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Available Datasets

Each client's Customer Insights has their 1st party data flowing to the dedicated Amazon Quicksight account, a BI tool provided by AWS. The analyses available in the Amazon Quicksight account are an example of how to leverage the data Kevel Audience collects and processes. If clients desire access to the datasets for constructing their own reports, they should contact their Customer Success Manager for assistance.

The data is first pre-processed and cleaned, resulting in the following datasets:

Please note that all datasets are refreshed twice a week (on Mondays and Wednesday, 00h05AM - WET).

Users

Each entry of this dataset pertains to a user and their attributes, and is a snapshot of the user at the time the data was refreshed. The default settings of this dataset include unsampled data on customers and a 1% sample of website users. By using this dataset, analysts can explore questions such as:

  • "After how many orders does the % of churn decrease significantly?"

datasets-users-churn

The users dataset's fields are based on:

  • User Attributes, e.g. each user's likelihood to buy in the next 7 days, their RFM scores, order data, and so on.
  • Events performed by a user, e.g. how many add to cart events were trigged by a given user in the last 28 days.
  • User Identifiers, e.g. this helps you investigate how many users have a given ID type.
  • New fields related to the dataset itself, aggregations based on order data (like category) and default segments (High Spenders, Active Shoppers). Refer to the table below.
  • Each client's users dataset will also have their own unique user attributes, related to the segments created in Kevel Audience, e.g. "users that bought <category> in the last 90 days".

Other available fields

FieldTypeDescription
export.firstOrderValuedecimalTotal of first order placed.
export.orders.netValueExcludingFirstdecimalSubsequent total spend by a customer, excluding the net value of their first order.
orderData.productCategoriesstringProduct categories purchased by a user, since their first order.
orderData.totalProductsintegerProduct quantity purchased by a user, since their first order.
orderData.totalUniqueProductsintegerUnique products purchased by a user, since their first order.
predictions.purchaseCycleintegerThe average number of milliseconds between a user's consecutive purchases.
predictions.purchaseCycle.daysdecimalThe average number of days between a user's consecutive purchases.
predictions.purchaseCycle.<category>integerThe average number of milliseconds between a user's consecutive purchases within the same category. Category-specific calculations are disabled by default. Contact your Customer Success Manager if you want to have specific categories taken into account here.
predictions.purchaseCycle.days.<category>decimalThe average number of days between a user's consecutive purchases within the same category. Category-specific calculations are disabled by default. Contact your Customer Success Manager if you want to have specific categories taken into account here.
sampleSizedecimalSample size will be 1 (100%) for customers. For user profiles without orders, a 1% sample will be used in the dataset.
snapshotDatedatetimeTime of the latest dataset updated.
segment_namebooleanName of the segment a user belongs to at the time of the snapshot.

Orders

Each entry of the Orders Data represents an order. It can be used to answer questions, such as:

  • "Which payment methods are used more often?"

datasets-orders

The orders dataset's fields are based on:

  • Events performed by any user who has made an order event, e.g. the timestamp of their first refund.

Other available fields

FieldTypeDescription
order.orderIndexintegerThe sequential position of an order place within a series of transactions, indicating the order in which the purchases occurred.
userHashstringUser ID associated with an order ID. Different orders by the same user will have the same user hash.

Line Items

It's similar to the Orders Data dataset, but with a more granular level: each entry is a lineitem of an order. By using this dataset, you can explore questions such as:

  • "What brands are most effective at acquiring new customers?" brands-buying-order

The lineitems dataset's fields are based on:

By leveraging joins in Amazon Quicksight, we derived two more datasets from the original Lineitems data dataset: one to explore category combinations within the same order, the other to explore category combinations by the same buyer (same or different orders). This type of analysis is aimed at exploring cross-selling opportunities.

Attribution

Each entry of this dataset is an attributable event (or session), that relates to a conversion event. There are some details that you need to consider:

  • attribution model: linear. This means that every attributable event in the journey of conversion is accounted for, and have the exact same value;
  • lookback window: 30 days prior to the conversion. This means that, for each conversion, only attributable events in the last 30 days are considered;
  • available period: 90 days of conversion data. Because the datasets aren't refreshed everyday, we're not considering the most recent 90 days all the time, but close to that.

For additional details regarding attribution, we encourage the Customer Insights users to explore the documentation available.

By using this dataset, we can help answering questions such as:

  • "Which channels were the most successful in attracting new customers?"

attribution-channels-new-customers

The Attribution Data dataset fields are based on:

  • User Attributes on the users that converted within the analysed conversion window, such as when was their first order, their lifetime value, and so on.
  • Fields exclusively related to attribution. See table below.

