Customer Analytics is a suite of reports that allows retailers to track customer behavioral trends, trends in engagement and purchases overtime. The Customer Analytics suite of reports currently includes:
Weekly Year-over-Year Comparisons by revenue, AOV and customer counts
Cohort Analysis of movement between key customer lifecycle stages
First Identification to First Purchase
First Purchase to Second Purchase
At-Risk to Active (Bluecore Predictive Lifecycle stages)
Access Customer Analytics by following the below steps.
Navigate to the chart (Analytics) icon on the left-hand navigation.
Then, select Customer Analytics.
The Customer Analytics page displays the following:
Performance Metrics
Cohort Analysis
Report Types
Performance Metrics
Weekly Year-over-Year Comparisons enable understanding of how you are doing as a business through a customer-centric lens. This includes overall metrics as well as the breakdown of that overall performance by customer type and purchase frequency.
Customer Types
New Customers
Retained Customers
Reactivated Customers
Purchase Frequency
One-Time Customers
Repeat Customers
Tracked Metrics
Revenue
AOV
Customer Counts
Use Cases
Understand how your business is performing compared to the same period last year and trending week to week over customized time periods (up to the last 2 years).
Identify which customers are driving this performance and why. Are they spending more/less per order? Are there more/less customers within any of these groups?
Should you be trying to get more New customers? Converting more one-time buyers to be Repeat customers? Reactivating more inactive customers?
Below is an example of the revenue focused section of the dashboard including:
overall performance versus the same week last year and the weekly trend below
breakdowns by customer types versus the same week last year and the weekly trend of revenue contribution by customer type stacked to equal the overall revenue for the week
Cohort Analysis
Cohort Analysis groups customers into cohorts based on an action that occurred in a given month and then shows how long it takes and how many of those same customers ultimately take the next important action.
The current cohort analysis reports focus on movement of customers from and to the following stages:
First Identification to First Purchase
First Purchase to Repeat Purchase
At-Risk to Active (Bluecore Predictive Lifecycle stages)
Use Cases
These cohorts can then be compared with each other to help understand key insights such as:
What percentage of my customers are taking the next action I want them to take overall (cumulative) and in each subsequent month (incremental)?
How long does it take for customers to typically take this next action? Are there patterns or trends that are indicative of higher converting cohorts?
What is the conversion performance of one cohort as compared to another? What other factors may have influenced this outcome?
Have my efforts influenced the movement of specific customer cohorts toward that next action? If yes, how can I further scale these strategies? If not, what new strategies should I be considering?
Below is an example of the First Purchase to Repeat Purchase cohort analysis section of the dashboard including:
current month first purchase to repeat purchase rate versus the same month last year
current month count of customers that have made a second purchase from the current month’s cohort along with progress toward this same count during the same month last year
the incremental conversion rate cohort analysis table with counts of total customers included in each month’s cohort and the incremental second purchase conversion rate in subsequent months (color coded to highlight the range of values and patterns or anomalies in the analysis)
Data Requirements
The more accurate & complete purchase data that is available to Bluecore, the better Customer Analytics reports will reflect the state of your business and the impact you are having on acquiring, retaining and reactivating your customers.
With that said, Bluecore recommends at least 2 years of data to ensure you are getting a complete & accurate view of all the metrics available within the Customer Analytics reporting suite.
In cases where there is more than 2 years of data available, Customer Analytics will only show trends for the last rolling 2 years but will use any purchase data beyond that point to classify customers within the appropriate Customer Type.
For example, if Customer A made a purchase 4 years ago and then purchased again today, Customer Analytics would classify this customer as Reactivated even though their initial purchase was not shown in any of the trends charts.
In contrast, if only 2 years of data were available to Bluecore, this same customer would be classified as New (since this is the first time ever in their lifetime that Bluecore has seen a purchase from them).
More information on Purchase Data Imports are available in the article here.
Purchase Data Validation
If you are unsure about the completeness and/or accuracy of your purchase data in the Bluecore platform, a view of the last rolling 2 years of purchase data by week broken out by revenue, order and customer counts is available for review at the bottom of the Performance Metrics > Weekly Year-over-Year Comparisons reporting dashboard.
