**See our complete guide:** [Predictive AI for banks and credit unions](/blog/banks-credit-unions-complete-context-guide).

---

[Community First Credit Union](https://www.communityfirstfl.org/) (CFCU) started in 1935 in a Jacksonville high school basement and has since evolved into 19 branches and over 135,000 members. CFCU's success is rooted in its commitment to its members, offering tailored products to meet individual needs and utilizing innovative technologies to deliver an exceptional banking experience.

## The problem: finding the right members

A mere 19% of Americans have three or more products with their primary financial institution–that is, where their checking account lives–despite the average American holding [eight financial products](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-kickingitupanotch-092614.pdf).

In order to remain competitive & ensure long-term success, CFCU knew that they had to grow their "wallet share" with [existing members](/solutions/customer-engagement). More members adopting CFCU's comprehensive financial solutions deepens the financial relationship between the credit union and its members, fostering loyalty and long-term engagement. This diversification helps in spreading risk and maintaining a more stable financial base, while reducing the need for costly marketing and acquisition efforts.

The challenge they faced was how to accomplish this effectively. CFCU lacked the resources to compete directly with larger banks and fintech companies. That meant simply shoveling more ad spend into their [direct mail](/blog/personalized-direct-mail-strategy) program wasn't an option - CFCU needed a more efficient approach that could increase response rates from both new and existing members _without_ increasing spending.

## The obstacle: reaching the right members efficiently

Before Faraday, CFCU used traditional segmentation, like a member's current product mix, their income, and identifying members to whom they could extend pre-approved offers of credit. What they found is that this approach often failed to capture the nuanced preferences and [behaviors](/features/customer-behavior-predictions) of individual members. Because segments were built off broad categories, the generic messages tailored to these segments didn't resonate with recipients. [Personalized](/blog/personalization-at-scale-guide) and relevant content were essential to better find and engage loyal members.

CFCU understood that AI offered opportunities for more effective segmentation, on top of an unlock for personalizing a member's journey.  Their existing member data provided the historical examples of customers who had grown wallet share from one product to many. Their existing direct mail providers had exposed CFCU to the immense trove of additional third party data that could be leveraged to create distinct segments.

At the same time, they knew that were signficant costs associated with this data, the infrastructure needed to run AI models, and the in house talent needed to develop that level of modeling.

## Why Faraday? 

### Identity resolution & easy product recommendations

Partnering with Faraday turned out to be the unlock that CFCU needed to realize this vision of using AI to grow wallet share amongst their members.

They used the Faraday platform to connect their member data to Faraday's identity graph, giving them instant access to [hundreds of data points](/features/faraday-identity-graph) beyond those they had used to segment members in the past. Using Faraday's modeling engine, CFCU was able to build product recommendations that were personalized for each member and for all prospects in their charter territory.

With this segmentation in hand, CFCU used Faraday to easily build mailing lists of the individuals they most wanted to target in their direct mail campaigns.

## The results

### Acquisition campaign

CFCU used the Faraday platform to predict which consumers were most likely to become their next loyal members, defined as a member with a high account balance and strong net interest revenue. To test the model, CFCU ran a direct mail campaign with a 50/50 audience split (50% from high-scoring model predictions; 50% from traditional segmentation techniques).

The new members generated using Faraday's targeting saw **61% higher average account balances** and **24% higher net interest revenue** than the new members acquired using traditional segmentation techniques.

### Next product recommendation

Faraday also predicted which members were most likely to need a checking account, auto loan, first mortgage, and more. To test the model's predictions and get in front of members with the highest potential for engagement, CFCU ran a direct mail campaign with the same technique in a 50/50 audience split (50% based on Faraday's next product recommendation; 50% from traditional segmentation techniques).

The members contacted with offers recommended by Faraday saw **54% higher net interest revenue** and **88% lift in projected ROI**.

With Faraday, Community First Credit Union used customer predictions to drive highly effective acquisition and engagement campaigns with great results.

How Community First Credit Union unlocks 61% higher account balances using Faraday’s predictive AI

**See our complete guide:** [Personalization tactics that actually drive conversions](/blog/personalization-tactics-that-drive-conversions).

---

Every marketer knows that personalization is critical. Once considered a nice touch to spruce up campaigns, today it’s a full-on necessity for digital-first brands. Plus, the bar for personalization is getting higher and higher, to the point that consumers expect real-time, highly-curated interactions on every marketing channel.

The groundwork for personalized marketing begins with knowing your customer. Before you can so much as sketch out a campaign idea, you need to know who you’re marketing to. However, even this seemingly simple question is getting a lot more complicated thanks to the phase-out of the third-party cookie.

Nowadays marketers need to take stock of their cookieless identity resolution options and ensure they have the rich customer profiles they need to achieve true personalization.

