Zeta Global Announces Successful Completion of Debt Refinancing

New term loan carries significantly lower interest rate, provides Company with additional capital and facilitates repayment of outstanding debt.

Zeta Global Logo

NEW YORK, NY – March 1, 2021 – Zeta Global, a marketing technology company that leverages unique data and predictive AI to help brands acquire, grow and retain customers, today announced that it has successfully closed a $222.5 million loan facility to provide additional capital to pursue new initiatives. This new debt is a combination of Term Loan A and Revolving Credit Facility, which gives the company increased flexibility.

“We are pleased to announce this refinancing, in conjunction with our banking partners, which serve a number of long-term advantages for Zeta’s business,” said David A. Steinberg, Zeta Global Co-Founder, Chairman, and CEO. “This will generate significant savings by reducing our interest by less than half the cost of previous loans and provide us with additional capital, which will serve to further expand Zeta’s cutting-edge capabilities. As we continue to improve the Company’s capital structure, we are working hard on new initiatives and are excited about the next chapter of Zeta.”

Previously, Zeta’s loans have been utilized as working capital to complete successful acquisitions and integrations of companies including AI pioneer BoomTrain, publisher commenting platform Disqus, content intelligence platform Temnos, and advertising suite platform Sizmek. All have strengthened the technology and data capabilities of the Zeta Marketing Platform, making it one of the leading marketing technology platforms in the industry today.

Chris Greiner, Chief Financial Officer states, “The strategic refinancing is a proactive step that meaningfully improves Zeta’s overall capital structure and strengthens our liquidity, reduces our borrowing costs and provides us with additional financial flexibility. We appreciate the continued support from our banking partners who see an abundance of opportunity in the future of our business.”

BofA Securities, Inc. served as Lead Arranger and Bookrunner. Barclays, Credit Suisse, and Morgan Stanley Senior Funding, Inc. are also participants in the facility. Matthews South served as the financial advisor to Zeta.


Zeta Global is a data-powered marketing technology company that combines one of the industry’s largest consumer data sets (2.4B+ global identifiers) with results-driven artificial intelligence to unlock consumer intent, personalize experiences, and power business growth for Fortune 1000 companies, such as GM, Wyndham, Sprint and Progressive. Zeta has been recognized as a Leader in the Forrester Wave™ and competes with marketing cloud offerings from Oracle, SAP, Salesforce and Adobe as well as programmatic platforms including The Trade Desk. Founded in 2007 by David A. Steinberg and John Sculley, the Company is headquartered in New York City. For more information, please go to www.zetaglobal.com.

3 Next-Generation Lookalike Modeling Trends for Marketing and Advertising

Is lookalike modeling important to your marketing efforts? Here are 3 next-gen lookalike modeling trends you need to know.

lookalike modeling trends

The exploding use of lookalike modeling within digital in the last decade is tied to the evolution of machine learning’s ability to drive ROI across the funnel. Yet there’s still a lack of clarity on the best lookalike modeling trends to use and where to use these approaches. Additionally, as most of the attention goes to the algorithms that power machine learning, very little consideration is given to the actual inputs (data) that fuel lookalike modeling trends in the first place. Given the complexity of lookalike modeling, this pattern is concerning. 

Lookalike modeling is both an art and a science.

The inputs (and how those inputs are combined) influence the eventual predictive power of a lookalike model more than any machine-learning algorithm.

As complex lookalike algorithms continue to become readily available, data becomes the ultimate differentiator.

Having predictive and proprietary data that is relevant to your business is not only a significant competitive edge. Moreover, it increases the predictive power of lookalike models, irrespective of the application of the model (customer retention, personalization, acquisition, or other CRM activities).

For example, testing the 3,500 proprietary data signals contained within Zeta’s Data Cloud indicated a huge influence on the predictive power of Zeta’s lookalike modeling for both CRM and acquisition. (In other words, using the right kind of data in your lookalike models is both a real differentiator and an advantage.) 

