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Metrics: Definitions and examples

Ernst Vorndran
  • Updated

    In this document, you can find definitions for all metrics used in Insights. We not only provide detailed explanations but also examples for each metric.

    Important Definitions

    Here you can find a list of acronyms as well as other important term definitions that are relevant for the rest of the document.

    • PLP (Product listing page): Commonly known as category page; the page on the website that returns the list of products when a shopper navigates within the websites category structure
    • SLP (Search listing page): The page on the website that returns a list of products after a shopper has completed a search
    • PDP (Product detail page): The page on the website that provides all the detailed information about one specific product
    • Fredhopper triggered entity: A piece of configuration that is set within the Fredhopper platform and then applied to the website; including facets (filters), ranking rules, result modifications, and item campaigns (recommendations)

    Note: We do not track the performance of image campaigns (e.g. banners, adverts, clickable image links) or text campaigns.

    • User micro journey: A sequence of actions a shopper takes during a single session; a micro journey begins when a shopper interacts with a Fredhopper-triggered entity, such as loading a page or area influenced by it; it ends when the shopper makes a purchase within the entity’s influence, changes categories or navigates to a different part of the website  (e.g., selecting a new category or performing a new search); at this point, a new micro journey starts

    Metric Definitions

    1 Total Impressions | 2 Unique Impressions |  3 Total Searches/Searches | 4 Unique Searches |
    5     Views | 6 Adds to Basket | 7 View Conversions | 8 Click-through rate (CTR) |
    9 View to total impressions rate | 10 Basket Conversions | 11 Add-to-view Rate | 12 Purchases |
    13 Purchase Conversions | 14 Purchase-to-add Rate | 15 Min Results | 16 Max Results |
    17 Average Results | 18 Revenue | 19 Revenue Per Unique Search | 20 Average Micro Journey Value |
    21 Average Price of Purchased Item | 22 Average Unique Searches Per Shopper |
    23 Facet Interaction Rate | 24 Paging Interaction Rate | 25 Search Refinement Rate
     

    Total Impressions

    An impression is attributed to a product every time it appears within a Fredhopper triggered entity.

    An impression is attributed to a Fredhopper triggered entity every time a shopper takes an action that loads/reloads a Fredhopper triggered entity onto the front end.  Loading/reloading includes reloading/refreshing a page, navigating to that page, searching, filtering, paging, back button, clicks that land on the page.

    Example Scenario

    Example 1: Product Total Impressions 

    1. User searched for ‘blue winter scarf’.
      → SLP contains product_12.

    2. User viewed PDP of product_12.

    3. User searched for ‘winter scarf’.
      →       SLP contains product_12.

    4. User navigated to ‘Women' category.
      → PLP contains       product_12.

    In this scenario, product_12  gets three total impressions as it appears three times on SLPs/PLPs across all three user micro journeys (steps 1-2 is one micro journey, step 3 is one micro journey and step 4 is one micro journey).

    Example 2: Impressions

    1. User searched for ‘blue winter scarf’.
      → New Winter Accessories Ranking is triggered.

    2. User selects ‘Navy’ facet value under the ‘Colour’ facet.
      → New Winter Accessories Ranking is reloaded.

    3. User viewed PDP of product_12.

    4. User searched for ‘winter scarf’.
      → New Winter Accessories Ranking is triggered.

    In this scenario, the New Winter Accessories Ranking gets three Total Impressions as the ranking rule configuration is loaded three times on SLPs across both of the two user micro journeys (steps 1-3 is one micro journey and step 4 is one micro journey).

    Unique Impressions

    A unique impression is attributed to a product when it appears in a user micro journey for the first time. Thus, only one unique impression per journey is obtained. An additional unique impression will only get added to the total if a new user micro journey is started.

    A unique impression is attributed to the relevant Fredhopper triggered entity when the entity is loaded on a page at the start of each user micro journey.

    There are three actions that a shopper can take that will add to total impressions but not add an additional unique impression, as they are considered part of a single user journey:

    • Interacting with facets (that do not change the category)
    • Selecting a different page within the current category/location (for example, navigating to page 2 of the search results)
    • Navigating between the PLP/SLP and PDP

    Any of the above actions will not add additional unique impressions for a product or entity but they will contribute to the number of total impressions.

