Unify Your Lead Data with Ease: Learn How to Normalize Your “Country” Field in Adobe Marketo”

Is your CRM encountering bidirectional data sync errors due to conflicting values in the “Country” field of leads? Here’s a process to normalize this field for smoother data flow. The goal is to unify the data format by standardizing the abbreviated country names.

To begin, we needed a list of correct country names from Salesforce (SF) and a list of frequently encountered abbreviations in our database. The process involves four blocks with a tree structure, with the “Check and Distribute” block as the main block. 

  1. Check and Distribution.
  2. Fill Empty Field

  1. Normalization process

  1. If normalization fails, the lead is placed in the “Country is not in list” list or “Country is empty” list. To ensure consistent data flow, use a picklist in lead forms for introducing country data and regularly process the non-form leads’ “Country is not in list” or “Country is empty” lists.

To ensure standardized data in Marketo, introduce country data using a picklist in lead forms, and regularly process “Country is empty” and “Country is not in the list” lists for non-form leads.

Normalizing the “Country” field can help reduce errors and ensure data consistency between your CRM and Marketo. Stay tuned for more details about the normalization process.

Step by step  Guide to Preserving Custom Objects If you are merging leads with the same emails 

After merging leads in Marketo, you will lose custom objects (CO) if the merging process is not handled carefully. The connection between the CO and the final object-lead is always broken if CO does not belong to the winning-primary lead. At the same time, the object itself – CO remains in the Marketo without the reference to any lead. That’s why we call them – orphaned custom objects.

To avoid orphaned custom objects during the lead merge process in Marketo follow these steps.

  1. Before merging, you first need to get a list of records that have custom objects. For that create a smart list in Marketo with a filter that identifies all relevant leads who have your custom object. For example – filter “Has Car” if your custom object is called “Car”.
  2. Then using this list you need to get the actual data of each instance of a custom object. We advise to use the API and extract the data based on the smart list created in step 1.

    All fields are not needed completely, only link field and dedupe field. Marketo Custom Objects (CO) records are linked with person records (Leads) by a set link field value. By using the Marketo API and an external script (for example NodeJS), a CSV file should be generated as output.

    The final .csv table will look like:
Email addressCO 
[email protected]CO 1
[email protected]CO 2
[email protected]CO 3

In this example the link field is “Email address” and dedupe field is “CO” .

  1. After that, you can merge leads – manually or by bulk merging. 
  1. After the merge is complete, import the extracted file to re-establish any orphaned custom objects and link them to the primary leads. Please note that this activates all triggers that must be activated after getting a new CO.

With careful planning and completing all the above mentioned steps, it is possible to avoid losing any custom objects during the lead merge process in Marketo.

#Marketo #merge #lead_merge #deduplication #custom_objects #MarketoAPI #NodeJS 

Maximizing Sales Efficiency with Accurate Behavior Back Scoring in Adobe Marketo

Behavior back scoring is a method used in lead scoring to re-evaluate and adjust the scores of leads based on their past behavior. It can be particularly useful when a lead’s score has been inflated by a program scanning mail systems with automatic link clicking.

For instance, let’s say your lead scoring model assigns 3 points for visiting your website, 5 points for clicking a link, and 50 points for filling out a demo form. Now imagine that John used a program scanning mail system to click on all 10 links in each of the three emails, while Jane simply filled out a demo form, and Jennifer clicked a link and visited the website twice.

Without back scoring, John would have a score of 150 points, Jane would have 53 points, and Jennifer would have 11 points. However, after recalculating the scores, counting only 1 activity per period, John would have only 5 points, Jane would still have 53 points, and Jennifer would have 8 points. By recalculating, you can ensure that the most important and relevant leads are prioritized.

