Faraday has been pioneering the use of [Google Cloud's](https://cloud.google.com/gcp?utm_source=google&utm_medium=cpc&utm_campaign=na-US-all-en-dr-bkws-all-all-trial-e-dr-1710134&utm_content=text-ad-none-any-DEV_c-CRE_665735450627-ADGP_Hybrid+%7C+BKWS+-+EXA+%7C+Txt-Core-Google+Cloud-KWID_43700081237254438-kwd-6458750523&utm_term=KW_google%20cloud-ST_google+cloud&gad_source=1&gclid=CjwKCAiA2JG9BhAuEiwAH_zf3ipLZUgcmqUgvAG4mSazGgzYHHuc-Sxw_1kXaAED5OkUnKhoJ5VNGxoCrRIQAvD_BwE&gclsrc=aw.ds) BigQuery and with its recent addition of LLMs, we’ve unlocked even more powerful use-cases. In this video, Seamus dives into how LLMs are improving sentiment analysis, content generation, and streamlining our data processes—all within [BigQuery](https://cloud.google.com/bigquery?utm_source=google&utm_medium=cpc&utm_campaign=na-US-all-en-dr-bkws-all-all-trial-e-dr-1710134&utm_content=text-ad-none-any-DEV_c-CRE_724346015211-ADGP_Hybrid+%7C+BKWS+-+MIX+%7C+Txt-Data+Analytics-BigQuery-KWID_43700081227767773-kwd-47616965283&utm_term=KW_bigquery-ST_bigquery&gad_source=1&gclid=CjwKCAiA2JG9BhAuEiwAH_zf3rR-UNnTVF20r8oRT-f2T4EOwmKb8fymYRf1cOrh2cj3ZH1M7JjJThoCkDYQAvD_BwE&gclsrc=aw.ds), without needing complex infrastructure.

**In this video, you’ll learn:**

- How Faraday improves sentiment analysis accuracy with BigQuery LLM feature
- The benefits of generating personalized content directly within BigQuery
- How BigQuery’s LLM feature simplifies our workflows and data processes

<iframe width="560" height="315" src="https://www.youtube.com/embed/luRR3cyMqrM?si=IIbOHJLK5nwU6jaO" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Join Faraday’s CTO Seamus Abshere as he shares how we’re using Google Cloud’s BigQuery to transform our workflows with the new Large Language Model (LLM) capabilities.

How Faraday simplifies sentiment analysis and generates personalized content using BigQuery

**See our complete guide:** [Understanding why you need data (and what kind actually moves the needle)](/blog/what-data-do-you-need-for-marketing).

---

## Difference between first and third party data

Nowadays, the most successful brands are data-driven organizations. For a long time, brands harnessed both first and third party data to strengthen their understanding of their customers, improve decision-making, create more personalized experiences, and more. Let’s zoom out: what’s the difference between first-party data and third-party data?

**First party data** is data that your brand collects on your customers and leads. Page visits, form submissions, purchases, and email engagement are all examples of first party data, and each one provides unique insights and context into how your customers and leads interact with your brand. Depending on how and where your brand collects data, first party data can come in a variety of forms: engagement, demographic, transactional, financial, lifestyle, and more.

**Third party data** is data that’s been collected by a third party, or not your brand. It comes in the form of demographics such as age or gender, psychographics such as hobbies and interests, and financial data such as household income. Third party data providers like Acxiom allow brands to purchase the data for use in marketing.

Until recently, third-party cookies and social scraping were common sources for data enrichment meaning that browsers would creepily follow consumers across the internet to track their interests, behaviors, and other personal information that was never willingly shared for commercial use. It enabled personalization at the expense of consumer privacy, but responsible brands have been preparing for the [death of third-party cookies](/blog/death-of-third-party-cookies-strategies).

## Benefits of first and third party data for consumer marketing

All types of data have their own individual merits when it comes to consumer marketing, and in order to create a cohesive, impactful [customer insight strategy](/blog/how-to-create-a-customer-insight-strategy), you need more than just your first party engagement data, otherwise you run the risk of sending the wrong message at the wrong time. A good combination of first and third party data plays a key role in two pillars of a strong marketing strategy: customer insights and predictions.

### Customer insights

A strong foundation of data enables marketers to segment customers and leads into distinct subgroups so that they can be targeted with relevant engagement, and with both first and third party data fueling your customer analytics tools, you’ll find that your customers and leads are more than just purchases and clickstreams–they’re real people, with real interactions outside of your brand.

By unifying first and third party data, your customer insights upgrade from “person that abandoned their cart” to “John Doe, married father of two, golf enthusiast, cat owner, and brick-and-mortar shopper.” And the best part is that this can be done at a scale that applies to entire segments of your customer base. The patterns are there in your data, you just need to find them.

### Predicting customer behavior

The value of pure customer insights through data is clear, but you can take your strategy a step further by using that data to [predict virtually any customer behavior](/blog/propensity-models-predict-customer-behavior) your brand is interested in. Likelihood to churn, convert, make a purchase, purchase again–whatever strategy you want to reinforce or employ, customer predictions using propensity models can get you there quickly and accurately.

Propensity models pore over your customer and lead data to find historical patterns, and then use those patterns to make the customer predictions you need. Plus, by using both first and third party data, your predictive models become even more accurate because they have more data to work with. Being reactive just doesn’t cut it anymore, and by predicting customer behavior, you can be proactive to ensure that lead converts, or that customer decides to stay on board.

## First party data collection techniques

Perhaps the most important benefit of first party data is that your brand is in complete control of how you collect it. Some strategies are more effective than others, but it’s important to implement a variety of strategies to ensure healthy engagement with both customers and leads.

### Web forms & surveys

Web forms can be a great way to capture basic information like name, email, mailing address, and possibly some financial or lifestyle information as well. In a similar vein, albeit more resource intensive, surveys are a fantastic way to poll your customers and leads to learn more about what they’re looking for, what they like about your brand (or even what they don’t!). Consider offering a small incentive, such as a free coffee, for any who fill out a survey.

