A Customer Lifecycle Campaign is utilized to maintain and/or increase a customer’s engagement with the brand through multiple marketing strategies and offers at various times from their first interaction. Such types of campaigns are useful in creating personalized offers for different customers based on their recency. It also helps in reducing campaign costs while increasing customer retention and ROI, which can be increased by choosing the optimal time to target and which customers to target based on their previous transactions.

Methodology

In the case of the Advanced Segments Repeaters Lifecycle, the aim is to keep the customers Active and reduce their latencies. To find the touchpoints, we look at the behavior of the previous customers who have shopped with the brand before and have made at least 3 visits. The days taken between their nth and (n+1)th visit (latency) is taken as the reference (where n >= 2) and plotted in a graph (on the x-axis) with the corresponding number of customers (on the y-axis). This is done for each of the advanced segments.

The first step is to identify where the peaks are coming in the graph. These peaks correspond to the latency values which should be the ideal touchpoints for the Segments. The latency graphs can be seen in cmd 8, where for each advanced segment 6 graphs are made broken down into smaller time periods with 3 graphs displayed on each row. This makes it easier to observe the peaks.

We can see some clear peaks at 90,110 and 180 days, and 1-2 touchpoints can be taken from between 0 to 60 days depending on the latency table in cmd 9. Please keep in mind the values on the y-axis as they would be different for the 6 graphs.

After getting some initial touchpoints from the graphs, make use of the table in cmd 9. This table has the data of the same customers divided into 10 percentile buckets based on latency. For each percentile, the mean, min, and max latency values are shown. The column with the mean latency values is the most useful.

Combining the graph touchpoints with this table will give the final lifecycle touchpoints. The use of the table is to see how many percentiles of customers would come in the target range. It’s important to not choose touchpoints close to each other because contacting the ‘yet to transact’ customers too quickly again is not advisable. 

For each segment there will be a different lifecycle and different touchpoints as shown in the example below for the ‘Crown’ segment:

The final touchpoints for the 2 segments are shown below.

D is the day of purchase.

The graph in cmd 10 is a visualization of the max latency value for some important percentile values of all the Advanced segments. It shows what is the maximum number of days that many percentages of customers take to return. It is used just to understand the advanced segments repeaters' natural behavior.

Important Pointers

  • Don’t contact the customer too soon from the last touchpoint. Some brand knowledge is required here to determine the frequency of touchpoints. Ex: A supermarket brand will have a higher number of touchpoints with fewer days in between compared to an Apparel brand.
  • The y-axis values for the graphs (Customer counts) need to be seen carefully while determining the peaks.
  • Customer counts will reduce as the latency increases, but don’t just consider the first few peaks as the touchpoints since those customers are going to be coming back to shop without needing a push so no need to start targeting from very early. The best way is to first find the prominent peaks in the higher latencies and work backward from there.
  • The first 1 or 2 touchpoints offers can simply be product offers or product catalog information based on their previous purchases. Then the % or flat amount discount offers can be given with the aggressiveness of offers increasing as days since the last transaction increases. The avg. ATV and ABS of the segments can be used to figure out the aggressiveness. DVS campaigns can be run for Lifecycle.

Input Parameters

  • Org_id of the brand.
  • Start Date and End Date: Duration for which the notebook is to be run. Format: yyyy-mm-dd. (example: 2020-01-16)
  • Advanced Segments DB: Name of database where Advanced segments table is present.
  • Advanced Segments Table: Name of the table.
  • Adv Customer Column: User id column name in the Advanced segments table which is joined with dim_event_user_id in bill_summary.
  • Adv Segment Column: Column name in Advanced segments table which has the segment names.

Output

  • Cmd 8: Latency Graphs.
  • Cmd 9: Percentile table of Customers based on Latency.
  • Cmd 10: Latency curve.

Notebook Links

Open your cluster-specific link provided for the Notebook.

Notebook
Cluster links
Lifecycle Campaign Touchpoints - Advanced Segments
India, SEA, EMEA