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Zeta Live: 5 Key Themes Shaping the Future of Marketing

From NFTs to first-party data to the Metaverse and everything in between, these are some of the most engaging themes that marketers can’t afford to ignore from Zeta Live ’21.

The industry’s most forward-thinking marketers and business leaders came together at Zeta Live ‘21 to discuss the evolution to a digital-first world as well as trending topics on the future of marketing.

From NFTs to first-party data to the Metaverse and everything in between, we’ve collected some of the conference’s most engaging themes that marketers can’t afford to ignore.

1. Ecommerce Enters the Experience Era

The last 20 years of ecommerce and the technological progress made within the industry have offered solely static experiences for consumers. The old ways of selling online no longer work and brands must adapt to reach consumers in new ways. Enter the “experience era.”

The experience era enables marketers to give consumers the branded experience they crave. In the What Comes Next for Social Commerce session, industry experts from Verishop, Snap Inc., ThirdLove, and Yahoo! explain what’s next, with emphasis on one-to-one interactivity, sustainability, the Metaverse, and livestream selling being the holy grails. 

 

From a retail perspective, Sydney Stinson Ferguson, VP of Marketing at Sunglass Hut North America, noted in Engaging the Connected Consumer how through experiences, the Luxottica brand has been able to become the destination for consumers through the campaign, “The Sun Within,” which helped consumers create their own happiness in their own environment (yoga, dance, etc on social media) and feel safe when coming back in store. All in all, a personalized experience at every step of the path to purchase is the expectation of tomorrow’s consumer.

 

2. Web 3.0, NFTs, and Blockchain Technology GoMainstream 

Web 3.0 represents the next iteration of the evolution of the internet as we know it. Built upon the core concepts of decentralization, openness, and great user utility, the real value coming out of web 3.0 will be blockchain. 

At the center of this blockchain technology now sits non-fungible tokens, better referred to as NFTs. In our session 2022: The Year NFTS Take the Mainstage, industry leaders from TIME and CNBC weighed in on the ability of crypto technology to create a new type of relationship with consumers through exclusivity. Michael Rubin, CEO of Fanatics, supported this positioning in his session on Sports Marketing in The Digital Era, sharing why he is launching NFT trading cards to strengthen the fan experience. While we’re still a few years out, NFTs and other Web 2.0 products platforms will inevitably disrupt the MarTech industry as we know it.

 

“In the digital world, there’s a voracious appetite to own. Values create value.”

 – Keith Grossman, President of TIME

3. The Next Wave of Customer Engagement in the Metaverse 

Despite an uptick in big news surrounding the Metaverse lately, the reality is that this shift to virtual experiences won’t come overnight. When (and if) this trend fully catches on, it will have been a long time coming — just as the concept of AR and VR have existed for years with slow adoption.

Bob Sherwin, CMO of Wayfair, noted in the session on The Future of Commerce & Creativity that the home-goods brand has offered these experiences to its shoppers for years. Yet, the question of ‘when do consumers want to engage with it?’ still remains.

So what can marketers do in the midst of the Metaverse boom? A wait-and-see approach will be what works best as all the kinks with technology and user experiences get worked out.

 

“I think the whole Metaverse thing is an eventuality. It’s just when will that happen, when will we be there? We’ll realize we’re there before it’s declared, then it will all continue to evolve.”

 – Bob Sherwin, CMO of Wayfair

We’re now gamifying the game. Fans want to be even closer to the game than they were.”

Jene Elzie, Chief Growth Officer, Athletes First Partners

4. Identity Plays A Pivotal Role in the Digital Economy

Identity-based marketing – especially through the use of first-party data – has become increasingly important as consumers accelerate their transition to digital for everyday conveniences, transactions, and social interactions.

In the session Navigating the Golden Age of Identity, marketing experts from Zeta, LiveRamp, AWS Data Exchange, and T-Mobile unpacked the significance of identity being the only thing that unifies the customer experience. Allan Samson, SVP, Marketing of T-Mobile, built upon this notion by recognizing that finding the right algorithm and the right hierarchy in a massive amount of data is key. 

The theme of identity was supported throughout other Zeta Live sessions as well. In Using Technology to Achieve Your 2022 Goals, leaders from G6 Hospitality, MGM Resorts International, and Christmas Tree Shops shared their teams’ best practices for using ID-based data and technology to activate segment-specific growth strategies.