### Loyalty programs

One of the best ways to engage with and retain your current customers is to implement a loyalty program. Loyalty programs enable brands to consistently engage with their customers through features like allowing customers to rack up points per purchase that can be redeemed for discounts or freebies. On top of the rewards you can offer your customers, it gives you another avenue through which you can engage with them–like sending automated, personalized emails when they reach a certain point or purchase threshold.

### Online engagement

Users will engage with your website in different ways, and the more data you collect, the more you’ll understand what it is that your visitors are looking for. Digital ads on a platform like Instagram can help you find the platform that fits your ideal customers. Visitor behavior on your site, such as scroll depth or visits on a certain page can similarly help you find what pages resonate most with them, which you can then use to guide engagement copy & creative.

## First party data enrichment

Modern brands are adopting a first party data enrichment strategy through third party data providers to gain a true understanding of who their customers are so that every engagement is a delight. It’s possible to both respect your customers’ privacy and use third party data to personalize engagement–you just need to be smart about it. Faraday helps brands do just that.

With cookies in the rear-view window, it’s more important than ever to ensure you’re being [responsible](/features/responsible-ai) and privacy-centric with any data you use. Avoid data that involves shady practices like social scraping, and look for [opt-in, permissioned data](/features/data)–it’s the way of the future, and not utilizing it is leaving your brand at a disadvantage.

So what are your options for first party data enrichment? The obvious answer is licensing directly from third-party data vendors like Acxiom or Epsilon. Something to note, however, is that you’ll need to sift through all that data to find what’s most relevant to your brand at any given time–your ideal customer profile will likely change over time, after all. On the other hand, many brands are adopting customer prediction platforms like Faraday so that they can access all the third party data they need, with machine learning algorithms that tell them what’s most important, saving time & resources that are better put elsewhere, like launching campaigns.

## How to leverage first and third party data with Faraday

Faraday enables your brand to generate actionable insights and customer predictions using both first and third party data–or first party data enrichment. No matter what your stack looks like, Faraday has [hundreds of integrations](/integrations) to ensure your predictions are where you need them, when you need them, and without a single line of code. On the flip side, if your brand is developer-minded, Faraday offers a [robust developer API](/docs/reference) so that you can implement predictions your own way.

### Predict personas to make personalization a breeze through rich insights

Like I mentioned earlier, first party data enrichment completely transforms what you know about your customers, and allows you to visualize clear indicators for who’s represented in any segment of people your brand is interested in. Turn that newly-discovered John Doe, married father of two, golf enthusiast, cat owner, and brick-and-mortar shopper–part of the Middle-aged Married Mike persona–into internal talk track alignment, guided personalization on any platform, and more.

[![Faraday customer personas](/images/blog/first_party_vs_third_party/first-party-vs-third-party-personas.png)](/images/blog/first_party_vs_third_party/first-party-vs-third-party-personas.png)

### Predict propensity to take virtually any action

With your personalization in-hand, use propensity models to predict who’s (or where’s) most [likely to buy](/predict/category/likely_buyers) your product, who’s worth [retaining](/predict/category/churn_scores), how to [prioritize your leads](/predict/category/lead_scores), & more. Customer predictions with machine learning turn your reactive marketing proactive, so that you’re always delivering the right message at the right time, and personalized to boot.

[![Faraday propensity to buy outcome](/images/blog/first_party_vs_third_party/first-party-vs-third-party-outcome.png)](/images/blog/first_party_vs_third_party/first-party-vs-third-party-outcome.png)

**Ready to try it out? [Create a free account](https://faraday.ai/signup).**

An effective marketing strategy is data-driven, and to create one, you need a strong mix of first and third party data. Learn the difference between first and third party data in marketing, and how smart brands are using first party data enrichment to drive growth.

First party vs third party data: What's the difference?

The renewable energy sector is growing rapidly as consumers and businesses increasingly turn to sustainable solutions. While this growth presents a wealth of opportunities, it’s also been a bumpy ride, with market uncertainties and shifting dynamics creating challenges for providers. In this ever-changing landscape, success hinges on the ability to identify, prioritize, and place their bets on the right horse—or, in this case, the consumer segments with the most value-generating potential.
When a prominent solar energy provider faced this challenge in 2024, they realized that finding the best answer alone wasn’t necessary, or the best use of their specialized team’s energy. Instead they turned to Faraday.

## The problem: they were tired of guessing and wanted to make data-driven decisions they could feel confident in

This solar company faced a wide-ranging set of problems as they tried to grow their sales pipeline in 2024. Although they were determined to scale efficiently and outpace competitors, their existing targeting approach lacked precision, which led to wasted marketing spend in low-potential areas and with the wrong consumers. Additionally, they were overwhelmed by an abundance of raw data that made it difficult to derive actionable insights. Finally, rapid market growth had also attracted competitors, heightening the urgency for smarter acquisition strategies.

These challenges translated into tangible pain points like lower conversion rates, wasted time, and inefficient marketing spend. Without a clear roadmap to identify and prioritize high-potential customers, their ability to compete effectively in the fast-evolving solar market was under threat.

## The solution: Faraday’s predictive analysis tools gave them data driven answer and confidence

Realizing these challenges could hinder their growth, the company decided to seek outside expertise and while these problems might seem insurmountable, they’re what we get up for.

Almost immediately after onboarding and hearing about their issues, we identified two significant ways we could support their expansion goals and help them to better understand their market: Persona Analysis and Market Opportunity Analysis.

### Persona Analysis

Faraday’s [Persona Analysis](/templates/thematic-personalization) is a process that helps companies uncover the traits and behaviors most closely associated with their ideal customers. By analyzing first-party data from the client as well as rich third-party data from our proprietary [Faraday Identity Graph](/features/faraday-identity-graph), we create detailed profiles of a company’s highest-value personas. These represent distinct socio-demographic groups that share common characteristics and behaviors, making it easier to design marketing strategies tailored to their specific needs.

