1. being aware of churn after it has occurred and making up for it,
   
2. and not being aware of it at all.

According to a research on loyal customer relationships  conducted by  Bain & Company,  the cost of acquiring and onboarding a new customer is  5 times more costly  than retaining an existing one.

Zia's churn prediction

Permission required

There are two types of permissions available for the Zia Prediction feature in Zoho CRM: Manage configuration and view results. These permissions can be enabled for other users by CRM administrators.

1. Manage configuration : Only those who have this permission enabled for their profile can create, edit, view, enable, disable, or delete a rule.
   
2. View results : Users with this permission can only view the prediction results.
   

Note : By default, the view results permission is enabled for all profiles when Zia Prediction is toggled on. The manage configuration permission must be enabled manually.

Business scenarios

Zia analyzes your customer records for their churn possibility based on the configuration you set up. Irrespective of your revenue model—subscription-based or not—Zia will adapt its prediction to assess the churn percentage.

Predicting churn in a telecom provider

Zylker Communications is a telecom provider that provides phone, internet, and satellite connections. It offers two payment modes—prepaid and postpaid—to cater for different types of customers. Zylker's revenue model is therefore recurrent in that customers have to subscribe to enjoy its services. While one might assume that having a subscription indicates loyalty to the business, Zylker is losing a considerable number of customers each month. Zylker's goal is to identify which product is getting churned the most and to identify it as early as possible. This is where Zia's Churn Prediction can help. By analyzing the customer base and their transaction schedule, Zia spots records that are on the verge of churning and rates their churn probability on the record details page.

Zylker can thereafter take steps to recover the churning customers using automation like workflow rules.

Predicting churn in a retail store

Zia will offer an overall churn probability for customers on an ad-hoc basis wherever applicable. 

Configure Zia Churn Prediction

Step 1: Configure customer information

1. Imagine you have a quarterly revenue goal and you'd like to be informed about any shortcomings that could affect your revenue. You can enable churn prediction for all records in your module and be vigilant to address any churn that might occur.
   
   

1. If you'd like to know how many of your loyal customers are about to churn, you can set criteria based on their churn scores.
   

Step 2: Specify a payment module

After teaching Zia who the customers are, it's time to show Zia where their transaction information is stored in CRM. Information from this module will be used to learn their payment patterns. It can be any standard or custom module in CRM. Modules created as a result of an integration will not be listed.

Step 3: Define successful payment criteria

1. Specify the payment date : The payment date is any date field present in that module. You can use the starting date or closing date to indicate the payment schedule.
   
2. Specify your business's revenue model: Based on your business offerings, you use either a recurrent or one-time payment revenue model. Zia needs to know this information in order to understand payment frequencies in your business.
   
1. For a subscription-based revenue model , like a telecom provider, streaming service, or SaaS provider, the payment is expected to recur each month or each year. You can simply point to which field or module you're maintaining the information about the active subscription and Zia will observe the progress thereafter. 
2. For a non subscription-based payment model , like a purchase in an online store or FMCG transactions, Zia will refer to customers' previous transactions and payment frequencies to come up with churn predictions.
   

Step 4: Define customer churn for Zia

1. Go to Setup > Zia > Prediction > Churn Prediction.
   
2. If you're creating the churn configuration for the first time, click Get Started.
   
3. To create a new churn configuration, click on the New Prediction button.
   
4. In the Create New Churn Prediction page, do the following.
   
1. Provide a unique name f or the prediction configuration.
   
2. In the Where do you store your customer information? field, select a customer module.
   
1. In the Churn prediction for option, select All if you want to predict for all records in your customer module.
   
2. Select Selected if you want to predict churn only for specific records, and then provide the criteria.
   
3. In the Payment module field, select the module where your customers' payment information is stored.
   
4. In the Successful payment criteria field, enter the criteria that indicates a successful payment.
   
1. Select the Payment date.
   
2. In the Do your customers pay using a subscription-based revenue model? field,
   
1. Select Yes if your payments are recurrent and choose the field where you store information about the subscription in the Active subscription field .
   
2. Select No if your payments are occasional.
   
5. In the How would you define customer churn? field, select the criteria that define churn.
5. Click Save .

View the prediction detail page

Configuration summary

Zia will summarize how this churn prediction is configured. It will list all the criteria and fields used in the configuration for quick reference. 

Additional information

Model accuracy

1. Anything above 80 is a great score. It indicates that the prediction will be accurate.
2. Score between 60-80 is considered moderate. While the prediction can be used to an extent, the predictions cannot be relied upon at all times.
3. Any score below 60 is bad and Zia will conclude that the predictions are flawed.
   

Notes:

1. For Zia to learn and generate churn predictions, you need a minimum of 200 records per customer module, within which are at least 75 churned records and 75 active records.
   
2. Zia will take up to 24 hours to analyze the data sets to come up with predictions.
   
3. While there may not be any specific reason your model accuracy is low, you should ensure factors like null values, data duplication, multiple unique values that Zia cannot relate together, and so on, are addressed. You can amend validation rules, mark fields as unique to avoid data duplication, or auto-merge duplicates to prevent low scores.
4. Once the configuration is saved, you can't edit the rule name, customer module, payment module, or revenue model. You can, however, edit the criteria mentioned in the configuration.
   
5. The custom field will be created for all records, irrespective of whether the record contains predictions or not.
   
7. In the case of Churn usage data, we've incorporated robust error-handling measures to ensure data integrity. If there are issues like the deletion of essential data components in Google Analytics, Zia will cease to use that data source, thereby maintaining the accuracy of the churn predictions.
8. It's important to note that once a service like Google Analytics is selected and configured, the setup is final. Modifying or switching to a different service requires a new configuration. 

Waiting for data

In addition to the churn configuration summary, the prediction's status can be obtained from Zia notification panel.

View churn predictions

1. If Zia predicts a potential churn, a widget will be displayed on the record details page of the qualified record, along with the name of the prediction rule and the churn probability score.
   
2. If Zia predicts a customer is active, no predictions will be displayed.

1. Churn score: The score displays the probability of that record to churn.
   
2. Churn prediction: The prediction field displays the active subscription field associated with the customer record.

In the case of non subscription-based payment models, only the churn score will be displayed on the record detail page.

Notes:

1. The number of churn fields created in a record depends on the number of churn configuration the record satisfies.
   
2. The fields added to the record contain the same information as the prediction widget. However, in order to make further use of these findings in CRM—like generating reports, configuring automations, and sorting records using advanced filters—these fields will be useful.

Act upon Zia churn predictions in Zoho CRM

1. In Workflow rules, you can use the score field as a criteria and automate actions like create tasks, add tags, notify record owners, etc.
2. In Reports, where you can collate all the churn customers as a table and observe the pattern of churn score among customers.
3. In Module views and advanced filters, to sort churning customers from the rest of the customer base and tend to them with more attention.