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, which it uses to recommend the right product or service.
These suggestions are useful for the sales team, helping them be aware the right product to pitch to a customer, increasing the chances of conversion.
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 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:
Number of active recommendations
that are currently available. The recommendations will be divided between 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 four active recommendations. Two for existing customers (relationship and sequence type) and two for new customers (first time and bundle).
Success rate of the recommendation
tells us 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, if 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.
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 five recommendations, two progressed further with creation of rental agreement while three 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.
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:
The number of times it has been recommended by Zia.
The number of unique customers who purchased the particular property.
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 the most or the least recommended properties.
Note that only 10 data points will be displayed in the graph.
Recommendation trend over time
(this week/month and last week/month/quarter) to determine whether there has been a constant increase, decrease, or 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. Following a similar strategy for upcoming seasons would prove fruitful in increasing sales.
Performance of individual recommendation type can be analyzed by:
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.
- The number of deals created from each recommendation to determine the success rate. For example, if Zia made ten relationship based recommendations out of which only four deals were created then it would be advisable to monitor the sales funnel and understand the reason behind the churn out.
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. If a member creates less number of deals out of the recommendation, then 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.
Segmenting Zia's recommendations into groups that have similar characteristics will help users understand the pattern of such recommendations and equip them to handle the deals more efficiently.
The segmentation is performed by Zia without the need for user intervention.
Under each segment, the number of customers along with the condition for that particular segment is listed. For example, in the recommendation below, you can see how many records are associated with each segment and the criteria that constitute the segment.
Within the segments, you can look for common fields from the drop-down and the common fields in all the segments are highlighted. Businesses can use this information to arrive at a pattern among the segmented customers and plan efficient marketing or selling strategies.
Along with the condition, Zia also shows the top-recommended products for that segment, missed count and the product win rate.
You can view the recommendations that were missed across segments as a tabular report.
You can also see the success rate of Zia recommendation across different segments.
Managers can use this recommendation segmentation to target different groups of customers efficiently and sell the right product to the right set of customers.
Recommendation Analytics based on user feedback
Based on the users' response, Zia computes and displays the following insights:
In addition to the performance analytics of recommendations, Zia also displays analytics based on user feedback. For every recommendation, Zia will display a thumbs-up/ thumbs-down feedback strip in your records. Click here
to learn about direct feedback on Zia's recommendation.
- Overall feedback analytics
- Feedback analytics based on the recommendation type
- Feedback analytics based on the product type
- Feedback contribution
- Missed cross sell opportunities
Using these insights, you will get an idea if Zia's recommendations are being utilized and if so, how well the recommendations are valid. You can also see how your sales reps feel about these recommendations and assess which product recommendations are favorable.
Overall feedback analytics
Direct feedback is a candid review your sales reps are expressing through their votes. If the recommendation was useful at that moment, the user might vote useful, otherwise, by the down votes you can understand the recommendation was not useful and there was no conversion, whatsoever. This overall feedback-based analytics will reveal its efficacy in the sales battlefield.
It portrays the following insights using a donut chart:
- Total number of feedbacks given
- Number of positive feedbacks
- Number of negative feedbacks
Feedback analytics based on the recommendation type
Based on the recommendation type ( first time, repeat, sequence, relationship, or bundle), the number of positive and negative reviews will be tabulated.For instance, if the bundle recommendations has more number of down votes, you can infer that Zia needs more seasoning/ training to perfect the bundle recommendations.
Feedback analytics based on the product Zia computes the feedback for most recommended products and least recommended products. It will help in analyzing which of the product recommendations was most accurate and useful.
Feedback contributionThis analysis will tabulate all users who have participated in the recommendation review. It can indicate which users are actively using the recommendation in their routine activities.
Missed Cross Sell OpportunityCross-selling is an important aspect of Zia's recommendation, because it drives in more revenue strategically. Missing a cross-sell recommendation is a missing opportunity and Zia will display all the information about it. The table will display Who missed the opportunity for which record? What is the recommended product?, and What is the period of suggestion and last transaction date?
To view recommendation analytics
Go to Setup > Zia > Recommendations.
Scroll to individual analytics to view the results.
In the respective report, filter to view desired results.