## 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?

[Propensity predictions](https://faraday.ai/features/predictions) indicate the likelihood of an individual to perform a defined action (buy, buy again, churn, etc.), which enable businesses to grow and retain revenue by engaging with the right customers, leads, or audiences at the right time. To get accurate propensity predictions, you need a good propensity model. Here’s how Faraday approaches propensity modeling.

## Selecting the right propensity model

Our prediction system classifies against outcomes using an array of predictive algorithms, including individual decision trees, random decision forests, logistic regressions, and neural networks. At run time, we are attempting, in each case, to figure out which algorithm works best. Usually it’s random decision forests.

## How random decision forests work

Random decision forests are made up of individual decision trees, which are classifier algorithms that look like flow charts, showing the choices made to reach a certain outcome.

Predictions aren’t based on a single tree, though; we use dozens of trees to improve the accuracy of the algorithm, hence the random decision forest. We look at the universe of trees, their splits and branches, and choose the ones that are the most promising when it comes to creating a path towards the desired outcome. This path leads from seemingly arbitrary data to a classification: the “yes” or “no” to the outcome. In other words, we're defining someone as a “good potential customer.” (In the image above, the “yes” is “mostly converted” and the “no” is the “mostly did not convert.”)

While the classification seems binary, the reality is that often there's not a clear “yes” or “no.” We are dealing with the likelihood that someone performs an action, and that is never definitively 100% or 0%. During the prediction process, we employ propensity scores, which are a way of saying, “This person is more likely to be a good potential customer than not.” Every individual in the group we’re looking at is scored, and those in the top percentiles are recommended to the client as the best fits for the outcome they want to realize.

_Now, if there are so many predictive algorithms we can use, why do we choose random decision forests?_

## Benefits of predicting propensity using random decision forests

### Building explainable models

Well, for one, they’re the most explainable; with so many decision trees at play, we have a strong sense of the different importances at each node split in a tree. These importances show us which attributes contribute most toward the success of an outcome, or the most toward failure.

### Handling missing data

The random decision forest technique also handles missing data very well, especially compared to a logistic regression or neural network, which both tend to require values to make predictions. In a decision tree, if there is a missing value, it moves onto the next node because there are other decision paths in the case of missing data. When you scale that to a whole random decision forest, the prediction becomes very accurate and isn’t perturbed by missing data.

### Identifying collinearity

A third reason we use random decision forests is collinearity. Random decision forests may see that some data are correlated — say, mortgage value and income. A lot of the attributes the algorithm deals with are linearly related but don’t necessarily directly drive a certain customer behavior.

A random decision forest algorithm finds areas of greatest information gain, meaning that it may realize there is interdependence between attributes, but it will choose the feature that gives the algorithm the greatest leverage over the prediction. This is particularly helpful for us, as we deal with tremendous amounts of customer data, and data on humans is generally noisy and often subverts expected distributions and assumptions.

## How propensity predictions help improve marketing efforts

Propensity predictions are especially helpful in improving campaign targeting efforts across a number of marketing channels. By identifying individuals ranked in the top percentile of the analyzed group—those that have the highest likelihood of performing the desired action—businesses typically see improvements in efficiency metrics (cost per acquisition, return on ad spend, etc.). Here are some popular business use cases for propensity predictions:

- Reaching likely buyers
- Driving repeat purchases
- Scoring and prioritizing leads

Learn how propensity models help predict the likelihood of individuals to exhibit a defined behavior.

How propensity models help predict customer behavior

If you're in marketing or sales, you've probably already heard of the [Telephone Consumer Protection Act (TCPA)](https://www.twilio.com/docs/glossary/what-is-telephone-consumer-protection-act-tcpa), which regulates how businesses can communicate with consumers through telemarketing calls and texts. You also probably knew that in 2023, they introduced a new [1:1 Consent rule](https://docs.fcc.gov/public/attachments/DOC-408396A1.pdf), which aimed to close the [lead generator loophole](https://www.orrick.com/en/Insights/2023/12/FCC-Closes-TCPA-Lead-Generator-Loophole-Requires-One-to-One-Consent?utm), tightening consent requirements and limiting how consumer phone numbers are distributed. This would have required businesses to solicit individual consent from each consumer they planned to contact by phone.