    Example Scenario

    Example 1: Product Unique Impressions 

    1. User searched for ‘blue winter scarf’.
      → SLP contains product_12.

    2. User viewed PDP of product_12.

    3. User searched for ‘winter scarf’.
      → SLP contains product_12.

    4. User added product_12 to basket.

    5. User navigated to Women.
      → PLP contains product_12.

    In this scenario, product_12 gets three unique impressions since it appears at least once in three different user micro journeys (steps 1-2 is one micro journey, steps 3-4 is one journey and step 5 is another micro journey).

    Example 2: Product Unique Impressions 

    1. User searched for ‘blue winter scarf’.
      → SLP contains product_12.

    2. User selects ‘Navy’ facet value under the ‘Colour’ facet.
      → SLP contains product_12.

    3. User changed to page 2 of search results.

    4. User selected back button.
      → SLP contains product_12.

    5. User viewed PDP of product_12.

    In this scenario, product_12 gets one unique impression since it appears at least once in one user journey (steps 1-5 is one micro journey). 

    It is important to note that product_12 appears three times in the above example, which would mean it receives three total impressions but only one unique impression at the moment it appears for the first time in the journey.

    Example 3: Fredhopper triggered entity Unique Impressions 

    1. User searched for ‘blue winter scarf’.
      → New Winter Accessories Ranking is triggered.

    2. User viewed PDP of product_12.

    3. User searched for 'winter scarf'.
      → New Winter Accessories Ranking is triggered.

    4. User viewed PDP of product_76.

    5. User selected back button and returns to ‘winter scarf’ results.

    In this scenario, the New Winter Accessories Ranking gets two Unique Impressions since the rule configuration is triggered at least once in two different user micro journeys (steps 1-2 is one micro journey and steps 3-5 is another micro journey).

    Additional Information/Comments

    The unique impressions metric on Insights is calculated based on user micro journeys, as a opposed to a user session based journey.

    For merchandisers, this metric is an improvement on the session based definition of a unique impression, as it allows you to gain more granularity into interactions on the site. 

    Total Searches/Searches

    Similar to total impressions, total searches refers to the total number of times a search query (search term) is loaded in all user micro journeys. Loading/reloading includes reloading/refreshing a page, navigating to that page, searching, filtering, paging, back button, clicks that land on the page.

    Example Scenario

    1. User searched for Red dress.
      → Red dress term is loaded.

    2. User selected ‘Burgundy’ facet value under Colour Facet.
      → Red dress term is reloaded.

    3. User paged to page 2 of search results.
      → Red dress term is reloaded.

    4. User added a red dress from the SLP to his basket.

    5. User bought the red dress he added to his basket.

    In this scenario, search term red dress gets three total searches since the search query was loaded three times in the user micro journey.

    Unique Searches

    Similar to unique impressions, unique searches refers to the number of times a search query is searched for at the beginning of a user micro journey (one unique search per journey).

    Note: As with unique impressions, interacting with facets (that do not change the category), selecting a different page within the category and navigating between the SLPs and PDPs will not add to additional unique searches.

    Example Scenario

    Example 1

    1. User searched for Red dress.

    2. User selected ‘Burgundy’ facet value under Colour facet.

    3. User paged to page 2 of search results.

    4. User added product_10 from the SLP to the basket.

    5. User purchased product_10 in the basket.

    In this scenario, search term red dress gets one unique search since the search query was searched at least once in the user micro journey. (1-5 is one micro journey as steps 2 and 3 are not actions that initiate a new micro journey).

    Example 2

    1. User searched for Red dress.

    2. User selected ‘Burgundy’ facet value under Colour Facet.

    3. User paged to page 2 of search results.

    4. User added product_10 from the SLP to the basket.

    5. User purchased product_10 in the basket.

    In this scenario, search term red dress gets one unique search since the search query was searched at least once in the user micro journey. (1-5 is one micro journey as steps 2 and 3 are not actions that initiate a new micro journey).