So, with all of that said, here are 3 Next-Generation Lookalike Modeling Trends for Marketing and Advertising…

Trend #1 – Lookalike models transition from supervised to unsupervised learning approaches

Lookalike models are used to characterize the dominant traits of a specific set of records (be they audience segments, campaign convertors, or something else entirely), especially against another set of records opposite in nature.

For instance, lookalikes of a set of convertors are built by mathematically teasing out the dominant traits of said convertors and then comparing them to a set of non-convertors. 

The algorithms that power this comparison-based lookalike model are known as “supervised learning algorithms”, and they do a great job of describing “labeled” audiences.

In the example above, the labels indicate whether a record is a “convertor” or “not a convertor.”

But it is in situations where labeled data is not readily available that true marketing and advertising ROI is achieved. For example…

  • Why does a customer make multiple purchases from you, and what are the traits of those customers? 


  • What insights are you missing from your campaign because you’re looking in the wrong place?

Answering these questions (and addressing other situations where labeled data is not readily available) requires incorporating  “unsupervised learning algorithms” into lookalike modeling.

Unsupervised algorithms don’t require labeled data and they don’t need to be “told” what to find. They can analyze the available data to find hidden patterns within campaigns, audiences, and more.

The patterns discovered are a goldmine of actionable opportunity for marketers and advertisers which is why the shift from supervised to unsupervised learning algorithms is happening slowly but surely. 

Trend #2 – Keeping humans in the loop to ensure modeling accuracy

The algorithms used to power lookalike models regularly provide an accurate approximation of what’s happening in the real-world.

However, they’re not perfect.

When allowed to run loose without any human oversight, they can create inaccurate models.

For example, given a set of inputs, an algorithm designed to identify lookalikes of a prospect who signs up for a credit card can over-index on traits that aren’t compliant with fair credit lending practices.

As a result, marketers and advertisers might fail to reach key underserved populations. To ensure inaccuracies like this don’t happen in their lookalike models, marketers and advertisers are increasingly keeping people in the loop.

By allowing human eyes to review the initial findings of a model and approve (or reject) its use for downstream purposes, marketers and advertisers can mitigate their exposure to risk from the deployment of an inaccurate model. 

Trend #3 – Data distribution using sub-models will power lookalike modeling

“Data privacy” is a buzzword in marketing and advertising these days—it commands the attention of consumers, regulators, and agencies alike.

As a result, no marketing or advertising professional wants to obtain insights about customers or prospects that aren’t privacy compliant. This reality casts doubt on the future of data appending by way of sharing owned, first-party data with external, third-party data vendors that provide enrichment services. 

Going forward, lookalike models will be built and distributed using multiple sub-models which will determine where the data resides.

The outputs from these sub-models will come together in a secure, cleanroom location where they’ll be collated so a primary modeling decision can be made.

This will be a departure from the current state of lookalike modeling where all the data required to build a model is pooled in one location.

Wrapping up lookalike modeling trends for marketing and advertising…

In the years ahead, lookalike modeling will continue to embrace more sophisticated, accurate, and distributed techniques that increase ROI.

Marketers and advertisers would be wise to stay ahead of these emerging lookalike modeling trends so they can ensure they’re always obtaining the best business outcomes possible for the brands they represent. 

To learn more about lookalike modeling trends, please reach out to a member of our expert panel or visit our resources page for additional reading.

What Does a Next-Generation ID Graph Look Like?

What’s the difference between a traditional ID graph and a next-generation ID graph? We cover the differences in this blog.

There are two types of ID graphs in the world: the type that everyone is using today, and the next-generation ID graph that everyone will be using tomorrow. While discussions about ID graphing aren’t the most riveting in the world, they’re important for marketers. Especially, in a sputtering economy that’s struggling to recover from a global pandemic and recession. So, what does a next-generation ID graph look like? More importantly, how does it outperform the ID graphs that are out there today? Keep reading to find out.

A quick refresher on ID graphs

Any conversation about ID graphs starts with an understanding of mapping tables. Why?—Because all an ID graph does is map digital IDs from one company to another. The mapping is due to each company having different “name spaces” (= dictionaries of ids that map to their internal data stores). 