But a successful future of data is more than just finding effective ways to activate personalized strategies. It’s just as important to leverage clean room technology to understand your customer in a privacy-first, secure way, as consumer consent expectations will continue to evolve.

 

“A clean room in a silo can’t be an input. It is a function of its own purpose, which we don’t see much of today.” 

Dennis Ellis, VP of Product, GM of Identity Infrastructure at LiveRamp

 

5. CDPs Become The Marketer’s North Star

Legacy technology systems no longer meet the needs of omnichannel orchestration in marketing. Whether it’s added complexity that slows down processes, or siloed processes that lead to mono-channels, marketers are breaking through these roadblocks by implementing a Customer Data Platform (CDP) to power their MarTech stack.

In the session Technology Bets for the Modern Marketer, industry experts from Carter’s, Email Connect, Healthgrades, and SIMON weigh in on the importance of having such a CDP platform at the core of their marketing infrastructure. With first-party data at the core, marketing teams can execute personalized, coordinated, omnichannel programs across the customer lifecycle. 

Zeta and Snowflake also announced a partnership and a new suite of joint offerings focused around first-party data and the Zeta CDP+. Brands will be able to enrich and integrate Snowflake records with the CDP to activate campaigns faster with more precision, a key factor in ensuring this trend is efficient and effective.

 

“Do I need a CDP and ESP? There are overlaps between CDP and enterprise ESP. You can very quickly over-platform. You’re paying more than you should be and building inefficiencies. My prediction is, these will eventually become one platform.”

 – Chris Marriott, President & Founder of Email Connect

 

Want more insights to inform your 2022 marketing efforts? 

Access all the Zeta Live ‘21 session recordings until December 17th by navigating to the “theater” area of our virtual experience here.

Chasing Causal Holy Grails: Quantifying Advertising Success

Is your brand struggling to quantify your advertising campaign success? This historic statistical approach just might be the answer.

Have you ever wondered how valuable your marketing campaigns really are? Typically, companies use machine learning capabilities to target “ideal” prospects and may create profiles of audiences based off their data. These efforts, however, are fundamentally centered on the premise that a predictive model is equivalent to a descriptive or causal model, which is far from the truth. In this blog, we’ll explore the differences between the two, as well as the value of causal models and how we’re using them at Zeta.

The holy grails of advertising

In advertising, there are two “holy grails” for machine learning:

1. The ability to deduce the “true” effect of variables, such as location and certain search keywords, on the propensity to purchase your product.
Knowing this provides insight and value not only for the immediate campaign but also for your client’s marketing strategy.

2. Optimization of your campaign’s targeting ability to the people that are most affected by it (incrementality).
We don’t want to waste money by targeting people that are already going to purchase an item. Instead, we want to be defined by our ability to actively change behaviors purely by our advertising efforts.

While existing predictive models can achieve the above to a limited degree, there are serious underlying issues in their abilities. We’ll discuss these limitations below, and demonstrate how causal models can generate strategic insights, decrease wasteful spending, and increase the impact of your campaign on changing outcomes (henceforth referred to as incrementality).

Differences between predictive and causal models

So why is this important? If your method of describing a variable’s effect on outcome Y is through a predictive model such as logistic regression, then your method may be flawed.

In predictive modeling, all that you care about is how well your algorithm can predict a given outcome. Therefore, you are willing to accept conditions that violate key assumptions in your model such as endogeneity and ignore issues such as multicollinearity, as long as the overall predictive power is high.

To counter these problems, we need to carefully assess the sources of endogeneity and identify issues that may contaminate our interpretations and beliefs surrounding our models be it from the predictors (predictor is another term for variable), data generation process, or otherwise. Zeta’s curation of relevant predictors and modeling processes are constructed on precisely this premise to deliver value to our customers.

Given these problems, why are simple predictive models so successful? Well, strictly speaking for predictive models, it doesn’t matter if your estimate of an individual variable’s effect on outcome Y is accurate, as long as the model in its entirety is able to predict the right outcome.

