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Data source - Transactions

Learn how we process your transaction data

Lexer’s Customer Data Platform ingests data from a variety of sources, and one of the most common is transaction data, from both ecommerce and in-store. Sending Lexer this data allows you to understand your customers’ behavior towards your brand in terms of spend and frequency, to better market to the right crowd, at the right time. In this article, we'll walk you through how to integrate your account and which transaction attributes are available.

Integrate your account

First we need to connect your account, which will give us permission to retrieve transactions and customer profiles from your ecommerce platform.

What we do with the data

Once you’ve connected your account, Lexer is then able to retrieve your customer and product information to create a set of attributes, that you can later query and compare in Lexer.

This is what we do with your data to achieve this:

Auto-validation: We check the format of the file, and the records in the file, to make sure they can be imported.
Validation: We look at the data to make sure it’s what we expect it to be.
‍Enrichment: We transform the data to turn it from what it is into a set of value-add attributes.
‍Unification: We unify your transaction data to other data you have provided, as well as third-party data sets.
‍Import: We take the validated, enriched, and unified data and import it into the Lexer product.

What we build

Once we’ve retrieved your data, we will build a set of attributes you can query in Lexer. We either import it without transformation, or we change it so you can get more value from it. Here is a list of the attributes we can build with this data.

Standard attributes

Lexer cleans and transforms your data into straightforward representations of the raw data, making it easy for your team to search, analyze, and activate.

Attribute Code	Description	Value
Products purchased	Which products the customer has purchased.	Useful for understanding previous behavior, at a 1:1 level and across all customers. Users can gain a deeper understanding of product preferences, and tastes.
Product category purchased	Which category of products the customer has bought.	Useful for understanding previous behavior, at a 1:1 level and across all customers. Users can gain a deeper understanding of product preferences, and tastes.
Size Purchased	What product sizes a customer has purchased.	Useful for understanding previous behavior, at a 1:1 level and across all customers. Users can gain a deeper understanding of product preferences, and tastes.
Color Purchased	What colors a customer has purchased.	Useful for understanding previous behavior, at a 1:1 level and across all customers. Users can gain a deeper understanding of product preferences, and tastes.
Stores Visited	Which stores a customer has visited.	Used to infer the location of a customer, and to inform future Calls To Action

Value-add attributes

Lexer takes all the data you have provided and enriches it, often combining multiple data points to make them more useful than they are on their own.

Total Spend	The total amount spent by a customer across all channels.	Used to inform lifetime value of one or many customers.
Annual Spend	The total amount spent in a 1 year period (e.g. Last 12 Months) or annually (e.g. Total Spend in 2017).	Used to inform a customer’s loyalty, spend cycles, and recency of purchase.
Total Online Spend	The total amount spent on E-Commerce platforms.	Understand the value of a customer’s online experience.
Total Retail Spend	The total amount spent in-store.	Understand the value of a customer’s in-store experience.
Average Spend Per Product	The average amount the customer spends on each product.	Know, at a glance, how much customers tend to spend on products.
Average Spend Per Order	The average amount the customer spends per order.	Know, at a glance, how much customers tend to spend each time they interact with you.
Total Orders	How many orders a customer has made.	Understand how frequently a customer has bought from you.
Annual Orders	The total number of orders made in a 1 year period (e.g. Last 12 Months) or annually (e.g. Total Spend in 2017).	Used to inform a customer’s loyalty, spend cycles, and recency of purchase.
Last Order Date	The last time a customer ordered a product.	Understand if you have a new, loyal, or lapsed customer.
First Order Date	The first time a customer ordered a product.	Understand if you have a new, loyal, or lapsed customer.
Total Returns	How many returns the customer has made.	Know if a customer is happy with your products, so you can intervene for help.
Return Rate	What percentage of products were returned.	Know if a customer is happy with your products, so you can intervene for help.
Total Return Value	The total value of all products returned.	Know if a customer is happy with your products, so you can intervene for help.
Spend Decile	Split into 10 groups, how much does the customer spend.	Identify your low, medium, and high spend customers without having to do any data analysis.
Discount Buyer	Identifies customers who have bought at a discount.	Segment your customers into discount and non-discount buyers for targeting and offers.
Buys Premium Products	Whether the customer buys more expensive products, relative to all products in the category.	Know if this customer pays a premium for their products, so you can make a greater effort to have them return.
Transaction Segment	Splitting customers into single order, single product, and multi-order segments.	Assigning customers to these segments allows marketers to understand how engaged a customer is with your brand.
Channel Preference	The most common sales channel a customer purchases from.	Know where to direct a customer to complete their sale, based on previous behavior.
Closest Store	Which store is closest to the customer.	Understand the customers within 10km of each store, and direct them there to complete a purchase.

How we unify

The data we receive can be matched to first, second, and third party sources. This could be your own data, partner data, or public data sources. To match the data, we need certain fields to do certain jobs.

Customer ID

This field is typically used to match to your other data sources, such as CRM and Email Engagement platforms. If this ID is used across your business, it will be the field that links across all systems.

Email address

This field is used to unify your data to Twitter, enable activation on various sales channels, and can be used to unify your data to certain second and third party sources. If this field is only available in your CRM system, then the Customer ID in your Transaction file should match the IDs in the CRM file.

Mobile

This field is used to unify on other data sets, such as Experian. Lexer transforms your mobile numbers to make sure they’re in a consistent format, ensuring the highest possible unification rates.

Product ID/SKUs

This field is matched to product files, capturing richer descriptions of categories and products your customers purchase.

Order ID

This field is used to capture information about an order from a variety of sources. Order IDs are a great way to match all data about a purchase a customer has made.

Store ID/Name

This field is typically attached to a specific sales channel. Orders with store information can be unified to data about stores to understand channel behaviors.

Limitations

The volume of data being sent from each platform’s respective API will affect the time it takes to extract it. If we're trying to extract a large amount of data, it’s worth noting this could take a while, and has the potential to fail. This is based on each platform’s ability to send across the data Lexer is asking for, within a reasonable time, and without becoming overwhelmed.

And that's it! Now that you know how we process your transaction data and which attributes we can build with it.

Updated:

September 13, 2023