Identity resolution is the process of taking disparate IDs (addresses, emails, MAIDs, cookies, etc.) and relating them to a person-level or a household-level concept. Identity resolution effectively addresses the relationship among the identifiers (addresses, emails, MAIDs, cookies, etc.) and hence maps devices to the concepts of people and people to households. 

The more quickly and efficiently you can resolve the link between the identifiers and the concepts of real people or households, the more effective you will be in your marketing.

Batch-mode processing

Older ID graphs rely on batch-mode processing to give marketers the consumer insights they need. Unfortunately, batch-mode processing is no longer efficient or effective—it takes too much time.

When an ID graph operates using a batch-mode process, you (the marketer) are looking at a week-long turnaround time from when you submit first-party data to the data processor and when you get your enriched data back.

That’s at least five business days to process, clean, map, and then enrich your customer data. Then, and only then, is it ready to be syndicated to the various engagement platforms you use to match your content to your desired audience.

Latency is a big issue

Just because you have data in a database doesn’t mean it’s valuable.

Data isn’t valuable to a marketer—data is only valuable when it is used.

The utility of data is tied to actually matching content to people and improving this match.

Only then does data start to have an impact on reaching the right audiences, influencing media spend, hitting the prospects most likely to engage, and more. 

With batch-mode processing, you’re going to wait at least a week to get your enriched data back, and a lot can happen in that time.

A prospect who was in-market when you provided your data to the data processor, may no longer be in-market by the time your enriched data is ready for activation.

What to look for in a next-generation ID graph

Time matters when it comes to ID graphing, so look for a partner that can offer real-time, closed-loop enrichment and engagement (such as Zeta) versus a traditional batch-mode processor (such as Salesforce, Oracle, or LiveRamp).

A next-generation ID graph provider will be able to take from a robust, known-universe of customers and use the insights gleaned to help you obtain your next customers from unknown users. 

What about privacy? It is important with whichever partner you select, they respect people’s privacy rights—do they have consent to associate people’s activity to their identity?

Also look for partners that can help you with prospects with whom you do not yet have a relationship. After all, most advertising is to help acquire new customers who are demonstrating a propensity (i.e. interest and intent) to become your customer. 

Again, the distinguishing factor between the current generation of ID graph and the next-generation ID graph is the closed-loop, real-time optimization system.

This system reduces the latency between obtaining the data that lets you know WHO you want to talk to with your marketing, and actually activating that insight (i.e. putting your marketing right in front of them). 


  • Do you really want to work with a batch-mode partner whether they’ll be days and weeks of delay between data enrichment and activation? 
  • Or do you want to work with a company that’s built to provide enrichment and activation in real time?

Remember, data of and in itself isn’t a source of value.

Data at rest does nothing for your marketing.

The amount of data you have in your database is merely a curiosity metric—it doesn’t actually create value. If you want to put that data to use, you need to work with a partner running a next-generation ID graph so you can activate your data, extract value from it, and measure the significance of the value your marketing is creating over time.

Making the Most of Identity Resolution Technology

Identity Resolution (IDR) is the act of associating digital IDs across different devices and tying them back to one unique identity. This allows brands to clearly understand consumer activity so the right message is delivered to the right person at the right time — and can also scale to lookalike audiences.

As device ownership proliferates (the average American owns seven different devices as of 2020), so too does your opportunity to reach and engage customers. Unfortunately, more devices means more complexity along the customer journey. Today’s shoppers see an ad on their mobile device, research products on their desktop, visit a physical store to see the product in person, and finally order it from their tablet. With so many devices, touchpoints, and channels, how can marketers ensure they’re talking to the right people at the right time? The key to success lies in exceptional identity resolution technology.

What is identity resolution technology?

Identity resolution (IDR) is the act of associating digital IDs across different devices and tying them back to one unique identity. Done properly, IDR makes it easy to deliver the right message to the right person at the right time.

How does IDR work?

Let’s start at the beginning.

1. Onboard your data

Data onboarding is the process of getting all of your data tied to directly identifiable offline information associated with a single digital ID, and the key is being able to do this swiftly, easily, and accurately.

