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Einstein Lead Scoring with Kalpana Chauhan

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コンテンツは Salesforce and Mike Gerholdt によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Salesforce and Mike Gerholdt またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Today on the Salesforce Admins Podcast, we talk to Kalpana Chauhan, Lead Salesforce Consultant at Mar Dat. Join us as we chat about Einstein lead scoring and the importance of high-quality data.

You should subscribe for the full episode, but here are a few takeaways from our conversation with Kalpana Chauhan.

From math teacher to Salesforce Admin

Kalpana started out as a math teacher but, as her kids got older and she found herself with time on her hands, she discovered she had an affinity for Salesforce. That foundation has been incredibly helpful in her career as a Salesforce Consultant when she has to untangle something like validation rules. “Coding is easy once you understand the logic and flowchart of how it actually works,” she says.

Flows and automation have always held a special place in Kalpana’s heart. There is so much manual work that you can make unnecessary if you’re willing to sit down and thoughtfully analyze a business process. And new tools like Einstein are making that easier than ever before.

Scoring leads with Einstein

One of Kalpana’s clients needed help resolving issues related to proper lead qualification and marketing engagement. “The marketing team was eager to enhance their engagement with leads, while the sales team was seeking better clarity regarding which leads were sales-qualified so they could focus their efforts on conversion,” she says.

That’s when Einstein came to the rescue. They worked through the AI Wizard and came up with three scores:

  1. Behavior, to evaluate how warm a lead is based on engagement.

  2. Engagement frequency, to figure out what marketing cadence is best for each lead.

  3. Send time optimization, to automate marketing emails to go out at the best time for each lead based on their engagement history.

Together, these scores helped marketing engage prospects at the right cadence while sales could pursue an optimal nurturing and conversion strategy for each lead.

Getting started with Einstein

If you want to start using Einstein in your org, Kalpana recommends starting by taking a close look at your data quality. “Data is the key,” she says, “the more data you feed in, the more accurate your results will be.” That means Implementing solid data validation rules and using screen flows to remove duplicate records.

Getting up-to-speed on Einstein was easy with all of the resources out there from Salesforce. In addition to Trailhead, Kalpana found Bootcamps to be especially useful. “I love anything with visuals,” she says. She also recommends going to your local Trailblazer Community Group meeting to connect with other admins facing similar challenges.

Be sure to listen to the full episode to learn more about Einstein, email marketing, and data cleanup.

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152 つのエピソード

Artwork
iconシェア
 
Manage episode 380259227 series 2794780
コンテンツは Salesforce and Mike Gerholdt によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Salesforce and Mike Gerholdt またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Today on the Salesforce Admins Podcast, we talk to Kalpana Chauhan, Lead Salesforce Consultant at Mar Dat. Join us as we chat about Einstein lead scoring and the importance of high-quality data.

You should subscribe for the full episode, but here are a few takeaways from our conversation with Kalpana Chauhan.

From math teacher to Salesforce Admin

Kalpana started out as a math teacher but, as her kids got older and she found herself with time on her hands, she discovered she had an affinity for Salesforce. That foundation has been incredibly helpful in her career as a Salesforce Consultant when she has to untangle something like validation rules. “Coding is easy once you understand the logic and flowchart of how it actually works,” she says.

Flows and automation have always held a special place in Kalpana’s heart. There is so much manual work that you can make unnecessary if you’re willing to sit down and thoughtfully analyze a business process. And new tools like Einstein are making that easier than ever before.

Scoring leads with Einstein

One of Kalpana’s clients needed help resolving issues related to proper lead qualification and marketing engagement. “The marketing team was eager to enhance their engagement with leads, while the sales team was seeking better clarity regarding which leads were sales-qualified so they could focus their efforts on conversion,” she says.

That’s when Einstein came to the rescue. They worked through the AI Wizard and came up with three scores:

  1. Behavior, to evaluate how warm a lead is based on engagement.

  2. Engagement frequency, to figure out what marketing cadence is best for each lead.

  3. Send time optimization, to automate marketing emails to go out at the best time for each lead based on their engagement history.

Together, these scores helped marketing engage prospects at the right cadence while sales could pursue an optimal nurturing and conversion strategy for each lead.

Getting started with Einstein

If you want to start using Einstein in your org, Kalpana recommends starting by taking a close look at your data quality. “Data is the key,” she says, “the more data you feed in, the more accurate your results will be.” That means Implementing solid data validation rules and using screen flows to remove duplicate records.

Getting up-to-speed on Einstein was easy with all of the resources out there from Salesforce. In addition to Trailhead, Kalpana found Bootcamps to be especially useful. “I love anything with visuals,” she says. She also recommends going to your local Trailblazer Community Group meeting to connect with other admins facing similar challenges.

Be sure to listen to the full episode to learn more about Einstein, email marketing, and data cleanup.

Podcast swag

Resources

Social

  continue reading

152 つのエピソード

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