For example, with highly correlated predictors “A” and “B,” it’s perfectly possible that our model’s estimate for “A” is below the true value of “A,” as long as the model compensates for this by making the estimate for “B” higher than its true value. The total effect of “A” and “B” are the same, but the individual estimates for “A” and “B” are now off.

We are unable to necessarily interpret individual effects of the variables in our models as causal if our model is predictive in nature.

We here at Zeta actively combat against issues such as these to ensure our models are both reliably interpretable for our clients while maintaining high predictive power through the use of model constraining techniques, external validation, and a series of statistical tests and procedures.

Estimating the impact of our campaigns

For most advertisers, machine-learning algorithms are built to optimize on people who are most likely to convert (purchase an item). However, we’re asking the wrong question here. What we’re interested in isn’t who is likely to purchase an item, it’s whose behavior is most likely to change due to our ads.

This is a crucial difference! In the former case, we are sending ads to people who would have purchased an item anyways, with or without having seen your ad. Sure, the campaign might show a high number of conversions (people that have purchased after seeing your ad), but how much impact has the campaign actually had? In the latter, we avoid this wasteful spending and optimize purely on the impact we can have.

To understand this, we use incrementality testing, which splits our total prospects into two groups: treatment and control. We ensure both groups are homogenous in how likely they are to purchase an item. The only difference between the groups is that the treatment group receives an ad while the control group does not. Once the campaign is finished, we’ll compare the average probability of someone converting in the treatment group against someone in the control group. In statistics, we call this the Average Treatment Effect (ATE).

At Zeta, we asked if we could go one step further. Can we estimate the “true” or causal impact of our ads, at an individual level? If so, we can optimize our ads on an individual level to maximize our impact.

Causal modeling at Zeta

To answer both questions (the true effect of our predictors on an outcome and optimizing on incrementality), we need to understand how to convert predictive models into causal ones. To do so, we need to understand how the assumptions of our models were violated for predictive modeling and adjust our models accordingly.

Typically, models suffer from a variety of assumption violations, but most notable amongst these are selection bias and omitted variable bias. For brevity’s sake, we won’t discuss these in detail. But usually selection bias and omitted variable bias require thoughtful consideration of what predictors we put in our model. We also need to consider other forms of endogeneity, which is when our model’s predictors are correlated with the error term. The error term is the model’s way of understanding that variation in an outcome between data points of the same value is natural and ideally random. For example, if our model considers location and browser history, it is fine for people who have the same location and similar browser history to have variation in their decisions.

To deal with more complex forms of endogeneity, we have a variety of tools at our disposal, which stem from fields as varied as econometrics to epidemiology. Chief among these are instrumental variables, regression discontinuity, and Bayesian Networks.

At Zeta, we’ve found that while those methods succeed at isolating the “true effect” of variables by removing biases from our variables of interest, they often lack predictive power. This does, however, provide valuable strategic insight into predictors, accomplishing the first question.

To deal with the second question, we’ve borrowed some insights from the medical field. Within the medical field, practitioners face a serious problem of heterogeneous treatment effects. In other words, certain sub-groups in a population will respond to the same medical treatment differently. We thus transform the problem of estimating individual incrementality to a related but more tractable problem: can we identify sub-groups in our campaigns that have abnormally high response rates? If we can identify these sub-groups as well as remove sub-groups with lower-than-average response rates, we can use a series of other statistical modeling techniques to optimize specifically for these groups and increase our overall incrementality.

Want to further discuss how causal inference and incrementality may help your campaigns?

Contact us today!

Unwrapping the Biggest Holiday Retail Trends of 2021

From early shopping events to shop-in-shops and everything in between…these are the holiday retail trends you can’t ignore this season.

‘Tis the season of giving and retailers are eagerly anticipating a +7% YoY increase in sales between November and December alone. 

To make the most of this key consumer spending period, brands must adapt to the supply chain/inventory shake-up as well as the evolving digital customer journey. Packed with predictions, this article aims to give you insight into the trends expected to shape the biggest retail event of the year.

Early shopping events and independent delivery teams will help eliminate ‘Bah Humbug!’ 

As supply chain complications loom this holiday, consumers are shopping earlier to ensure they get the top products on their lists. Moreover, the December 15 cutoff date for ground delivery set by USPS, FedEx, and UPS will prompt consumers to hit the stores and browse online to get what they need in a more timely fashion. 

