail website any time between Jan 1, 2018 – Mar 31, 2018 will be returned as a result.
Replenished: Replenished refers to products that went out-of-stock at some point in its product lifetime and came back in stock within the timeframe selected.
For example, any products that came back in stock on the retail website any time between Jan 1, 2018 – Mar 31, 2018 will be returned as a result.
Full price: Full price refers to the products that were listed without a markdown within the timeframe selected.
For example, any products that were listed as full price any time between Jan 1, 2018 – Mar 31, 2018 will be returned as a result. If the said product went on discount on Feb 1, 2018, it remains qualified as a result.
Discounted: Discounted refers to the products that were listed with a markdown within the timeframe selected.
For example, any products that experienced a markdown in price any time between Jan 1, 2018 – Mar 31, 2018 will be returned as a result. If the said product experienced a price increase returning its retail price back to full price say on Feb 1, 2018, it remains qualified as a result.
In-Stock: In-Stock refers to products that are available for purchase on the retailer’s website.
For example, any products that are listed as available any time between Jan 1, 2018 – Mar 31, 2018 will be returned as a result. If said product went Out-of-Stock say on Feb 1, 2018, it remains qualified as a result
Out-of-Stock: Out-of-Stock refers to products that are no longer available for purchase on the retailer’s website.
For example, any products that are no longer listed on the retailer’s website or labelled as Out-of-Stock by the retailer anytime between Jan 1, 2018 – Mar 31, 2018 will be returned as a result. In the former scenario, the product was marked as Out-of-Stock for 14 days before the dashboard ceased collecting stock status information from the product.
Using a combination of Price & Status filters can add powerful insights into your analysis. For example, using a combination of the following filters can give a better indicator of products that are popular.
Out-of-Stock & Replenished: This filter combination highlights products that were sold out and subsequently replenished, a sign of sustained demand for the product.
Out-of-Stock & Full Price: This filter combination highlights products that were sold out at full price, a sign of value as consumers were willing to pay at full price.
In-Stock & Replenished: This filter combination highlights products that were topped up by retailers despite being in stock, presumably to prevent an out-of-stock scenario so anticipated demand can be continuously met.
How to filter by price & status? (video)
Filtering by date & availability of data
Filtering by date enables you to narrow down the data sets to a particular time frame relevant to your analysis. For example, to analyse the market performance for the first quarter of 2018, you will select Jan 1, 2018 – Mar 31, 2018.
On the Omnilytics dashboard, you can select a time frame as far back as Aug 1, 2017.
How to select a timeline? (video)
What does “product” mean?
Product refers to the stock keeping unit (SKU). For example, on the Omnilytics dashboard it is shown that Zalora carries 45 products under the T-shirt subcategory. This is interpreted as Zalora having 45 different T-shirt SKUs (breadth) in its inventory instead of 45 units of T-shirts (depth/volume).
Why are there no information on sales and volume (depth)?
As an analytics platform, we are only able to access and monitor publicly available data on a retailer or brand’s website, such as SKU details and product attributes like colours, patterns, and materials. Data on sales and volume is considered private information belonging to the business proprietor alone and disclosing these numbers would be considered a data breach.
What can I do with information on the breadth or assortment mix?
Breadth refers to the choices available for the consumer to purchase. In other words, the different categories and styles available. Understanding the science behind building a range with the right breadth is equally as vital as understanding volume or depth since a range too narrow may lack visual appeal on the website.
For example, imagine that an online consumer visits a retailer’s website because she was attracted by the trendy polka-dot dress she saw on an online ad. However, she ends up purchasing the safer option, which is the plain black dress.
Without the trendy polka-dot dress in the assortment mix, it may never have hooked the consumer to visit the retailer’s website. Similarly, without the plain black dress, the consumer may have left without making a purchase.
It is highly recommended that you use Omnilytics’ data together with your internal sales data to build a range that has both the right options available (breadth) as well as the right quantity (depth) to meet demand.
How to read the assortment graph?
There are two viewing options for assortments on the dashboard – bar chart and pie chart. Both viewing options offer the same information and are presented as an option to suit different user preferences. The default viewing option is the bar chart.
We will use the bar chart to explain how to read the assortment graph, as illustrated below.
