Add additional calculated metrics to the customer profiles.
This box provides the workflows necessary to enrich customer profiles with standardized metrics that describe the customer’s behavior. These metrics can then be surfaced within the Treasure Data segment builder to enable marketers to build highly intelligent segments.
The current version enriches customer profiles with the following metrics:
1.td_total_activity_perc–This metric measures (0-100) the number of activities a user has performed compared to other users.
This metric is measured as a percentile, meaning that the users with the highest activity will receive a total of 100, where users with the lowest activity will receive a total of 0. This metric can help marketing teams isolate the customers who are most engaged with their brand from others who merely aware of their brand. Marketers can use this metric to personalize their communication with customers based on the customers' level of engagement with the brand. For example, a campaign targeted to a segment with a high td_total_activity_perc could encourage customers to refer their friends and family, where a campaign td_total_activity_perc targeted to a segment with low td_total_activity_perc could offer a first-time-shopper discount.
2.td_intent_percentile–This metric measures (0-100) based on how recently a customer has performed a behavior in comparison to the rest of the master segment. The more closer in the past a user interaction is, the higher this metric is compared to other users. This metric helps the marketing team to find the users with intent to buy in a certain period of time (the last month, last two months, etc). Marketers may want to target customers with a high td_intent_percentile more aggressively, in order to get them to convert quickly while our brand is top of mind. Marketers can also use td_intent_percentile to identify lapsed customers and target them with a win-back campaign.
3. td_ad_exposure_percentile- this is a percentile measure (0-100) of how much a particular customer has visited URLs with UTM tags compared to the rest of the master segment. Customers who have a high number of visits through URLs that include UTM tags will have a higher td_ad_exposure_percentile than those who have visited the site through URLs that do not contain UTM tags. This behavior is used as an indicator of how much this user has been targeted with our brand's advertising. Marketers may want to cap advertising to customers with high a td_ad_exposure_percentile but no conversions, as they likely have low intent to convert. They may also want to cap advertising to customers with a low td_ad_exposure_percentile and several conversions, as these are likely brand-aware, loyal customers that will continue to convert without advertising intervention.
4. avg_visit_monthly_trend - This metric gives a Boolean value (0 or 1) to each user based on whether the number of visits in the most recent month is an increase or decrease compared to the average monthly visits of the last 6 months. Marketers can use avg_visit_monthly_trend can use this metric as estimate of whether the customer is increasing their brand-affinity or intent to purchase ("0"), or if they are in the process of lapsing as a customer ("1").
5.td_engagement_percentile - This metric measures (0-100) the level of engagement in a customer's web visits, as compared to average web visits across all users in the master segment. Here, engagement is measured as the average number of web pageviews per session. A user with high number of average pageviews per session gets a high rating, while a user with a low number of average pageviews per session will get a low rating. Here, a low rating could indicate the customer is just browsing without intent to purchase, and marketers could potentially save ad spend for customers who are more engaged with the brand.
- Personalize communication with customers based on the customers' level of engagement with the brand (td_total_activity_perc)
- Convert customers quickly while the brand is top of mind (td_intent_percentile)
- Identify lapsed customers and target them with a win-back campaign (td_intent_percentile)
- Cap advertising to customers with high advertising exposure but no conversions (td_ad_exposure_percentile)
- Estimate of whether the customer is increasing their brand-affinity or intent to purchase, or if they are in the process of lapsing as a customer (avg_visit_monthly_trend)
- Estimate engagement of customers by session (td_engagement_percentile)