2. Clean your data

Once you have all of your data uploaded, it’s vital to clean it. Data hygiene is the process of cleaning the attributes tied to these digital IDs; as the saying goes, garbage in, garbage out.

3. Match digital IDs with attributes to other digital IDs

After you’ve eliminated duplicate records and rid yourself of outdated information, it’s time to match the digital ID with this enriched set of attributes to the other digital IDs associated with that consumer, a core component of Identity Resolution. The technology creates a graph of different IDs, both directly identifiable IDs (physical address, email, telephone number, etc.) and non-directly identifiable IDs (mobile advertising IDs, cookie IDs, etc.).

4. Personalize consumer experiences

The activity associated with each ID can now be used to better personalize messaging and measure the effectiveness of your marketing.

5. Lookalike modeling

Targeting only the identifiable consumers that can be matched to digital IDs is a smaller pool. But using this high fidelity information for better insights to target everyone that are “lookalikes” widens opportunity for marketers to amplify their acquisition campaigns and better engage the right user. (All in accordance with privacy-by-design by the way.)

What are the benefits of identity resolution technology?

  • Eliminates marketing waste

By tying all activity back to one individual instead of 5 different IDs, we are improving the messaging frequency when engaging the same person hence eliminating wasted ad spend.

  • Enables accurate, consistent messaging

Tying all activity back to one individual allows marketers to get a 360-degree view of their customer—knowing their full purchase history, intent, and interests. Remember—knowledge is power. Marketers empowered with the right information create better messaging messaging across channels. More importantly, they can deliver that message at the right time and on the right device.

  • Amplifies the opportunity to reach a wider audience through lookalikes

It enables marketers to receive better insights to target a wider audience and better engage the right user.

  • Creates better attribution and reporting.

It enables you to create detailed customer profiles that deliver deeper insights and reporting for future campaigns.


Are you curious to learn more about IDR or how it can elevate your approach to marketing (especially omnichannel personalization)? Reach out to Zeta ASAP!

Zeta Celebrates Data Privacy Day 2021

A quick run through of what you should be paying attention to on Data Privacy Day this year…

Data Privacy Day 2021

Data Privacy Day is a good time to take stock of what is going on in the world of privacy.

What should you pay attention to on data privacy day?

In November, California voters approved a series of amendments to the CCPA (which hadn’t even been in effect for a full year). The new CPRA doesn’t become enforceable until 2023, but due to its “look back” period of 12 months, most companies will need to be compliant by the end of 2021.

A number of other state legislatures are considering bills similar—but not identical—to the CCPA/CPRA. Local differences in the application of that legislation will make the data privacy landscape more complex than it already is.

The focus of US federal regulatory agencies is also changing with the arrival of the Biden administration. As such companies should expect to see new enforcement priorities and cases at the federal level. Could the new Congress finally enact the federal privacy bill that’s 20 years in the making? Maybe. But after 20+ years of waiting, who really knows anymore. Certainly the conditions for a federal law are more favorable than ever (but that’s been said before).

Internationally, there are new (or changing) privacy rules to comply with in Brazil, South Korea, Japan, and Canada. On the other hand, the EU’s long-awaited ePrivacy Regulation is still in limbo despite years-long rumors that it’s almost ready to be enacted.

A changing technology landscape

There are significant developments on the horizon for the technology landscape. Today, most digital advertisers relies on third-party cookies and mobile advertising identifiers to do business. But changes announced in 2020 by Google with respect to its Chrome browser, and by Apple relative to data collection on iPhones could severely restrict (or even eliminate) the use of those cookies and identifiers by advertising technology companies. As such, there’s been an industry-wide effort in the last 12 months to develop new technologies that can replace the third-party cookie and MAID as cross-publisher identifiers.

But these replacements might not (ultimately) be necessary. Last year, regulators officially announced antitrust investigations into these ostensibly pro-privacy moves by Apple and Google as anti-competitive measures. It’s too early to tell what will happen in this conflict, but the drama will be “must-see TV” in 2021.

