New enhancements in Zia

Dear All,

We hope you are well!

We are glad to present a few new enhancements in Zia that will help you organize, measure, and scale your business activities. Let us get into the details!

Following are the enhancements we have opened for all users:

New advanced filters to find records with similarity recommendation
Enrichment history and enrichment usage
New components added to the native prediction builder

New filter options to find records with similar characteristics

Earlier this year, we opened Zia Similarity Recommendation, where Zia suggests records with similar characteristics in your database. You were able to know the presence of similar records, understand why they are similar, compare their characteristics, and follow best practices. Also, you were able to filter and classify them based on the availability of recommendation using the advanced filter.

Now for a higher granularity, we have added the following filter parameters to the advanced filters for similarity recommendations.

Filter based on similarity scores
Filter based on similarity suggestions

You can now filter the records in a module based on a score or you can simply refer to an existing record as value and get all records with similar characteristics, listed.

For more information about Zia Similarity Recommendation, click here.

Enrichment history and enrichment usage

Data enrichment is Zia's ability to populate your CRM records with information obtained from the expanse of the internet. In just two steps - enrich and format mapping, you can fill your CRM records with information about your leads, contacts, or accounts, on-the-go.

However, when you enrich your record using Zia, the data will be populated showing no difference from the manual user updates, whatsoever. Also, you don't get to know how the data enrichment limits are being used or distributed in your organization. For these reasons, we have introduced enrichment history and enrichment usage.

Enrichment history

Enrichment history is a log of all enrichment activities performed in your organization. It lists the user name, record name, date and time of enrichment, and the field updates, in a chronological order. Like the audit logs in CRM, you can also filter the events based on different parameters like module name, user name, and time.

You can thus monitor enriched data and tell from manual record updates from the enrichment configuration page.

Enrichment Usage

As you might know, an organization with Enterprise CRM edition can enrich about 500 records per user license and go up to 50,000 records per month and for Ultimate edition, the limit is 2000 records per user license up to 50,000 records per month. These limits are for the whole organization and not module-based. While these limits should be sufficient, there was no facility for you to know the available balance, what is module-wise usage, how enrichment limits are being consumed each month, and so on. In this update, you get all of these under the Enrichment Usage tab.

It displays the following information:

Monthly limit and average consumption per month
A pie chart, illustrating the total number of enrichments made across different modules.
A trend chart, illustrating the adoption trend of enrichment till date
A table, distributing the number of enrichment limits consumed across modules, each month.

Enhancements to the native prediction builder

One of the most coveted features of Zia is its Prediction. It can predict different aspects of your business based on the configuration like conversion probability or potential purchase by a prospect, and so on. Zia Prediction will thus, help you set the right expectations by projecting its findings for all qualified records.

Click here for more information on Zia Prediction.

Now, to assess Zia's learning efficacy and ensure more accurate prediction, we are introducing a couple of enhancements to the prediction summary page of the builder.

Feature rename announcement:
Before we get into the enhancements, we'd like to inform you that we have renamed this native prediction feature as Field Prediction, for we have added another prediction feature for churn. Click here for more information.

Let's get back to the enhancements!

Enhancement to Learning pattern score

Renaming learning pattern score to Model accuracy
Introducing version history and module comparison
Moderatng desirable contributing factors

Introducing Prediction accuracy

Enhancements to learning pattern score

In this update, the erstwhile learning pattern score - the measure of Zia's learning, is renamed as Model accuracy. Also, to understand how Zia's learning has evolved through different versions, we have added version history and the ability to compare them one on one.

Zia's learning is an ongoing process. Once a prediction model is built, Zia will retrain and recondition its learning, every 15 days and each model is logged as versions. With the version history, you can now view up to four versions versus the original configuration to get an idea as to how the data sets have evolved. It displays version number, factors included during learning at that version, and date.

Furthermore, you can compare up to three versions for a more detailed analysis of how your data is being used to learn. If you are not satisfied with the current learning pattern, you can also moderate the contributing factors and the pattern will be followed in the subsequent versions.

Introducing Prediction Accuracy

Just as how Zia measures its learning accuracy, Zia will also assess its prediction accuracy. It is a measure that shows how reliable the prediction will be. The higher the score, the higher will be the reliability. It will display the number of records predicted and of them how many of them were successful. This will help you decide whether to proceed with Zia's prediction or not.

That's all about enhancements on Zia, for now.

Like mentioned, we have opened these features to all users in EU, US, and IN DCs. We hope these features will help you administer and manage your business, at ease.

Should you have more questions or feedback feel free to open a comment, we'd be happy to join the conversation. Thanks and have a good one!

Resources: Zia Similarity Recommendation | Zia Field Prediction | Data Enrichment

Thanks and have a good one,

Saranya

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