We recommend that you revisit the filters you have selected frequently and use that as a starting point in understanding the story the data presents. For example, the graph above tells the following story:
In the Malaysian market since March 29, 2018, Zalora has a total of 107,877 products that are in stock, both discounted and full price. In contrast, ASOS has a total of 54,967 products that are in stock, both discounted and full price.
The top three product categories at Zalora are Tops (19%), Shoes (15%) followed by Bags (13%). ASOS, on the other hand, has an assortment mix that is led by Tops (28%), Outerwear (12%) and Dresses (12%).
Grouping by retailer vs grouping by brand
On the dashboard, you can choose to view the assortment graph grouped by retailer or by brand.
Group by Retailer
Grouping by retailer will show you the assortment mix of each retailer.
Here’s an example on how to read the assortment graph, grouped by retailer.
In the Malaysian market since Mar 29, 2018, Mango (brand) has 2,644 products in stock on Zalora (retailer) and 222 products in stock on ASOS (retailer). These in stock products could be at full price or at a discount.
Group At Brand
Grouping by brand will show you the assortment mix of each brand as a total from all retailers selected.
Here’s an example of how to interpret the assortment graph, grouped by brand.
In the Malaysian market since Mar 29, 2018, Mango (brand) has a total of 812 tops in stock as full price or discounted on Zalora (retailer) and ASOS (retailer).
How are products categorised?
Product categorisation on the Omnilytics platform is classified using a combination of retailer’s tagging, text and image recognition technology.
Found a product that is miscategorised? Send us the dashboard link of the product and a screenshot so we can train our classification models to improve.
How Omnilytics manages products with multiple subcategories?
For products that fall under multiple subcategories such as a Plaid Midi Dress, it will be counted as one product under the Plaid Dress category and similarly, as one product count under Midi Dress.
Therefore, you may notice that the total product count of a category does not match with the totalling of product count for each subcategories.
What is a price architecture?
Price architecture refers to the pricing structure of a retailer or brand.
Step-by-step guide to using the price architecture analysis (video)
What can I do with the price architecture information?
Understanding the pricing architecture of market comparables or adjacent retailers or brands is vital information in the evaluation of your pricing strategy.
A common use case would be to evaluate your pricing strategy against a market comparable to understand possible pricing gaps.
Let’s assume you are the hypothetical retailer The Wool Factory selling knitwear and your competitor, Peaches & Knits, is a comparable retailer with similar offerings.
Prior to mapping your price architecture against Peaches & Knits, you have assumed that both businesses employ the same pricing strategy. However, from the price architecture above, it is clear that the assumption is in fact inaccurate.
As it turns out, Peaches & Knits is more focused on lower price point products while The Wool Factory is focused on higher price point products. Understanding this can lead to several implications. As The Wool Factory, you may like to experiment with a new range of lower price point items to capture some of Peaches & Knit’s market. Conversely, you may no longer see Peaches & Knits as your competitor and continue crafting out a niche market for higher-priced knitwear.
How to read the price architecture graph?
There are two viewing options for the price architecture graph – summary and distribution.
View as Distribution
The distribution view allows you to get an overview of the distribution of product counts in each price range across the entire pricing structure.
Here’s an example on how to read a distribution view. Once again, we return to the filters selected as a starting point to understand the story told via the data presented.
In the Malaysian market since Mar 29, 2018, there is a higher proportion of products on Zalora lies in the MYR0-50 price range, whereby 17.5% of its SKUs are within this range compared to ASOS, as there are only 3.6% of its SKUs priced in the price range of MYR0-50.
This is the default view for Price Architecture on the Omnilytics dashboard.
View as Summary
The summary view is presented in a box-and-whiskers plot. This plot is most informative when the goal is to view the spread of prices across the entire price structure.
The Min is interpreted as lowest price observed in the entire pricing structure. Conversely, the Max is the highest price observed while. Med refers to the median price.
Q1 (first quartile) is the price observed halfway between Min and Med while Q3 (third quartile) is the price observed halfway between Med and Max.
Here’s an example on how to read a summary view.
In the Malaysian market since Mar 29, 2018, the lowest price observed amongst all Mango’s (brand) in stock products across Zalora (retailer) and ASOS (retailer) is MYR73.44, at either full price or discounted. On the other extreme , the highest price observed amongst all Mango’s (brand) in stock products across Zalora (retailer) and ASOS (retailer) is MYR4,292.54, at either full price or discounted.