Here at Zeta…

We remain focused on operating our services in compliance with existing laws and reducing any privacy-related risks for our clients.

Right now, we’re updating our privacy documentation (as we do at the start of each new year), and continuing to develop enhancements for our rights request page (where people can see, copy, or delete data linked with Zeta cookies). In doing so, we’re tracking the “big picture” without losing our daily focus  on the routine aspects of compliance. 

It bears mentioning that Zeta was NOT affected by the SolarWinds breach—or any other data breach for that matter—so we have not had to make sudden, drastic changes to our systems as many other companies have.

Looking ahead…

At Zeta, we see 2021 as a time to expand our data footprint.

We also see it as a time to expand overseas offerings. Accordingly, we will maintain our commitment to privacy compliance in Europe, Brazil, Singapore, South Africa, and more.

As always, we will continue to evolve the automation of our internal privacy-related processes. We will also continue supporting the compliance needs of our clients as they grapple with CPRA, a potential new law in India, and more.

With all of that said, Happy Data Privacy Day from everyone here at Zeta. We look forward to making 2021 the best year yet for our clients!

Activating Consumer Data to Deliver Personalized Experiences at Scale

Do you know how to activate consumer data? If you don’t your marketing will never thrive. Learn how to unlock data to your advantage.

Marketers like you love to talk about the importance of consumer data. But too few marketers know how to activate consumer data to deliver personalized experiences at scale. As a result, the brands these marketers represent lose market share.

Knowing how to harness signals and utilize the full value of consumer data is essential in today’s ultra-competitive environment.  Whether it’s for acquisition, experience optimization, or retention improvement, activating the insights locked inside consumer data is critical. But once it’s unlocked, it becomes possible to deliver personalized experiences at scale. 

Remember that data by itself is just an asset (and a costly one at that). But if you can make data actionable—if you can put it to practical use—it’s going to stop being something ethereal and start being something tangible. You know…something that’s tangible and capable of driving revenue.

Learning how to activate consumer data is crucial for growth

Marketers exist in a growing digital landscape. One that provides overwhelming challenges as well as unfathomable opportunities. But avoiding those challenges and realizing those opportunities is reliant upon for data activation.

To activate consumer data, you need the right combination of tools. The right tools will allow you to identify interested individuals and capitalize on their purchase intent with targeted, valuable engagement. 

About two years ago, the Director of Gartner’s Marketing Advisory team, Alex De Fursac Gash, delivered an informative presentation at the Gartner Marketing Symposium. During his talk, he urged marketers to movbeyond personalization for “personalization’s sake.” Instead, he encouraged marketers like you to spend time creating true, individualized engagements. In today’s environment, these kind of engagements are the most likely to provide value. 

Alex went even further by breaking down the potential value brands can deliver into five categories:

    1. Direct Me – Guide consumer to products of interest
    2. Teach Me Something New  Make consumer aware of new products
    3. Save Me Time – Expedite consumer’s purchase process
    4. Reassure Me – Reduce consumer’s pain of purchase
    5. Reward Me – Provide consumer with exclusive benefits

By activating consumer data to deliver an individualized engagement in one of these five categories, marketers can create more resonance and more effectively turn shoppers into customers.  

Brands unable to harness signals and utilize the full value of consumer data will lose market share. Continued failure to implement data-driven marketing efforts will result in missed conversions, off-putting experiences, increased customer churn and wasted resources on disinterested consumers.

How you can unlock and activate consumer data


Zeta carries your data from consumer signals to individualized experiences at scaleWcan supplement the quantity of those signals with 750 million deterministic digital profiles in our permission-based Data Cloud.

You can combine your customer files with our Data Cloud to generate tangible and valuable insights for your organization. From these insights, you can identify real interested individuals to deliver valuable, truly personalized experiences and at scale.  

Hegarty touches on using insights to deliver relevant communications “regardless of the channel.” This is key to optimizing your individualized experiences. You must be present on the right channel at the right time with the right messaging. 

