Zia - Recommendation Analytics | Zoho CRM

Zia Recommendation Analytics

Zia can analyze customer data such as purchase details, interests, and requirements to recommend similar items for other customers. It also compares the behavioral pattern of customers and identifies similar attributes based on which it recommends the right product or service.

These suggestions are useful for the sales team as they would know the right product to pitch to a customer, thereby increase the chances of conversion.

Availability 
Permission Required
Administrators and users with Manage Configuration permission can access recommendations analytics.

The recommendation analytics provides a consolidated result where one can view the total number of active recommendations available on a particular date, number of deals or transactions created with the help of these suggestions and more. Overall, these analytics will enable the decision-makers to understand the performance of the recommendation tool and amend it as per their business requirements.

Let's take an example of a real estate business that uses recommendation tool to suggest suitable properties to its customers.

Based on the above example, the recommendation analytics will highlight:
  1. Number of active recommendations that are currently available. The recommendations will be divided among new and existing customers. The analytics will display the number of recommendations available for each type of recommendation. See also Types of recommendations.
    In the image below, you can see that there are 4 active recommendations. 2 for existing customers (relationship and sequence type) and 2 for new customers (first time and bundle). Number of active recommendations analyzed by Zia
  2. The Success rate of the recommendation tells how well the tool is able to analyze the data and give useful suggestions. Success rates take into account the fact whether certain recommendation is used by the sales rep. For example, a deal is created for the product that was recommended for customer B, it means Zia's suggestion was appropriate.
    For higher success rates, you can scrutinize the reason certain recommendations were declined by the customers and revisit the configuration to align it with their expectations.
  3. A chart that displays the breakdown of the total success rate for a certain period (this week/month and last week/month/quarter). In the image below you can see that out of 5 recommendations, 2 progressed further with creation of rental agreement while 3 were declined. Out of the two successful recommendations, one is partial and one is exact.
    A partial recommendation means the recommended property was considered in addition to other property types such as villa along with individual house.
  4. Bar graph displaying the products that are most and least recommended and their breakup.
    In the image below you can see the most recommended properties and for each property type:
    1. The number of times it has been recommended by Zia.
    2. The number of unique customers who purchased the particular property.
    3. Total number of transactions that is deal closed, rental agreement created or payment made for that property.
      Likewise you can filter the data to view most or least recommended properties.
      Note that only 10 data points will be displayed in the graph.
  5. Recommendation trend over time (this week/month and last week/month/quarter) to determine whether there has been a constant increase, decrease, sudden surge or dip in the recommendations.
    Based on the trend you can analyze the reason and make amends to the existing sales or marketing strategies to meet your expectations. For example, if you observe a surge in recommendations during the holiday season you can infer that most customers were inclined to purchase what was recommended to them, hence following a similar strategy for upcoming seasons would prove fruitful in increasing sales.
  6. Performance of individual recommendation type can be analyzed by: 
    1. The number of times a particular recommendation was made, for example, if relationship based recommendations are higher compared to other types, you can infer that the system identifies relationship between the entities based on customers interests.
    2. Number of deals created from each recommendation to determine the success rate. For example, if Zia made 10 relationship based recommendations out of which only 4 deals were created then it would be advisable to monitor the sales funnel and understand the reason behind the churn out. 
  7. Top 10 employees who use these recommendations to create deals can help identify the reason other team members were unable to proceed with the transactions or why the customers rejected the suggestion etc. Also, if a member creates less number of deals out of the recommendation further analysis of customer interest and buying pattern would be helpful.
    These insights can be used to improve the existing configurations to get more accurate recommendations. 

To view recommendation analytics

  1. Go to Setup > Zia > Recommendations .
  2. Click the Analytics tab. 
  3. Scroll to individual analytics to view the results. 
  4. In the respective report, filter to view desired results. 

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