    Views

    A view is attributed to a product when its PDP is loaded by a shopper.

    A view is attributed to a Fredhopper triggered entity each time a shopper requests a PDP by clicking on an item/product returned by the entity.

    A view is attributed to a search term each time a shopper requests a PDP by clicking on an item/product returned by the search term.

    Example Scenario

    Example 1

    1. User searched for ‘blue winter scarf’.
      → Popular Now Ranking is triggered.

    2. User viewed PDP of product_19.

    3. User navigated to ‘Women’ category.
      → Popular Now Ranking is triggered.

    4. User viewed PDP of product_19 PDP.

    In this scenario, product_19 gets two views since the shopper viewed the PDP for that product once in two user micro journeys (steps 1-2 is one micro journey and steps 3-4 is one micro journey).

    Popular Now Ranking gets two views since there were two product views in two different user journeys where this ranking was triggered.

    'Blue winter scarf' gets one View since there was one product viewed from the SLP returned by the search term.

    Example 2

    1. User searched for ‘blue winter scarf’.
      → Popular Now Ranking is triggered.

    2. User viewed PDP of product_12.

    3. User selected the back button.

    4. User viewed PDP of product_12.

    In this scenario, product_12 gets two views since the shopper viewed the PDP for this product twice in the user micro journey (steps 1-4 is one micro journey).

    The Popular Now Ranking gets two views since there were two product views in one user micro journey where this ranking was triggered.

    'Blue winter scarf' gets two views since there were two products viewed from the SLP returned by the search term.

    Views (for Facets report)

    The number of times a facet is selected by a shopper in a user micro journey.

    Note: The de-selection of facets is not considered in this metric and should not be sent by the tracker.

    Example Scenario

    1. User searched for ‘winter scarf’.

    2. User selected ‘Blue’ facet value under the ‘Colour’ facet.

    3. User paged to page 2 of search results.

    4. User selected ‘Dark Green’ facet value under the ‘Colour’ facet.

    5. User viewed PDP of product_4.

    In this scenario, the ‘Colour’ facet gets two views for facets since two facet values were selected in one user micro journey.

    Additional Information/Comments

    This metric is useful in providing an indication of how often a particular facet is interacted with.

    Add-to-Basket Rate

    An add-to-basket is attributed to a product each time the product is added to the basket by a shopper in a user micro journey.

    An add-to-basket is attributed to a Fredhopper triggered entity each time a shopper adds a product to their basket that was returned by the entity.

    Note: Adds to Basket counts are counting the number of add-to-basket events in a journey, not the volume of units added to basket.

    Example Scenario

    Example 1

    1. User navigated to ‘Shoes’ category.
      → Popular & New in Ranking is triggered.

    2. User paged to page 2.

    3. User viewed PDP of product_19.

    4. User added product_19 to basket from the PDP.

    5. User navigated to ‘Women’ category.
      → Popular & New in Ranking is triggered.

    6. User added product_40 from the PLP to the basket.

    In this scenario, product_19 and product_40 both get one add-to-basket since both were added to the shoppers basket during a user micro journey (steps 1-4 is one micro journey and steps 5-6 is one micro journey).

    The Popular & New in Ranking also gets two add-to-basket counts since there were two products added to the basket in two user micro journeys where this ranking was triggered.

    Example 2

    1. User navigated to ‘Shoes’ category.
      → Popular & New in Ranking is triggered.

    2. User viewed PDP of product_19.

    3. User added two units of product_19 to basket from the PDP.

    In this scenario, both product_19 and Popular & New in Ranking get one add-to-basket since there was one add-to-basket event that occurred in the user micro journey.

    View Conversions or Click-through rate (CTR)

    The Click-through rate (CTR) is defined as the ratio of views to unique impressions (views / unique impressions), i.e. the average number of views given a unique impression.

