Before we dive into the specifics of how to leverage answer engine optimization to power your business growth, let’s clarify what exactly an answer engine is and how the cultural shift from search engines to answer engines came about.

What Is An Answer Engine?

An answer engine harnesses the power of artificial intelligence and natural language processing to understand the user’s query and deliver a direct answer, eliminating the need for users to sift through multiple search results.

While traditional search engines like Google and Bing provide a list of relevant web pages in response to a query, answer engines take it a step further by directly providing concise and accurate answers to specific questions — all with zero-clicks and little to no wait time.

These engines are designed to provide instant and precise responses, making them invaluable tools for quick and efficient information retrieval. Whether you need to know the weather forecast, the capital of a country, or the definition of a word, an answer engine can provide you with the answer in a matter of seconds, revolutionizing the way we seek and consume information.

What are Some Examples of Answer Engines?

There are two main categories of search engines: generative AI bots such as ChatGPT and Gemini, and AI-powered voice search assistants such as Alexa, Siri and Google Assistant. Gen AI uses sophisticated artificial intelligence algorithms to generate human-like responses to queries. These answer engines excel at understanding and generating textual information, making them valuable resources for answering a wide range of questions. With their ability to comprehend context and engage in dynamic conversations, Gen AI is becoming increasingly popular for providing detailed and informative answers.

Open AI launched ChatGPT in November of 2022, and it quickly gained traction, capturing the interest of researchers, developers, and users alike. It set the record of being the fastest-growing platform in history, reaching 100 million users two months after its initial launch. It became the go-to place for users to ask questions and get quick answers to hyper-specific search queries.

The model’s ability to engage in dynamic conversations and provide coherent responses made it a breakthrough in Gen AI. Users interact with ChatGPT by simply inputting text prompts and receiving detailed and informative answers. While ChatGPT is praised for its conversational abilities, it also comes with limitations, such as occasional inaccuracies or generating responses that may seem plausible but are not factually correct.

Following the success of ChatGPT, Google launched their rival chatbot, Bard, in March of 2023. While Bard was functionally similar to ChatGPT, the most significant difference between the two was that Google’s service pulled its information from the web. Bard itself was an entirely new concept prompted by a sense of urgency around competing with ChatGPT. However, it was powered by Google’s Language Model for Dialogue Applications (LaMDA), which had been unveiled two years prior.

In February 2024, Google renamed Bard to Gemini in hopes of streamlining the different Google AI models and hinted at their shift in approach. The change to Bard wasn’t solely in the name; the intent was also to improve the Gen AI’s processing ability. This updated algorithm processes entire sentences and provides a deeper understanding of context and nuances in language.

What are AI-powered Voice Search Assistants?

Another type of answer engine separate from chatbots is AI-powered voice search assistants. These answer engines utilize voice recognition technology to understand spoken queries and provide immediate responses. With a simple voice command, users can ask questions, get directions, set reminders, and perform various tasks without touching a screen. Voice search assistants provide quick and convenient answers, catering to users who prefer hands-free interaction and real-time information. As these assistants continue to evolve and improve, they become an integral part of our everyday lives, seamlessly integrating into our homes, smartphones, and other smart devices. In contrast to the relative newness of Gen  AI chatbots, voice search assistants are already v integrated into consumers’ daily lives, with 41% of the U.S. population using voice search daily. What’s more, 72% of people who own a voice search device say it has become a part of their daily routine, and 65% of people who own a Google Home or an Amazon Echo don’t want to go back to keyboard searches. There’s no denying that voice search will continue to be an integral part of the day-to-day user experience, meaning marketers will need to know how to successfully optimize their content to succeed.

How Did We Shift From Search Engines to Answer Engines?

The evolution from traditional search engines to answer engines represents a significant shift in how we seek and consume information. While search engines like Google and Bing have long been the go-to platforms for retrieving relevant web pages based on keyword queries, the rise of answer engines has transformed the landscape of information retrieval. This shift can be attributed to advancements in artificial intelligence and natural language processing, enabling answer engines to understand queries in a more nuanced manner and provide direct and accurate answers to specific questions.