This means about 50% of products from the total product count are priced at MYR587.06 or below and the other half are priced at MYR587.06 or above. Moving on, about 25% of the total product count lies at MYR342.25 (Q1) or below, another 25% are priced between MYR342.25 (Q1) and MYR587.06 (Q2), the other 25% are priced between MYR587.06 (Q2) and MYR771.16 (Q3), while the remaining 25% of products are priced more than MYR771.16 (Q3).
How Omnilytics manages products with prices across multiple price bands?
A product may fall into multiple price bands due to a markdown from full price and vice versa within the timeframe selected. In this case, the product will be counted as one product count in each of the price range.
Therefore, you may notice that the total product count of an entire pricing structure does not match with the total of product count for each price range.
Step-by-step guide to using the colour analysis tab (video)
How are colours categorised?
Similar to product categorisation, colours on the Omnilytics platform are classified using a combination of retailer’s tagging, text and image recognition technology. The system of colours used by Omnilytics is the ISBCC-NBS system.
Found a product that has its colour miscategorised? Send us the dashboard link of the product and a screenshot so we can train our classification models to improve.
How to read the colour graph?
There are two viewing options for colours on the dashboard – bar chart and pie chart. Both viewing options offer the same information and are presented as an option to suit various preferences. The default viewing option is the bar chart.
To illustrate how to read the graph, we will use the pie chart to explain how to read the colour graph.
Again, we recommend that you revisit the filters you have selected frequently and use that as a starting point in understanding the story that the data presents.
For example, the graph below tells the following story.
In the Malaysian market since Mar 29, 2018, majority of Mango’s (brand) products that are in stock on Zalora (retailer) are composed of black (21% with 563 SKUs). On the other hand, most of Mango’s (brand) in stock products on ASOS (retailer) are in blue (25% with 55 SKUs). These black-coloured items analysed are either on discount or at full price.
What does “Others” mean?
Products are classified under “Others” when the system is unable to detect the product’s colour or when the product is multicoloured.
This is dependent on how the products are treated on the retail website itself. If the retailer lists different colours of the same product as a different SKU, each of these colours will contribute as one product count on the dashboard. The price associated to each colour would then be tied to the individual SKU.
However, some retailers will list different colours of the same product as a single SKU. Therefore, the different colours will not be considered as an individual product count. Instead, all the available colours associated with the product are considered as one product count. In this scenario, the difference in prices between different colours would not be as accurately captured by the dashboard as the former case. This is a shortcoming our team is actively addressing. If you would like to learn more, please feel free to reach out to your dedicated Client Success Manager or email us at firstname.lastname@example.org
How to determine which category is most discounted? (video)
How to find the sellout rate? (video)
Step-by-step guide to using the size analysis tab (video)
How to read the size graph?
Here’s an example on how to read a distribution view. As we did before, we return to the filters selected as a starting point to understand the story the data is presenting.
In the Malaysian market since Mar 29, 2018, Zalora (retailer) has 2,479 size UK14 dresses in stock at full price or discounted while ASOS (retailer) has 6,396 size UK14 dresses in stock at full price or discounted.
How Omnilytics manages products with multiple sizes offered?
For products with multiple sizes, each size is counted as one product count in the respective size. For example, a top with sizes UK 8, UK 10 and UK 12 will be counted once under UK8, once under UK10 and once under UK12.
Therefore, you may notice that the total product count does not match with the total of product count for each size.
How Omnilytics manages products with different prices at different sizes?
Similar to how Omnilytics manages products of different colours with different prices, it is dependent on how the retailer lists the product on the retail website. To learn more, see ‘How does Omnilytics manages products with different prices at different colours?’
Omnilytics size conversion charts
How to read the discount graph?
There are two viewing options for discounts on the dashboard – grouped or stacked. Both viewing options offer the same information and is presented as an option to suit various preferences. The default viewing option is the bar chart.
To illustrate how to read the graph, we will use the grouped chart to explain how to read the discount graph.
In the Malaysian market since Mar 29, 2018, 16% (15,653 SKUs) of the discounted products on Zalora that are in stock are being discounted in the 50-54% discount range, while most (9.6%) of the discounted products on ASOS are discounted in the 30-34% discount range.
How Omnilytics manages products that have been discounted multiple times and falls under several discount ranges?
Products that have been discounted multiple times within the same time frame may fall in more than one discount range, depending on the size of the mark down.