Looking for cookieless options for enhanced customer data? Eager to add more sophisticated personalization strategies to your arsenal? Here's what this article covers:

- Customer insights and segmentation
- Personalized advertising
- Personalized product recommendations
- Personalized promotions

## Personalization starts with segmentation

Third-party cookies have always gone hand in hand with personalized marketing, since they’ve unlocked access to swaths of data that marketers can use for surgically precise segmentation. But [the end of the cookie](https://faraday.ai/blog/death-of-third-party-cookies-strategies) doesn’t have to sound the death knell for highly personalized ads. If anything, it’s ushering in a new era of more meaningful personalization strategies, where people see content that speaks to them as people, not a click to be retargeted.

### Adding consumer data to the mix

Segmentation is still key to achieving human-centric interactions. Without third-party cookies, you can certainly segment your first-party data, but frankly, it won’t offer enough nuance to craft personalized campaigns. Now more than ever, marketers need to prioritize [data enrichment from responsible sources](https://faraday.ai/features/responsible-ai). This cookieless consumer data enables brands to build out rich customer profiles based on a wealth of demographic, psychographic, and firmographic data.

Once you’ve enhanced your first-party data, you’ll have the full picture you need to segment your audience in a meaningful way. This might mean segmenting by income level, household size, or even hobbies and interests. The savviest brands go even further and leverage machine learning algorithms to parse through billions of data points, surface the most significant patterns in their audience, and cluster their customers into highly accurate persona groups.

All of this paves the way for true [personalization at scale](https://faraday.ai/predict/category/persona_assignments). By segmenting your audience into clusters or personas, you can craft highly personalized campaigns that speak to each unique segment and orchestrate more meaningful interactions across channels.

If that sounds like a plan, read on for the strategies you can use to start bringing those campaigns to life.

## Personalizing ad campaigns

Personalized advertising is a fantastic tool for connecting with good fits across the buyer’s journey. Since consumers are bombarded with ads all day long — social media, TV, radio, podcast, flyers, billboards — meaningfully personalized ads help cut through the noise and create an immediate connection.

### Using personas to create custom audiences

It’s one thing to segment your existing audience based on [rich consumer data](https://faraday.ai/features/data), but quite another to personalize ads for potential customers who aren’t yet known to you. Lookalike audience options from platforms like Facebook used to reliably expand brands’ reach to new pools of good fits, but with the advent of iOS 14 and Apple’s privacy updates, these lookalikes have lost a lot of signal and are less cost-effective by the day. You’ll need additional strategies up your sleeve to reach new audiences.

One such strategy involves seeding Facebook lookalikes with AI-driven personas, which gives Facebook a more defined seed audience to work with and enhances its ability to surface consumers that fit your personas. Think of it like giving Facebook’s algorithm a bit of a boost. Then, you can craft highly personalized ad campaigns for individuals you would never have reached otherwise. Brands doing this typically see increased conversion rates and lower CPA.

Another option is to skip Facebook’s native lookalikes and leverage AI to build your own custom lookalike audiences. In this strategy, algorithms can uncover great fits from an identity graph and match them to your brand’s personas, providing you with fresh audiences to upload to Facebook or any other ad platform — greatly expanding your audience beyond what these platforms would normally surface.

With these in place, and your personas already established, you can personalize your ad campaigns with confidence in spite of having never interacted with the audience before.

## Personalized product recommendations

Once someone has made themselves known to your brand— for instance, subscribing to your email list, creating a free online account, or signing up for a rewards program — personalized product recommendations are another invaluable strategy. You can leverage insights in your CRM to create a truly personalized experience on your website, in-store, via email marketing, and more.

Crucially, consumers have high expectations for these experiences. In such a competitive market, where shoppers can find dozens of similar products and services online with just a quick Google search, it’s the real-time, personalized interactions that help brands stand out from the crowd and earn their trust. Orchestrating this type of personalization requires sophisticated automation and algorithms to make recommendations instantaneously and at scale.

### Personalized recommendations based on journey stage

Truly personalized recommendations tend to come back to great life cycle marketing. Once you have an accurate picture of where current and potential customers fall on the buyer’s journey, you can map recommendations to each stage — such as promoting a product quiz or survey to your social followers in order to collect more first-party data, launching email campaigns that are tailored to your unique personas, or nudging customers about new releases.

With most modern ESPs and email marketing platforms, such as Iterable, Klaviyo, and MailChimp, you can also build powerful workflows to trigger personalized product recommendations based on specific actions. For a financial organization, this could look like an email with more information on home loan options after a lead schedules a consultation. For an apparel brand, it could mean triggering an alert that an item has come back in stock for every rewards member who'd added it to their virtual wish list.

By aligning recommendations with an individual’s journey stage and automating them to be delivered at the perfect moment, you create the real-time, personalized interactions today’s consumers crave — and you’ll see the results in your revenue.

### Personalized recommendations with predicted purchases

For marketing teams that have already dialed up product recommendations and want to offer even more valuable suggestions to potential customers, there’s also the option to take it one step further with predictive journey mapping. Rather than make an assumption about what a person is going to purchase, this entails using AI to actually predict what they’ll get.

