| May 20, 2019

Deriving Better ROI with ‘Agent Capacity function’ in Contact optimization – Part -2

In part one of this blog, we learned about the first four steps about configuring Agent capacity function. These first four steps were

A, Creating Agent tables: Agent information table and agent-customer relationship table
B, Defining Agent tables in Optimize Configuration
C. Create Agent channel
D. Create Contact Optimize Session providing agent tables and agent channel.

Please click here to read the Part -1 of this blog
We shall continue with the next steps in this article which are –

E. Define Agent Capacity rules in Optimize session
F. Create Pre-opt flowchart and then run it
G. Run Optimize session
H. Run Post opt flowchart to deliver offers to optimized contacts.

E .Defining Capacity Rules in CO session:

Agent_rule: Provide Agent Channel in the channel box. You can either select specific offer/offer list or select “Any offer” which means all offers which has a channel as “agent” will be selected by this rule.
Other options like “over the time period” and offer range will be disabled by default when you select agent channel.

Email rule: Select channel as “Email” or any other channel you have defined. We are using EMAIL in this example. You can define offer range here, so we have defined 0-2. That means at max only 2 offers will be selected by this rule which has a channel as “email”.

F. Creating a pre-opt flowchart:
Pre-opt:

Select process box “Agent”:

Optimize process box “Opt_Call_1”:

Offer “CO_Offer2” parameters:

Select process box “Email” configuration:

Optimize process box “Opt_Email” configuration:

Now run the flowchart and check count in PCT table:

Once the pre-opt flowchart finishes run, it populates the PCT table which is known as the proposed contact table. You can find PCT table name from debug level flowchart.log.

We can see above that 4 customers are selected to deliver offer via Email channel and 5 customers via Agent.

G. Run Optimize session to optimize the records:

Before running the session, we have a total proposed contact as 9. These proposed contacts are populated when you run a pre-opt flowchart:

Now, let’s run Optimize session: We are selecting production run here. You can select either Test run or Production run.

After it finishes run, it shows “Run Complete” message:

To check total optimized records, click on either the analysis tab. It shows the date and time of the last run along with the optimized records count:

Before Optimize session run, total proposed records were 9. Out of which 4 customers were selected to deliver offer via the “Email” channel and 5 customers were selected to deliver offers via Agent.

We defined 2 rules in the CO session: Agent_rule and Email_rule.
In Email_rule we defined maximum offer as 2. So out of 4, 2 are selected and 2 are excluded.
Agent rule will include and exclude offers based on agent capacity defined in UACO_AGENT_INFO table. It will work in the below manner:
Agent 1 has capacity 3 and total customers proposed are 5 (customer id’s-1,2,3,4,5). So out of 5 customers, it has randomly taken 3 customers and excluded 2.

Thus, Email rule excluded 2 and Agent rule excluded 2 that makes total optimize records as 9-4= 5

Same can be validated via OCV view which is known as Optimized contact view:

H. Create and run post-opt flowchart:
Take input from the optimized table and deliver offer using call list/mail list process box:

Extract process box should take input from the optimized list as shown below:

Mail list process box assign offer by its own. You don’t need to manually assign any offer in treatment tab since it is taking offers from optimize session:

Now run this post-opt flowchart to deliver an offer to the client. Once the flowchart finishes run, check history tables:

Once the flowchart runs, you can check contact history/ detail contact history tables:

Jyoti Verma is a technical analyst in a support role for over 5+ years in customer problem solving and advocacy. Adept at learning various industrial niches and producing effective content for clients, she currently helps clients of Marketing Suite at HCL. She plans to present more informative articles on various marketing products in the future that will help clients to implement their marketing strategies efficiently.

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Jyoti Verma

Senior Product Support Engineer

Jyoti Verma is a technical analyst in a support role for over 5+ years in customer problem solving and advocacy. Adept at learning various industrial niches and producing compelling content for clients, she currently helps clients of Marketing Suite at HCL. She plans to present more informative articles on different marketing products in the future that will help clients to implement their marketing strategies efficiently.

Topics in this article:

Contact Optimization Unica Optimize

Deriving Better ROI with ‘Agent Capacity function’ in Contact optimization – Part -2

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