For example, if a product is given an additional 10% discount on top of it’s existing 15%, the product will be counted as one count under the 10-15% discount range and one count under the 20-25% discount range.
Product Match (Beta)
What is product match?
Product match analysis is designed with the goal of making product comparison and tracking across multiple platforms easier and faster.
Product match is especially useful if you are
An own-brand retailer or wholesaler: Tracking price changes and comparing it across your distribution partners to ensure they do not engage in undesirable pricing behaviours.
Marketplace or multi-label platform: Compare prices across different eCommerce players to ensure you remain competitive. This is especially so if your key value proposition is to offer your online consumers the lowest prices guaranteed.
How are the products matched?
In this beta version, products are matched based on the category, subcategory, gender, colour, product name and product description.
What happens when there is no exact match found?
In the event there is no exact match found for the product, our algorithm will sort through our database to return similar products. Similarity is determined based on the category, subcategory, gender, colour, product name and description.
How do I view the product images and its associated information?
There are two ways to access the product images and the associated information – drill down and View Product
Drilling down involves clicking through the graph itself, which filters data all the way down to the final layer, which is the product view.
Alternatively, you could click on the ‘View Product’ at any point in your workflow to view the products associated with the filters you have applied.
Clicking on the product image will reveal further information related to the specific product and offer more viewing options of the product.
What is the definition of Best-Selling?
Best-Selling is determined by a scoring system that takes into consideration the replenishment rate, discount proportion and frequency as well as whether the product sells out at full price.
The stock status bar charts the status of a particular SKU over its product lifetime. Using the example above, the product was first available for purchase on Mar 29, 2018. It then went out-of-stock for the first time on Apr 10, 2018 and was replenished again on Apr 17, 2018 before going out-of-stock again on Apr 20, 2018. The product then went through a few more cycles of being out-of-stock and replenished.
To find out which sizes were replenished, simply hover over the yellow segments of the bar. In this example, for the replenishment on Apr 17, 2018, the retailer stocked up on sizes XS, S, M and L.
From observing the movement of the SKU over its lifetime via the Stock Status bar, we can formulate an initial inference that this product is well-received by online consumers as it has repeatedly gone out-of-stock and replenished multiple times.
There are several reasons why data may not be available. For example, it could be attributed to a technical error on our data scraping system or an error on the retailer’s website itself. In general, it translates to a scenario whereby we were unable to complete the process of obtaining data from the retailer’s website. In the event of this happening, the days of which data is unavailable is marked in grey as illustrated in the example above.
What does it mean by ‘Replenished’ when there was no prior ‘Out-of-Stock’?
It is possible that the stock status bar shows a replenishment without a prior occurrence of out-of-stock. This happens when a particular size of the SKU has:
1) gone out-of-stock and is subsequently replenished
2) been added to the SKU when it was not previously
Therefore, when you hover over the section of the bar indicating replenished, it will show you the size that was replenished. In the example above, we can interpret that a day after the product was launched (Apr 28), UK6 ran out-of-stock and was replenished the following day (Apr 29).
How is New In and Last Seen defined?
In this section, we will use the same example from ‘How to read the Stock Status bar?’to illustrate the definition of New In and Last Seen.
New In: New in date refers to the first time the product was seen by the dashboard. For example, the product in the example was first seen by Omnilytics on Mar 29, 2018.
Last Seen: Last seen date refers to the last time the product was seen by the dashboard. For example, the product in the example was last seen by Omnilytics on Apr 24, 2018.
What does it mean by Last Seen Price, First Seen Price, Highest Price & Lowest Price?
Below are the definitions for the following price-related terms.
Last Seen Price: Last seen price refers to the last registered or most up-to-date price picked up by the dashboard.
First Seen Price: First seen price refers to the price first picked up by the dashboard.
Lowest Price: Lowest price refers to the lowest price registered by the dashboard throughout the entire lifetime of the product.
Highest Price: Highest price refers to the highest price registered by the dashboard throughout the entire lifetime of the product.
Using the example above, it can be observed that the product’s first seen price was SGD67.00. Sometime between Apr 1, 2018 and Apr 8, 2018, the retailer reduced the price to SGD57.36 before raising the price to SGD67.99 on Apr 21, 2018. Along the entire product lifetime, the highest price registered was SGD67.59.
Still have questions that was not addressed here? Feel free to reach out to your Client Success Manager or drop us an email at email@example.com.