## So, what happened to the rule?

Although originally scheduled to take effect today (January 27, 2025), at the last minute, a postponement was announced, pushing the effective date to January 2026\. However, just hours after this delay, the [Eleventh Circuit Court vacated the rule entirely](https://natlawreview.com/article/eleventh-circuit-vacates-tcpa-one-one-consent-rule-immediately-after-fcc-postpones), eliminating its implementation altogether. So for now, the existing TCPA consent requirements remain unchanged.

**With the TCPA 1:1 Consent rule:**

For companies that rely on purchasing large lists of leads to grow, this rule would have required a significant shift in strategy. By curbing the "spray and pray" approach of casting a wide net with unqualified prospects, businesses would need to pivot toward precision targeting and engagement with smaller, more qualified audiences. While this might initially seem like a challenge, it offers a tremendous opportunity to refine marketing campaigns and focus resources on higher-value prospects.

The benefits of this shift would be multifold. Businesses would see an increase in ROI from more intentional and personalized campaigns, as every interaction would be designed to resonate with the recipient. Consumer trust would also improve, as individuals would have a clearer understanding of who is contacting them and why. The era of vague, broad-based consent would give way to meaningful, 1:1 engagement that prioritizes quality over quantity.

This approach would not only reduce waste but also empower companies to connect with the right consumers at the right time. While these changes might seem daunting, they would pave the way for more effective, trust-driven marketing—a strategy that businesses could turn into a competitive advantage with the right tools and expertise.

**Without the TCPA 1:1 Consent rule:**

We’re back to the world of massive, unqualified lists. Without the additional compliance requirements, businesses can continue to cast a wide net in their telemarketing efforts. However, this approach risks higher opt-out rates, lower engagement, and potential reputational harm if messages are perceived as spam. 

## You don’t need to wait for the government to tell you try smarter targeting, here’s how Faraday can help today

Whether the 1:1 Consent rule returns or not, targeting remains a critical factor in marketing success. At Faraday, we prioritize quality over quantity, helping businesses focus on the most qualified prospects instead of chasing unqualified leads. With our predictive tools, companies can:

- **Target the right consumers**: Faraday can analyze market trends and [customer data](https://faraday.ai/features/faraday-identity-graph) to identify the [consumer segments](https://faraday.ai/templates/customer-targeting) most likely to show interest in engagement. This ensures companies can focus their efforts on earning explicit consent from likely-to-convert individuals.  
- **Use the right tools**: With fewer leads available, focusing on the right prospects is more crucial than ever. For instance, instead of focusing on [lead rejection](https://faraday.ai/templates/lead-rejection), since the lowest common denominator of leads won’t be there already, use that budget instead on tools like [lead routing](https://faraday.ai/blog/how-to-use-ai-to-deliver-a-better-quality-pipeline) to make sure the new leads make it to the ideal sales rep and [adaptive discounting](https://faraday.ai/templates/adaptive-discounting) to pique their interest.  
- **Maximize value:** After you win your best-fit customers, leverage the various predictive tools available through Faraday like [churn scores](https://faraday.ai/blog/churn-prevention-machine-learning) and [product recommendation](https://faraday.ai/templates/next-best-offer) to ensure that you maximize their lifetime value.

Regulatory changes may come and go, but Faraday’s data-driven approach ensures that businesses stay ahead, delivering effective and efficient marketing strategies no matter the landscape.

## Need help navigating this change?

The world is full of unexpected outcomes, like the 1:1 Consent rule getting canceled hours before it went into effect\! At Faraday, we’re ready to help you adjust your strategy to target the highest-quality leads and stay ahead of industry trends regardless of how regulations change. Reach out today to ensure your lead generation stays ahead of the curve\!

The TCPA's new 1:1 rule would have required individualized consent to contact a lead by phone but was vacated at the last minute.

TCPA 1:1 Consent rule vacated, but smarter targeting with Faraday is already here

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

Is the artificial intelligence hype cycle approaching the inevitable trough of disillusionment, as hype and reality collide head-on? Is the AI bubble, much like the internet bubble of years past, about to burst?