    Example Scenario

    Explanation per Report

    • Product report: The rate at which the PDP is viewed compared to the number of times the item has been returned by any Fredhopper triggered entity, i.e. displayed on a listing page
    • Campaigns and Ranking report: The average number of product views received, given the Fredhopper triggered entity was loaded on the page
    • Facet report: The average number of times a facet was clicked on, given the facet was loaded on the page
    • Search report: The average number of clicks on a product, given a specific search term was searched

    Additional Information/Comments

    A high view conversions value indicates that the navigation page or the search results contain relevant products for the users.

    This metric can be above 100% since it is possible that for a specific navigation page (which would lead to one unique impression), a user might look at multiple products (leading to multiple views).

    View to Total Impressions Rate

    This rate describes the ratio of views to total impressions (views / total impressions).

    Example Scenario

    Explanation per Report

    • Product reports: The ratio of the total views on a product compared to the total impressions that product received during shoppers' journeys
    • Campaigns and Ranking report: The ratio of the total views on a product that occur from interacting with a specific entity and the total number of times that Fredhopper triggered entity was loaded/reloaded onto the front-end
    • Facet report: The ratio of the total number of times a facet was clicked on and the total number of times the facet was loaded on the page in a shoppers journey
    • Search report: The ratio of the total views on a product that occur if the product is returned by a specific search term and the total number of times the search term is searched

    Additional Information/Comments

    It is recommended to use the view conversion as the measure of merchandising success rather than this metric. It is important to remember that the total impressions metric is a ‘noisy’ metric and the direct relationship with the views count is not clear in this metric.

    For example, an item gets additional impressions if the shopper navigates between SLPs/PLPs and PDPs, pages to a different page or interacts with facets in the user journey. This creates significant ‘noise’ for the metric, which can skew the results and understanding of success for a product or a Fredhopper triggered entity.

    Basket Conversions

    The basket conversions metrics describes the ratio of adds to basket to unique impressions (adds to basket / unique impressions), i.e. the number of add-to-basket's given a unique impression.

    Example Scenario

    Explanation per Report

    • Product report: The average number of adds to basket that a product received, given the product appeared in the shoppers journey
    • Campaign and Ranking report: The average number of adds to basket that an entity received, given the entity was triggered
    • Search report: The average number of adds to basket a search term received, given a specific search term was searched

    Add-to-view Rate

    This rate describes the ratio of add-to-basekt events to views events (adds to basket / views), i.e the number of add-to-basekt events given a view event.

    Example Scenario

    Explanation per Report

    • Product report: The average number of adds to basket that a product received, given the product was viewed in the shoppers journey
    • Campaign and Ranking report: The average number of adds to basket attributed to an entity, given that the entity returned a product that the shopper viewed/clicked on in the shoppers journey
    • Search report: The average number of adds to basket attributed to a search term, given that the search query returned a product that the shopper viewed/clicked on in the shoppers journey

    Purchases

    A purchase is attributed to a product if the product is purchased by a shopper.

    A purchase will be attributed to a Fredhopper triggered entity each time a shopper purchases a product that was returned by the entity.

    Note: Purchase counts are counting the number of purchase events in a journey, not the volume of purchases made of a particular product. For example, purchasing 10 units of a product in a journey is counted as one purchase for that product, not ten purchases.

    Example Scenario

    1. User navigated to Kids -> Accessories category.
      → Popular Now ranking is triggered.

    2. User viewed PDP of product_73.

    3. User added 10 units of product_73 to basket from the PDP.

    4. User purchased 10 units of product_73.

    In this scenario, product_73 gets one purchases assigned as one purchase event occurred for that product in a user micro journey.

    The Popular Now Ranking will get one purchases assigned as one purchase event occurred in a user micro journey that triggered the ranking.

    Additional Information/Comments

    Purchases in Insights are based on the last-click attribution model, where the conversion is attributed to the last touchpoint that the shopper clicked on. This means that the purchase event is attributed to the last micro-journey that included the product the shopper purchased.

    Purchase Conversions

    This metrics describes the ratio of purchases to unique impressions (purchases / unique impressions), i.e. the number of purchases given a unique impression.