One of the key drivers behind this shift is the increasing demand for instant and precise information. Traditional search engines present users with a list of relevant web pages, requiring them to navigate multiple sources to find the desired answer. This process can  be time-consuming and often requires the user to skim through lengthy articles or websites. Answer engines, on the other hand, leverage AI algorithms to interpret the user’s query, extract relevant information, and deliver a concise answer. This streamlining of the search process eliminates the need for users to sift through search results, providing them with the desired information quickly and efficiently. In short, consumers are becoming more and more impatient — and answer engines are rising to the challenge to combat that desire for faster, more accurate information delivery.

The emergence of voice-enabled technologies has also played a significant role in the rise of answer engines. Voice assistants like Alexa, Siri, and Google Assistant have made it effortless to ask questions and receive immediate answers. Users can simply speak their queries, and the AI-powered voice assistants leverage natural language understanding to provide spoken responses. This shift towards voice-based interaction has further propelled the demand for answer engines, as they offer hands-free access to information and cater to the growing preference for voice search.

What is Answer Engine Optimization (AEO)?

Now comes the fun part.

AEO, or Answer Engine Optimization, refers to the practice of optimizing content to provide direct zero-click answers to user queries. This can be on a Generative AI-powered chatbot like Gemini or ChatGPT; search engine results in pages like Google or Bing, or voice search products like Alexa or Siri. Answer engine optimization is the process of optimizing large language models (LLMs) and influencing their learning and training data and feedback loop to ensure that your brand is intentionally present in the right spaces at the right times.

AEO is arguably a subfield or an evolution of search engine optimization (SEO), with a similar principle of needing to understand the user’s search intent and how best to answer it. While AEO shares a common goal with SEO to provide highly useful content that answers users’ queries as accurately and concisely as possible, AEO specifically focuses on creating content that is a direct answer to the question so that it’s easily readable, crawlable, and presentable by AI answer engines and voice search assistants.

How does SEO differ from AEO?

SEO and AEO are two distinct approaches to optimizing content for search engines, each with its own focus and objectives. SEO primarily revolves around improving website visibility and rankings on traditional search engines like Google and Bing. It involves optimizing various aspects such as keywords, featured snippets, structured data markup, link building, and site structure to ensure that a website ranks higher in search results pages. SEO aims to attract organic traffic by providing valuable and relevant content that matches user search queries, aiming to increase website visibility and drive traffic to the site.

On the other hand, AEO is a more specialized approach that specifically targets answer engines or AI-powered platforms that directly provide answers to user queries. AEO focuses on creating content that directly answers specific questions in a concise and accurate manner. It involves understanding user intent, optimizing content structure, and using language that is easily crawlable and comprehensible by AI answer engines like ChatGPT or Gemini. AEO aims to position content as the direct answer to user queries, ensuring it is easily discoverable and displayed prominently on answer engine platforms.

While SEO and AEO share the goal of improving online visibility and attracting relevant traffic, they differ in their execution and target platforms. SEO strategy is broader in scope, catering to traditional search engines and aiming for higher rankings, while AEO is more specialized, targeting answer engines and focusing on directly answering user queries. Implementing SEO and AEO marketing strategies can be beneficial, as it allows businesses to optimize their content for a wider range of search platforms and cater to different user preferences in seeking information.

How to Perform Answer Engine Optimization for Your Business

With the rapidly increasing popularization of answer engines, brands and marketers must grasp the art of optimizing and influencing the outputs of these engines. You can begin by identifying, understanding, and influencing the data sources these engines rely on for training. AEO involves ensuring that the content and data you publish are easily accessible and can be utilized to train answer engines, enabling you to impact their output. This is where the power of community building and user-generated content shines, as socially validated content pieces can serve as the fundamental sources from which answer engines extract their information.

To truly comprehend what Gen e AI deems an optimal answer, a valuable approach is to input the questions commonly asked by your target audience into the platforms they use for inquiry. By reverse-engineering the structure of your content based on the resultant answers, you can create a foundation that aligns with the expectations of users. To enhance the quality and value of your content, you can then infuse it with a unique brand perspective or add value that sets your business apart. This approach ensures that your content not only satisfies user queries but also adds a distinct touch that resonates with your brand identity.

To Kick-Start Your AEO Process, Here are 5 Key, Actionable Steps to Implement:

1. Identify the sources of data to which AI language models are trained on,
2. Create a clear “About Us” page,
3. Build reputability by working to be included in media mentions, review websites, award pages etc.,
4. Ensure that the content and data that you are publishing is easily crawlable by AEOs,
5. Reverse engineer the structure of your content by inputting questions that your audiences typically ask.

