Release Features Date: December 28, 2020
We have some new features available on Insights+.
Following are the details:
- Top N per registered store filter
- Top N per last purchased store filter
- Top N offer propensity filter
- Payment mode filter
We have some enhancements available on Insights+.
Following are the details:
- Onboarding requests
- Transaction timestamp in data export
- Improved error messages on user segments uploads
Top N per registered store filter
We now have an audience group filter for selecting top customers (ranked on the lifetime purchases, transactions, or quantity of items purchased with the brand) based on the store they are registered. For instance, you can create a list of top 100 customers - based on lifetime spend with the brand - registered in one or more stores of your choice using this filter.
Consider the following example where the first table shows the top customers by lifetime purchases at the org level and the second table shows the top 3 customers by lifetime purchases as per each store of the org according to stores at which customers registered.
Top 10 customers at the Org-level
|Customers||Lifetime purchases||Registered stores|
|Customers 1||$5700||Store C|
|Customers 2||$5400||Store B|
|Customers 3||$5100||Store A|
|Customers 4||$5000||Store C|
|Customers 5||$4900||Store B|
|Customers 6||$4800||Store A|
|Customers 7||$4700||Store B|
|Customers 8||$4600||Store C|
|Customers 9||$4600||Store C|
|Customers 10||$4500||Store B|
|Customers 11||$4400||Store C|
|Customers 12||$4200||Store A|
Top 3 customers by lifetime purchases per each store
|Stores||Top customers||Lifetime purchases|
|Store A||Customers 3||$5100|
|Store B||Customers 2||$5400|
|Store C||Customers 1||$5700|
For more information on this filter, see top n per registered store customers.
Top N per last purchased store filter
This filter is similar to the Top N Per Registered Store filter except for one key difference: instead of selecting customers based on their registered store, the filter will give you a list of top customers (ranked on lifetime spend, transactions, or quantity purchased with the brand) based on the store they last purchased.
Consider, you can create a list of the top 100 customers - based on lifetime spend with the brand - who last transacted in one or many stores of your choice using this filter. Similar to the top n per registered store filter, if you select the top 100 customers and two last purchased stores, you will get 200 (100 top customers per selected last purchased store) customers when you use this filter. For more information on this filter, see top n customers per last transacted store.
Top N offer propensity filter
The top n offer propensity filter helps you build an audience group who are likely to respond to specific types of offers.
For example, consider you have a cashback offer enabled and have a budget for targeting or need to target only 1000 customers with the offer. Instead of seeking assistance from the analytics team to identify the most relevant customers, you can utilize the top n offer propensity filter to get top customers who have the highest inclination towards cashback offers.
|Only orgs that are mapped to the data science vertical will have this filter enabled.|
Relevant tickets: CAP-59126
Payment mode filter
We now have a filter based on available payment modes. The design is similar to our existing filters.
You can select a list of customers who paid using one or more payment methods for a specific range of transaction amount. Advanced options include filters for the day of the week, hour of the day, a specific day, and store/concept.
For example, you can select a list of customers who:
- Paid using a credit card for bill amount between the range of USD $100 to USD $200
- As mentioned above, the transaction happened on Monday between 10:00 hours and 11:00 hours
- The transaction happened in a Concept - sub 3 (refer to the following screenshot)
For more information on this filter, see payment modes.
|Only orgs that are mapped to payment modes vertical will have this filter enabled|
Relevant tickets: CAP-56026
Insights+ team has set up a process for picking up any onboarding tasks. The details of different types of onboarding tasks and steps are mentioned here.
|Please read the document before raising any onboarding task ticket.|
Transaction timestamp in data export
We have now added a bill event timestamp field to the standard Transaction export template as following:
Till now, we only had a separate date and time dimensions in data export. Due to several requests, we reconcile transaction data captured by the Capillary system vis-à-vis client-side transaction data dump and made this field available as a measure in Data Export. For enabling this field, please create a vertical mapping request on Jira.
Relevant ticket: CAP-51101
Improved error messages on user segments uploads
We have updated the error messages that you will observe while working on file uploads in User Segments. When you try to upload a User Segment file with incorrect junk values in the user_id field. An error message will appear with the exact row numbers where junk values are present so that you can identify and enter the correct value.
Relevant ticket: CAP-48878