AI is moving at a frantic pace, and with its advancement comes a massive amount of investment, which has led to inevitable market saturation and a lot of inflated expectations. As of the summer of 2023,[ ](https://www.gartner.com/en/newsroom/press-releases/2023-11-28-gartner-hype-cycle-shows-ai-practices-and-platform-engineering-will-reach-mainstream-adoption-in-software-engineering-in-two-to-five-years)**[Gartner](https://www.gartner.com/en/newsroom/press-releases/2023-11-28-gartner-hype-cycle-shows-ai-practices-and-platform-engineering-will-reach-mainstream-adoption-in-software-engineering-in-two-to-five-years)** had already identified that generative AI was reaching the 'peak of inflated expectations' and was about to enter the dreaded 'trough of disillusionment'.

Even the ever-promoting, all-is-well tech titans are starting to sound the alarm on overpromising about the power of AI. Recently both Amazon and Google have reportedly been[ ](https://www.theinformation.com/articles/generative-ai-providers-quietly-tamp-down-expectations?rc=kz8jh3&ref=wheresyoured.at)**[telling their sales teams](https://www.theinformation.com/articles/generative-ai-providers-quietly-tamp-down-expectations?rc=kz8jh3&ref=wheresyoured.at)** to tone down on hyping AI too much to customers. Today's AI leaders are concerned that they are overpromising, and underdelivering.

But all is not hype when it comes to AI, there are many real-world business applications where AI is making a big difference and delivering true value to both the business and its customers. Accordingly,[ ](https://www.gartner.com/en/webinar/567177/1278324)**[Gartner](https://www.gartner.com/en/webinar/567177/1278324)** expects 2024 to be the year where the companies that successfully deploy AI do so through a more targeted and disciplined approach.

So, what is AI hype and what is AI reality? And how are people using AI successfully in the real world to make a measurable difference to a business's bottom line?

## AI hype

The amount of AI hype is extraordinary, even in the world of technology hype. Many of us who have been around the block a few times have heard this story before. An amazing new technology arrives that will change the world, and then companies hype the technology endlessly, leading to market saturation, end-user fatigue, and missed expectations. Eventually, the technology makes its way into mainstream adoption, but typically through more distinct use cases and targeted real-world applications. This process is what **[Gartner's Hype Cycle](https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle)** attempts to represent graphically.

What is AI hype? When you go to a technology conference and an AI futurist talks in vague generalities about how AI will solve every future problem while giving no specific use cases. Or when omnipresent tech CEOs recycle their chatbot marketing from 30 years ago, put an AI in front of it, and tell you your AI assistant will help you in every conceivable way, like a virtual butler. This is AI hype.

For the past year or so, the AI hype discussion has been dominated by generative AI. And while some demos of generative AI were promising, their real-world application at this time has been oversold somewhat to the general public and business world.

## AI reality

While up to now generative AI has gotten much of the attention, predictive AI has become the hot topic of conversation in business circles for delivering immediate business value in challenging economic times. Predictive AI can work in tandem with generative AI leveraging the best of both technologies, but you need the right software to combine the algorithms for true value.

With predictive AI, more specifically predictive modeling and predictive analytics, companies today generate real business value in the short to mid-term by predicting customer behaviors by enabling more effective and efficient customer engagement within the customer experience.

By using software like the[ ](https://faraday.ai/)**[Faraday](https://faraday.ai/)** predictive analytics and predictive modeling platform, you can have the AI infrastructure needed to predict customer behaviors like likelihood to convert, likelihood to churn, forecast spend, and product recommendations. With these predictions, you can drive data-driven customer experience use cases like AI lead scoring, churn prevention, adaptive discounting, and next-best offer.

Platforms like Faraday are real-world targeted AI, not some general-purpose AI platform for generating news headlines. Faraday focuses on predicting the behavior of individuals, with built-in Responsible AI, including features like bias mitigation and prediction explainability.

## More real-world AI use cases

While improving the customer experience with predictive modeling and predictive analytics is a top use case for real-world AI implementation, there are several other primary use cases where AI is making significant inroads.

One of the areas already making a huge impact is predictive AI and generative AI being used to empower software developers, aka employee empowerment, allowing them to code quicker, improve time-to-market, and enhance their data with predictive algorithms.

Let's face it, existing developers are maxed out with work, and they can't build things as fast as companies want, plus they don't enjoy their coding jobs when they work on an assembly line. Today's AI coding assistants are intended to handle more routine tasks, allowing developers to focus on higher-value work. But to work well, they need to be combined with predictive AI engines like Faraday powered with Responsible AI.

