--- title: "How do I boost personal emails tied to qualified accounts to at least a good score?" slug: "how-do-i-boost-personal-emails-tied-to-qualified-accounts-to-at-least-a-good-score" updated: 2025-12-08T12:36:50Z published: 2025-12-08T12:36:50Z canonical: "help.madkudu.com/how-do-i-boost-personal-emails-tied-to-qualified-accounts-to-at-least-a-good-score" --- > ## Documentation Index > Fetch the complete documentation index at: https://help.madkudu.com/llms.txt > Use this file to discover all available pages before exploring further. # How do I boost personal emails tied to qualified accounts to at least a good score? As a Marketing Ops leader, there are many business questions we need to tackle every day. With MadKudu, we help make answering those questions easier. ## **Story** One of the **business questions** that we've seen many customers have is: "How do I boost a personal email tied to a qualified account to at least a good score?" The **trigger** for this is that leads with personal emails that are tied to a good account are not shown as qualified to Sales. These personal emails typically come from ads (Facebook, LinkedIn, etc) where prospects would enter their email addresses that are non-corporate. Likely personal emails are not a population of leads that convert well and this is the reason why the model scores them low. The **objective** is to boost a personal email tied to a qualified account to at least a good score to make sure these good leads are sent to Sales. ## **How to do this via the MadKudu platform?** **Pre-requisites** - You have the permissions of the [Architect](/v1/docs/user-roles-permissions) role - You know what an [Attribute](/v1/docs/attribute-mapping) is - You know what a [Computation](/v1/docs/what-are-computations) is - You know what an [Override](/v1/docs/customer-fit-overrides) is ### **Option 1: The account enrichment is available in fields on the Lead record or the Account it is attached too** **[Available for Salesforce, HubSpot and Segment]** We can use enrichment sitting on your Lead or Account record to score the personal leads and contacts. - For example, if the Lead record [john@gmail.com](mailto:john@gmail.com) is enriched by a 3rd party filling the fields such as `Title`, `Industry`, `Country`, `NumberOfEmployee`, or other custom fields, then you can boost [john@gmail.com's](mailto:john@gmail.com) score with an override based on this SFDC enrichment. - For example, if [john@gmail.com](mailto:francis@gmail.com) is explicitly attached to the account ibm.com which is flagged as a target account in an SFDC field, or your SFDC enrichment says `Industry` = Internet Software & Service, `NumberOfEmployee` = 20,000, then we can boost [john@gmail.com's](mailto:john@gmail.com's) score with an override based on your SFDC enrichment. Here are the steps to accomplish this: ### **Step 1: Pull relevant fields** 1. Pull from your Salesforce into MadKudu relevant `Lead`, `Contact` and `Account` fields that could be used to separate good from bad personal emails even when tied to known accounts (company size, industry….) [following these instructions](/v1/docs/how-to-pull-additional-fields-to-use-in-the-madkudu-platform). 2. Wait for the confirmation email. ### **Step 2: Map the fields in the Attribute mapping** 1. In your [MadKudu app account](https://app.madkudu.com/), map the Salesforce fields containing the relevant enrichment points in the [Attribute mapping](/v1/docs/attribute-mapping) to make them available in the Data Science Studio so you can create an override ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(89).png) 2. Wait for a confirmation email. ### **Step 3: Create a computation for each of the fields** 1. Navigate to the [Data Science Studio](https://springbok.madkudu.com/). 2. Duplicate the Customer Fit model with the labels: "**Live as Standard**" and "**batch**". ![springbok-duplicate.png](https://support.madkudu.com/hc/article_attachments/360091991431) 3. Go to the Customer Profile > **Computations** to [create a Computation](/v1/docs/how-to-create-a-computation-in-the-data-studio) 1. **Type:******String 2. **Name**: field_lead_salesforce 3. **Configuration mode**: OneToOne 4. **Field**: select the attribute you created in step 1 ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(91).png) > ***Tip****: If you have an field Industry on both the Lead, Contact and the Account, use the Configuration mode "Advanced mode" and write***`COALESCE (attribute1,attribute2, attribute3...)` > > > > *Example:*`COALESCE (salesforce_lead_industry, salesforce_contact_industry, salesforce_account_industry)`***which means "get the value of the industry first in the Lead record, otherwise in the Contact record, otherwise the Account record it is attached too" -> the order is important.* 1. Repeat the same for each field you'll want to use in the override ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(92).png) ![mceclip5.png](https://support.madkudu.com/hc/article_attachments/4406133411597) 2. Once created, Click on "Release & Recompute" The training and validation datasets will be enriched with this new computation to append the Account fit score to each lead in the training and validation datasets ### **Step 4: Create the Override on personal emails** In the Data Science Studio, in the model you duplicated, [add the Override](/v1/docs/customer-fit-overrides). The override should boost personal email based on the same traits as your ICP. Example 1. 