For this solar provider, Persona Analysis revealed the unique traits that drive solar adoption. By understanding these factors, the company could refine its messaging, focus on the most relevant customer segments, and craft outreach strategies that resonated with their target audience.

But that on its own might not be enough. This tools allows us to know what _type_ of person to target, but not necessarily how many of them exist and are reachable

### Market Opportunity Analysis

That’s where [Market Opportunity Analysis (MOA)](https://faraday.ai/blog/unlocking-growth-with-market-opportunity-analysis) comes in. Building on these insights and persona profiles, Faraday conducted a comprehensive Market Opportunity Analysis, a process which combines the client’s first-party data with broader market trends and dynamics to quantify their existing market. To conduct this analysis, Faraday builds a complex and individualized predictive model which compares that quantified market to the population at large, represented through [Faraday’s Identity Graph](https://faraday.ai/features/faraday-identity-graph)—a robust and proprietary dataset containing detailed information on nearly every U.S. adult. This enables the identification of the totality of potential new customers who resemble the company’s top-performing personas available in the U.S. An example is pictured below.

![Example of our Market Opportunity Analysis](/images/blog/moa_map.png)

The analysis doesn’t stop there though. It then visually maps these high-potential customers by state, zip code, and even down to the individual level. These dynamic predictions enable clients to focus on specific regions and neighborhoods with the highest concentration of ideal customers. These insights are paired with additional predictive tools, such as lead routing and direct mail targeting, ensuring marketing efforts can be directed with precision and confidence.

Through this dual approach, the solar provider gained actionable insights about market potential and customer behaviors. These findings informed advertising campaigns, local outreach initiatives, and third-party logistics strategies, enabling the company to allocate resources strategically and maximize their impact.

## The results

The impact of Faraday’s Persona and Market Opportunity Analysis changed the company’s entire outlook. Where as before, they had marketed broadly based on vague demographic factors, they could now target their spend down to the top zipcodes, and even to the individuals most likely to convert, suggesting products that were more likely to sell and talking to their ideal consumer in a way that they were most likely to respond to.

A particularly telling example of the power of these tools came when the solar provider, who primarily relies on their installation trucks for marketing, discovered through Market Opportunity Analysis that some of the highest-potential zip codes were in rural areas—places where the trucks wouldn't typically reach. To ensure these high-fit prospects were engaged, they decided to run a targeted direct mail campaign in those regions, making sure they could be seen by the people who mattered most.

Faraday's tools didn’t just provide actionable data—they gave the solar provider the confidence to act without hesitation. In this case that meant switching up their marketing strategy significantly. By moving away from broad, guesswork-based targeting, they were able to focus their efforts and dollar specifically on high-potential customers and this makes all the difference.

## Conclusion

Faraday's persona and market opportunity analysis provided clarity that allowed the company to make informed decisions on where to invest their resources, cutting out low-value areas and optimizing their target audience. With the right data guiding them, they gained the confidence to expand in competitive markets and scale efficiently. This success story underscores the value of data-driven insights in driving market expansion and delivering long-term success.

What's next for them? We're going to predict (with _high confidence_) more sales\!

Using Faraday's predictive tools, this company transformed their marketing strategy by targeting only the highest-potential customers.

How a leading solar provider improved customer targeting with personas and market opportunity analysis

Imagine you’re leading a growing retail business with 25 thriving locations. Now, it’s time to decide where to open the next five. Do you expand into an emerging market where demand is heating up, or double down on your presence in regions where your brand is already well known? Is there some other critical factor you haven’t considered? The stakes are high, and even seasoned executives can fall prey to personal biases.

Take, for example, a story we recently heard from a client about an experienced restaurant CEO who was set on opening a location in his beloved hometown—a move driven by emotion rather than data. Fortunately, his team knew to assess the market, which revealed the location wasn’t actually viable. While this upended their original plan, it ultimately saved them the effort and cost of opening a doomed restaurant. This story highlights how critical these decisions are for sustained growth and why relying on data-driven insights (from the beginning) is essential to avoid costly missteps and make confident, strategic choices.

At Faraday, we understand the challenges businesses face when it comes to making strategic growth decisions. That’s why we offer **market opportunity analysis (MOA)**, a powerful tool that helps you confidently identify actionable opportunities that are backed up by data.

## What is market opportunity analysis?

MOA equips your team with a clear understanding of your current market reach and untapped potential. By analyzing your existing customer data and comparing those datapoints to the population at large, we can help you pinpoint exactly where the next big opportunities lie. Whether you're looking to expand geographically, target new customers, or optimize product offerings, our approach ensures your growth is backed by solid insights.

## How it works at Faraday

MOA provides your team with a data-driven roadmap to your most valuable and actionable growth opportunities, but how does it actually work? Here's how we approach it:

![](/images/blog/moa_analysis_diagram.png)

First, we conduct a detailed assessment of your current market penetration. By analyzing your existing customer base, we identify key traits such as purchasing behaviors, regional preferences, and lifetime value to create an ideal customer profile. Then, leveraging data from the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph), we develop a tailored predictive model to evaluate the broader U.S. market and identify prospective customers who closely match your existing audience. This allows us to quantify the untapped potential within the customer segments most likely to drive growth.

Once we’ve identified the high-potential customer segments, we dig deeper into the specific opportunities within those groups. This involves determining which product or service offerings are best suited to each persona to maximize their lifetime value. We also take a close look at geographic trends, mapping out areas where your target customers are most concentrated from the regional level all the way down to zip code, metro area, or even postal route. This data empowers businesses to strategically allocate resources with significantly more confidence and precision than before.