If there are unexpected gaps, anomalies and and/or overall volume is generally lower than expected, please consult with your CSM on the best course of action to resolve.
This will likely appear similar to the examples below and require one or a combination of the associated resolutions.
Problem: “My purchase data has unexpected gaps or appears incomplete.”
Resolution:
Bluecore does not have enough purchase data to provide a complete & accurate view of all the metrics available within the Customer Analytics reporting suite thus additional purchase data should be imported to resolve this problem (and improve the accuracy of other Bluecore capabilities that are dependent on purchase such as Predictive Audiences or Next Best Purchase recommendations).
More information on Purchase Data Imports are available in the article here.
Problem: “There are unexpected spikes or drops in my purchase data.”
Resolution:
This is likely due to a purchase data import where individual purchases were all labeled with the same created date thus it appears that many customers all made purchases on the same day causing the anomaly. Alternatively, a drop in purchase activity would suggest a similar problem - purchases were missing or labeled with an incorrect created date and misallocated in the timeline resulting in an anomaly in the weekly trend line.
This will require technical support from Bluecore to remove the inaccurately labeled purchase data and once complete the same steps as described above to import accurate purchase data will be required to backfill this historical purchase activity accurately.
Problem: “The trends in my purchase data appear normal but the overall volume is too low.”
Resolution:
This is likely due to a missing purchase source. Bluecore is collecting all of your online purchase activity as it is occurring on your site but is not collecting purchase activity that is occurring in store or through other non-site sources.
This will require technical support from Bluecore to set up a recurring import of this data and backfill of historical data from the same source to ensure there is a complete view of all purchase activity from your customers.
Consult with your CSM on next steps to set up these recurring imports.
Terminology & Definitions
Below is terminology that you will see used throughout Customer Analytics reports along with definitions for each.
Term | Definition |
Customers | a person that has made at least one purchase in their lifetime |
Overall | metrics that are inclusive of all customers |
Customer Type | a classification of customers based on the recency and quantity of purchases in their lifetime |
New Customers | customers who made their first purchase during the last week of the selected time period |
Retained Customers | customers who made a purchase in the prior 1 year, and purchased again in the last week of the selected time period |
Reactivated Customers | customers who have purchased previously but had not made a purchase in the prior 1 year, but purchased again in the last week of the selected time period |
Week | a time period from Sunday to Saturday |
Same Week Last Year | the same Sunday to Saturday time period but from the prior year |
Revenue | the total spend of customers independent of any campaign attribution rules (which is reported separately under Campaign Analytics) |
AOV | ‘Average Order Value’ which is calculated by dividing the total revenue by the total number of orders overall or for a specific customer group |
Count | the total number of unique customers that made a purchase during the selected time period |
Weekly Trend | a snapshot of the weekly values for a given metric over the selected time period |
Purchase Frequency | a classification of customers based on the total number of purchases in their lifetime |
One-Time | customers who have made one purchase in their lifetime |
Repeat | customers who have made two or more purchases in their lifetime |
Cohort | a group of customers with a shared characteristic (e.g. made their first purchase in the same month) |
ID’d Visitors | a count of unique persons that have been identified by their email address but have not yet purchased |
CVR | ‘Conversion Rate’ which is calculated by dividing the total number of customers in a cohort that have moved to the next stage (e.g. made a second purchase) by the total number of customers in the cohort (e.g. made a first purchase in a given month) |
Incremental Conversion Rate | the ratio of customers in a cohort that have moved to the next stage in a specific month and the total number of customers in the cohort representing the difference in conversion rates between months |
Cumulative Conversion Rate | the ratio of total customers from a cohort that have moved to the next stage and the total number of customers in the cohort representing the accumulating conversion rate over consecutive months |
Bluecore Predictive Lifecycle | |
At-Risk (Bluecore Predictive Lifecycle) | At-Risk is a classification by Bluecore Predictive Lifecycle model of customers who have deviated from their typical buying cycle and are at risk of never purchasing again |
Active (Bluecore Predictive Lifecycle) | Active is a classification by Bluecore Predictive Lifecycle model of customers who have made a purchase within their typical buying cycle |
Months to First Purchase | the time in months it takes for a customer to move from First ID’d to making their first purchase where 0 is the same month they entered the cohort, 1 is the subsequent month and so on up to 24 months later. |
Months to Repeat Purchase | the time in months it takes for a customer to move from First Purchase to making their second purchase where 0 is the same month they entered the cohort, 1 is the subsequent month and so on up to 24 months later. |
Months to Active | the time in months it takes for a customer to move from Bluecore Predictive At-Risk to Bluecore Predictive Active where 0 is the same month they entered the cohort, 1 is the subsequent month and so on up to 24 months later. |
Frequently Asked Questions
What is the time period that Performance Metrics are shown for?