Zeta empowers brands to reach individuals at scale

By using the Zeta Marketing Platform, you can map your customers to our permission-based Data Cloud, allowing you to activate higher-value engagements in real-time.  

For example, what would you do if you could provide individualized value to millions of “Zacks”?

activate consumer data in Zeta's identity hub

With Zeta, you can:

  • Deliver valuable, individualized experiences to interested consumers within your customer files and Zeta’s Data Cloud
  • Optimize outreach to eliminate non-viable channels and reduce media wasted on uninterested individuals
  • Implement true deterministic attribution to account for all marketing ROI

Let’s connect to see how Zeta can empower your marketing organization to do more with less.

Zeta’s Approach to Programmatic Advertising

What is Zeta’s unique approach to programmatic advertising and how does it help marketers acquire new customers and grow an audience base? Uncover the answers below.

Programmatic advertising (automated bidding on ad inventory in real-time for the opportunity to show an ad to a specific customer, in a specific context) is one of the key digital investments every marketer must consider. 

  • In 2020, marketers invested 84.5% of their total digital display ad spending in programmatic—and that number will rise in 2021. 
  • New programmatic channels are now available including Connected TV, Audio, and Digital Out-Of-Home.
  • AI and personalization are now a core function of programmatic and they’re thriving in real-time activation environments.

At Zeta, we take a strategic approach to programmatic advertising to help marketers build customer awareness, engagement, and audience growth in real-time. 

A data-driven approach to programmatic

Historically, retargeting was used to tie a customer’s identity to their intent to purchase. This can now be more easily achieved through programmatic advertising. The Zeta Marketing Platform connects to a larger data set which enables marketers to not only reach their existing audience but also discover lookalikes within a unified platform

Real-time decisions using AI and machine-learning technology

The Zeta Marketing Platform uses AI and machine learning to make real-time programmatic decisions based on audience definitions developed using interest and intent signals pulled from the Zeta Data Cloud (e.g., people interested in luxury fashion). In other words, Zeta’s approach to programmatic ensures your media dollars aren’t wasted and your programmatic bids are priced dynamically.

At Zeta, we “market in the moment” when it comes to programmatic, which means we can more efficiently find the individuals most likely to convert on your campaigns.  

Zeta’s approach to programmatic also analyzes thousands of attributes (e.g. time of day, site activity, device) to learn which consumer actions matter most. This predictive technology can then be used to create a simple visual showing campaign performance improvements to better understand the moments most influential among customers.

Want to learn more about Zeta’s approach to programmatic advertising?

Contact us here or read our recent client success story with The RealReal.

Top 5 Financial Lessons Learned From COVID-19

With such renewed consumer focus on career advancement and fiscal responsibility among consumers right now, let’s hope 2021 can deliver the goods.

Last year was a painful one for the American economy and its workers, which is why the majority of consumers are resolving to ‘save more money’ in 2021. Here are the Top 5 Financial Lessons Learned From COVID-19. (To learn more, check out Zeta’s Chief Data Officer, Neej Gore, on Nasdaq or contact us here!)

Top 5 Financial Lessons Learned From COVID-19

What Is Zeta’s Approach to Website Personalization?

What is Zeta’s approach to website personalization, and how can it help marketers enhance engagement across the customer lifecycle?

Here’s something important to note: 80% of consumers are more likely to make a purchase from a brand that provides personalized experiences. That’s why the implementation of any omnichannel marketing plan must place emphasis on website personalization. Doing so will maximize the impact of both your paid and owned media efforts. Translation? It will make it easier for you to turn website visitors into paying customers. So, how does Zeta’s approach to website personalization create the optimal customer experience? This blog will dive into our comprehensive data-backed suite of solutions that can help enhance engagement across the customer lifecycle.

A data-driven approach to website personalization

Everything we do at Zeta is backed by data (including our approach to website personalization). Leveraging our Data Cloud within the Zeta Marketing Platform, marketers are able to derive key insights (including website content consumption and behavioral signals) from over 200MM U.S.-based consumers. This data provides a 360-degree view of each website visitor and is then activated to properly personalize your website. 