These [predictions](https://faraday.ai/features/predictions) are far more accurate than any other product recommendation you could make, allowing your marketing efforts to become less like a sales pitch and more like the advice of a trusted friend. Just like automated email campaigns, these predictions can be triggered from a platform like Iterable to deliver the perfect recommendation to a given individual at the perfect time.

## Personal discounts and pomotions

Sometimes consumers need a little extra encouragement to get them to commit. For these individuals, a well-timed promotion or discount can make an enormous difference. The trick is to tailor the promotion to the consumer in order to protect your bottom line — after all, you don’t want to offer 15% off or a first month free to someone who’s likely to pay full price, but you definitely want that to be an option for valuable lead that’s on the fence.

### Using personalized discounts to build brand affinity

To be certain that you’re only offering discounts to the customers that matter most, customer lifetime value (LTV) predictions can eliminate an enormous amount of guesswork. LTV is difficult to determine on your own, but a wide variety of AI tools available to marketers make it a piece of cake.

With LTV predictions uploaded to Salesforce, HubSpot, or wherever you track your customer information, it becomes much clearer how to personalize discounts for distinct audiences. You may want to try rewarding your most valuable customers with a discount, as a small thanks for their brand loyalty, or you may want to test out special offers for customers with middling LTV to see if that extra push inspires them to spend even more.

### Using personalized discounts to retain high-value customers

Another proven discount strategy relies on [churn prediction](https://faraday.ai/predict/category/churn_scores). As with all things predictive, AI is the best tool to utilize here, with churn predictions at least five times more accurate than those that traditional methods produce. Once you understand exactly which customers are at the highest risk of falling away, you can intervene proactively, with a personalized touch, rather than reactively.

For instance, you can tailor the discount to the level of churn risk. High-value customers at great risk of churn might deserve a generous discount offer, whereas customers with lower risk can get a smaller one. And just like with product recommendations and LTV predictions, churn predictions can be delivered seamlessly to wherever your team needs them most, whether that’s triggering an email drip or refreshing the customer profile in your CRM — empowering you to provide a seamless customer experience.

## Level up your personalization strategies today

Whether you’re just getting started with personalization or are looking to truly master 1:1 communication at scale, you’ve got numerous opportunities available at every stage of the buyer’s journey. Consumers overwhelmingly prefer personalized offers, and an increasingly crowded digital space is making it harder and harder to gain an edge — so it’s more important than ever to double down on personalization before it’s too late.

Explore practical personalization strategies that leading brands use to create more enjoyable experiences across the customer lifecycle.

5 ways to personalize experiences across the customer lifecycle

With brand trust at an [all-time low](https://www.havasgroup.com/press_release/havas-meaningful-brands-report-2021-finds-we-are-entering-the-age-of-cynicism/), it’s a good time to ask: is your organization doing the right thing with consumer data? 50% of consumers say they’d pay more for a brand that takes a stand on important issues. With privacy at the heart of a global reassessment of social media, big tech, and surveillance, organizations at the vanguard of this issue will win in 2022.

The good news is that playing fair isn’t all that complicated. “When in doubt, follow your heart,” says Andy Rossmeissl, CEO of Faraday, the leading customer prediction platform. “Our motto from the start has been ‘don’t be creepy,’ and it’s remarkable how far that can take you.”

Looking for specifics? Here are the top 3 rules for using customer data ethically:

## Rule 1: Use identifiers people can recite from memory.

This rule sounds simple, but it’s surprisingly powerful. “Ask your mom if she knows any of her tracking cookies by heart,” suggests Rossmeissl. “Ask yourself what your mobile device ID is.” The digital surveillance panopticon relies on these [long, unique strings](https://www.quora.com/What-does-the-code-for-a-tracking-cookie-look-like) of gibberish assigned to virtually every eyeball in the world.

Instead, ask yourself what identifiers you have that you can actually recite. “I have an address on my mailbox for all the world to see, and you probably do too,” says Rossmeissl. “I put my number in the phone book, and my email address is all over the Internet.”

Relying on real-world identifiers, or “known identities,” has the additional benefit of steering brands away from collecting and using anonymous behavior, such as data collected from unidentified web and mobile visitors. “Until somebody has identified themselves to you," explains Rossmeissl, "tracking their behavior is, frankly, creepy.”

## Rule 2: Use data that comes from commercial behavior

“I call it the ‘laying in bed rule,’” Rossmeissl shares. “When I’m laying in bed and I read a news article on my tablet or like something on Instagram, that’s private. Watching me do that is creepy.”
The surveillance of private behavior has rapidly normalized, much to the dismay of both privacy experts and, increasingly, consumers alike. A 2019 study found that nearly two thirds of Americans believe they can’t go through daily life without their data being collected.

And yet marketers have long known the power of data-driven growth practices. How can brands [use consumer data while still respecting privacy](https://faraday.ai/features/responsible-ai)? Rossmeissl shares:

> We thought long and hard about this when we started Faraday in 2012. We drew on anthropology, architecture, history, and other disciplines for our research, but the result was fairly simple.

> It turns out that commerce is one of the main reasons humans started to live in communities. Markets are some of the earliest forms of non-residential architecture we know of. No matter how you look at it, commerce and the marketplace have been with us longer than virtually any other social structure.