## Unleashing the power of the Faraday Identity Graph (FIG)

A comprehensive MOA requires access to a detailed dataset of the broader population to serve as a benchmark for comparison. Fortunately, Faraday’s **Faraday Identity Graph (FIG)** provides exactly that, and much more. With rich data on nearly all American adults, the FIG offers a robust and comprehensive view of the population, significantly enhancing the reach and depth of our MOA.

The FIG goes beyond basic demographic information, offering nuanced insights into consumer behaviors, preferences, and buying patterns. By tapping into these granular details, businesses can understand their target market in a way that’s far more accurate and actionable than with competing data sources.

## Ready to explore what's next?

MOA can greatly benefit businesses by identifying high-potential customer segments and emerging market trends. By understanding where the highest-value opportunities lie, companies can optimize their marketing strategies and resources, focusing on areas with the highest growth potential. This leads to more targeted and personalized campaigns, which can improve conversion rates, enhance customer engagement, and increase profitability. MOA additionally provides companies with the confidence to know that their strategies are backed by data.

Growth doesn't happen by chance. With Faraday's MOA, you can take the guesswork out of targeting strategy and focus on results. Contact us today to discover how we can help unlock your business's full potential.

MOA empowers businesses to make strategic growth decisions by uncovering untapped potential and high-value opportunities in their market.

Unlocking growth with market opportunity analysis (MOA)

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

Restaurants love Toast because it focuses on _their_ problems:

<a href="https://pos.toasttab.com/blog/il-primo-pizzeria" target="_blank"><img src="/images/blog/new-imperative-for-vertical-saas-built-in-ai/toast-pizzeria.png" width="372px" /></a>

Toast is a VSaaS (vertical software-as-a-service) - software **purpose built for unique industry workflows**. This means faster time to value compared to generic "horizontal" solutions. For example, Toast has a dedicated "Pizza POS" system with [easy topping swaps!](https://pos.toasttab.com/restaurant-pos/pizza-pos) A pizzeria wouldn't want to reinvent that wheel - but they also wouldn't want to pick a POS system that doesn't support it.

For the roofing industry, you _could_ customize Salesforce to track "Roof Area" - or you can use a [roofing CRM like Acculynx](https://acculynx.com/). There is a wonderful explosion of examples like this - companies that build exactly what their clients need, because they ONLY build for those clients:

- [bloomerang](https://bloomerang.co/) - for non-profits
- [mindbody](https://www.mindbodyonline.com/) - for wellness centers and spas
- [ALINE](https://alineops.com/) - for senior living
- [archy](https://www.archy.com/) - for dental offices
- and hundreds more.

With growth comes competition, and the question is: what's next for Vertical SaaS?

I would argue it is **incorporating AI features** that are specific to their industries. For VSaaS companies that haven't yet incorporated an AI feature, or are [looking to upgrade beyond first party data-powered features](/blog/why-first-party-data-and-rfm-is-not-enough), there are many options on the market. Here, we organize them into 3 levels - purpose-built, data warehouse-native, and low-level API. Each is useful for different use cases and organizations.

## Traditional focus on control points

Best-in-class VSaaS players have been focusing on [capturing "control points"](https://www.tidemarkcap.com/vskp-chapter/control-points-2-0). The control points are "back office" and "front office." Back office is the system of record, and VSaaS often starts by trying to become this - the "last system you turn off". Acculynx tracks roofing jobs and leads, while another Xero, an accounting VSaaS, [keeps the books](https://www.xero.com/us/security/data-protection/). From there, they can focus on integrating with or directly providing front-office technology - whatever the employee or customer interacts with. Every adult in the United States has probably held a [Toast POS](https://pos.toasttab.com/hardware/toast-go) in their hand.

## The new imperative - built-in AI features

What's next for VSaaS? How can you extend further into the value chain - or compete with an incumbent who has captured a control point? How can you [avoid ChatGPT-based extinction](https://www.tidemarkcap.com/post/ai-extinction-or-evolution)? One possibility is adding AI features specifically tuned for an industry, which only VSaaS can provide.

There are at least 2 reasons VSaaS is well positioned for AI.

- **Data**. As a system of record, a VSaaS might be the _only_ place a business can get the data it needs to make a critical decision or prediction.
- **Semantic layer**. Since VSaaS understands the semantics of its vertical - in other words, it knows what pizza toppings or roof sizes _mean_ - it can automatically build and use AI models.

As [Tidemark](https://www.tidemarkcap.com) puts it, **"flip your product from being reactive to proactive."** For example, Acculynx rates every roof lead 🟢 🟡 🔴 and [doubled conversion rates for clients who focused on green-light leads](https://www.prweb.com/releases/2023/4/prweb19276673.htm).

## Taking the first step

Assuming building AI into their product is critical path, VSaaS vendors must decide where to start. It can be helpful to think of the options as organized into levels from most out-of-the-box to lowest level. Different VSaaS companies will find different levels to be the most appropriate.

![](/images/blog/new-imperative-for-vertical-saas-built-in-ai/level-advantages.png)

### Level 2 - easiest - purpose built AI

The most accessible way for VSaaS to incorporate AI into its products is to adopt an existing lower-level component whose unit of prediction is the same - one that is purpose built for predicting against people, products, or search. For example, any system that deals with people can build predictions about their behavior by using Faraday's [business outcomes](https://faraday.ai/docs/product/outcomes). A system in charge of inventory can add intuitive AI-based search by incorporating Bloomreach's [product search](https://documentation.bloomreach.com/discovery/reference/product-search-category-api). Systems for managing creative assets can build next generation functionality by incorporating AI features like Cloudinary's [automated image editing and generative background replacement](https://cloudinary.com/products/cloudinary_ai).

