Overview

eCommerce Maturity Index

The eCommerce Maturity Index offers a granular evaluation of the customer and user experience of eCommerce websites from all five continents. The final index includes 40 countries and considers 28 criteria covering 5 areas that relate to stages in the customer journey, from finding to purchasing products. Those areas are:

How were the websites selected?

For data collection, we manually selected 3 eCommerce websites per country. Websites were selected based on their local or regional relevance. Relevance was determined by using website traffic as a proxy.

First, we would select 3 locally relevant eCommerce websites, which are based in the pertaining country. If this was not possible, we would substitute with regionally relevant eCommerce websites that operate across borders in these regions, as was the case for countries in the Benelux and the Baltics. Only if locally or regionally relevant eCommerce websites could not be identified would we resort to including the most relevant large global players in a specific country market.

The guiding premise was to avoid including global multinational eCommerce websites to reflect a country’s eCommerce maturity as closely as possible.

Browsing (7)

Browsing captures the ease by which products can be found and saved for later. With a focus on the quality of search, product indicators, filtering, and wish list functionality:

• Search assistance and real-time suggestions with the ability to refine search suggestions
• Product popularity indications (e.g., number of bought products, indications of trending)
• General product indicator (e.g., “exclusive”, “new”, “deal”, “limited time deal”)
• Advanced search functionality (results highlights or dynamic filtering options)
• Content integrations in search or product results
• Product filtering availability
• Wishlists functionality

Reviewing (7)

Reviewing captures the possibilities of finding out more about the functionality and specifications of a product, comparing, asking questions, finding reviews from clients and experts, and directly communicating with customer service. Advanced features were not applied in the Fashion category.

• Similar item list
• Item comparisons (advanced)
• 3D product models (advanced)
• Advanced review sorting and classification (e.g., popular categories, groups of buyers, sentiment)
• Differentiated expert or video reviews (advanced)
• Q&A sections on the product page
• Customer service options integrated into product pages

Investigating (4)

Investigating captures whether an eCommerce website provides background information on its products, such as product warning and safety information, sustainability and recycling, and ethical sourcing.

• Product warnings and safety information (e.g., warnings about product safety, usage, hazards, materials, proper application, components)
• Sustainability information
• Recycling options
• Ethical sourcing information

Expanding (3)

Expanding captures the extent to which customer are provided with options to add additional services, finance their purchase, or bundle it with related products. Advanced features were not applied in the Fashion category.

• Related services like extended warranty (advanced)
• Financing options availability (advanced)
• Bundling options availability

Decisions (7)

Decisions category captures the extent to which an eCommerce platform makes it easier to find out about and select between multiple delivery options, determine inventory availability, and set restock and price alerts, as well as find additional information on pricing and return policy.

• Multiple delivery options
• Delivery timings indicated on the product page
• Inventory availability
• Price match guarantee
• Recommended retail price as a reference for pricing
• Back in Stock and other customizable alerts
• Return policy indicated

Data analysis

After manually reviewing for the presence of all features and coding them with 1 (present) or 0 (partially present or absent), we created percentage scores for each area by dividing the number of present features by the number of all features multiplied by 100.

This provided us with scores representing the maturity in each dimension from 0 to 100.

The final index score was created based on an equal weighting average across all dimensions.

eCommerce Dynamic Pricing

The eCommerce Dynamic Pricing usage score reflects the extent to which eCommerce websites around the world use and incorporate dynamic pricing practices in their business while also taking into account the complexity of these pricing practices. The data collection and analysis included data from 40 countries and 120 websites, each featuring 12 products, which were retrieved at regular four-hour intervals over a one-month period with the Smartproxy eCommerce Scraping API. The final score takes into account:

• Share of dynamic pricing practices
• Frequency of price changes
• Pricing model complexitys

Data collection

The eCommerce Dynamic Pricing Index data collection took place over 33 days from April 25th to May 27th. In that period, we tracked pricing changes of 12 hand-selected products, comprised in equal proportions of high and low-value products. Price tracking and item name extraction were automated using the Smartproxy eCommerce Scraping API.

The study focused on 40 countries, and the top three eCommerce websites in each country were selected based on visits and revenue. For each website, three categories were selected from a website's front page. Within each category, four products were chosen. Two high-value and two low-value. This approach resulted in 36 products per country.

The data collection script was executed every four hours, resulting in eight data captures per day and a total of 264 scraping sessions. Over the 33-day period, a total of 245833 price data points were successfully collected.

Data collection methodology

Data analysis

After data cleaning, including manual and semi-automated data review, failed response removal, and finally, data formatting, the analysis proceeded in two ways.

Dynamic pricing framework building

First, data on each tracked product was turned into price change plots that visually captured pricing changes within the price tracking period.

Manual review and complementary research on dynamic pricing models were used to develop the Dynamic Pricing framework. This framework captures key pricing models based on which price change patterns were classified. The key pricing models are:

• Consistent price adjustments model that features gradual price adjustments over time, reflecting natural market shifts without abrupt changes.
• Discount and peak adjustment model includes applying one or two discounts and increasing prices during up to three peak periods.
• Dynamic discounts & peak adjustments model features multiple discounts (three or more) and three or more price increases.
• Consistent micro adjustments model that involves multiple small-scale price adjustments often aimed at testing and understanding price elasticity over the long term.
• Supply and demand adjustments model that features price adjustments that fluctuate above and below a base price in response to changes in supply and demand dynamics.
• Automated pre-set changes model featuring frequent, consistent price adjustments based on pre-defined rules or algorithms. Real-time adjustments model involves price changes occurring at random intervals with no predictable pattern or structure.

Score development

Following the construction of the dynamic pricing framework, the collected pricing data points were analyzed to derive scores for each dimension and then constructed from the dimensions of each country’s overall score. The country scoring system assesses the usage and complexity of dynamic pricing in each country and features 3 key components that are normalized on the average and then weighted equally to derive the overall score.

• Dynamic products share by country. The proportion of dynamic products within a specific country compared to the total number of products in that country.
• Price change frequency. The average number of times prices were changed within a month, aggregated by country.
• Pricing model complexity. Additional points assigned for using more complex structure pricing models*.

*Higher complexity models include consistent micro pricing adjustment (1 point), supply-demand adjustment (1 point), automated pre-set changes (2 points), real-time adjustment (2 points). Lower complexity model was scored with 0 points.

Dynamic pricing score structure

*Higher complexity models include consistent micro pricing adjustment (1 point), supply-demand adjustment (1 point), automated pre-set changes (2 points), real-time adjustment (2 points). Lower complexity model was scored with 0 points.