This provides a clue: humans are quite used to buying and selling, and they’re used to doing it in public. We have evolved an innate ability to operate in the marketplace, and an accompanying understanding that this behavior is public.

“What we put in our cart at the grocery store is public,” says Rossmeissl. “What we buy walking around a farmer's market is public. And when we buy online, there's an extension of this understanding. Engaging in commerce is a collaborative act — there's a buyer and a seller — and also a performative one.”

The upshot is that we instinctually treat data arising from commercial transactions as fair game. Luckily, this kind of data is not only the most ethical, but also some of the most powerful available for predicting subsequent consumer behavior. Faraday includes over 300 data points like this on virtually every US adult, but you can also procure your own from reputable licensing groups like Epsilon.

## Rule 3: Let people easily opt out

“Imagine being in a store and being told you can’t leave,” says Rossmeissl. “That goes from creepy to nightmarish very quickly.”

The right to opt out is at the heart of CCPA, the landmark privacy legislation. And while the law only protects California residents, brands should meet its requirements across the board. Luckily, that’s quite simple: provide an easy to use opt-out link, and pass along opt-outs both up- and down-stream to your partners.

Beyond the fact that [honoring opt-outs is actually good for business](https://sendgrid.com/blog/honoring-opt-outs-is-good-business/), it’s the right thing to do.

Is your organization doing doing the right things with consumer data? Here are 3 common-sense rules to follow.

3 common-sense rules for ethical data use 

Audience overlap is a common issue marketers face when advertising on platforms that build native audiences or lookalikes. Often marketing teams will create two separate ad sets for a campaign, but end up reaching more or less the same audience despite trying to target different audiences. So rather than expanding their reach, brands are inadvertently oversaturating their audiences — which can frustrate consumers and result in wasted spend.

## What causes audience overlap with Facebook Ads?

Honestly, we aren’t sure. Many advertising platforms that generate audiences using brands’ uploaded first-party customer data in combination with the platform’s third-party data — Facebook, for example — don’t provide much insight into how they build those audiences. Faraday Data Scientists have seen time and time again Facebook pulling the same people into audiences, regardless of the target result (e.g. add-to-carts, clicks). We chalk it up to the training data.

## How audience overlap impacts brand reach

Audience overlap costs marketers valuable opportunities to reach potential customers. To serve the best ad to as many of the best leads as possible, brands must know who their customers are — but tracking just consumers’ digital footprints to guide ad creative isn't enough. One way to mitigate this issue is to upload custom audiences built with real-world, offline data.

We've seen a significant reduction in overlap between Faraday's custom audiences (from our third-party consumer database) and Facebook Lookalike audiences.

This approach has two key benefits: it addresses the problem of oversaturation and expands advertisers' potential reach beyond the ad platform's built-in capabilities.

## Tools to monitor and reduce audience overlap on Facebook and other channels

When ad sets overlap, they can compete against each other for visibility and unnecessarily increase marketing costs. You can (and should) monitor [overlapping audiences in Facebook](https://www.facebook.com/business/help/1679591828938781?id=176276233019487) monitoring overlap in the Facebook Audience Overlap Tool and uploading custom audiences that limit any duplication will be key here when thinking about ways to optimize spend and lower customer acquisition cost.

Overlap can happen on any channel where you build lookalike audiences, whether it’s Facebook or Hulu or Snapchat. Some platforms have taken measures to decrease the risk of oversaturating audiences by installing [frequency caps](https://www.advertisingweek360.com/hulu-a-better-experience-for-viewers-a-better-home-for-advertisers/?ref=faraday-blog) on ads. Hulu, for example, now makes sure that no ad is shown more than twice in an hour. But some customers have still reported seeing the same ads many times. This may be because of audience overlap — when Hulu runs an ad against multiple audiences, customers that are in more than one audience could see it again and again.

So, just because platforms are working to make ad experiences better doesn’t mean that brands don’t also have to put in the work. Because ultimately, this sort of viewer exhaustion can drive customers away, rather than reinforcing brand recognition and leading to conversions.

Learn how custom third-party audiences help reduce audience overlap across Facebook Ads campaigns.

Reducing audience overlap with custom audiences

Consumer spending has changed drastically during the pandemic. We saw a boom in household supplies and food categories as quarantine lockdowns took effect, and the trend continued through 2020. Discretionary spending became a luxury for many as unemployment rates hit reached [nearly 15%](https://sgp.fas.org/crs/misc/R46554.pdf) in April 2020. With such drastic changes, businesses had to shift their tactics and appeal to a new type of audience: the at-home consumer, who is fueling the “homebody economy.”

As the name suggests, these are the consumers who are spending the majority of their time at home. They’re likely working remotely, and certainly making most of their purchases online. Because of this, e-commerce has taken center stage for retailers, forcing brands that hadn’t implemented strong consumer-facing digital strategies before 2020 to adapt quickly.