### Level 1 - data warehouse native

Going a level down, VSaaS looking to develop its own AI capacity outside of what's already on the market in level 2 should evaluate the machine learning components [built into data warehouses](https://cloud.google.com/bigquery/docs/bqml-introduction). These are not just for one-off analyses; entire applications can be developed on top of them. This level sits at the sweet spot between specific units of prediction in level 2 and raw HTTP APIs in level 0. Generally controlled by SQL commands, they treat structured, unstructured, and even [file/object data](https://cloud.google.com/bigquery/docs/annotate-image) as tables and allow feature engineering, modeling, evaluation, and prediction as SQL queries. They are designed to process data at any scale, automatically shuffling data and managing memory without requiring manual partitioning or configuration by users. So, if you build a solution on top of them, you get scaling essentially built in.

Almost all data warehouses now allow you to do [generative AI](https://faraday.ai/blog/quickstart-use-bard-in-bigquery). This is much more attractive for batch use cases than creating error-prone HTTP connections to LLM APIs across many rows of data (you wouldn't use this for building chatbots that require low latency - see level 0). Don't forget about predictive use cases too - [time series modeling](https://towardsdatascience.com/time-series-forecasting-in-snowflake-using-sql-6ce7cedce862), for example, is available on all of the major data warehouses and allows sophisticated projections with built-in and custom holidays. [SQL-based classification and regression](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree) using XGBOOST and random forest are bread-and-butter use cases that fit perfectly with tabular input and output data; once you've done feature engineering with standard SQL functions, it's hard to imagine a simpler way. [Unsupervised clustering](https://docs.aws.amazon.com/redshift/latest/dg/tutorial_k-means_clustering.html) is also available.

### Level 0 - raw API

A very rich set of services, from Google's [Vertex AI](https://cloud.google.com/vertex-ai) to [OpenAI's Enterprise-friendly models](https://openai.com/enterprise), exist as HTTP APIs. This is the lowest common denominator and is accessible from all systems. It is also particularly appropriate for just-in-time calculations such as generative prompts used by chatbots or online fraud predictions using random forest models mounted alongside [feature stores](https://cloud.google.com/vertex-ai/docs/featurestore) for instant response. When planning an AI feature for a VSaaS, it might seem necessary to master an entirely new API. However, low-level systems are often better suited for specific real-time use cases, while asynchronous or batch workflows should first explore the potential of level 2 or level 1 solutions.

## Choosing a level

A few questions can suggest where to start:

- Is there already an out-of-the-box prediction infrastructure for your use case? Companies should always evaluate level 2 purpose-built solutions before committing to building low-level systems.
- What is the time between a prediction being created and its use? For example, if you're incorporating search, people expect results immediately; using an existing level 2 vendor may be the fastest approach. However, if no solution aligns with your industry needs, you may need to skip Level 1 and develop a [level 0 solution](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions). Conversely, if you plan to rank a lot of records in asynchronous batch use cases, a level 1 solution is likely sufficient.
- Is there industry data that can extend a client's own data? For example, fraud detection vendors incorporate geographic fraud data in their scores. Faraday has a [national dataset of all US consumers](https://faraday.ai/features/data) that automatically enriches predictions made on its platform.
- What kind of scrutiny will your AI receive? For example, if you are making predictions about people, consider how you will implement [Responsible AI](https://www.brookings.edu/events/what-is-responsible-ai/). Some level 2 vendors [automatically build in reportable controls and metrics](https://faraday.ai/features/responsible-ai); some level 0 vendors [let you build your own](https://www.datarobot.com/blog/bias-mitigation-with-datarobot/).

## The imperative

If you were starting a VSaaS product from scratch, what would be your wedge? Some form of AI is a good guess. And if you are a VSaaS incumbent, how do you defend your position? You have an advantage in the data and semantic layer you already hold. A thoughtful approach to AI infrastructure options can boost your efforts and minimize your time to value.

Vertical SaaS (VSaaS) is software built exactly for a particular industry. The next generation of VSaaS is incorporating exactly the AI that people need.

A new imperative in vertical SaaS: built-in AI features

Companies are looking for the [fastest way to build AI into their software](/blog/new-imperative-for-vertical-saas-built-in-ai). Using techniques like RFM ([recency-frequency-monetary](<https://en.wikipedia.org/wiki/RFM_(market_research)>)), many are implementing AI features that learn from how people use websites, respond to emails, and pay for products. This process is called "first party data-based modeling". Companies like [Twilio](https://www.twilio.com/blog/first-party-data-marketing) and [Klaviyo](https://www.klaviyo.com/blog/predicting-churn-risk-our-new-model) have used this simple approach to rapidly add features to existing large products.

At the same time, increasing privacy regulation (did you know [Colorado](https://leg.colorado.gov/bills/sb21-190) and [Virginia](https://law.lis.virginia.gov/vacodefull/title59.1/chapter53/) have their own versions of [CCPA](https://oag.ca.gov/privacy/ccpa)?) and the [retirement of "third party cookies"](https://developer.chrome.com/en/docs/privacy-sandbox/third-party-cookie-phase-out/) have made companies [think "first party data only" is the only option left](https://digitalmarketinginstitute.com/blog/why-first-party-data-is-more-important-than-ever-and-how-to-use-it). It turns out this is not quite right - the very same transaction and customer records that make up first party data can be linked to [traditional, legal, consented offline data](https://www.acxiom.co.uk/blog/understand-your-online-and-offline-data-to-build-defined-data-strategy/) to create more powerful technology. Over time, companies that start with RFM [upgrade to using offline data too](https://www.postpilot.com/lookalike-audiences).

# Problem

Why is first party data not enough? **The total information contained in a user's activity may be insufficient to predict their future behavior** - the model may be weak. Over time, repeat customers _may_ build up enough data to be useful. But new users will always be a mystery. And once a first party model is built, it must be constantly monitored for new patterns in relatively noisy data. In other words, building AI features into software but limiting them to first party data makes them weaker, less widely applicable, and less stable.

