Filter out the best ones amongst good
Score your users in response to their interaction with your portal.
Overview:
Segmenting users by their lead score is a common practice in B2B/Subscription service business.
Lead/user engagement scoring is a normalised score given to cumulative activity/inactivity for a user across sessions. A typical lead scoring algorithm may contain -
- User actions ('what the user has done' including both favourable and non-favourable to business goals)
- User persona values ('who the user is' including demographic and business attributes)
Each of these 2 sets occur at a specific frequency and with specific probability of occurences. Users may visit product pages 100 times before transacting once. Hence, each of these event sets need to be assigned a different weight. eg. Page view may be assigned a score of 10 vis-a-vis a transaction completion event may be assigned a score of 100. These score also may have positive and negative numerical values depending on whether they are favourable to business goals or not. eg. User clicking an email link is favourable whereas users unsubscribing to email is unfavourable.
Each of these actions lead to cumulatively updating the 'engagement/lead score' for each of the users (both anonymous and known users). This attribute may be stored in user profile within the marketing CRM. Segments can be created on the basis of this attributes score in real time. eg. Hot leads may be defined as 'Users with lead engagement score value greater than 400' etc.
Entry criteria to such segments may trigger a set of activities -
- Trigger communication with a user
- Update an internal stakeholder
- Push data in real time to 3rd party tools like CRM
- Fetch data from 3rd party systems like recommendation engine
This workflow attempts to showcase how 'Journey designer tool' can help your business create a flexible yet highly comprehensive and powerful lead scoring algorithm.
Industry:
B2B
Use Case:
Lead scoring of users
Events:
Can be any number of events.
page_viewed
WebEngage.Track(‘page_viewed’, {
/*string*/
“Email” : dummy@dummy.com,
“Name” : Dummy,
“Contact” : 123456,
“Industry” : e commerce,
“Acc_created Date” : 12/12/17,
“Acc_created time” : 16:00
“Last login date” : 16/12/17
“Last login time” : 14:00
“Country” : India
“Plan” : Free
“Page URL” :
“View date” :
“View time” :
“Acquisition source” :
/*Number*/
“Phone number” : 9842657921
“Number of sessions” : 6
“Lead score” :
/*Boolean*/
“On website” : 1
"On mobile" : 0,
“Newsletter subscribed” : 1
“Account_verified” : 1
})
Step-by-step explanation:
1. Journey is triggered at various user actions for each user. User enters the journey via these entry triggers, his/her lead score gets updated and the same user exits the journey in the same flow without wait.
2. On positive actions like, sign up, email clicked, first campaign set, pricing page viewed etc, score is increased by 30 or 50, depending on their weightage to your business.
3. On negative actions like push rejected, email unsubscribed, score is decreased by 10.
4. If user is in one of the segments D-30, D-60 or D-90, check if lead score is less than -200.
4.1. If yes, then directly set the score as -400 (This sets up a floor for preventing user's score from going too low)
4.2. Else decrease it by 100. This is done to gradually reduction of the score on inactivity of users with linear score decay model.
Protip: You may choose to update the lead score on the basis of persona values like job title, company information, subscription_type etc.