Other available fields

FieldTypeDescription
attributionRow.attributableEventIdstringThe attributable event identifier.
attributionRow.campaignstringUTM campaign in the URL that initiated the session of the attributable event.
attributionRow.citystringUser location at the moment of the conversion, grouped by city.
attributionRow.clvdecimal12 months future CLV of the acquired customers, distributed across the attributable events.
attributionRow.conversion.amountdecimalTotal conversion value.
attributionRow.conversion.orderSourcestringSource from where the order was placed.
attributionRow.conversion.productCategoriesstringProduct categories present in the conversion event.
attributionRow.conversion.siteIdstringThe client's site identifier.
attributionRow.conversionEventIdstringThe conversion event identifier.
attributionRow.countrystringUser location at the moment of the conversion, grouped by country.
attributionRow.isEmptystringThe conversion event came from an offline import, and did not match any conversion event in the system. Consequently, that conversion has no attributable event.
attributionRow.isFirstConversionbooleanWhether the attributable event is related to a user with no previous orders.
attributionRow.mediumstringUTM medium in the URL that initiated the session of the attributable event.
attributionRow.modelstringAttribution model. Currently we only support linear attribution.
attributionRow.sourcestringUTM source in the URL that initiated the session of the attributable event.
attributionRow.timestampdatetimeTime of the attributable event.
attributionRow.valuedecimalAttributed value of the attributable event. It will depend on the attribution model.
conversionEventIdstringThe conversion event identifier.
numberOfAttributablesintegerNumber of attributable events for a conversion event.
attributionRow.modelstringAttribution model. Currently we only support linear attribution.
attributionRow.sourcestringUTM source in the URL that initiated the session of the attributable event.
attributionRow.timestampdatetimeTime of the attributable event.
attributionRow.valuedecimalAttributed value of the attributable event. It will depend on the attribution model.
conversionEventIdstringThe conversion event identifier.
numberOfAttributablesintegerNumber of attributable events for a conversion event.

Attribution Conversion

Each entry of this dataset is a conversion event, and it was built with the aim of answering questions about touchpoints of a customer journey, for example:

-"How many touchpoints does a user need to finally convert?"

attribution-conversion-paths

Available fields

FieldTypeDescription
campaignPathsstringList of all UTM campaign that participated in a given conversion, sorted by the first to the latest. Empty values mean that there was an attributable event without a UTM campaign defined (e.g. Direct traffic).
contentPathsstringList of all UTM content that participated in a given conversion, sorted by the first to the latest. Empty values mean that there was an attributable event without a UTM content defined (e.g. Direct traffic).
conversionEventIdstringThe identifier of the conversion.
conversionValuedecimalThe value of the conversion.
firstCampaignstringFor a given conversion, the UTM campaign of its first attributable event.
firstContentstringFor a given conversion, the UTM content of its first attributable event.
firstMediumstringFor a given conversion, the UTM medium of its first attributable event.
firstSourcestringFor a given conversion, the UTM source of its first attributable event.
lastCampaignstringFor a given conversion, the UTM campaign of its most recent attributable event.
lastContentstringFor a given conversion, the UTM content of its most recent event.
lastMediumstringFor a given conversion, the UTM medium of its most recent attributable event.
lastSourcestringFor a given conversion, the UTM source of its most recent attributable event.
mediumPathsstringList of all UTM medium that participated in a given conversion, sorted by the first to the latest. Empty values mean that there was an attributable event without a UTM medium defined (e.g. Direct traffic).
numberOfAttributablesintegerNumber of attributable events for a conversion event.
orderSourcestringSource from where the order was placed.
productCategoriesstringList of categories purchased in a conversion event.
sourcePathsstringList of all UTM source that participated in a given conversion, sorted by the first to the latest.
timestampdatetimeThe date when the conversion event occurred.

Churn

Each entry of the dataset represents one month of data.

Please note that churn definition here isn't the same as in the RFM Map: it’s a slightly simplified definition, in which we determine the global median period between purchases and consider a user to be churned if they have passed that period in their purchase cycle.

churn

Available fields

FieldTypeDescription
activeintegerNumber of active customers at the begining of the period. Active customers are customers whose next order is in the future.
churnedintegerNumber of active customers that had become churned at the end of the period. Churned customers have exceeded their expected next order date.
cumulativeChurnedintegerCummulative sum of customers who became churned.
sampleSizedecimalThe default is 100%, the whole customer base is considered.
startdatetimePeriod to which active and churned are attached. They represent a month / year.

Session

Each entry of this dataset is a session, based on a 1% sample of users with activity in the last 90 days. This dataset provides insight on the percentage of sessions that reached a given Funnel Location.

sessions

Available Fields

FieldTypeDescription
addToCartsintegerCount of add to cart events within a user session.
checkoutStepsintegerCount of check out step events within a user session.
finishdatetimeThe time when the session ended.
orderPlacesintegerCount of order place events within a user session.
pageViewsintegerCount of pageview events within a user session.
productCustomizationsintegerCount of product customization events within a user session.
productImpressionsintegerCount of product impression events within a user session.
productViewsintegerCount of product views events within a user session.
sampleSizedecimalThe default is 1% sample of users with activity in the last 90 days.
startdatetimeThe time when the session started,
userIdIndexintegerIndex of the user in the User Database.

For guidance on how to leverage the datasets, please refer to the documentation on Getting Started with Customer Insights and Navigating the Dashboard guides.