(Image source: USA Today)

But even with all of the early shopping happening, there’s still the question of ‘how is it going to be delivered?’ Retailers will be reliant on independent delivery teams within popular DMAs to avoid late deliveries, being more relevant with communications as it relates to in-stock/out-of-stock inventory.

Pop-up and shop-in-shops will deck the halls to create convenience

Despite a lot of shopping happening online this year, 45% of people are still looking for hybrid or in-store shopping experiences that are convenient to their shopping needs. Retailers will be looking to double up on their product offering with Pop-up and shop-in-shops (e.g. Target’s shop-in shop with Disney). By introducing merchandise they may not otherwise have access to, brands will be able to increase foot traffic and help customers turn a ten-stop shopping event into five. It’s a win/win situation.

(Image source: Target)

The success of a retailer’s pop-up or shop-in shop will be reliant on partnering with a brand that doesn’t compete with their current offering. This will be a built-out experience in an existing store or via excess real estate in a new test market. To tie the bow on top, retailers will connect in-store offerings to an ecommerce storefront to promote partnerships and enable consumers to jump to various online marketplaces.

Livestream selling goes mainstream selling

Livestream selling, also known as the modern-day QVC, will help humanize the online shopping experience and generate excitement for specific product categories by leveraging social media-savvy associates and influencers. This will enable retailers to generate new lines of revenue and make way for new inventory by showcasing products from categories that were overbought in. Moreover, it will be a pivotal tactic to help reach anxious consumers concerned about shopping in-store due to the COVID-19 Delta variant.

(Image source: Bloomberg)

The brands that will win with livestream selling will have tested timeframes to get an understanding of when their audience is most engaged (e.g. early mornings versus afternoon versus post-dinner, etc.), and activate across multiple social channels including YouTube, Instagram Live, Facebook, and TikTok.

Personalized gift guides will bring good tidings

Gift guides are imperative this year after a boom in ecommerce shopping and online product discovery in 2020. In fact, a recent study reveals that 63% of shoppers now enjoy discovering items they weren’t actively looking for.

Curating gift guides is in every retailer’s nature, but oftentimes these guides turn into overly promoting excess or old inventory. This holiday season, retailers will be replacing their top 10 gift guides with individualized guides for customers using proprietary customer data (e.g. purchase history, interests, website behavior, etc.). This will ensure customers are seeing the products they’re most interested in and in turn increase their purchase intent.

‘Buy now, pay later’ options will be used to create comfort and joy

Despite another round of child tax credits, many consumers are still holding on to their wallets this holiday season as a direct response to the pandemic. To afford shoppers the opportunity to buy a special gift for everyone on their list, retailers will be offering consumers the option to buy now and pay later with services such as Klarna and AfterPay.

(Image source: Sensor Tower)

Purpose-driven ‘hero’ products will make spirits bright

As many as 30-40% of U.S. consumers continue to switch brands or retailers to those that place more emphasis on purpose-driven alignment. This holiday season, retailers will be promoting ‘hero’ products that have a deep content strategy related to a greater purpose attached to them (e.g., donating a portion of proceeds to charity, highlighting sustainable materials, partnering with local businesses, etc.). Hiring influencers (both well-known and everyday people) that are connected to these hero products to strengthen their promotion will be crucial, as well as linking back to one of this year’s trending categories — Luxury, Fashion, or Adventure.

(Image source: Freestocks)

Retail brands will also use virtual cart capabilities on their e-commerce site to make it easy for consumers to buy these purpose-driven products. This might be the ability to automatically charge the full amount to a card on file when an item comes back in stock, or charging a down payment and holding an item for a limited amount of time until the customer decides if they want it or not.

Give the gift of value this holiday season

Looking for more tips and trends for the 2021 holiday season? Unwrap additional insights and learn how to win at the biggest shopping event in our holiday guide

Data-Driven Intelligence for Full-Funnel Marketing

In this blog, we’ll unpack how to better understand the nuances of the marketing funnel and how to best reach consumers at each stage.

Marketing, as it relates to customer lifecycle management, maps a customer journey from the prospecting stage to becoming a high-value, loyal customer. This journey is frequently represented as moving through the marketing funnel.

