Image and Data Analysis¶
One of the techniques eSales Lifestyle applies to deliver high quality results with low effort implementations, is image and data analysis.
Providing data in a truly structured manner is often simply not possible. In some cases, structure requires understanding of complex data relations. Neither producing nor consuming those relations are supported in all systems. Furthermore, manual tagging is hard and without exceptional discipline a lot of important information is left out. This leaves important parts of a retailers' assortment unreachable for visitors. Especially for visitors with a clear purchase intent who uses search or applies filters. This causes a poor experience for the visitor as well as a lost opportunity of sales.
Due to the importance of quality data, a lot of resources are often spent on creating product content both in terms of imagery and textual information. By automatically extracting important concepts from that data, eSales Lifestyle is able to both complement and utilise these efforts as if it were structured data.
The image analysis supports images in the following file formats:
Over 400 different colours and nuances are recognised by eSales Lifestyle through image analysis, and it is all done automatically using deep neural networks. No more need for manual colour tagging.
eSales Lifestyle can analyse a variety of image types including cutout images and images depicting fashion models.
The analysis includes a built in understanding of colour synonyms and how colours relate to each other. For example, bright red products won't be seen as
burgundy. A product in a darker shade of red will however automatically be considered as
dark red, as well as
red, in both search and filter contexts.
The colour analysis not only determines the colour nuances of a product, but also the distribution. This means that a shirt that is entirely white will be recognized as "more white" than a shirt that is striped blue and white.
color_default attribute in the data feed is beneficial for faster enabling of colour search, filters, and swatches as this will take effect before the image analysis is completed. This means that colours that are available can be provided and if so, will be used until overridden by the automatic colour analysis.
In cases with very complex imagery, for example boxes and packages that display other colours than the product, the colour analysis can always be overridden using the
Proper imagery is crucial when it comes to visually driven product industries such as fashion and lifestyle. Who wishes to interact with garments without visuals?
This is in general handled well but accidents with image handling do occur, and when it does it needs to be resolved fast. To avoid the scenario of products without images filling up a site's most valuable retail space, eSales Lifestyle automatically checks the image availability at regular intervals.
A product can have multiple images and a priority order of which these images are to be displayed. Should the highest prioritised image be faulty, a fallback mechanism triggers, and the second most prioritised image is displayed. Should none of the images be accessible, the product will automatically be heavily demoted in all contexts. This gives a retailer minimum site impact during the time needed to work out any imagery issues.
Image resolution analysis¶
For performance reasons it is important to show images in the resolution that is appropriate for the context. Mobile devices typically use smaller images, thumbnail images are even smaller regardless of device, and for images in product listings it is important that the resolution is sufficiently high to showcase details. All images in the data feed are automatically analysed with respect to their width to provide a
width value. This value can also correct an incorrect provided value.
width for an image in the data feed, even if the width is not fully accurate, is beneficial for faster enabling of performance gains as these will take effect before the image analysis is completed. This means non-exact, guiding, widths can be provided that with time will be corrected if exact widths are inaccessible.
Image type analysis¶
Imagery depicting products normally come as either cutout or model images, meaning it either only displays the actual product with a plain background it is showcased on or together with a model. Proper separation of image types enables faster colour analysis and improved image consistency for hover or image gallery features on product cards, as well as allows for more flexible image prioritisation by merchandisers.
All images are automatically analysed with respect to their image type and an image type will automatically be provided to images without types, as well as correct any incorrectly given guiding image types.
type_override attribute in the data feed for known image types is beneficial for faster enabling of performance gains as this will take effect before the image analysis is completed. The
type_default attribute can be used to provide non-exact but best guess guiding image types, where as
type_override attribute is recommended if well structured data is available and the image types that are provided are known with a high degree of certainty.
Sizes often come in different scales, formats and standards. Getting sizes right and keeping these in a unified easy-to-understand manner, is especially important in an industry such as fashion and lifestyle.
Getting it right manually is possible, but costly. It requires lots of initial working hours to get in place followed by continuous disciplined work. To alleviate retailers of this issue, eSales Lifestyle comes with a size cleaning feature. It automatically extracts and interprets size information from uncurated size values in mixed formats from the data feed.
The built-in size cleaning uses several methods to curate values including:
- Splitting values such as
Srespectively for the size facet while maintaining the correct presentation in the product card.
- Normalising and combining values such as
one size, and
O.Sto a single value. The selected representative is determined by the most common value in the data feed.
- Splitting of multiple sizes provided in different formats such as
- Automatic separation of formats.
Product type extraction¶
A lot of effort is often put into product categorisation and grouping. However, many times the categorisation lacks the necessary structure for it to be utilised in an optimal way.
One aspect of categorisation that is especially important is the concept of product types. Product types are often lumped together with other types of concepts such as
Knitted, or a combination of multiple product types such as
Hats & Scarves. The data often lacks the proper granularity, i.e. the distinction between product types and collections in general. Furthermore a significant number of products are often miscategorised, despite lots of manual effort.
Through sophisticated data analysis, eSales Lifestyle automatically identifies and extracts the product type.
To acquire a high precision product type identification, several methods are used. One is applying built-in relational information about product types and concepts. An example of this is applying the knowledge of
T-shirt being a common fit of a bra. Rather than categorizing a
T-shirt bra as both
T-shirt and a
bra it will correctly only be categorized as a
bra with a
Another method is utilising built-in language and terminology knowledge. For example, a
Tee can be identified as a
Another example is the knowledge that English terms might very well appear in Swedish content, allowing weighted cross language product type identification from single attributes. The word
good in Swedish, could either be interpreted as something insignificant or as the actual product type
bra. Based on context and linguistic knowledge, eSales Lifestyle can assess that a
bra T-shirt in Swedish likely is a
T-shirt while a
plunge bra likely is a
Finding similar items is a fundamental task for an information retrieval system like an e-commerce platform. Based on current technology, two types of item similarity are distinguished: functional and visual.
Functional similarity is achieved using concept extraction. In effect, it is assumed that functional similarity is implied by the product type. Visual similarity uses product images as input. Images are transformed into a metric space in which visually similar items are close, and dissimilar items are far away from each other.
To find similar items in the alternatives recommendation function, both techniques are merged to make sure that items are similar in both respects.