But this adaptation goes beyond setting up an e-commerce platform — many businesses have adjusted their digital strategies to understand the swaths of new customers coming in the door and reel in at-home consumers with more personalized ads. And now consumers expect better online experiences, with [68% saying](https://www.forbes.com/sites/forbestechcouncil/2020/12/17/how-the-homebody-economy-is-shaping-digital-customer-experience/?sh=5807ede562eb) they have higher expectations of brands' digital abilities since the start of the pandemic. So, how can brands deliver?

## Get to know your new audience

The adage “Know your audience” has never been more relevant, with somewhat unpredictable consumer trends changing the audiences brands may have thought were set in stone. As more consumers were forced to shop online because of the pandemic (e-commerce now makes up 16.1% of all U.S. sales, up from 11.8% in the first quarter of 2020) businesses everywhere encountered unexpected customers.

To provide the enjoyable customer experience people expect, marketers need to dig into their customer data to better understand who they’re serving. Personalization isn’t just about meeting the expectations of the customers you already know and love — you’ve got to capture (and retain) the interest of these new customers, too.

Leveraging responsibly sourced consumer data can help round out brands’ understanding of their customers and aid in personalization efforts that go beyond web-based activity. By enhancing customer profiles, third-party data also bolsters targeted marketing strategies by making segmentation by customer type or persona more precise.

## Advertising to the homebody economy

When executing targeted marketing strategies, an omnichannel approach is more necessary than ever. Right now, brands have the chance to get in front of consumers they may never otherwise engage with because so many marketing channels are in play.

Between social media, OTT, and digital ads, brands should be capitalizing on the availability of the at-home consumer audience. Consumers’ media consumption is at a high, opening up lucrative marketing opportunities — not because there’s necessarily more ad space, but because there are more eyes on screens these days.

Radio and podcast ads aren’t out of the question either — though not as many people are commuting to work, [Nielsen reported](https://www.forbes.com/sites/bradadgate/2020/08/21/nielsen-how-the-pandemic-changed-at-home-media-consumption/?sh=765b07e45a28) that 75% of people say they listen to music during the workday, and 40% of those listeners do daily. In light of this, brands should consider including placing ads where their audiences tune in.

As for offline marketing channels, direct mailers are a great way to deliver personalized promotions to the at-home consumer — at home. It’s been reported that [70% of mailers that were personalized](https://www.sequeldm.com/direct-mail-trends-v) in some way as marketers vied for consumers’ attention. They were even integrated into digital strategies: QR code usage in direct mailers rose 69% in 2020 from 2019, making attribution easier for brands relying on mailers to convert customers online.

## At-home habits die hard

Even as Covid-19 vaccinations become more widely available and businesses are able to return to previous modes of operation and capacity, the shift to a homebody economy is most likely here to stay. [According to McKinsey](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/survey-us-consumer-sentiment-during-the-coronavirus-crisis), 3 out of 4 Americans have tried new shopping habits (changed brands, stores, or way of engaging) and intend to stick with them after the pandemic. So, businesses must continue to adapt because meeting the customer where they are is more urgent than ever before.

People are spending more time than ever at home so brands are adapting to reach them there. Here's data-driven guidance for the homebody economy.

How to reach at-home consumers in a homebody economy

Headlines abound on the [opportunities](https://www.techrepublic.com/article/companies-are-doubling-down-on-artificial-intelligence-and-machine-learning-due-to-pandemic/) and [dangers](https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing) of machine learning. Now armed with [plenty](https://www.propublica.org/article/breaking-the-black-box-when-algorithms-decide-what-you-pay) of [examples](https://www.nytimes.com/2019/08/20/upshot/housing-discrimination-algorithms-hud.html), the Data Science community is at least aware that our data lakes and algorithms can make life more difficult for real people if not properly used. But the root problem can be summed up in pretty simple terms:

> An algorithm contains the biases of its builder.

Examples of bias include:

- **Reporting bias** _(at the source)_ — Maybe the folks who filled out your online survey were mostly young, male, and computer-literate.
- **Implicit bias** _(the call is coming from inside the building)_ — Perhaps your image of an ideal job candidate fits into a mental archetype of which you're only semi-aware, and that image excludes those who don't match the archetype.

At Faraday, we have a handful of approaches we use to minimize these effects.

## Data sourcing

It's possible to get your hands on a pretty broad range of data points these days. Everything from the basics of home value, age, and income to the more esoteric pet ownership and liklihood to buy high-end men's apparel. **Faraday actively excludes data points relating to protected classes**. Race, ethnicity, primary language, and religion are excised from our database entirely and therefore excluded from our predictive models and audience definitions.

Of course, seemingly-harmless demographic variables can often act as proxies for these features, but our stance begins with the most effective levers we have available to pull. And further on in the pipeline we do check _results_ against features like race, but we do it at an aggregated level using standardized census data (more on that below).

## Model priors

We investigate the examples in our training data before they become machine-actionable profiles. In particular, we look at how well they're balanced by age, gender, and geography against the population at large. If there's a significant skew versus the baseline, we know there's a chance the resulting predictive model will show bias, so we rework the inputs.