"AI-augmented and machine learning (ML)-powered software engineering is changing the way software is being created, tested and operated, and the need for responsible AI is growing," said Dave Micko, Senior Director Analyst at Gartner. "Practices such as platform engineering will begin injecting insights from deployed systems into the systems being developed."

## Conclusion

The AI hype machine has done its job. People are inundated with AI marketing to the point no one knows what is hype and what is reality. Yes, generative AI is exciting and has tons of future promise. But right now it's more in the demo stage of development.

While many generative AI apps do not need predictive algorithms, for many traditional business applications, using predictive AI plus generative AI can be a good optimization to move the needle on your business.

Predictive modeling and predictive analytics are where businesses need to put their technology investment dollars to start to see real returns from AI, in the short-to-mid term.

But how a company moves forward in this process is critical. AI must be done safely and responsibly to ensure that the consumer's, business's, and community-at-large best interests are maintained.

This post explores the current state of AI, contrasting the widespread hype with real-world applications that deliver tangible business value. It highlights the overpromising nature of generative AI and emphasizes the immediate benefits of predictive AI, urging companies to adopt a targeted and responsible approach to AI implementation.

AI hype vs reality: AI in the real world

The first step to grow your sales pipeline is to improve your lead generation management. By optimizing your lead generation with predictive lead scoring, you can generate more qualified leads, improve profitability, and ROI, and lower costs.

Some of the most forward-looking businesses in the world are leveraging AI and predictive lead scoring to help them identify and prioritize the most promising and valuable leads based on the criteria that matter most to their particular organization and industry. AI and ML algorithms can ingest and analyze all sorts of data, from behavioral to transactional to demographics, and from multiple sources, including CRM platforms, websites, email, social media, and analytics platforms, to score those leads based on the likeliness to convert.

AI, predictive lead scoring, and predictive modeling allow your company to focus on the leads that matter to the health and profitability of your business, aka your most valuable customers, and avoid wasting your time and resources on low-quality leads.

By using AI, companies can optimize their sales and marketing pipeline by automating and personalizing some or all of it. Depending on the software and technology you apply to your pipeline, AI-driven tools can assist in the optimization of your customer communications in their buyer journey, triggering them at the right times, frequency, and channels to improve response rates, customer engagement, and conversions.

## How to use AI to forecast spend and optimize sales

Leveraging AI and predictive insights, businesses can now get a better handle on forecasting and optimizing sales. By feeding AI/ML algorithms with not just historical and real-time observable data, but also loading them with predictive data informed by machine learning, you will be better able to predict accurate outcomes, estimate revenue, and forecast spend.

With AI and predictive modeling, business can now optimize their sales and marketing pipelines. Predictive AI software allows companies to recommend next best actions, like being offered a discount or following up, to trigger them and move them along the customer journey toward a sale. The right AI-driven software will embrace predictive AI algorithms to detect churn and determine the likelihood of a prospect or customer buying, converting, or churning.

## The power of modeling lead prioritization and lead assignments

Lead routing, or lead assignment, is where a lead goes and who it gets assigned to when it comes into your company. Lead routing can be done manually like a lot of other processes, but as your company grows, and your customers become more diverse, automating lead routing, lead prioritization, and lead assignments with AI and predictive modeling to better route those leads. With predictive AI, you can do much more advanced routing and prioritization, based on criteria such as geography, deal size, industry, and other factors germane to your sales makeup. With the right AI-driven software, organizations can use rules-based routing to assign leads to the right sales associates, ranking and prioritizing those leads based on the likelihood to convert.

## Get AI lead prioritization with Faraday  

By using **[Faraday](https://faraday.ai/)** predictive modeling and AI platform, businesses can convert more leads by using AI-driven predictions to optimize how you prioritize, route, and engage leads. With their software, companies can anticipate the behavior of each lead in their funnel, essentially turbocharging your lead handling workflows with AI.

Faraday provides the data and the predictive AI modeling infrastructure (IaaS) that enables organizations to iterate on their business challenges, instead of their technology infrastructure. With our platform, brands, and the software companies who support them, can now develop end-to-end customer experiences, at scale, driven by predictive insights.