## Activity vs affinity

First party activity data is traditionally thought of in terms of "recency" (time stamps), "frequency" (aggregation over time), and "monetary" (revenue impact). Product choice can be added to this (this person bought a pillow!) Does this contain sufficient data to predict their next purchase or how much they will spend over their lifetime? Often, it does not. It would be more predictive to know, in addition to RFM, their [lifestyle preferences and financial resources](https://faraday.ai/features/data). Activity data is a relatively incomplete view into a person's overall affinity for a product.

## Cold start problem

The first time a person shows up at your business, you don't know anything about them - so you can't make any first party-based decisions about them. This is called the "cold start problem" with first party data. Over time, you will know more about their purchase behavior, activity on your website, opens/responses to emails, etc., and RFM models will become more powerful (though still not as powerful as the same model with lifestyle and financial data mixed in). However, if a software company's clients need to make predictions at first contact, first party data will not help.

## Noisy data

Activity data is noisy. Many random factors can affect when a user opens a browser, comes to your site, stops, buys a product, or opens an email. First party-based models must be able to adapt quickly to the same changing environment. In some industries, like fraud detection, the ability to update models in real time to address ever-changing threat actors is a key feature. In other industries, the need or capacity to constantly refresh models based on noisy data it not necessarily positive. For example, a model that uses lifestyle or financial aspects to predict next best product is likely to exhibit more stable performance over time, especially if it incorporates (but does not rely solely on) first product purchased and time spent on site.

# Solution

What possible solutions are there? One is to use offline data - a supply chain that has existed for decades. Ever since sewing magazines realized their mailing lists offered valuable insight into people's hobbies, they have been asking their readers for permission to share their data with marketing partners. In terms of regulation, this data is consented - unlike web scraping or even some forms of first party data. Sewing is only one of the many niches with dedicated providers who can derive affinity data from purchases. And every major data aggregator - who has relationships with all the niche providers - has a vested interest in defending its continued use and teams dedicated to adapting to changing laws.

There are a few challenges to using offline data directly. The contracting process is lengthy and requires a very secure infrastructure to receive and store the data - not something all companies have (or even want). Also, there is a lot of data - hundreds of columns. It can take data science teams years just to understand what data is valuable to each market segment and build repeatable workflows to create models. Even if this is done, issues like proxy discrimination and bias propagation arise - and may disqualify the product's use in certain industries.

Are we back to square one - first party data only? Not necessarily. AI infrastructure companies like [Faraday](https://faraday.ai) that have offline data built-in are solving contracting, security, model discovery, and responsible AI concerns with standardized components. The most important benefit is time to value - software companies feeling the imperative to add AI can release it an order of magnitude faster.

## Contracting

Traditionally, buying offline data has been a multi-month contracting process with "what the market will bear" pricing. AI infrastructure companies like Faraday have standardized contracts and usage-based pricing, making it affordable to try new AI product initiatives without high upfront data costs.

## Security

Data security requirements for holding offline data are high. A data breach may release not only customers' order history but also more personal data. Having a SOC 2 that specifically audits for the protection of consumer data is a common requirement. AI infrastructure companies store offline data on behalf of other companies, letting them use it in batch and real time use cases without ever coming into possession of it.

## Legal and regulatory

Holding offline data always increases legal requirements. For example, if you hold more than first party transaction and activity data, responding to CCPA (and other) data access and data deletion requests becomes much more expensive. It also reclassifies a company as a data broker, forcing it to register in many states. AI infrastructure companies respond to such requests on behalf of users and are registered data brokers so that other companies don't have to be.

## Time to value

Data scientists need time to figure out what data matters, but data usually arrives in an unusable state. It must be cleaned and reformatted. Different techniques are then used to measure each data point's value to the business. Experimental models are built in Jupyter notebooks; finally, the whole process is handed over to a development team to make ready for production. AI infrastructure companies can accelerate this process, for example, by automatically matching first party transaction data to offline data and generating information gain metrics and models that can be backtested, field tested, and iterated on.

## Responsible AI

In addition to general public concern about malicious use of AI, specific industry regulators and trade groups have been adding requirements. Longstanding requirements - such as fair housing and fair lending - apply to AI just as much as traditional methods. Other requirements - such as model transparency and elimination of proxy discrimination - take on new forms when using automatically generated models. AI infrastructure companies can provide these as out-of-the-box reporting and mitigation tools instead of individual company data science or compliance teams implementing them on a one-off basis.

# Don't leave money on the table

First party data is not a magic bullet; offline data has a steep learning curve. AI infrastructure providers like Faraday, Twilio Segment, and Snowflake can help add AI to existing products faster and with less complexity, while also bypassing the limiting first party data-only stage.

1st party data-based AI modeling struggles with limitations like noisy data and the cold start problem. By integrating 3rd party data through providers like Faraday, companies can enhance predictions and accelerate AI deployment.

Why first party data (and RFM) is not enough

The Special Ad Categories setting on Facebook applies to ads that promote content related to sensitive areas such as:

- **Housing** — Ads about real estate, home rentals, or housing-related services.
- **Financial products and services** (expanded category as of October 2024) — Ads offering credit cards, loans, or other financial services such as insurance and investment advice.
- **Employment** — Ads about job opportunities or career services.
- **Social issues, elections, or politics** — Ads related to social causes, political campaigns, or elections.

## Why the Setting Exists

This feature ensures compliance with laws and regulations that prohibit discrimination based on factors like age, gender, ethnicity, and location. If your company’s ad falls under these categories, Facebook limits your targeting options to promote fairness and equal opportunity.

### Key impacts on ad targeting

- No targeting by age, gender, or ZIP code.
- Location targeting is restricted to a 15-mile radius.
- No Lookalike Audiences: You cannot use Lookalike Audiences for Special Ad Categories. Instead, Facebook offers “Special Ad Audiences” based on similar behaviors but without using protected characteristics.
- Custom Audiences are allowed, but with additional restrictions: you can still use custom audiences, but you must certify compliance with [Facebook’s advertising standard](https://transparency.meta.com/policies/ad-standards/).