    Example Scenario

    Explanation per Report

    • Product report: The number of purchases of a product, given the product appeared in a shoppers journey
    • Campaign and Ranking report: The number of purchases attributed to an entity, given that entity was triggered
    • Search report: The number of purchases attributed to a search term, given a specific search term was searched

    Purchase-to-add Rate

    This rate describes the ratio of purchases to add-to-basket events (purchases / add-to-basket's), i.e. the number of purchases given an add-to-basket event.

    Example Scenario

    Explanation per Report

    • Product report: The number of purchases of a product, given the product was added to the basket
    • Campaign and Ranking report: The number of purchases attributed to an entity, given that the entity returned a product that the shopper added to their basket
    • Search report: The number of purchases attributed to a search term, given that a specific search term returned a product that the shopper added to their basket

    Min Results

    Note: This metric is related specifically to searches.

    This metrics describes the minimum number of product results returned when using a specific search term.

    The number of results returned when selecting a Facet, after a search, is taken into consideration when finding the minimum results (see Example 2).

    Example Scenario

    Example 1

    1. User searched for ‘kid's umbrella’ (day 1) and the search returned 4 products.

    2. User searched for ‘kid's umbrella’ (day 7) and the search returned 5 products.

    3. User searched for ‘kid's umbrella’ (day 8) and the search returned 6 products.

    The Min Results for the search term ‘kid's umbrella’ over the 8 day period (day 1 to day 8) will be 4.

    Example 2

    1. User searched for ‘green shoes’ (day 1) and the search returned 20 products.

    2. User selects ‘Dark Green’ facet value under the ‘Colour’ facet and 18 products are returned.

    3. User searched for ‘green shoes’ (day 4) and the search returned 22 products.

    The Min Results for the search term ‘green shoes’ over the 4 day period will be 18.

    Max Results

    Note: This metric is related specifically to searches.

    This metrics describes the maximum number of product results returned when using a specific search term.

    The number of results returned when selecting a Facet, after a search, is taken into consideration when finding the maximum results.

    Example Scenario

    1. User searched for ‘kid's umbrella’ (day 1) and the search returned 4 products.

    2. User searched for 'kid's umbrella' (day 7) and the search returned 5 products.

    3. User searched for 'kid's umbrella' (day 8) and the search returned 6 products.

    The Max Results for the search term ‘kid's umbrella over’ the 8 day period (day 1 to day 8) will be 6.

    Average Results

    Note: This metric is related specifically to searches.

    This metrics describes the average number of product results returned when using a specific search term.

    The number of results returned when selecting a Facet, after a search, is taken into consideration when finding the average results (see Example 2).

    Example Scenario

    Example 1

    1. User searched for ‘kid's umbrella’ (day 1) and the search returned 4 products.

    2. User searched for ‘kid's umbrella’ (day 7) and the search returned 5 products.

    3. User searched for ‘kid's umbrella’ (day 8) and the search returned 6 products.

    The Average Results for the search term ‘kid's umbrella’ over the 8 day period (day 1 to day 8) will be = (4+5+6)/3 = 5.

    Example 2

    1. User searched for ‘green shoes’ (day 1) and the search returned 20 products.

    2. User selects ‘Dark Green’ facet value under the ‘Colour’ facet and 18 products are returned.

    3. User searched for ‘green shoes’ (day 4) and the search returned 22 products.

    The Average Results for the search term green shoes over the 4 day period will be (20+18+22)/3 = 20.

    Revenue

    This metrics describes the quantity of purchased items multiplied by the unit price.

    This revenue will be attributed to a Fredhopper triggered entity if the product was reached via that entity.

    Example Scenario

    1. User navigated to Men -> Trousers category.
      → New Arrivals Campaign is triggered.

    2. User viewed PDP of product_73.

    3. User added product_73 to basket from the PDP.

    4. User purchased 2 units of product_73 (unit price = 53 euros).

    In this scenario, New Arrivals Campaign gets a revenue of 2*53=106 euros since there were two units of product_73 with a unit price of 53 units that were purchased in the user micro journey where the New Arrivals Campaign is triggered.

    Additional Information/Comments

    Warning: A single currency per tracker key should be used and the currency conversation needs to be done before the events are sent to Insights in order to have meaningful revenue values.