Understanding the marketing funnel

At the top of the funnel, new prospects present opportunities for brands to reach them with brand awareness messaging. Moving through the funnel to the interest and consideration stages, these prospects may show interest in receiving initial brand communications and act as hand-raisers to learn more about the product or service presented. These hand raisers would then need to be further shepherded through their journey towards conversion and beyond to be turned into a repeat purchaser, better known as a loyal customer.

The challenge this poses for marketers, however, is separating noise from quality prospects. As such the business question at hand becomes ‘how does one decide on whom to spend marketing dollars?’ There are a few marketing levers that can be used to address this challenge.

Leaning on time-tested, data-driven techniques

This is one of the best ways to qualify who among the prospect universe has the best chance of moving through the funnel. The characteristics that may make someone a prime prospect may not be predictive of how likely they will move into the next stage of the marketing journey. Understanding these characteristics will go a long way in tailoring messages, channels, and offers to prospects so that they are set up to move to the next stage of the funnel.

For instance, consider this 4-stage funnel where an individual needs to pass from being a prospect to becoming a hand raiser, then to an inquirer and ultimately to a buyer.

At each stage of this funnel, the attributes, behaviors, and preferences of the individual that will determine their likelihood to make it to the next stage can easily change. For instance, an individual with a certain persona (e.g., a middle–aged female who lives in a rural area) may be the right target for prospecting and may be very likely to become a hand raiser. However, out of such hand-raisers, there may be individuals in a specific life stage with demonstrated brand preferences, media consumption preferences ,and other attributes that make them more likely to go to the next stage of the funnel (inquiry stage). Of those who go through with the inquiry, there may be individuals who have exhibited specific interests who are more likely to buy the product or service.

Looking at these characteristics one at a time has its benefits in terms of being able to construct audiences quickly. However, understanding the change in high likelihood personas as they move through the funnel provides better marketing levers such as:

  • How to talk to these audiences
  • Which channel would resonate best with them
  • What kind of messaging would appeal to them
  • What their pull is towards the product or service (e.g., price vs. exclusivity)

Multivariate modeling

Multivariate modeling can incorporate several attitudes, preferences, and attributes of individuals into an algorithm and assign them a likelihood score. These models can then be hyper-tuned to the funnel stage they are about to enter. As it relates to our marketing funnel above, look-a-like modeling techniques can be applied to identify which individuals have a high propensity to move from the prospect to the hand raiser stage, from hand raiser to inquirer stage, and ultimately into the buyer stage.

Such multi-stage modeling presents marketers with the granularity needed to limit marketing efforts only towards high-likelihood audiences. An additional benefit of stage-specific look-a-like models is that they present an opportunity to investigate potential leakage points and to improve flow rates. These multi-stage models can also be combined to provide the ultimate likelihood of a prospect converting into a buyer while incorporating the ‘in-between stage’ propensities.

The insights that are driven from profiles of high–propensity converters at each stage can either validate or challenge existing assumptions about a brand’s customer profile.

For instance, a recent multi-stage modeling exercise for a mid-size company in the hospital and health care industry revealed that the persona of high-propensity prospects in the first stage of the funnel is very different than the conventional wisdom existing within the company of their customer persona. While the results were counterintuitive as a consumer progressed down the funnel, we found that the ultimate conversion took place at Stage 4. These insights brought to light the need for the company to diversify their targeting efforts to cater to the persona that is likely to become a hand-raiser and customize their customer journey along the funnel to cater to potential shifts in the customer base.

Wrapping Things Up

In conclusion, multi-stage look-a-like modeling not only provides insights for rightfully allocating marketing dollars but also highlights other preferences for individuals to pass through the funnel stages.

Today’s Traveler: Marketing with Agility to a Moving Target (Brandweek Session Recap)

Learn how brands are successfully connecting with today’s consumer while planning for the expected travel resurgence in 2022 and beyond in Zeta’s recent Brandweek session.

The pandemic has changed booking behavior with spontaneity and optionality replacing long-term dreaming and planning. Reaching consumers with tailored offers at just the right moment has become critical to win share.

In this Brandweek session, Crystal Eastman, CMO of Zeta, and Stephen Fitzgerald, VP of Digital Commerce and Distribution at G6 Hospitality, discuss how brands are successfully connecting with today’s consumer while planning for the expected travel resurgence in 2022 and beyond.