## Model predictions

When a predictive model is fully-built and ready to be put into action, we typically assign a predictive score to everyone in the country as a matter of efficiency. But this also allows us to check whole populations against statistics that reflect the inclusion or exclusion of protected groups at a census block group level.

If we see - for example - that the lowest scores are concentrated in the block groups with the highest proportions of particular racial or ethnic groups, we'll know that _some_ bias has made it into the model, and we'll revise it.

The bottom line is that these strategies are only a start, and **the best defense against unfair algorithms is multi-pronged and in for the long haul.** If you have questions about how your own applications might be veering away from fairness, I suggest checking out:

- Google's [Marketers' guide to ML fairness](https://www.thinkwithgoogle.com/feature/ml-fairness-for-marketers) or [ML bias crash course](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture)
- ["Abolish big data"](https://d4bl.org/videos/19-abolish-big-data) from [Data for Black Lives](https://d4bl.org/)
- The machine bias case studies at [diversity.ai](http://diversity.ai/#case)

How to reduce the effects of implicit bias in machine learning, from data sourcing to model predictions.

Mitigating implicit bias in machine learning

The use of third-party data isn't new, but it's becoming a necessity as businesses shift to a customer-centric approach. Marketers everywhere are realizing that first-party data is only the tip of the iceberg when it comes to understanding their customers.

The rise in digital technologies and interactions has increased the need for bringing outside data into their companies. While it may be as simple as understanding your existing customers' household and financial standing, with third-party data you can also gain deep insights into your leads on a known-identity basis.

Overall, leveraging third-party and first-party data adds breadth and depth to your customer insights, informing better branding and messaging decisions, personalization efforts across channels, and taking much of the guesswork out of targeted and location-based marketing campaigns.

## Customer identity resolution

The capability to bring together first-party data from various sources and third-party data to compile a unified picture of each customer is increasingly valuable. Spending dedicated to identity data assets [grew by more than 50% in 2018](https://www.iab.com/wp-content/uploads/2018/12/IAB_The-State-of-Data-2018_2018-12-05_Final.pdf?ref=faraday-blog), to a total of $846MM.

Personally identifiable information (PII) is crucial to connecting the dots across multiple touchpoints (online and offline) to form a holistic picture of your customers and prospects so you can reach them with content that will resonate with them through the channels that matter.

## Using third-party data for personalization

In a time when [almost all consumers](https://www.bondcap.com/report/it19/?ref=faraday-blog#view/148) prefer ads and products that are relevant to them, knowing how and when to serve your target market is crucial to effectively engaging your customers. According to [Forbes](https://www.forbes.com/sites/insights-treasuredata/2019/05/01/the-path-to-personalization/?ref=faraday-blog&sh=5bd84dc7a76b) 46% of marketing executives are not where they want to be in terms of delivering personalization, and 57% say that they hope to advance their personalization initiatives over the next year.

Enriching your first-party data with third-party information on your customers' financial, household, transactional, or lifestyle categories gives you a clearer idea of what your customers look like in real life.

To take it a step further, enriched customer data helps machine learning algorithms build better models. Cluster models, for example, sort individuals into distinct groups based on common attributes. These groups, or personas, can affirm your intuitions about what your customer base looks like, but may also surprise you by revealing significant groups you hadn't known about or previously considered important.

## Using third-party data for ad optimization

If you're using predictive lead scoring capabilities, the accuracy and precision of those scores improve with the quality and quantity of third-party data that you introduce. This not only benefits your leads' experiences, as they'll receive content that better aligns with their interests and where they are in their customer journeys, but it will save you time and money when you're deciding how to prioritize your outreach.

A popular way of leveraging third-party data with predictive modeling for digital marketing is Facebook lookalike audiences. But since the platform shut down advertisers' access to this data in April of 2018, gaining deep insights from Facebook has been a struggle for many businesses that are trying to better understand their audiences and target the right consumers.

If you enrich your first-party data with third-party attributes, you can gain insights on a granular level that you aren't getting with Facebook and other marketing channels. You can then segment known-identity audiences based on certain attributes — or develop in-depth personas for segmentation — and push the appropriate ad creative to those individual segments with the goal of increased, personalized engagement. This goes for not only new leads you're nurturing, but also for the customers you're actively engaging.

Another advantage of leveraging third-party data is the ability to match site's captured emails to real people. This allows you to bucket leads into certain personas for more personalized nurture campaigns that will pull them through their customer journeys with you. Your leads are more likely to receive messaging that aligns with where they are in their individual journeys, and it will save your team time and money when you decide how to prioritize your lead outreach.

## Using third-party data for location-based marketing efforts

Geographic data has obvious benefits, but incorporating additional third-party in geographic analyses improves the quality of the insights. Knowing where your ideal customers live is one piece of the geomarketing puzzle, but gaining an understanding of how your audiences will best interact with your ads offline is achieved by knowing more than just their geographic location.

