Scoring Rules - Zia Scores
Availability
Editions: Enterprise | Zoho One | Ultimate | CRM Plus
Release criteria: All Enterprise and Ultimate users with 20+ user licenses.
DC: All DCs
Zia Scores - Scope and benefits:
Every lead or customer a business encounters is unique, and they all have different thought processes before making a purchase. While there are many ways to understand a customer's point of view, segmentation is a proven method. By segmenting customers in a CRM system based on various factors, such as interactions, support approach, demographics, customer journey, deal history, revenue spent, and conversion period, businesses can better understand which customers require attention, which ones to focus on, and what the next step in the sales cycle should be.
Why Zia Scores?
What if instead of you having to set up these rules manually, you had Zoho's artificial intelligent assistant automatically score records of any module in your system and make your job easier? Zia Scores is an AI-based scoring methodology that takes into account all the information related to a particular record, then calculates a score for it. This feature enables users to configure Zia Score to score any module records automatically based on their tailored business requirements.
Zia scores are open to a variety of scoring types, such as Field Attribute Score, Engagement Score, Follow-up Score, Health Score, and Conversion Score across all modules. Zia Scores can also be used to set automation triggers since they work similar with other Scoring Rules. These flexibility allow businesses to tailor the scoring mechanisms to align with their processes better, enhancing decision-making and outcome prediction.
What sources does Zia consider?
Zia might take into account the module data, related modules' data, related activities data and other information obtained from SalesSignals, and other features integrated with your CRM. Zia can analyze this information to arrive at a score that represents the health, engagement, follow-up, likelihood of conversion, and field attribute for a particular record, making it easier for businesses to prioritize their efforts and allocate resources more efficiently.
Benefits of Zia Scores:
Here are just a few ways that Zia Scores will elevate the way you conduct business:
- Automatic prioritization of records in any module, eliminating the need to set criteria manually and assign scores
- Analyzing scores assigned to records and formulating relevant strategies to deal with them.
- Grouping/segmenting customers based on their scores and implementing targeted sales/marketing efforts.
- The Scorecard lists the top positive and negative factors, helping businesses understand what marketing strategies implemented have worked and where to improve.
- Building an effective communication strategy with customers.
- Increased productivity amongst sales reps because these scores will give them clarity on the next step.
Types of Scores and their training data:
For Zia to analyze the data, training data is essential as it allows for the identification of meaningful patterns by comparing ideal and non-ideal records and assigning scores accordingly. It is important to establish criteria for these conditions, referred to as training data, for the records. A minimum requirement of 75 records for each ideal and non-ideal conditions should be available as training data to help Zia generate patterns.
Scores that do not mandatorily require training data
Scores that apply to interactions-based data include a definite set of channels and factors, including calls, emails, and email sentiment count (positive negative count), as well as insights, meetings, and deal history. These score types do not compulsorily require training data to generate scores since they automatically follow an algorithm.
1. Health score
Health scores represent the overall health or status of a customer relationship based on a holistic evaluation of various data points, such as customer satisfaction, usage patterns, payment history, and support interactions.
Generating a customer's health score is more relevant for modules that do not depend on the record's definitive journey. Here, every touchpoint is taken into consideration, as customer health is a common approach that key on products purchased, their deals, communication made, tickets raised, campaigns made, and so on. It also considers customer engagement and user follow-up interactions.
Example: A customer account's health score in a subscription-based service might include factors like how often they use the service, which features they have adopted, their history of support tickets (resolved versus open), and their Net Promoter Score (NPS). A high health score suggests a happy and involved customer.
2. Engagement score:
Generating engagement score consumes data from the selected module and the particular set of related modules, including Calls, Emails, and Meetings, as listed earlier.
Determining a customer's engagement score involves considering the level of interest the customer has in us, sourced from incoming signals. This score is derived from various signals, indicating the level of engagement with a customer or prospect. It considers factors like email opens, clicks, website visits, social media interactions, and responses to marketing campaigns.
Example: An engagement score could be calculated by tracking how often a customer engages with your social media posts. A higher score indicates active engagement and interest.
3. Follow-up score
Generating a salesperson's follow-up score involves capturing outbound signals to analyze the effectiveness of their follow-up actions.
This score assesses the timeliness, relevance, and impact of their follow-up communications or activities.
Example: After a sales call, the follow-up score may take into account factors like the timeliness of sending a personalized follow-up email, whether a follow-up meeting was arranged, and if any promised information or resources were delivered. A high follow-up score indicates proactive and efficient follow-up.
Scores that mandatorily require training data
Zia must have training data for the Conversion and Field Attribute Scores to be effective. These scores are most useful for records moving towards a specific outcome, which could be positive or negative. To help Zia identify which field values are positive or negative, establish the appropriate conditions for each scenario.
4. Conversion score
For modules that have customer journey-based use cases (such as Leads or Deals), the customer's conversion score is more relevant.
This score predicts the likelihood of a record to be converted into a successful outcome, trained by additionally clubbing the information from all the chosen related modules' to calculate the winning score.
Example: If a user creates Scoring rules for the module Deals and sets ideal criteria as Stage is Deal won and for the related module ideal criteria as Deals with Sales Orders. Zia takes into account all data from the sales order the deal is associated with as training data for the chosen records. Similarly, it could consider all the record's data, like calls, emails, meetings, and so on.