Session Insights

  • Traveler behaviors have changed, and understanding the new customer journey is more critical than ever.
  • Successful Travel and Hospitality brands are using data to power agile, omnichannel marketing strategies to capture booking share.
  • There are several lessons marketers across industries can learn from leading Travel & Hospitality brands about balancing revenue goals with loyalty. This includes evolving services to account for how consumer expectations have changed.

To learn more about how top travel brands are winning share, watch the full 20 minute discussion here.

Zeta Acquires Technology Platform and Data from Apptness to Strengthen Identity Solution and Omnichannel Marketing Platform

Zeta announces its latest acquisition of Apptness, a digital technology company with proprietary audience engagement technology.

NEW YORK, NY — Zeta (NYSE ZETA), a cloud-based marketing technology company that empowers enterprises to acquire, grow, and retain customers more efficiently, today announced the acquisition of the technology platform and data from Apptness, a digital technology company with proprietary audience engagement technology.

Founded in 2015, Apptness operates a digital survey platform that provides comprehensive capabilities to engage consumers on sites across the open web, deliver proprietary insights and audiences to marketers, and provide publishers with new monetization opportunities. The acquired platform will be directly integrated into the Zeta Marketing Platform, expanding and enriching the Zeta Data Cloud with over 45M monthly incremental high fidelity consumer signals.

“Apptness’ technology to empower publishers is second to none and we anticipate it will be accretive to Zeta from a technology, data and financial perspective. For example, we expect it will enrich our data footprint, strengthen our actionable 360° view of the consumer, and help Zeta customers achieve even stronger results,” said Co-Founder, Chairman, and CEO David A. Steinberg. “I am also thrilled to welcome the talented Apptness team to Zeta, which grows our specialized sales capacity.”

The acquisition accelerates several of the growth drivers laid out as part of Zeta’s IPO:

Strengthens the Data Cloud. Adds incremental consumers to the Zeta identity graph and enriches consumer profiles with 45M+ monthly behavioral signals tied to consumer lifecycle and financial metrics. Additionally, this is expected to grow Zeta’s pool of first-party tracking pixels.

Revenue Efficiencies. The acquired Apptness business will be included in and is expected to increase Zeta’s platform revenue and lower Zeta’s cost of revenue as a percentage of revenue.

Grows Sales Capacity. The Apptness executive team includes sellers with deep experience in the digital marketing ecosystem.

Expands Publisher Capabilities. The Apptness platform will be made available to Zeta’s network of 6 million+ websites as a vehicle to increase consumer engagement and grow subscription revenue.

Dominik Szabo, CEO of Apptness, stated, “Apptness has differentiated itself with a strong team focused on driving innovation through dynamic and cutting-edge technology. We believe our product will complement Zeta’s approach to delivering high-value return for customers. Our sales and engineering teams are proud to join this incredible organization and be a part of this rapidly growing company.”

About Zeta

Zeta Global Holdings Corp. (NYSE:ZETA) is a leading data-driven, cloud-based marketing technology company that empowers enterprises to acquire, grow and retain customers more efficiently. The Company’s Zeta Marketing Platform (the “ZMP”) is the largest omnichannel marketing platform with identity data at its core. The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by leveraging sophisticated artificial intelligence to personalize experiences at scale. 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.

Forward-Looking Statements

This press release, together with other statements and information publicly disseminated by the Company, contains certain forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. The Company intends such forward-looking statements to be covered by the safe harbor provisions for forward-looking statements contained in the Private Securities Litigation Reform Act of 1995 and includes this statement for purposes of complying with these safe harbor provisions.

Any statements made in this press release that are not statements of historical fact, including statements about our beliefs and expectations, are forward-looking statements and should be evaluated as such. Forward-looking statements include information concerning possible or assumed future results of operations, including descriptions of our business plan and strategies. These statements often include words such as “anticipate,” “expect,” “suggests,” “plan,” “believe,” “intend,” “estimates,” “targets,” “projects,” “should,” “could,” “would,” “may,” “will,” “forecast” and other similar expressions.