Here’s how to do it:

  1. Start by creating an operational program in Marketo.
  2. Set the token values for each action (visit web page, open email, click non-unsubscribe link in email, fill Demo form, and any other non-unsubscribe form). These token values are the same as in your normal scoring program and will be used to recalculate the Behavior Scoring.
  3. Create a campaign that will reset the lead Behavior score to zero for all leads in the database. This will allow you to start with a clean slate and recalculate the score based on past actions.
  4. Create one smart campaign for each activity being scored (visit web page, open email, click link in email, fill out a Demo form, and fill out other forms), but apply a time limit in the filters. In our example, the time limit is three months. For example, for all those who have filled out the Request Demo form in the last 3 months, Behavior Score will be increased by 50. And the 50 volume of Behavior scoring is the value needed to become MQL.
  5. Turn off all existing Behavior Scoring Campaigns. Then, run the reset campaign to ensure that all lead Behavior scores are set to zero. Launch the Behavior Back Scoring campaigns one time. Finally, launch your Behavior Scoring with protection against link-clicking programs. Here’s a post on how to do it.

In order to obtain relevant and accurate results, the recalibration process must be performed on your entire lead database rather than a select few.

Keep in mind the consequences: Behavior scoring is a vital aspect of the revenue cycle model transition. Its recalculation affects MQL count and Sales Insight indicators, which track sales pipeline progress and forecast revenue.

Despite the challenges, recalculating behavior scores is a critical step in ensuring that your lead scoring model is accurate and effective.

How to Detect Bot-Inflated Behavior Scores in Adobe Marketo

Have you ever encountered leads with suspiciously high behavior scores, like 2000 and above, that end up not being interested in a deal? This could be due to bots or email systems automatically clicking all links in emails, inflating their behavior scoring. If you’re utilizing Sales Insight to get a deeper understanding of buyer behavior, that inaccurate information can skew your overall picture and cause your sales team to waste valuable time sorting through it.

To prevent this from happening and to ensure the accuracy of your lead scoring system, there are several strategies that can be implemented.

  1. Using the standard tools in the Marketo Admin section click the Bot Activity tab. Choose to Match with IAB List, Match with Proximity Pattern, or both. On choosing “Filter Bot Activity”, all email opens and email link clicks that match with their respective identification methods will not be logged in your subscription.
  1. Limit the number of trigger activations for link clicks in emails in behavior scoring to once per day in the qualification rules. This will prevent bots from manipulating the system by clicking all links in an email, leading to an inflated score.
  1. There are programs that are capable of clicking links within emails without leaving any traces of email opening. To avoid bot scoring for that you may want to add a “Click link in email” trigger in conjunction with a “Opened an email’ filter with about an hour constraint.
  1. Backscoring is recommended to normalize scores for accurate reflection of lead engagement. This campaign should be run only once to avoid skewing the data. Mark your calendars for next Wednesday! We will be publishing a dedicated post on the topic of correct backscoring. Learn how this valuable technique can assist in providing a precise representation of lead engagement. 

By implementing these strategies, you can maintain the integrity of your lead scoring system and ensure it accurately reflects the engagement of real prospects.

#marketo #marketingautomation #marketingoperations 

#leadscoring #bottracking 


#martech #marketingtechnology #marketoengage

#datahygiene #cleandata #datadrivendecisions

How to use segmentations for RCE reporting.

We had a task to create a report in RCE, based on the “Lead Source” attribute, considering the custom segments “Mailable” and “Unmailable”. This was necessary for analyzing the origin of high-quality leads. However, we have noticed that among the standard lead attributes in RСE, there is no segmentation. 

To solve this problem, our team created a new field in Marketo, setting up its update logic, and synchronizing it with RCE. Here’s how we accomplished this:

  1. Log in to your Adobe Marketo account and go to the “Admin” section.
  2. Select “Field Management” and then “New Custom Field.”

  1. Choose “string” for the field type and enter “Mailable” as the field name.

  1. Use a trigger smart campaign to track segmentation changes and populate the field “Mailable”.
  2. Next, navigate to the Revenue Cycle Explorer in your Adobe Marketo account.
  3. Select the field you’ve just created and click Edit Sync Option.
  4. Now you need to choose between a Dimension and Metric. Dimensions are used to describe what the data is about, while Metrics are used to quantify the data. So, your choice is “Dimension”

  1. Save the changes. Please, note that synchronizing data with Revenue Cycle Explorer takes approximately 24 hours Plan ahead!

By creating a new field and synchronizing it with RCE, you can greatly enhance the usefulness and impact of the information you’re able to gather, and help you make informed decisions.

#marketo #marketingautomation #marketotips #RCE #RevenueCycleExplorer