Tailor your lead conversion score
Customize the likelihood of converting leads or prospects into customers based on their behavior, demographics, and engagement patterns.
Example: A conversion score could consider factors such as lead source effectiveness, lead nurturing activities, lead demographics, and past conversion rates for similar leads. A higher conversion score indicates a higher probability of successful lead conversion.
Tailor your deal conversion score
Customize the likelihood of winning a deal, retaining a customer, or achieving a desired business objective. Here, Zia could consider historical data, predictive analytics, and contextual factors.
Example: In a sales CRM, a winning score could analyze past deal outcomes, lead interactions, competitor analysis, and market trends to predict the probability of winning a specific sales opportunity. A higher winning score suggests a higher likelihood of success.
5. Field Attribute score
This record score is automatically calculated based on the attributes or properties of specific fields of the selected module and the data in the record.
It assesses the relevance, completeness, and accuracy of the data presented in those categories.
Example: In a CRM module for sales, a "Lead Quality" field could be assigned a score based on factors such as lead source, lead industry, lead size, and lead contact information completeness. A high score indicates a well-qualified lead with comprehensive data.
Permissions
Users with Manage Configuration permission can access the Configuration page.
Configuring Zia Scores for your organization:
- Navigate to Setup → Automation → Scoring Rules.
- Select Zia Score as Type.
- Select the module and the layout you want to assign Zia scores.
- Add a description if needed and click Next.
- Select the type of Zia score you would like to apply to the records and choose the records to generate scores for.
- Set criteria for Zia to classify the ideal and non-ideal records of the module under Training data. Zia considers the record information that matches with this set of criteria as training data and learns from it. You can include both the primary and related modules to define the conditions.
Note: This step is mandatory for the conversion and field attribute scores. -
Click Save.
Note:
- Zia scoring requires a minimum amount of data to calculate the scores. Zia will need a minimum of 200 records to start scoring.
- Once the feature is enabled, the model will check if this criteria is fulfilled. If not fulfilled, then the model will state the possible reason for the failure of generating scores.
- It typically takes anywhere from a few minutes to 24 hours for Zia to analyze your data and come up with a score.
- Even if you have enabled Zia Scores, you can still setup Manual scores rules for the same module in Zoho CRM as usual.
Once you have configured Zia Scores, you will be notified when the Zia scores have been generated for the rule. A dedicated widget appears to show the scores in the individual record details page of the module under a custom field 'Zia Score'.
The score given for a record will be out of 100. We have categorized the score as: 'Need improvement', 'Good', 'Excellent'.
- Needs improvement signifies a score range of 0-50.
- Good signifies a score range of 51-75.
- Excellent signifies a score range of 76-100.
Zia also provides a Scorecard that delves deeper into why that particular score was assigned to that record. It will provide details on what went right with that particular record and what went wrong. With this, you can get a deeper understanding of the record and formulate strategies to address what went wrong.
The Scoring Rules section in the related list of the record detail page lists all the scoring rules and the scores they contribute to the record.
Smart filter
Users can filter records based on scoring rules and perform bulk actions such as sending emails, assigning or scheduling tasks, updating multiple records, adding tags, and more.
For example, if a user wants to send bulk emails to records with low scores of less than 30 in customer health, they can make use of the smart filter to do so.
Possible Model Insight Cases
You can check the status of your model insights for any scoring rule inside the scoring rule page.
Model created successfully
This insight means that the model has generated the scores for the records chosen in the scoring rule. You can check the module records' details page for the Zia Score widgets where you find the score, insights, and scorecard. For easy access to the list of records for which the scores from the particular scoring rule are generated, you can also click on the number mentioned.
There are possibilities that Zia might fail to create patterns or score records due to one of the reasons listed below.
Low Data Quality
This might happen when the module fields contain duplicate, null, or repeated data values or lack contextual information. In this case, you can modify the training data or revise conditions accordingly.
Waiting for data
If the minimum requirement of 200 records for Zia Scores is unavailable, the insufficiency could cause the failure to generate patterns. Zia would keep checking and waiting for incoming data until it is sufficient to analyze and calculate the scores. In another case, insufficient training data when the minimum requirement of 75 records for each ideal and non-ideal conditions are unavailable, this insight could happen.
System error
System error could happen if there is some component failure. You can raise tickets or contact Zoho support regarding the issue.
Feedback
Every record that has Zia score generated also has buttons for positive and negative feedback. You can leave a thumbs-up if you are satisfied with the score, insights, and scorecard details.
Otherwise, give a thumbs-down to let us know where Zia Scores needs improvement.
Zia Scores and other CRM features:
Since there is a field creation ('Zia score') involved when you enable Zia scores for your organization, it will have an influence on other CRM features where we use fields. Zia score custom field can be used as a criteria or input in other features like: when creating a smart filter, creating a custom view, layout rules, workflow rules, Blueprint, the approval process, copy customization, and audit log/timeline.
Limitations:
- This feature is currently not available for the old (already existing) records. It applies to any existing record only if it is newly edited after enabling Zia Scores. If the existing records stay idle, they are considered old records.
- In manual scoring rules, a pop-up appears asking if the rule should apply to only new records or old records as well. For Zia scores, by default, the rule applies to all fresh records added from the next day of creating the rule.
- In the Enterprise edition, the limit is 5 scoring rules per CRM account.
- In the Ultimate edition, the limit is 10 scoring rules per CRM account.
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