We base these forward-looking statements on our current expectations, plans and assumptions that we have made in light of our experience in the industry, as well as our perceptions of historical trends, current conditions, expected future developments and other factors we believe are appropriate under the circumstances at such time. Although we believe that these forward-looking statements are based on reasonable assumptions at the time they are made, you should be aware that many factors could affect our business, results of operations and financial condition and could cause actual results to differ materially from those expressed in the forward-looking statements.

These statements are not guarantees of future performance or results. The forward-looking statements are subject to and involve risks, uncertainties and assumptions, and you should not place undue reliance on these forward-looking statements. Factors that may materially affect such forward-looking statements include, but are not limited to: the impact of COVID-19 on the global economy, our customers, employees and business; potential fluctuations in our operating results, which could make our future operating results difficult to predict; our ability to innovate and make the right investment decisions in our product offerings and platform; our ability to attract and retain customers, including our scaled customers; our ability to manage our growth effectively; our ability to collect and use data online; the standards that private entities and inbox service providers adopt in the future to regulate the use and delivery of email may interfere with the effectiveness of our platform and our ability to conduct business; a significant inadvertent disclosure or breach of confidential and/or personal information we process, or a security breach of our or our customers’, suppliers’ or other partners’ computer systems; and any disruption to our third-party data centers, systems and technologies. These cautionary statements should not be construed by you to be exhaustive and are made only as of the date of this press release. We undertake no obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by applicable law.

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What Is People-Based Search and How Does It Work?

Search is easily one of the top digital channels at driving higher conversion rates. Savvy marketers are always looking to leverage this medium to optimize their search campaigns. Google takes most of the guesswork out by providing recommendations using user data gathered from web activity. This is where Zeta Global steps in — bringing in offline data to enhance Search Engine Marketing (SEM) efforts through People-Based Search.

What Is People-Based Search?

People-Based Search (PBS for short) serves to optimize a search campaign bid strategy. But marketers often question what is meant by the term “People-Based.” PBS takes an audience-based approach to determine appropriate bid multipliers. A lookalike model is built using a sample of your top-tiered customer list and then is used to score the search audience into propensity deciles (aka a consumers’ likeliness to convert). Bid multipliers are then set based on these deciles, with the bid increased for higher propensity converters and lowered for those who are least likely to convert. PBS can target either your CRM list — like leads sitting in your funnel or website visitors — or cold prospects.

PBS is largely audience agnostic, but the demand nature of paid search favors mass-market verticals or known brands, especially when targeting prospects. Niche markets and boutique brands can still benefit from the PBS approach, but at a slower pace with strategic implementation. One way to measure demand and determine how long it would take to reach statistical significance is to roughly measure the search rate of a similar audience.

Tips for Using People-Based Search

Search impression share is the percentage of impressions that your ad receives compared to those that it was eligible for, so it is crucial to mind your ad quality, keyword match types, and list of negative keywords because testing over Google Search will be interdependent of the Google ecosystem. You can use the optimization score to help you estimate how well the PBS test will perform within the Google environment. Google will take the PBS goal to improve the conversion rate and make recommendations for bidding, keywords, and ad improvements in the goal of improving overall performance and efficiency.

One way to leverage the optimization score of a standing campaign without affecting its performance is to launch PBS over Google’s Drafts and Experiments. You can use drafts to segment out the campaign and apply the PBS bid strategy, then create an experiment to measure results. When a user performs a search, either the original campaign or the experiment will randomly load based on how you’ve split the traffic share between the two. The added benefit to this is that the original campaign serves as another level of control to measure against.

Speaking of test and control, PBS is designed so that the higher propensity to convert segment (deciles 6-to-10) receive a bid increase of 50% and are then measured against their hold-out group with no bid adjustment. The low propensity to convert segment (deciles 1-to-5) have their bid decreased by 50% and are also measured against a control with no change to their bid. In order for this bid adjustment to take effect, a bid strategy of Enhanced CPC (cost-per-click) is used. Otherwise, the bid adjustment feature is used as a means to determine attribution for reporting.

The Use Case for People-Based Search

PBS has driven lifts in conversion rates as high as 20% by helping capture 38% more users actively searching. Moreover, it can help lower cost-per-acquisition by 13% on average and improve ROAS by 11%.

Want to Learn More?

Contact us today!

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