## Facebook’s October 2024 special ad category announcement

In October 2024, Facebook announced a few significant changes to special ad categories:

1. A new special ad category “[Financial products and services](https://www.facebook.com/business/help/510724041294968)” was introduced, replacing the previous Credit ads category. This expanded the type of companies and ads that were subject to special ad category limitations.
2. Certification that any ad set based on a customer list complies with Facebook’s advertising standards.
3. [Restriction on usage of shared custom lists](https://www.facebook.com/business/help/1452187872132363) for custom audiences.
   1. Ad accounts within the same business portfolio that share customer list custom audiences should make sure that all people with permissions to manage ad campaigns have the same business email domain. If the domains don’t match, then those shared customer lists will be unavailable for ads about housing, employment, and financial products and services.
   2. Usage of shared audiences from different business portfolios will not be permitted for ads about housing, employment, and financial products and services.

### Use of Faraday in Compliance with the Special Ad Category

Since the emergence of the Special Ads category, Faraday has supported businesses whose ad sets are subject to its rules and restrictions. The primary impact of Facebook’s October 2024 announcement is to increase the type of businesses that will be subject to these rules and restrictions.

If you are a business, whose Facebook ad sets are labeled a special ad category, Faraday recommends the following steps to ensure compliance of any custom list generated through the Faraday platform:

1. **Segmented audiences** — If you are using Faraday cohorts to generate a custom list, you should avoid building cohorts that specifically segment on restricted traits like age and gender, restricted locations like zip code, or other [prohibited information.](https://www.facebook.com/business/help/361948878201809?id=188852726110565)
2. **Modeled audiences** — If you are using a Faraday outcome (aka predictive model) to generate a modeled audience, you should:
   - Use trait blocking when you [build your outcome](https://faraday.ai/docs/academy/dashboard/making-predictions) to ensure that all traits associated with protected classes (e.g. age, gender, ethnicity, religion marital status, etc.) are not considered when building a predictive model. This ensures that your models will not knowingly consider any data that would violate Facebook’s policy against discrimination.
     ![Screenshot of Faraday dashboard](/images/blog/facebook-special-categories-1.png)
   - Use Faraday’s built in bias mitigation to account for implicit bias. Simply blocking traits from model training, does not account for the fact that there is often a correlation between other Faraday traits and certain protected class traits. [Faraday’s bias mitigation,](https://faraday.ai/blog/faraday-bias-mitigation-machine-learning) allows a user to neutralize or even correct for historical bias within their lead and customer base.

![Screenshot of Faraday dashboard](/images/blog/facebook-special-categories-2.png)

Please see [this blog post](https://faraday.ai/blog/how-to-optimize-ads-for-compliance-in-finance), for a more in depth look at common Faraday supported ad strategies that comply with financial regulations.

How to use Faraday for ads when you're in a special category

Facebook special ad category overview

I’m really happy to share some company updates with you, including new financing and additions to our leadership here at Faraday.

![Robin Spencer](/images/authors/robin.jpg)

First up is that [Robin Spencer](https://www.linkedin.com/in/robinbspencer/) has joined as COO. She arrives here from Clearbit, where she was COO for several years leading up to their acquisition by Hubspot in December. Robin had been working with us quietly behind the scenes for much of the year and has had a tremendous impact on our team and company strategy already. I couldn’t be more pleased that she’s now fully on board to steer our go-to-market teams and business operations through the next stage of scaling. I can already tell Robin is a game changer for us.

Here’s what she has to say about joining Faraday:

“It is special to return to Vermont and continue doing the work I love for a company built here. I'm grateful for the opportunity to work with a team that genuinely cares about our clients and each other. Faraday has a lot of potential, and I'm eager to help us realize it so we can support more companies.”

![Peter Levine](/images/authors/peter.jpg)

I’m also thrilled to announce that my friend [Peter Levine](https://a16z.com/author/peter-levine/) has joined our board as an independent board advisor. (While Peter is a partner at a16z, this position is in a personal capacity.) From his days as an operator to his role now at arguably the best tech venture firm in the world, Peter brings a massive amount of best practices knowledge on how great software companies grow and thrive.

I also asked Peter to share some thoughts on why he’s excited about Faraday:

“As soon as I met Andy and the team at Faraday I knew this company was something special. In a world where AI makes big promises but rarely delivers on them, Faraday has found a genuinely practical application that’s been helping thousands of brands for over a decade.”

Finally, we’ve closed some additional financing. Longtime Faraday ally Intercap, headed by board member Jason Chapnik, led the round as part of a coalition that included some of our earliest angels. Jason noted: “Faraday had been developing its technology for several years before the rest of the world caught on to AI. Now is the time for us to capitalize on our lead.”

Other founders will know that a startup is a community—and this remains the case even after almost 12 years. When longtime neighbors double down and new ones show up to help, really magical things can happen. 2025 is going to be a great year.

Robin Spencer joins as COO, Peter Levine joins the board, and we closed some additional funding.

New additions to the team, board, and financing

Faraday’s [customer behavior predictions](https://faraday.ai/features/customer-behavior-predictions) have a wide variety of use cases. From enhancing your customer engagement through personalization via [bespoke personas](/stories/persona_analysis) to embedding predictive lead scores in your [field sales app](/blog/salesrabbit) to increase rep productivity and drive growth, there’s no shortage of applications.

One application that’s proving to be more and more popular with our clients is geotargeting audiences based on the locations of physical stores and branches to enable personalized campaigns with a focus on retail shoppers. What this means is that these clients are able to specifically find the most likely buyers within a certain geographic radius around a point of interest, then run campaigns to draw people to their outlets, branches, and other physical locations.

In this guide, we’ll walk through how to create a geotargeted audience with Faraday.