In the end, a data-driven CX is a better CX. Faraday is the only predictive AI platform, both for **[software teams](https://faraday.ai/solutions/powered-by-faraday)** and **[brands](https://faraday.ai/predictions-for/brands)**, dedicated to predicting customer behavior. Everything you need to make customer experiences predictive is built into our software.

## The new normal

Predictive customer behaviors will increasingly drive the unified customer experience until all customer interactions are predictively AI-driven. Brands and organizations looking to leverage AI and predictive insights for business and customer value should start by using AI and predictive modeling to improve the quality and routing of their marketing and sales pipeline.

By optimizing your lead generation with AI-powered routing, lead scoring, and lead prioritization, organizations can run programs more effectively and efficiently, improving business results and consumer value.

Businesses can enhance their sales pipelines by using AI and predictive lead scoring to improve lead generation, prioritize high-value leads, and optimize customer interactions. AI-driven tools can help forecast sales, route leads, and create predictive customer experiences.

How to use AI to deliver a better quality pipeline

When you are marketing and selling consumer products, every lead counts. However, not all leads are created equal. As your business matures, it's crucial to move beyond simply filling your pipeline to effectively prioritizing leads that are most likely to convert. This is where the power of predictive lead scoring comes into play.

## What is lead scoring?

Lead scoring is the method marketing and sales departments use to rank the quality of a lead, typically in the form of a numerical point system. Leads get scored based on attributes critical to the business, including demographics, transactional behavior, and customer engagement, giving sales professionals the most promising potential customers to contact. Lead scoring systems are fed with past leads and historical data to inform the current scoring model and are typically handled within an enterprise's CRM system.

But traditional, manual lead scoring has its drawbacks. It's time-consuming for employees who could be spending their time in more productive ways, and it can result in best guesses and other types of inconsistencies in the application of the methodology.

## What is predictive lead scoring?

Predictive lead scoring represents a huge step forward for marketers and sales departments for qualifying leads at scale. By using machine learning algorithms, organizations can analyze enormous amounts of data and identify patterns to sort and prioritize leads, improving the quality of lead scoring mechanics. Predictive AI engines can ingest anything from CRM data, online data, IoT data, and behavioral data to help predict what leads will be the most likely to convert into customers.

During challenging economic times, budgets are tight, and sales and marketing teams are looking for ways to reduce costs and improve the efficiency and efficacy of their sales funnels. By leveraging AI-driven predictions, businesses can convert more leads by using those predictions to optimize, prioritize, route, and engage leads. Marketers can anticipate each lead's behavior in their funnel by infusing AI into their lead-handling workflows.

Predictive lead management works better than traditional lead management. The challenge is good predictions require robust and accurate data, which means resolving every detail down to the identity level. Only the right predictive software infrastructure will be able to address the data-oriented challenges to deliver effective predictive AI lead scoring.

## The benefits of predictive lead-scoring

With predictive lead scoring, organizations can expect to see a variety of benefits. They will be more data-driven overall, leveraging not just observed data, but predictive data to improve algorithm performance. You will get better efficiency in your prospecting with better conversation rates and shorter sales cycles. Plus, you can look forward to an improved alignment and democratized data connection between marketing and sales establishing a single source of data-driven truth for both departments to rally around.

## Three kinds of predictive lead scoring to consider

There are different types of predictive lead scoring, so it's important whatever software you choose can accomplish these three primary activities.

1. **Lead rejection** — Does your organization purchase leads from lead generators and/or aggregators? Many of these leads are not worth buying. If your lead platform supports it, you can use predictive lead scoring to reject leads that don't meet a minimum conversion probability threshold, saving you significant time and money.
2. **Lead suppression —** Does your organization spend money engaging every lead—for example, with direct mail—whether self-generated or purchased? Not every lead is worth the expense. You can use predictive lead scoring to suppress leads below a minimum conversion probability threshold—another way to save significant time and money.
3. **Lead prioritization —** Does your organization engage your incoming lead funnel with a resource-limited sales team? In many cases, this means not every lead can get the same attention, and few can get the kind of attention and engagement you know will maximize conversion. You can use predictive lead scoring to ensure your best leads get handled first and with the most robust level of engagement. This is another way to boost revenue with predictive lead scoring.

