Operational Efficiencies in Lead Allocation For Agents

The traditional method of lead allocation can burden one agent while depriving another of sufficient opportunities. To address this issue, sales engagement platforms have streamlined and improved lead allocation using a rule-based system. An ML-based system utilizes both the leads' and the agents' data fields to calculate a 'conversion propensity score' that finds the optimal matches for conversion. Implementing ML-based lead allocation can be a game-changer for carriers looking to sharpen their competitive edge and take the lead.

Generating quality leads through the website, social media, events, and mailer campaigns are something insurers are doing aggressively to win customers and gain market share. Alright, you get those leads - what next? You allocate it to the next available agent, via the trusted round-robin or a random allocation method. At least this is what would happen ten years ago. At best, an agent in the vicinity would be given that specific lead to chase. End of story.  Actually, the story never takes off.

With such an approach,

• There is a burden of leads on one agent, while another agent has insufficient opportunities
• A lead’s need may not be addressed enough for them to convert into a customer
• Managers need to constantly monitor this method making it unscalable

With technologies like sales engagement platforms, lead allocation has become more streamlined and intelligent. The application uses a rule-based allocation system to help allocate leads better, quicker. How does this work?

The application typically provides users (agent managers) an intuitive interface to build their parameters. For example, 

1. They could allocate new leads based on the lead source; from social media, from the website, or from a call center etc. and route it to a certain agent
2. Or, they could assign leads based on product - if it is health insurance, it can go to Jack, life insurance can go to Andy and so on
3. Agent managers could also allot their leads based on geographical locations, if in-person interactions is a huge factor in the lead journey
4. Or understand if the lead prefers in-person conversations, or not, in which case the lead could go to an agent in a different location but tenured in a specific type of insurance selling.
5. Beyond this, agent managers could assign leads to the first person who responds to the lead notification, the agent with the highest conversion success, or simply based on agent availability. 

With a combination of these parameters, based on the insurer’s requirement, a successful rule-based lead allocation system can be implemented. This method of allocating leads has significantly boosted lead allocation practices helping insurance organizations gain more conversions, with faster movement through the lead journey.

Happily ever after? ChatGPT says no, there’s more! 😈

With the world looking at AI and Generative AI applications as the next frontier, cutting edge sales engagement platforms are leveraging ML-based allocation methods to improve things further! 

ML-based rules allocation can bring in a superlative improvement in lead allocation efficiencies.

How does this work?

Here the rule-based allocation engine works in tandem with the ML-based allocation algorithm. So not only does the system comprehend lead attributes, it also recognizes the actions performed by the lead over time. As a starting point the lead passes through the rules-based allocation system that has been customized based on the parameters defined by the carrier. After filters on source, location, product need and more, the results are fed to the ML-based allocation system. 

Here’s where the magic happens.

An ML-based system uses both the leads’ and the agents’ data fields to calculate a ‘conversion propensity score’ - what is the percentage of success if lead A is paired with agent X? 

The match with the highest score also has the highest chance of conversion.

If it sounds like ‘matches made in heaven’ - it actually is!

The ML model keeps learning from each record and adjusts the algorithm to find best possible matches for conversion. 

ML-based allocation of leads assure 80% + accuracy in mapping the right lead to the agent - this can prove to be a game-changer for carriers seeking to sharpen their competitive advantage and take a lead. 

As insurance sales leaders seek ways to optimize operational efficiency in the distribution chain, lead allocation is an important area with a large scope for improvement. It is time to measure the,  

1. Leads being generated versus allotted
2. Lead allocation efficacy
3. Tools that can help in improving the efficacy  with a focus on using technology like AI and ML to optimize this further. 

Wait, there is room for a sequel too!🥁

The inherent ability of AI is to learn and improvise. With time, the algorithms gather the data presented to them, combine it with experience and are able to allocate leads with even more accuracy!  Insurance leaders can leverage this for better product positioning and faster conversions.