The future of search is in AEO, so if you’re not already optimizing for answer engines, this is your sign to get started. If you need help, you can download our free guide on Answer Engine Optimization to get everything you need in order to start powering your business growth through the rise of answer engines.

The post What is Answer Engine Optimization? A Guide to AEO-Powered Growth appeared first on NoGood™: Growth Marketing Agency.

So what does this mean for brands? It’s about understanding where your audience is searching and tailoring your strategy for each of these ecosystems. TikTok requires a totally different SEO approach than Google, for example—its algorithm rewards trends, user interaction, and even the virality of sound. If you’re focused only on Google, you’re missing a massive chunk of how users are discovering brands and products in 2024.

What’s more, search intent is evolving too. On Amazon, someone’s already primed to buy, but on TikTok, they might just be browsing for inspiration. Understanding the mindset behind each platform is critical to success. Brands need to think omnichannel — not just optimizing keywords but also diversifying across the platforms that matter to their target demographics.

Expand Your Search Strategy Starting With Social Platforms

Almost Anything Can Be a Search Engine

As the landscape of search continues to evolve and change, the concept of a search engine has expanded far beyond its traditional definition. While Google, Bing, and Yahoo! still take up a large majority of search activity, the emergence of niche and specialized platforms demonstrates that almost anything can serve as a search engine in its own right. This evolution highlights how diverse the tools and methods for finding information have become.

Traditionally, search engines were designed to index the web and retrieve information based on keywords and algorithms. However, the definition of a search engine has broadened to include a variety of tools and platforms that cater to specific needs and interests. These specialized search engines function not just as repositories of information but as tailored guides that help users navigate complex domains with ease.

Specific Answers Require Specific Search Engines

Google isn’t enough anymore. We’re seeing the rise of specialized search engines and vertical-specific platforms that help users get better answers, faster. Instead of turning to Google as the source of truth for all search queries, users — particularly younger Gen Z consumers — are using Beli or Yelp to find restaurants, Zocdoc to find doctors, Expedia to find hotels, and Spotify to discover new music.

While Google remains a powerful tool for general information, it often returns a vast array of results that can be overwhelming or less targeted. Google’s strength lies in its ability to index and search vast amounts of web content, making it a powerful tool for broad inquiries. However, this breadth can sometimes lead to an overwhelming number of results, many of which may be irrelevant or not precisely what the user is looking for. This is particularly evident in areas such as local dining, healthcare, travel, and entertainment, where specialized platforms offer more tailored and precise answers. For complex or niche queries, Google’s generic search results can fall short in providing the depth and specificity needed.

Specialized search engines and vertical-specific platforms are designed to cater to particular industries or user interests, offering several advantages over general search engines:

• Enhanced relevance: Platforms like Yelp and Beli offer targeted restaurant recommendations based on user reviews and preferences, delivering results that are more relevant to diners than a general Google search might. Similarly, Zocdoc provides detailed doctor profiles and appointment scheduling tools, which are far more specific than what a general search query would yield.
• Improved user experience: Specialized platforms are built with features and interfaces that cater directly to their niche markets. For example, Expedia offers comprehensive travel planning tools, while Spotify provides personalized music discovery. These tailored experiences can be more engaging and effective than the broader, less focused results of a general search engine.
• Focused data aggregation: Specialized platforms aggregate and filter data in ways that general search engines may not. This targeted approach allows users to access highly curated information, reducing the noise and irrelevant results that can clutter search engine results pages.

The rise of specialized search engines and vertical-specific platforms poses several challenges to Google’s dominance in the search market. As users increasingly turn to these niche platforms for specific needs, Google’s share of search traffic could diminish, leading to potential erosion of its market dominance. The introduction of these specialized platforms not only adds more competition but also captures valuable advertising and data-driven revenue from their targeted markets. Additionally, as users become accustomed to the tailored experiences offered by these platforms, their expectations for search accuracy and relevance may shift, putting pressure on Google to adapt and innovate beyond its traditional general search model. To maintain its leadership, Google may need to invest in developing or acquiring specialized technologies and enhance its offerings to address the growing demand for more personalized search solutions.