    The customer is responsible for sending us the right number of products, in the correct order, with the correct corresponding unit price. Insights does not fetch the unit price from the customers FHR data, therefore it is important to ensure that the correct information is being sent to Insights.

    Revenue Per Unique Search

    This metrics describes the total revenue made by purchasing products in a micro journey started from a specific search divided by the number of unique searches.

    Example Scenario

    1. User searched for ‘skirts’.

    2. User viewed PDP of product_73.

    3. User added product_73 to basket from the PDP.

    4. User purchased product_73 (unit price = 20 euros).

    5. User searched for ‘skirts’.

    6. User added product_1 to basket from PLP.

    7. User purchased product_1 (unit price = 60 euros).

    In this scenario, search term ‘skirts’ gets a revenue per unique search of (20+60)/2=40 euros since two products with unit prices of 20 and 60 euros were purchased and there were two unique searches for ‘skirts’ (steps 1-4 is one micro journey and steps 5-7 is one micro journey).

    Additional Information/Comments

    If your chosen success metric for search is based on revenue, then it is recommend to use this metric as it removes fluctuations due to traffic levels.

    Since, for example, if traffic halves then this number won’t halve, so merchandiser actions cannot be blamed.

    Average Micro Journey Value

    This metrics describes the average revenue gained from purchase events within a specific micro journey.

    If all revenue gained from purchase events from a specific journey are summed and then divided by the number of purchase events for that journey, this will provide the average order value.

    Note: This metric is not the average order value of the customers entire basket but rather the average order value of the items purchased or the average order value of items purchased from a specific triggered entity.

    Example Scenario

    1. User navigated to Men -> Trousers category.
      → New Arrivals Campaign is triggered.

    2. User clicked on PDP of product_73.

    3. User added product_73 to basket from the PDP.

    4. User purchased 2 units of product_73 (unit price = 53 euros).

    5. User searched for ‘jeans’.
      → New Arrivals Campaign is triggered.

    6. User added 1 unit of product_77 to basket from the SLP.

    7. User purchased product_77 (unit price = 30 euros).

    In this scenario, New Arrivals Campaign gets an average order value of (53*2 + 30) / 2 = 68 euros, since there was a total revenue of 53*2+30 and total of two purchase events that occurred across these two journeys (steps 1-4 is one micro journey and steps 5-7 is one micro journey).

    product_73 gets an average order value of (53*2) / 1 = 106 euros and product_77 gets an average order value of (30*1) / 1 = 30 euros.

    Additional Information/Comments

    The Average Micro Journey Value is useful to understand revenue per impression. This metric can help you to understand how the revenue figure is reached. For example, this could be due to a high volume of lower priced items being purchased or a low volume of higher priced items being purchased. This can help to inform the merchandiser of the types of items that are generating revenue.

    Average Price of Purchased Item

    This metrics describes the average price of items purchased from a specific triggered entity.

    The revenue made from each purchased item are summed and then divided by the total number of items purchased.

    Example Scenario

    1. User navigated to Men -> Trousers category.
      → Top Sellers Ranking is triggered.

    2. User viewed PDP of product_73.

    3. User added product_73 to basket from the PDP.

    4. User purchased 2 units of product_73 (unit price = 53 euros).

    5. User searched for ‘jeans’.
      → Top Sellers Ranking is triggered.

    6. User added product_90 to basket from PLP.

    7. User purchased 1 unit of product_90 (unit price = 30 euros).

    In this scenario, Top Sellers Ranking gets an average price of purchased item of (53*2 + 30) / 3 = 45.33 euros since there are three items of prices 53, 53 and 30 purchased across two user micro journeys where the ranking was triggered.

    product_73 gets an average price of purchased item of (53*2) / 2 = 53 euros and product_90 gets an average price of purchased item of (30*1 )/ 1 = 30 euros.

    Average Unique Searches per Shopper

    This metrics describes the ratio of the number of unique impressions of a search to the number of unique users who used the search term (unique impressions for search / unique users for search).

    Note: A unique users is a shopper that is only counted once regardless of how many times they visit the site over a set period of time.