Customer Analytics Performance Metrics values are shown in Weekly Year-over-Year Comparisons for the last complete week of the selected time period and trends of weekly values over the duration of the selected time period.
In both instances, a complete week is Sunday to Saturday.
How do you determine revenue? Is it specific to Bluecore?
Revenue is not constrained by any campaign attribution rules (see Campaign Analytics for this information) and will account for all purchase feeds that have been uploaded and/or provided to Bluecore on an ongoing basis including purchase activity from your website.
If I see some anomalies in the data in Customer Analytics what do I do?
If you are seeing less than two years of purchase data in the Data Validation section this would mean that you do not have a complete purchase data set available within the Bluecore platform.
If you are seeing unexpected spikes or drops in your purchase data, this will require technical support from Bluecore to clear the incorrect purchase activity and once complete an import of accurate purchase data will be required to backfill the data that has been removed.
If the trend of your purchase data looks correct but the volume of data is too low, this is likely indicative of a missing purchase source or sources and will require technical support from Bluecore to set up a recurring import of this data and backfill of historical data from the same source(s) to ensure there is a complete view of all purchase activity from your customers.
Will this reporting show me whether I had impact on a specific cohort (ie. one-time buyers) when I have activated a bunch of campaigns against this cohort?
Yes, Cohort Analysis will show you directionally whether or not the campaigns you have activated have had any impact on that specific cohort. However, you would need to know the time (month) in which you started running the campaigns to understand which campaign(s) provided the most impact during that time period.
If the reports are only showing me two years worth of data does that mean I cannot classify a two year inactive customer or consider other purchase activity older than two years?
Our models will accurately classify customers based on the amount of data that we have; trends are just limited to a rolling two year period.
Are New, Retained and Reactivated Customers the same as the Bluecore Predictive Lifecycle stages?
No, they are different. The following customer types are rules based definitions specific to Customer Analytics Reporting and not the same as the Bluecore Predictive Lifecycle stages, unless otherwise noted.
New customers = customers who made their first purchase during the last week of the selected time period,
Retained customers = customers who made a purchase in the prior 1 year, and purchased again in the last week of the selected time period)
Reactivated customers = customers who have purchased previously but had not made a purchase in the prior 1 year, but purchased again in the last week of the selected time period).
The one instance that Bluecore Predictive Lifecycle stages are used is in the Bluecore Predictive At-Risk to Active Cohort Analysis, which is clearly labeled in the report itself. To learn more about predictive audiences, review this article.
Is ID’d to First Purchase only taking into account website visitors?
No, it would take into account any purchase feeds where we are seeing a customer by their email address for the first time. Most of the time this would come from a web visit but can also come from a purchase feed or one-time import.
Why do I see spikes in the initial months of my First ID’d cohort analysis and why does this data not go back a full two years?
As Bluecore has continued to make investments in accurately identifying and reporting on identification related metrics with your customers (see more in the article Bluecore Transparent Identification) this may have required re-identification of customers at a system level resulting in an anomaly in the trends of identification counts since that point in time.
Counts of First Purchase activity within this cohort are still accurate and reflective of customers purchasing in the same or subsequent months but you will likely see that the month 0 rate is lower than subsequent cohorts.