Apps Are Becoming More and More Specific

The overarching theme of all the changes happening in the search landscape is this demand for greater specificity and helpfulness. As users increasingly seek precise, relevant, and contextually tailored information, apps and platforms are evolving to meet these needs with remarkable specificity. This shift is driven by the desire for more efficient and effective ways to find information, services, and products that align closely with individual preferences and requirements.

As apps become more and more specific, the search landscape, therefore becomes more and more fragmented as a result. Even in the dating and relationships space, the so-called “search” for a romantic partner has reflected the same shift towards greater specificity and segmentation. The (relatively) generic dating apps like Tinder and Bumble are starting to share the market with more specialized dating apps like Hinge, Feeld, The League etc. Hinge focuses on fostering deeper connections through detailed profiles and conversation prompts, aiming to build meaningful relationships rather than casual encounters. Feeld is designed for those exploring alternative relationship dynamics, including polyamory and open relationships, providing a space for non-traditional dating. The League offers a curated experience, targeting career-focused individuals by emphasizing professional accomplishments and social status. Drybaby, on the other hand, caters to those seeking sober dating experiences, creating a supportive environment for individuals who prioritize sobriety.

Gen Z Search Behaviors are Changing

Apps and search engines are evolving and adapting because Gen Z search behaviors are changing — and it’s a good thing they are. Gen Z uses social media more than search engines for brand discovery and information. In 2023, more than half of global Gen Z respondents said they use social media for brand information, compared to 45% who used search engines. TikTok is also a major part of the search engine discourse for Gen Z. A survey found that 51% of Gen Z respondents chose TikTok over Google as their search engine. This is a huge change from the past as Gen X and Millennials have pretty much turned “google” into a verb, yet now we’re seeing Instagram and TikTok come out on top as the preferred search engines for Gen Z.

The shift is evident, but it’s not just enough to note which platform Gen Z users are using as their go-to search engines — the more important question lies in the why. Why do Gen Z users prefer social media platforms like TikTok and Instagram to traditional search engines like Google? Here are a few reasons.

1. Discovery over immediacy

Unlike traditional search engines, which often prioritize quick answers to specific queries, social media encourages exploration and discovery. Users can scroll through endless feeds filled with diverse content, allowing them to stumble upon new trends, ideas, and interests that they might not have actively sought out. This approach transforms searching into an enjoyable experience, where the journey of finding something unexpected is just as valuable as the end result. The ability to discover unique and engaging content fosters a deeper connection to the platform and enhances overall satisfaction.

2. Visual-first experiences

Social media platforms excel in providing visual-first experiences that Gen Z users have grown to favor (and expect). With a heavy emphasis on images, videos, and creative content, these platforms create a more immersive environment compared to the text-based format of traditional search engines. This visual appeal makes information more digestible, memorable, and engaging, as users are naturally drawn to eye-catching visuals and dynamic storytelling. Whether it’s a quick tutorial on TikTok or a highly-curated carousel on Instagram, the emphasis on visuals allows for a more compelling and entertaining way to consume information, making it easier for users to connect with the content and retain what they’ve learned.

3. Social proof and community validation

Social proof and community validation significantly influence Gen Z’s search habits, driving them to rely on social media platforms for information and recommendations. Content created by peers, influencers, or trusted figures is often seen as more authentic and relatable than traditional advertising or search engine results. When users see others engaging with a product, service, or idea, it instills a sense of trust and credibility, leading them to prioritize these platforms for decision-making. This communal aspect fosters a shared experience, where users not only seek information but also engage in discussions and share their opinions, further validating their choices and enhancing their sense of belonging within the community.

Ironically, the “best” search engines for Gen Z users tend to be apps that simply serve as a platform for others’ opinions (e.g. Reddit, TikTok, Beli) instead of the ones that are designed to deliver a single, accurate answer. With the rise of the creator economy, paid partnerships and influencer deals, users are becoming skeptical of even the content that’s organically posted on social media. This has led to the practice of reading TikTok comments as another level of search and discovery, where users are able to form their own opinion based on varying inputs from a community of multiple users.