Attributes like household information, transportation preferences, and lifestyle data helps you prioritize where you place ads, as well as where you might establish new brick-and-mortar presences. A subway ad or billboard placed in your customers' neighborhoods can be a great way to advertise your e-commerce business for those who don't live near a storefront, but for customers who are in your brick-and-mortar trade zones, perhaps a direct mailer advertising an in-store sale would better drive business.

From a creative perspective, audiences can vary widely depending on their physical locations, so the messaging and images chosen for your campaigns should be curated for specific audiences in particular geographies. Providing relevant content increases your chances of audience engagement, so capitalizing on the insights provided by third-party data is a necessity.

This is the case with SEM advertising, which segments audiences by geographic location. With the insights gained from third-party data, you can optimize your SEM spend by targeting areas where higher-value customers reside, spending less on areas that aren't as likely to bring in revenue.

## Using third-party data responsibly

With the rise of regulations like GDPR, privacy and validity of data are prominent concerns when using third-party data and PII. However, there are ways to ensure that your customers' and prospects' privacy is protected. You want to be sure your data comes from a verified source, isn't dangerously invasive, and is leveraged to benefit the customers you're targeting.

Social scraping is a common tactic, and it can provide large categories of data on behavioral tendencies, consumer habits, and some personal information, though this data may not always be entirely accurate. At Faraday we avoid social scraping in favor of canonical sources of information from established third-party data vendors. Vendors like Epsilon verify the accuracy of the high-quality data we license and leverage for our partners.

Faraday's Director of Data Science, William Morris, notes that we sidestep many consumer privacy issues by dealing with datasets that categorize data, such as consumer purchases, by type of product and frequency, rather than the exact stores where those purchases are made. This leads to a significant lift in machine learning models' predictive capabilities without being invasive from a privacy standpoint. Additionally, each of our clients' customer data is siloed using industry-standard practices to protect the privacy of each clients' customers.

Faraday also actively works to build models that are agnostic to a person's membership to protected classes. We exclude potentially harmful categories like ethnicity, religion, or primary language data, as these can disenfranchise certain groups of the consumer population. As an additional security measure, we employ a suite of tools for post-model checking to ensure that attributes that could indicate a person's race or class don't make the models biased or adversely impact a population.

Taking into consideration these types of ethical practices is important when exploring your own use of third-party data.

Using third-party data responsibly benefits brands and consumers alike. Learn how brand marketers are doing it right.

Using third-party data in marketing responsibly 

**UPDATED FOR 2017** Now with easy subquery support and a more sensible argument order!

We adapted [this excellent piece by Dmitri Fontaine](http://tapoueh.org/blog/2014/02/21-PostgreSQL-histogram) and turned it into a function.

It can be invoked like this:

```
SELECT * FROM histogram($table_name_or_subquery, $column_name);
```

. . . to give sweet results like this, in a check of the distribution of 2016 political contributions in Vermont:

```
fec=# SELECT * FROM histogram('(SELECT * FROM small_donors_vt LIMIT 50000)', 'transaction_amt');

 bucket |   range   | freq |       bar
--------+-----------+------+-----------------
      1 | [0,9]     | 2744 | ******
      2 | [10,19]   | 5630 | *************
      3 | [20,29]   | 6383 | ***************
      4 | [30,39]   | 1290 | ***
      5 | [40,49]   |  369 | *
      6 | [50,59]   | 3541 | ********
      7 | [60,69]   |  174 |
      8 | [70,79]   |  313 | *
      9 | [80,89]   |  171 |
     10 | [90,99]   |   65 |
     11 | [100,109] | 2363 | ******
     12 | [110,119] |   51 |
     13 | [120,129] |  115 |
     14 | [130,139] |   32 |
     15 | [140,146] |   11 |
     16 | [150,159] |  187 |
     17 | [160,169] |   24 |
     18 | [170,177] |   33 |
     19 | [180,189] |   19 |
     20 | [191,199] |   24 |
     21 | [200,200] |  795 | **

```

Use it yourself by adding this to your postgres setup:

```
CREATE OR REPLACE FUNCTION histogram(table_name_or_subquery text, column_name text)
RETURNS TABLE(bucket int, "range" numrange, freq bigint, bar text)
AS $func$
BEGIN
RETURN QUERY EXECUTE format('
  WITH
  source AS (
    SELECT * FROM %s
  ),
  min_max AS (
    SELECT min(%s) AS min, max(%s) AS max FROM source
  ),
  histogram AS (
    SELECT
      width_bucket(%s, min_max.min, min_max.max, 20) AS bucket,
      numrange(min(%s)::numeric, max(%s)::numeric, ''[]'') AS "range",
      count(%s) AS freq
    FROM source, min_max
    WHERE %s IS NOT NULL
    GROUP BY bucket
    ORDER BY bucket
  )
  SELECT
    bucket,
    "range",
    freq::bigint,
    repeat(''*'', (freq::float / (max(freq) over() + 1) * 15)::int) AS bar
  FROM histogram',
  table_name_or_subquery,
  column_name,
  column_name,
  column_name,
  column_name,
  column_name,
  column_name,
  column_name
  );
END
$func$ LANGUAGE plpgsql;
```

Note:

1. You don't need to use a subquery - you can also just provide a table name as the first argument.
1. Subqueries are useful for sampling. For example, `(SELECT * FROM bigtable LIMIT 500 ORDER BY RANDOM()`. Don't forget parentheses!
1. You can call this as `SELECT histogram()` or `SELECT * FROM histogram()`. The latter form is much more legible!