There are two steps to successfully obtaining a holistic visitor view…

  1. Identify and understand your customer

This can be aggregated transactional and behavioral data including sites visited, past purchases, shopping preferences, intent signals, and more.

  1. Enrich customer data with third-party data

This is what powers successful website personalization. Leverage a variety of first-party data including site data (page, frequency, recency, context), customer/CRM data (loyalty, value, purchase history), Zeta intent data (intender audiences, sentiment, consumption), and Zeta identity data (email, deterministic identities, location).

Zeta’s solutions to website personalization

Once you understand your customer, you can start activating website personalization features. At Zeta, we offer a wide variety of features depending on your brand-specific goals.


These are interactions above a website in pop-up form. Overlays provide flexible creation options and support various stages of the customer journey and business use cases. A great example of this can be seen with Everlane, which uses an overlay to offer new customers 10% off their first purchase if they provide an email address.

Website personalization with Everlane
Via Everlane


Better know as onsite recommendations, these are in-line offerings such as “you may be interested in.” These are typically a pre-defined size and placed in a specific position on page (e.g. when viewing a cart).

Websiter personalization win-page
Via Boy Smells Candles

Site optimization

This feature is integrated with the structure of your website and organizes content on parts of the page to personalize the experience. This feature allows for custom integrations but requires heavier implementation.

Landing page optimization

These are custom landing pages meant to drive traffic and are optimized based on conversion metrics (towards best converting). Similar to site optimization, LPO allows for custom templates but requires heavier implementation.

Key use cases to deepen engagement on your website

Since overlays and in-page are the easiest website personalization features to implement, let’s unpack some of their fundamental use cases.

Overlay use cases

The number one use case for overlays is acquiring new customers. This can be done through lead generation (quote requests and scheduling a consultation/visit), promotions (discounts and sales, or personalized messaging tied to paid media). To strategically acquire new customers through this feature, consider suppressing existing customers and engaging new visitors at the optimal time—not at the initial time of site visit.

Another use case for overlays is to drive conversion or avoid abandonment. Use product recommendations with a variation of sizes, colors, and styles as well as related products to grab attention. Moreso, implement incentivized messaging such as alternate styles/sizes, promotional discounts, or a countdown clock. Be sure to interact at the point of abandonment and re-engage on revisit for the best customer experience.

Lastly, overlays can be used to increase retention. Prompt returning customers to review or rate their last purchases, implement an onsite chat, and facilitate in-store returns. These strategies are all proven ways to encourage repeat purchases and drive lifetime value.

In-page use cases

In-Page features are best used for product recommendations and content recommendations

For product recommendations, consider leveraging promotional discounts and be sure to show a variation in sizes, colors, styles, locations, and categories (i.e. Recommended For You’). Personalized offers are also a great in-page tactic, and Zeta’s AI can determine the optimal product selection for each customer. In general, 70% of companies that use personalization involving AI see a 200% ROI or more.

Via Uniqlo

For content recommendations, be sure to include a variety of images, messaging, and order (i.e. ‘Curated for You’ and ‘Top Stories’).

The benefits of working with Zeta for website personalization

Zeta website personalization tracks individuals’ real-time activity on your website and enables rich personalization to boost conversion, increase order value, and enhance the overall customer experience. With our wide range of features you can…

Uncover real-time insights of each consumer on your website

Through accumulated website activity on the individual level, you can continuously enrich data to enable 1:1 personalization on your website.

  • Track each customer’s real-time website activity, including products of interest, levels of engagement, conversion history, etc.
  • Tie onsite activity to signals from offline CRM data to create a holistic customer view.

Scale your personalized interactions and content for each consumer

Create personalized experiences with content and messaging that reflects a consumer’s real-time interests.

  • Target consumers based on their website activity and other signals.
  • Personalize messaging, including interactions and in-line content based on any attribute.
  • Encourage engagement by presenting relevant offers for each stage of the customer lifecycle.

Watch our recent webinar to uncover insights on how website personalization can create engagement, drive leads, and nurture long-term customer relationships.