    Example Scenario

    1. User searched for 'skirts'.

    2. User viewed PDP of product_73.

    3. User added product_73 to basket from the PDP.

    4. User purchased product_73 (price = 20 euros).

    5. User_2 searched for ‘skirts’.

    6. User_2 added product_1 to basket from PLP.

    7. User_2 purchased product_1 (price = 60 euros).

    In this scenario, search term ‘skirts’ gets two unique impressions as the search team appeared in two distinct user micro journeys and two unique users as two different users searched for the term. The average unique searches per shopper is 2 / 2 = 1.

    Additional Information/Comments

    This metric can be used as a guide to search utilisation and can indicate how often search is being used. It is useful in the context of a Search A/B test.

    If this metric is going up over time, then search utilisation is increasing. This shows that shoppers are frequently returning to search and can be an indication that previous shopper experiences using search were successful.

    Facet Interaction Rate

    This rate describes the ratio of facet interaction count (how many times facets were selected) to unique impressions (facet interaction count / unique impressions).

    Note: Facet Interaction Count is counted each time a shopper selects a facet that is loaded on the site.

    Example Scenario

    1. User searched for ‘blue winter scarf’.
      → New Now Ranking is triggered.

    2. User applied ‘Sky Blue’ facet value under the 'Colour’ facet.

    3. User viewed PDP of product_19.

    4. User selected the back button.

    5. User applied ‘Medium’ facet value under the ‘Size’ facet.

    6. User added product_19 to basket from the SLP.

    In this scenario, New Now Ranking gets two facet interactions since a facet was selected twice in user micro journeys where this ranking was applied.

    Furthermore, New Now Ranking gets one unique impression. Therefore, the facet interaction rate of the New Now ranking is 2 / 1 = 2.

    Additional Information/Comments

    A high rate of facet interactions indicates that shoppers are having to manually refine the results they are seeing to find the products they are looking for. This can indicate that the merchandising strategies in place are not promoting the best products for shoppers.

    For this reason, this metric can be useful when looking at A/B testing results to measure the success of a merchandising configuration.

    Paging Interaction Rate

    This rate describes the ratio of paging interaction count (how many times a different page was selected) to unique impressions (page interaction count / unique impressions).

    Note: Paging Interaction Count is counted each time a shopper pages backwards and forwards between different pages of the PLP on the site.

    Example Scenario

    1. User searched for ‘blue winter scarf’.

    2. User went to the second page of the search results.

    3. User went to the third page of the search results.

    4. User viewed PDP of product_12.

    5. User added product_12 to basket from the PDP.

    6. User purchased product_12.

    In this scenario, search term ‘blue winter scarf’ gets two paging interactions and one unique impression making the paging interaction rate 2 / 1 = 2.

    Additional Information/Comments

    As with facet interactions, a high paging interaction can indicate that shoppers are having to manually page between PLPs to look for the product they want, instead of finding it on the first page. This can indicate that the merchandising strategies in place are not promoting the best products for shoppers.

    For this reason, this metric can be useful when looking at A/B testing results to measure the success of a merchandising configuration.

    Search Refinement Rate

    This rate describes the ratio of search refinement count to unique impressions (search refinement count / unique impressions).

    Note: Search Refinement is counted for a search term when a shopper immediately searches again after performing a search.

    Example Scenario

    1. User searched for ‘blue sandals’.

    2. User applied ‘Sky Blue’ facet value under the 'Colour’ facet.

    3. User searched for ‘blue summer sandals’.

    4. User viewed PDP of product_12.

    5. User added product_12 to basket from the PDP.

    6. User purchased product_12.

    In this scenario, search term ‘blue sandals’ gets one search refinement and two unique impressions making the search refinement rate 1 / 2 = 0.5

    Additional Information/Comments

    A high search refinement rate indicates that shoppers are having to search more than once for what they are looking for because their initial search does not yield the correct results. This can indicate that the merchandising strategies in place are not promoting the best products for shoppers.

    For this reason, this metric can be useful when looking at A/B testing results to measure the success of a merchandising configuration.

     

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