Happy querying!

Learn how to create in-table histograms with PostgreSQL.

How to do histograms in PostgreSQL

**Update** _We used to call this `jsonb_collect`, but then we realized it was very similar to `json_object_agg(name, value)`... except that there is no version of that function that just takes an expression. So, copying `jsonb_agg(expression)`, we give you..._

How do you aggregate (aka merge aka combine aka collect) JSONB fields in Postgres 9.5+? You define a new aggregate `jsonb_object_agg(expression)`:

```sql
CREATE AGGREGATE jsonb_object_agg(jsonb) (
  SFUNC = 'jsonb_concat',
  STYPE = jsonb,
  INITCOND = '{}'
);
```

Here's how you use it:

```console
# select * from greetings;
         data
-----------------------
 {"es":"Saludos"}
 {"en":"Hello"}
 {"ru":"Здравствуйте"}
(3 rows)

# select jsonb_object_agg(data) from greetings;
                        jsonb_object_agg
-------------------------------------------------------------
 { "es" : "Saludos", "en" : "Hello", "ru" : "Здравствуйте" }
(1 row)
```

If you're curious about the aggregate we just added, note that `jsonb_concat(jsonb, jsonb)` is the [function backing `||`](https://github.com/postgres/postgres/blob/REL9_5_STABLE/src/backend/utils/adt/jsonfuncs.c#L3349) that was [introduced in Postgres 9.5](https://www.postgresql.org/docs/9.5/static/release-9-5.html).

It's just that simple!

Learn how to aggregate (aka merge aka combine aka collect) JSONB fields in Postgres 9.5+.

How to aggregate JSONB in PostgreSQL 9.5+

At Faraday, we've long used [csvkit](https://csvkit.readthedocs.io/en/latest/) to understand, transform, and beat senseless our many streams of data. However, even this inimitable swiss army knife can be improved on - we've switched to [xsv](https://github.com/BurntSushi/xsv).

xsv is a fast CSV-parsing toolkit written in [Rust](https://www.rust-lang.org/en-US/) that mostly matches the functionality of csvkit (including the clutch ability to pipe between modules), with a few extras tacked on (like smart sampling). Did I mention it's fast? In a standup comparison, I ran the "stats" module of XSV against "csvstat" from csvkit, on a 30k-line, 400-column CSV file:

- Python-based csvkit chews through it in a respectable-and-now-expected 4m16s.

- **xsv takes 1.8 seconds.** I don't even have time for a sip of my coffee.

The difference between csvkit and xsv is partly defined by scale; both tools are plenty fast on smaller datasets. But once you get into 10MB-and-upward range, xsv's processing speed pulls away exponentially.

If you've been using csvkit forever (like me), or if you want to be able to transform and analyze CSVs without loading them into a DB, give xsv a shot:

### Install Rust

```bash
curl https://sh.rustup.rs -sSf | sh
```

. . . which also gives you the rust package manager `cargo`, which lets you:

### Install xsv

```bash
cargo install xsv
```

Then be sure your PATH is configured correctly:

```bash
export PATH=~/.cargo/bin:$PATH
```

. . . and try it out on a demo CSV with 10k rows, some messy strings, and multiple data types:

```bash
curl https://gist.githubusercontent.com/wboykinm/044e2af62fc0c7f77e17f6ccd55b8fb0/raw/fca391e6c03a06a7be770fefca6c47a9acdd2305/mock_data.csv \
| xsv stats \
| xsv table
```

_(`xsv table` formats the data so it's readable in the console):_

```bash
field           type     sum                 min                  max                  min_length  max_length  mean                stddev
id              Integer  5005000             1                    1000                 1           4           500.49999999999994  288.6749902572106
first_name      Unicode                      Aaron                Willie               3           11
last_name       Unicode                      Adams                Young                3           10
email           Unicode                      aadamsp5@senate.gov  wwrightd8@upenn.edu  12          34
gender          Unicode                      Female               Male                 4           6
ip_address      Unicode                      0.111.40.87          99.50.37.244         9           15
value           Unicode                      $1007.98             $999.37              0           8
company         Unicode                      Abata                Zoovu                0           13
lat             Float    243963.82509999987  -47.75034            69.70287             0           9           24.42080331331331   24.98767816017553
lon             Float    443214.19009999954  -179.12198           170.29993            0           10          44.36578479479489   71.16647723898215
messed_up_data  Unicode                      !@#$%^&*()           𠜎𠜱𠝹𠱓𠱸𠲖𠳏       0           393
version         Unicode                      0.1.1                9.99                 3           14
```

Happy parsing!

A data science hack to process CSVs faster using XSV.

All posts