How we onboard transactional 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’ behaviour towards your brand in terms of spend and frequency, to better market to the right crowd, at the right time.

Integrate your account

Getting your data into Lexer is easy - simply integrate your transaction/ecommerce account through our handy Integrate tool, and we’ll do the rest.

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 behaviour, 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 behaviour, 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 behaviour, 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 behaviour, 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.

Attribute Code Description Value
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 behaviour.
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 ot 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 behaviours.

Limitations

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