## How to geotarget with Faraday

### Connect your data

To start, you’ll want to make sure that you have a [connection](/docs/abstractions/connections) created to wherever your data is stored. Doing this allows your customer predictions to seamlessly deploy back to your stack. If you’ve already got your connection created, double check that your [dataset](/docs/abstractions/datasets) is ready as well, as that’s how you’ll describe your data to Faraday–who’s who and what pieces of data mean what.

Both of these resources can be created with Faraday's API via the [createConnection](/docs/reference/createconnection) and [createDataset](/docs/reference/createdataset) requests, respectively. While these aren't required in order to complete the steps in this guide, they're required for you to be able to deploy your predictions directly back to your stack, or to add additional event-specific parameters mentioned in the [cohort section](/blog/how-to-create-a-geotargeted-audience-api#create-your-cohort).

### Create your place

In Faraday, you can create a point of interest–a **place**–around which you can create radius-based cohorts, or groups of interesting people. To do this, you’ll need your Faraday API key, which can be found in the _account_ tab of your [settings page](https://app.faraday.ai/settings/account).

[![Location of API key in Faraday](/images/blog/product/api-key-location.png)](/images/blog/product/api-key-location.png)

Once you’ve copied your API key, head over to the [createPlace](/docs/reference/createplace) request in API reference docs. Here, you’ll paste your API key into the `Token` field, then update the `addresses` field to include the address of the point of interest.

[![Location of createPlace API request in Faraday docs](/images/blog/product/geotargeted-audience/createplace-request.png)](/images/blog/product/geotargeted-audience/createplace-request.png)

A place can include more than one address, so if you’d like to take advantage of a radius around _all_ of your locations, you can add them all to a single place request within the `address` parameter. Here’s what your request might look like for a single address, though:

```bash
{
  "addresses": [
 {
   "city": "Burlington",
   "house_number_and_street": "431 Pine Street",
   "postcode": "05401",
   "state": "VT"
 }
  ],
  "name": "My Storefront"
}
```

Once your address info has been entered–don’t forget to give the place a name, like “My Storefront” above–then click **send API request** to create the place.

In the response window, grab the **place ID**, which you’ll use to create your radius-based cohort around your new place.

[![Location of createPlace API response in Faraday docs](/images/blog/product/geotargeted-audience/createplace-response.png)](/images/blog/product/geotargeted-audience/createplace-response.png)

### Create your cohort

With the place you’d like to geotarget your predictions around ready, you’re now ready to create the [cohort](/docs/abstractions/cohorts) of people that you’ll target with that place. To do this, you’ll use the [createCohort](/docs/reference/createcohort) request.

[![Location of createCohort API request in Faraday docs](/images/blog/product/geotargeted-audience/createcohort-request.png)](/images/blog/product/geotargeted-audience/createcohort-request.png)

In your request, you can optionally add parameters for things like `min_count` / `max_count` or `min_value` / `max_value` if you've created a connection and a dataset, but we’ll focus on the `place_conditions` parameter as that’s where you’ll specify the place you just created.

You can customize the cohort with `recency`, `frequency`, and `value` parameters based on the event you use to define the cohort, but for this example we’ll keep it simple and create a cohort using the following parameters:

1. A **name** for the cohort.
2. A **distance** of 10,000 meters around the place created above. Note that this value _must be in meters_.
3. The **place ID** saved from the createPlace request.

```
{
  "name": "Leads within 10km of storefront",
  "place_conditions": [
	{
  	"distance": 10000,
  	"place_id": "b1ab0efb-f732-478f-8310-6882383c4107"
	}
  ]
}
```

You can also optionally include an [event stream](/docs/abstractions/events) using the `stream_name` parameter if you’d like this radius-based cohort to also filter people that have completed an event of some kind.

With the createCohort request ready, click **send API request**, after which you should see a **201 created** response for a success.

Voilà, you’ve got a radius-based cohort created based on all your leads within 10km of your storefront.

With it, you can add it to a [pipeline’s](/docs/abstractions/pipelines) _population to include_ in order to target this group of people with your predictions. Add both their likelihood to convert via [outcome](/docs/how-to/predictions/likelihood-to-convert) as well as their customer persona via [persona set](/docs/how-to/predictions/customer-personas) to maximize effectiveness for your next direct mailer.

A guide on creating a geotargeted audience using Faraday's API.

How to create a geotargeted audience with Faraday’s API

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

Our [Powered by Faraday](https://faraday.ai/solutions/powered-by-faraday) partners work with Faraday to supercharge their features with customer predictions, ensuring their customers receive the best solutions on the market.

To accomplish this goal, they need a subaccount for each of their customers so that all data is siloed and secure. In this guide, we'll walk through the steps to create a subaccount.

## Finding your Faraday API key

The first thing you'll need in order to create a subaccount is your Faraday API key. You can find this in the Faraday Dashboard, under **Settings** in the left-hand nav, then **Account**. Click **copy** next to _production environment_.

[![Location of API key in Faraday's Dashboard](/images/blog/product/api-key-location.png)](/images/blog/product/api-key-location.png)

## Creating a subaccount with the Faraday API

Next, head over to our [API reference docs](/docs/reference), and specifically the [createAccount](https://faraday.ai/docs/reference/createaccount) request.

Here, you'll want to do three things:

[![Arrows pointing to three steps to create a subaccount in Faraday's reference documentation](/images/blog/product/api-request.png)](/images/blog/product/api-request.png)

1. First, paste your API key into the **token** field.
2. Next, replace "Acme Wind Turbines" with the name you'd like to give your subaccount. We suggest something like "Your Company Name - Customer Company Name."
3. Last, click **send API request**.

With these three steps done, your new subaccount will be accessible via the account switcher in the upper left corner of the Dashboard.

[![Location of account switcher in Faraday](/images/blog/product/account-switcher.png)](/images/blog/product/account-switcher.png)

A short guide on creating a subaccount using Faraday's API.

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