In this guide, we’re going to walk through the technical steps Boll & Branch, a prominent D2C linens brand, took to configure Faraday to make the [customer persona predictions](https://faraday.ai/templates/thematic-personalization) they needed, and how they activated.

## Integrating with their BigQuery warehouse

Boll & Branch has a wealth of customer data in their BigQuery warehouse, and Faraday’s seamless [integration](https://faraday.ai/integrations/predictions-with-bigquery) with BigQuery was the first step in connecting their data with Faraday. The only info they needed on their end to get this connection set up was the ID of their project in BigQuery and the dataset’s name.

[![Boll & Branch's connection configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-connection.png)](/images/blog/howto/boll-and-branch/boll-and-branch-connection.png)

From there, they created a Faraday [dataset](https://faraday.ai/docs/abstractions/datasets) to describe their data: which rows in their data represent what pieces of customer data? Creating this dataset served the brand two purposes, both key cornerstones in powering their customer predictions:

1. It enabled Faraday to identify people in their data through an identity set.
2. It pointed Faraday at the [events](https://faraday.ai/docs/abstractions/events) occurring in their data, which is how they’ll make their predictions–more on that later.

[![Boll & Branch's dataset configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-dataset.png)](/images/blog/howto/boll-and-branch/boll-and-branch-dataset.png)

In their identity set, the team at Boll & Branch matched the people in their data–their customers in this particular use case–into the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) by telling Faraday which rows in their data represented personally identifiable information (PII): name, email, physical address, and so on.

They then defined two events:

- “Customers,” which looked at the date of someone’s first order to define them as a customer.
- “Orders with product category,” which pointed toward an event property that specified which category in their catalog the product purchased is in. They planned to use this to see which category their customers were purchasing from so that they could offer recommendations.

## Defining their customers

With their data connected, the next step was to create [cohorts](https://faraday.ai/docs/abstractions/cohorts) of interesting people, or people that they wanted to make predictions on. As their team was initially focused on identifying who their current customers really are as people and consumers, their [_Customers_](https://faraday.ai/docs/how-to/cohorts/customers) cohort was where they began.

The brand defines their customers as people that have made a purchase, and so they chose the “customers” event that they created earlier on in their dataset to define their _Customers_ cohort.

[![Boll & Branch's cohort configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-cohort.png)](/images/blog/howto/boll-and-branch/boll-and-branch-cohort.png)

Creating this cohort grouped everyone in the brand's data that had made a purchase together, and any time a new customer made a purchase, they’d automatically join this _Customers_ cohort thanks to the connection to BigQuery they made.

## Predicting customer personas

Now that their pieces were in place, Boll & Branch's team was ready to make their first prediction: [personas](https://faraday.ai/docs/abstractions/persona_sets). They navigated into [persona sets](https://app.faraday.ai/personas) and created a new persona set using the _Customers_ cohort they just created.

[![Boll & Branch's persona configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-personas.png)](/images/blog/howto/boll-and-branch/boll-and-branch-personas.png)

In taking a look at the freshly-clustered persona set for their customers, they were able to quickly identify that their demographic didn’t skew quite as old as they thought it did. Additionally, they found that their highest-value and largest persona at 55% of their persona set, nicknamed “Jen,” were heavy social media users through Faraday’s relevant traits like `Active on social media` and `Instagram user`. This insight gave them clear direction as to where they should focus marketing efforts.

## Deploying their predictions with a pipeline

Analyzing their persona set within Faraday’s Dashboard gave their team a high-level overview of their customer makeup, and they were soon ready to deploy their predictions back into their BigQuery warehouse for activation. To do that, they needed a [pipeline](https://faraday.ai/docs/abstractions/pipelines).

In their pipeline, they added their _Customers_ cohort to the **population to include**, as they wanted to make predictions on their customers. Next, they grabbed their _Customer personas_ persona set for their **payload** so that they could apply their persona predictions to all of their customers.

[![Boll & Branch's pipeline configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-pipeline.png)](/images/blog/howto/boll-and-branch/boll-and-branch-pipeline.png)

While the pipeline was building in the background and persona predictions were being applied to their customers, the team moved on to deploying their predictions back to their stack with a [deployment](https://faraday.ai/docs/abstractions/deployments).

They knew they wanted to sync their predictions back into their BigQuery warehouse, so they created a referenced deployment, which was an easy way to sync data back into their stack via a reference key–a way for them to specify which column in their data Faraday should use to match their predictions to the appropriate person.

[![Boll & Branch's deployment configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-deployment.png)](/images/blog/howto/boll-and-branch/boll-and-branch-deployment.png)

With their reference key set as _email_, every customer had their persona applied to them, and their data was then synced back into BigQuery based on the email column.

Now that everything was in place on the Faraday end for their persona predictions, they turned off [preview mode](https://faraday.ai/docs/abstractions/pipelines#preview-mode) so that their pipeline would constantly stay up-to-date as new customers entered their _Customers_ cohort.

## Making practical use of their predictions

In BigQuery, Boll & Branch’s analysts were able to manipulate their Faraday deployment to discover which of their customer personas over-indexed on what products by tallying products-per-persona, what colors of products they preferred, and more. This was key for them, as it enabled them to recommend personalized [next best offers](https://faraday.ai/templates/next-best-offer). Once they were satisfied with their findings, they pushed their predictions out to [Iterable](https://iterable.com) \- their goal was to launch an email campaign with their newfound personalization insights.

Drawing on the analysis they performed on each persona in Faraday’s Dashboard and the post-analysis they performed for product recommendations, they created a different [Iterable List](https://support.iterable.com/hc/en-us/articles/204780549-Lists-Overview) for each persona, then applied copy & creative based on the traits they discovered in Faraday’s Dashboard.

The result of applying these persona predictions to their email efforts was a **30% lift in conversion rate**.

Part of our "How they did it" series, this entry highlights exactly how Boll & Branch predicted their customer personas in Faraday, then deployed their predictions to Iterable for email activation.

How to: enhance your customer data with rich personas