## The challenges of predictive lead scoring

One of the top challenges of deploying any type of predictive engine is making sure it is fed with quality data. The accuracy of the predictions will be driven directly by the quality of the data input into it. As the old saying goes, garbage in, garbage out (GIGO). You must have the right software platform necessary to make sure that data is complete, consistent, and accurate.

The other primary issue with predictive AI lead scoring is there is an ongoing process that involves the consistent refinement and retraining of data sets so that the predictive models remain accurate as new data comes in. These patterns can change over time.

## Faraday delivers predictive lead scoring

Patterns that predict behavior in the early stages of a customer journey eventually stop working and new ones take over. Only[ ](https://faraday.ai/)**[Faraday](https://faraday.ai/)** uses time-linked model ensembles to tell the true customer story. Faraday detects and selects the best tools for the job, from time-tested algorithms to cutting-edge generative AI Everything you need to predict customer behavior is built-in to the platform. With Faraday's rich, built-in data, you can skip expensive licensing deals and start predicting behavior immediately.

**[Faraday](https://faraday.ai/)** provides the data and the predictive AI modeling infrastructure (IaaS) that enables organizations to iterate on their business challenges, instead of their technology infrastructure. With our platform, brands, and the software companies who support them, can now develop end-to-end customer experiences, at scale, driven by predictive insights.

In the end, a data-driven CX is a better CX. Faraday is the only predictive AI platform, both for **[software teams](https://faraday.ai/solutions/powered-by-faraday)** and **[brands](https://faraday.ai/solutions/lead-management)**, dedicated to predicting customer behavior. Everything you need to make customer experiences predictive is built into our software.

Predictive lead scoring uses machine learning to analyze large datasets and prioritize leads most likely to convert, enhancing efficiency and effectiveness for marketing and sales teams. By leveraging AI, businesses can improve conversion rates, shorten sales cycles, and ensure alignment between marketing and sales, provided they maintain high-quality data and regularly refine their predictive models.

Why you need predictive lead scoring today

Customer experience is THE differentiator for a modern, successful business. Being able to provide a compelling, valuable CX can lead to a variety of benefits, including improved customer satisfaction, better retention, and improved business performance.

Today's businesses are well aware of the critical nature of the customer experience in their future. **[80% of companies](https://www.zippia.com/advice/customer-experience-statistics)** that focus on CX report seeing an increase in revenue, while brands that are more customer-centric and focused on the customer experience report **[60% higher profits](https://cxindex.com/company/blog/20-statistics-to-understand-the-rise-and-value-of-cx/)**. Companies that update their CX to be more data-driven find that by improving the customer experience they can **[reduce customer support costs by up to 33%](https://cxindex.com/company/blog/20-statistics-to-understand-the-rise-and-value-of-cx/)**. And companies are putting their money where it counts when it comes to the customer experience. Global CX spending is expected to reach **[$641 billion in 2024](https://cxindex.com/company/blog/20-statistics-to-understand-the-rise-and-value-of-cx/)**.

It's not just businesses that want a quality, value-driven CX. Consumers are willing to pay good money when interacting with a high-level customer experience, so long as they get something in return for it. For instance, consumers are willing to pay a **[16% price premium](https://cxindex.com/company/blog/20-statistics-to-understand-the-rise-and-value-of-cx/)** in return for a superior customer experience. Data shows that customer spending increases by up to 140% following a positive customer experience.

And don't kid yourselves, today's consumers are more empowered than ever before and will bolt for another brand if they don't have a good customer experience. **[49% of customers](<https://chainstoreage.com/bad-customer-service-puts-brands-risk#:~:text=Roughly%20half%20(49%25)%20of,24%2F7%20customer%20service%20support>)** who left a brand that they are loyal to say it's due to poor CX.

But for brands to be successful in executing a superior CX, they must come to terms with the fact that the foundation of a value-driven customer experience is quality, accurate customer data. The reality is a data-driven customer experience is a better CX.

And while up to now the CX was predominantly fed with more traditional "observable" data, including first-party data like pages visited, clicks, adds to cart, and time on site, for the customer experience to proactively understand customer behaviors it must deploy predictive AI algorithms to deliver to consumers what they desire before they have had a chance to act on it.

## What is predictive AI and generative AI?

The two primary AI models being deployed today in the enterprise business world are predictive AI and generative AI.