The Higher Standard for Personalization

Gen Z grew up with social media and tends to have a very high standard for personalization that google just isn’t achieving. Searchers are getting lazier — meaning they want more accurate and personalized answers to their specific questions faster. Google’s AI Summary feature has attempted to answer to this need, but even with that level of speed and convenience, it still lacks the social proof and personalization that alternative “search engines” have.

Let’s take the search for content as an example. If a user wants to listen to music or watch a movie, they aren’t going to Google to search for that because they know that there are platforms that can give them that deeper level of personalization. For music, users can go straight to Spotify and type in a specific mood or vibe and immediately get a curated mix of songs tailored to that search term that takes into account their unique music preferences. Similarly, users can type a movie genre into their Netflix search bar and get content recommendations tailored to their tastes based on their viewing history. Non-traditional “search engines” like Spotify and Netflix are able to offer a higher degree of personalization because of the large amounts of user data they have on every individual user — something a simple Google Search simply isn’t able to achieve. As a result, users are turning to streaming platforms and apps for their content search needs, and Google loses a whole chunk of search traffic to these specific, personalized search experiences.

How Brands Should React to The Fragmentation of Search

The search landscape is fragmented, whether we like it or not; however, this isn’t necessarily a bad thing for brands. In fact, the fragmentation of search provides brands with a unique new opportunity to optimize their presence across a variety of different platforms to create a holistic, cross-channel brand experience.

To remain competitive, brands must adjust their strategies to align with this new search paradigm. Here are a few key actions they can take:

1. Embrace niche platforms:

Brands need to identify which specialized search engines or apps their target audience is using. For example, a restaurant may benefit more from being visible on Yelp, Beli, or OpenTable than simply relying on Google searches. Similarly, lifestyle brands should focus on social media platforms like Instagram or TikTok to engage Gen Z, who increasingly prefer these for discovery.

2. Tailor content for specific search behaviors:

As search habits become more platform-specific, brands should craft content that fits each platform’s unique format and user expectations. Short-form, visual content thrives on TikTok, whereas detailed, review-centric content may perform better on Yelp or niche communities like Reddit.

3. Leverage personalization:

Specialized platforms often excel in delivering highly personalized experiences. Brands should focus on providing tailored content, product recommendations, or services that cater to individual user preferences. This will require harnessing user data and employing personalization strategies to meet higher expectations.

4. Capitalize on discovery:

Unlike traditional search engines that focus on direct queries, platforms like TikTok and Instagram promote discovery through algorithms that suggest content. Brands can create engaging, shareable content that encourages exploration and taps into trends, positioning themselves to be found organically by users.

5. Incorporate consumer input in your brand strategy:

Your consumers play an active role in shaping your brand. Through reviews, social media posts, and user-generated content, your audience is a vital part of how your brand is perceived. Brands should listen closely to customer feedback, engage in two-way conversations, and encourage users to create content that aligns with their values. When consumers feel heard, they become loyal advocates, contributing to a more authentic and trusted brand image.

6. Empower loyal advocates to amplify your brand:

Your most loyal customers are your best advocates. With the rise of social platforms and niche search engines, consumers trust peer recommendations more than traditional advertising. Brands can leverage this by fostering strong relationships with advocates through loyalty programs, exclusive content, and ambassador opportunities. By empowering these advocates to share their positive experiences, brands can organically extend their reach, driving trust and engagement across the fragmented search landscape.

By recognizing the shift from a singular search engine model to a fragmented, app-based search ecosystem, brands can remain relevant and competitive, meeting users where they already are.

Optimize Your Brand for The New Landscape of Search

The post The Fragmentation of Search: Why Search and Discovery Just Isn’t the Same Anymore appeared first on NoGood™: Growth Marketing Agency.

A brand has a 60-70% chance of making a sale to a retained existing customer, but that percentage drops dramatically to 20% for net new customers. All this is to say: keeping and valuing your existing customers is both strategic and important.

One big benefit of pursuing customer retention is that you already have a foundational level of user data that you can leverage to retain these existing customers. This data may include metrics like total revenue, past purchase frequency, browse history, any zero-party data the user chooses to provide, and more, which can all be used to inform predictive analytics for retention optimization.

What Is Predictive Analytics?

Predictive analytics is a powerful tool in growth marketing that leverages historical data, machine learning, and statistical algorithms to forecast future customer behaviors and trends. By analyzing patterns in past customer interactions, such as purchase history, browsing habits, and engagement metrics, predictive analytics helps marketers anticipate what actions customers are likely to take next.