1. - IF **is_personal** IS **1** - AND **country_salesforce** IS ANY OF **United States, Canada** - AND industry_salesforce IS **Internet Software & Services** - THEN should at least be **good** You could also add additional AND conditions based on the person's title for example to restrict the boosting of personal emails only to leads from qualified accounts and with a good title. Alternatively, you can leave this override like this and create a second one like - IF **is_personal** IS **1** - AND **country_salesforce** IS ANY OF **United States, Canada** - AND **industry_salesforce** IS **Internet Software & Services** - AND******title_salesforce******contains******data** - THEN should be******very good** ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(93).png) > ***Tip****: Instead of using the computation***`salesforce_industry`*, create another computation named***`target_industry_salesforce`*or***`target_title_salesforce`*to list all the good industries and titles you would like to use in the overrides, and signals to avoid reusing the same logic several times* ### **Step 5: Check the performance of the model** Before deploying your changes you'd like to make sure the override does not boost too many leads otherwise you may flood your marketing and sales teams with too many new leads. Check the performance of the model in the **Validation tab** to make sure the performance still looks good. Open in a separate tab of your browser the model live you duplicated the model from and **compare side by side** whether the performance has greatly decreased in terms of conversion rates for each segment (refer to the second section in the screenshot). ### **Step 6: Update signals** So far your Sales team has been seeing in the Customer Fit signals in Salesforce a negative signal `x Lead is using a personal email`and no other information. 1. Go to the Signals tab and [add Signals](https://support.madkudu.com/hc/en-us/articles/4404497474061-Customer-Fit-Signals) based on the computations you've used in the Override ### **Step 7: Review** Before deploying any change to production, review a sample of leads in the Spot check tab, see if you can find personal emails boosted and with the new signals. ### **Step 8: Deploy the model and announce it to the Sales team** Now that the override has been added to the model, you will need to push this update to your model in production to update the scores in Salesforce. Keep these considerations in mind when deploying overrides: 1. All your Leads, Contacts, or Accounts that are usually scored by MadKudu will get rescored with the next batch scoring within the next 4-12 hours (see when the next **Analysis** then **Sync** process should run in the [Processes Page](/v1/docs/how-to-read-the-processes-page-how-long-does-it-take-for-madkudu-to-score-my-leads-and-accounts) ). 2. Adding, editing, or deleting overrides that increase or decrease prospect scores may trigger automated workflows in your CRM which are based on the customer fit or lead grade score (like your MQL workflow). 3. Don't forget to **give a heads-up to your Marketing and Sales team** about the override you added and why they'll see personal emails now scores good or very good. When you are ready, go to the Deploy tab - If the live model you are editing is flagged "Live as Standard", then go into the first sub-tab "deploy as standard" and click **Primary deploy** - If the live model you are editing is flagged "Live as Multi-fit" please submit a support ticket to our support team, and we'll help you with that. Well done! ### **Option 2: you have an Account Fit model scoring your Salesforce Accounts** **[Only available in Salesforce on plans including the Account Fit]** We can use the Account Fit score sitting on your accounts to score the personal leads and contacts explicitly attached to the accounts through an account ID. - For example, [john@gmail.com](mailto:francis@gmail.com) is explicitly attached to the account ibm.com which is scored very good, then we can boost [john@gmail.com's](mailto:john@gmail.com's) score with an override based on ibm.com account score. Here are the steps to accomplish this ### **Step 1: Map the Account Customer Fit segment field in the Attribute mapping** 1. In your [MadKudu app account](https://app.madkudu.com/), map the `mk_customer_fit_segment__c` Salesforce field in the [Attribute mapping](/v1/docs/attribute-mapping) to make the Account score available in the Data Science Studio so you can create an override. - **Object**: Account - **Field**: mk_customer_fit_segment__c - **Attribute name**: salesforce_account_mk_customer_fit_segment ![mceclip0.png](https://support.madkudu.com/hc/article_attachments/4406116018061) 2. Wait for a confirmation email. ### **Step 2: Create a computation with the Account Customer Fit segment** 1. Navigate to the [Data Science Studio](https://springbok.madkudu.com/). 2. Duplicate the Customer Fit model with the labels: "**Live as Standard**" and "**batch**". ![springbok-duplicate.png](https://support.madkudu.com/hc/article_attachments/360091991431) 3. Go to the Customer Profile > **Computations** to [create a Computation](/v1/docs/how-to-create-a-computation-in-the-data-studio) - **Type:******String - **Name**: customer_fit_segment_account_salesforce - **Configuration mode**: OneToOne - **Field**: select the attribute you created in step 1 ![mceclip2.png](https://support.madkudu.com/hc/article_attachments/4406124028813) ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(95).png) 4. Once created, Click on "Release & Recompute" The training and validation datasets will be enriched with this new computation to append the Account fit score to each lead in the training and validation datasets ### **Step 3: Create the Override on personal emails** In the Data Science Studio, in the model you duplicated, [add the Override](/v1/docs/customer-fit-overrides). Conditions: 1. 1. - IF **is_personal** IS **1** - AND **customer_fit_segment_account_salesforce** IS ANY OF **very good, good** - THEN should at least be **good** You could also add additional AND conditions based on the person's title for example to restrict the boosting of personal emails only to leads from qualified accounts and with a good title. Alternatively, you can leave this override like this and create a second one like - IF **is_personal** IS **1** - AND **customer_fit_segment_account_salesforce** IS ANY OF **very good, good** - AND******title_lead_salesforce******IS ANY OF******...** - THEN should be******very good** ![](https://cdn.document360.io/a55e7ea5-b8ac-4456-874d-10cc92097370/Images/Documentation/image(97).png) ### **Then follow Step 5 to Step 8 described above. Well done!**