Predictive AI uses historical data to find patterns and anticipate future potential outcomes, while generative AI is more focused on the creation of new content assets, like images, text, and video from that data.

With predictive AI, businesses can better understand what their customers need, and what they care about. In this, businesses can finally move beyond making best guesses when detecting churn, scoring leads, recommending next best actions, forecasting spend, and making smarter decisions based on solid predictive data.

These two approaches to AI can be complementary if their algorithms are combined with the right software infrastructure that addresses data-oriented challenges like data quality, unified data integration, data integrity, identity resolution, and global data privacy compliance.

## To be proactive, not reactive

Making decisions from aging data is always going to put a business on the back foot. They will be forever reacting to something that happened in the past. But if an organization can predict future outcomes, it can be prepared ahead of time for the actions of customers, market fluctuations, and even challenging economic headwinds. Predictive AI delivers the knowledge of the possibilities of what might occur and allows a brand to prepare a response before it happens.

To do this companies must invest in the right AI technology infrastructure that will give them robust, dynamic predictive data to feed their CX systems. **[19 out of every 20 CX leaders](https://www.cxtoday.com/data-analytics/96-cx-leaders-are-expanding-their-customer-data-strategies/)** have invested in or plan to invest in data integration, data integrity, or data enrichment technologies.

**[Faraday](https://faraday.ai/)** provides the data and the predictive AI modeling infrastructure (IaaS) that enables organizations to iterate on their business challenges, instead of their technology infrastructure. With our platform, brands, and the software companies who support them, can now develop end-to-end customer experiences, at scale, driven by predictive insights.

In the end, a data-driven CX is a better CX. Faraday is the only predictive AI platform, both for **[software teams](https://faraday.ai/solutions/powered-by-faraday)** and **[brands](https://faraday.ai/features/customer-behavior-predictions)**, dedicated to predicting customer behavior. Everything you need to make customer experiences predictive is built into our software.

## Conclusion

How important is the customer experience to the modern business? According to **[Gartner](https://www.gartner.com/en/doc/3874972-realizing-the-benefits-of-superior-customer-experience-a-gartner-trend-insight-report)**, 80% of organizations expect to compete mainly based on CX. **[90% of businesses](https://cxindex.com/company/blog/20-statistics-to-understand-the-rise-and-value-of-cx/)**, regardless of their vertical market, have said they have made CX their primary focus.

For companies to realize this potential, they must have quality, accurate data to feed the CX system. By only using observed data companies remain inherently reactive, not proactive. To be proactive in serving their customer's needs, brands must deploy predictive AI software technology to deliver the dynamic, enhanced data needed to power the CX.

Through predictive analytics, brands can deliver a superior omnichannel customer experience, anticipate future trends, identify customers' behaviors and future needs, and proactively prevent customer churn.

To deliver the best customer experience, businesses must utilize predictive AI to anticipate and meet customer needs proactively, leveraging quality, accurate data to enhance customer satisfaction and drive business performance.

How to deliver the best customer experience possible with predictive AI

The customer experience continues to evolve. As consumers become empowered due to digitization, and their interactions with brands become more trackable and data-driven, companies must understand their customers more completely and intimately than ever before to differentiate themselves from their competition. This level of in-depth understanding of the consumer comes from robust, clean, and accurate customer data — both observed and predicted. Today, a data-driven customer experience is a better CX.

To achieve this type of dynamic customer experience organizations are investing in advanced tools like customer data platforms (CDPs), enhanced with [predictive AI software](/), enabling companies to gather and combine data into unified profiles so they can understand their customers more cohesively. Companies use those AI-driven insights to tailor a superior CX which can help improve the quality of customer interactions and resulting behaviors, like reducing churn. The predictive AI / predictive analytics software market is so integral to the development of a dynamic, data-driven CX that it is estimated to grow to $14.52 billion by 2028, according to [Statistica](https://www.statista.com/statistics/1286871/predictive-analytics-market-size/).

Customer acquisition costs (CAC) continue to grow, and in challenging economic times, organizations everywhere are looking to contain costs while maximizing value from their most valuable customers. When one considers that the cost of acquiring a new customer can be as much as five times as to retain one, improving retention strategy should be top of mind for any company that wants to survive and thrive in the modern business world.

By deploying a more AI-driven customer-centric approach to the CX, organizations can meet the rising expectations of today's customers, delivering the right CX at the right time to the right person, according to consulting firm [Capgemini](https://www.capgemini.com/insights/research-library/shaping-the-ai-enabled-customer-experience/).

Companies who deploy an AI-enabled customer experience can deliver a:

- 10% boost in revenue
- 20% increase in customer satisfaction
- 60% reduced effort in audience selection
- 13% decrease in operational costs

Unlike more traditional approaches, AI enables companies to continually tune their understanding of customers and dynamically reshape the CX to provide them, and the business, with true value. All to build long-term value (LTV) with your best customers.

## The challenges of observed data

What type of data is shaping today's modern data-driven CX? A few years ago, that would have been mostly from data mined from traditional survey-based methodology, what is today called zero-party data.

Zero-party data, according to Forrester, is 'data that a customer intentionally and proactively shares with a brand.' In other words, it is solicited data. It's also observed data, which is data that was collected by observing and recording events and behaviors as they occur without manipulation. While zero-party is very desirable data, due to its accuracy and direct relationship with the consumer, it's not the bread and butter of today's CX. That would be reserved for observable first-party data.

The customer data shaping today's CX is primarily made up of unsolicited first-party data like pages visited, clicks, adds to cart, time of day, and time spent browsing. This is the real data making up the data-driven CX. However, this data is reactive, and in the past. It needs to be enhanced with more predictive capabilities to deliver the dynamic data needed to meet a modern consumer's customer experience expectations.

The solution to providing the type of forward-looking data needed to create great customer experiences is for companies to begin leveraging predictive AI capabilities, like predictive modeling and predictive analytics derived via machine learning (ML), to enable companies to anticipate customer behaviors and see CX challenges and exploit opportunities in real-time.

## Predictive data drives the customer journey

Organizations that are powered by predictive insights can understand their customers as full people, interpret their interactions more completely, and trigger them at the right moments in their path along the customer journey for a desired action.

The use of predictive analytics and predictive modeling establishes a fundamental sea-change in how a dynamic customer experience is delivered to consumers. The reality is predictive customer behaviors will increasingly drive the unified customer experience until all customer interactions are predictively driven. Is your organization prepared to leverage AI-powered consumer insights for both business and customer value?

Leveraging consumer data for value is THE key differentiator for a successful modern business. But having quality, accurate data is only part of the challenge. The rubber meets the road when you determine how you will deploy that data to improve business performance, customer value, and ROI.

But all data is not equal. To do more forward-looking activities with your customer data, like train machine learning algorithms with it to inform AI, you will need to enrich it with third-party data and deploy the right software that enables predictive AI capabilities.

## How Faraday can get you there

[Faraday](/) provides the data and the predictive AI modeling infrastructure (IaaS) that enables organizations to iterate on their business challenges, instead of their technology infrastructure. With our platform, brands, and the software companies who support them, can now develop end-to-end customer experiences, at scale, driven by predictive insights. Predicting customer behavior enables more effective and efficient customer engagement within the CX. In business terms, this translates to better conversion rates which in turn creates higher revenue and reduces cost.

In the end, a data-driven CX is a better CX. A quality predictively driven CX will mean better retention for your most valuable customers, helping to reduce new customer acquisition costs in favor of a more long-term value retention strategy.

Faraday is the only predictive AI platform, both for [software teams](/solutions/powered-by-faraday) and [brands](/features/customer-behavior-predictions), dedicated to predicting customer behavior. Everything you need to make customer experiences predictive is built into our software.

## Looking forward

Predictive AI and Predictive Analytics deliver to companies the tools they need to increase customer retention and improve the customer experience. By leveraging customer data and predictive modeling, brands can understand their customer's behavior better than ever before and calculate churn for each customer.

Customer experiences everywhere are becoming more and more predictive, driven by advanced AI. It's only a matter of time until all customer experiences are predictive.

For more information on how to make your customer experience predictively driven by AI, contact [Faraday](/talk-to-sales).

As customer experiences evolve to be more data-driven, companies must leverage predictive AI and customer data platforms to anticipate customer needs, enhance interactions, and improve retention, thereby reducing costs and boosting long-term value.

Tech