This insight allows businesses to tailor their marketing efforts, optimize customer journeys, and deliver personalized experiences that drive retention and increase lifetime value. Essentially, predictive analytics turns data into actionable insights, enabling more strategic decision-making in customer retention strategies and lifecycle marketing.

What Is Customer Retention?

Customer retention refers to the strategies and actions a business undertakes to keep its existing customers engaged, satisfied, and loyal over time. Unlike customer acquisition, which focuses on attracting new customers, retention is about nurturing and maintaining relationships with those who have already made a purchase or engaged with the brand. Effective customer retention involves understanding — and often predicting — customer needs, providing consistent value, and delivering exceptional experiences that encourage repeat business. This can involve leveraging data analytics to understand customer lifecycle stages, predicting potential churn, and proactively engaging at-risk customers with targeted interventions. The goal is to increase customer lifetime value (CLTV), which measures the total revenue a customer is expected to generate over their relationship with the brand.

Why Is Customer Retention Important?

The primary reason customer retention is important is its direct impact on profitability. Studies consistently show that it costs significantly less to retain an existing customer than to acquire a new one — some estimates suggest up to five to ten times less. Moreover, retained customers tend to have higher lifetime value (LTV), meaning they are likely to spend more over time, engage more deeply with your brand, and be less price-sensitive. This leads to a more predictable and steady revenue stream, which is essential for planning and scaling a business.

Additionally, retained customers often become brand advocates, spreading positive word-of-mouth and referring new customers to your business. This organic advocacy not only reduces your marketing costs but also enhances your brand’s credibility and reach. Loyal customers are more likely to provide valuable feedback, participate in loyalty programs, and engage with new products or services, creating a virtuous cycle of growth and improvement.

How Is Predictive Analytics Used to Drive Customer Retention?

Predictive analytics is used to drive customer retention by transforming extremely large amounts of data into digestible and actionable insights. By analyzing patterns in customer behavior, preferences and historical interactions, brands can forecast which customers are at risk of churning and intervene before they are fully lapsed. For instance, predictive models can identify subtle signs of dissatisfaction, such as a drop in usage frequency or negative feedback, allowing businesses to proactively address issues with targeted offers, personalized communications, or enhanced support. On the flip side, predictive analytics can also identify the factors that lead to high engagement or sustained loyalty, giving brands data-backed insights on how to further strengthen or double down on the strategies that do work.

What Are Strategies to Reduce Customer Churn Using Predictive Analytics?

Predictive analytics is a powerful tool for reducing customer churn, ensuring that you are leveraging the data that you have in order to keep existing customers engaged over time, no matter where they are in their customer journey. Using user data like purchase history and customer feedback, you can develop predictive models to identify customers who are at high risk of churning. Patterns such as declining engagement, reduced purchase frequency or negative feedback can raise a red flag for possible churn, so that businesses can pinpoint which customers are likely to leave and take proactive steps to retain them. Here are 5 key strategies for using predictive analytics to drive customer retention:

Customer Journey Mapping

Customer journey mapping is a practice that’s important with any retention or lifecycle marketing strategy, but this can be greatly enhanced with insights gained from predictive analytics. You can utilize predictive analytics to create detailed maps of each different customer journey, highlighting the most common paths that lead to churn — or oppositely, sustained engagement.

By understanding these journeys on a more granular level, you can optimize the customer experience at each critical touchpoint, ensuring a smoother progression from step to step that reduces the overall likelihood of going down that path toward churn.

Behavioral Triggers for Engagements

Based on past customer churn data, predictive analytics can identify key behavioral triggers that indicate when a customer is about to disengage. For example, if a customer stops using a particular feature they previously engaged with regularly, an automated email or popup with a special offer can be sent to reengage them by reminding them of the value they derive from said product or service.

Customer Lifecycle Segmentation

You can segment your customer base according to their lifecycle stage (e.g. new, active, at-risk, lapsed, etc.) and use predictive analytics to then further refine these segments based on behavior and preferences. These segments can then serve as the basis for personalized retention campaigns across multiple channels.

Personalized Retention Campaigns

Once you have identified the different customer segments to target, you can use predictive analytics to create personalized retention campaigns that offer incentives, such as discounts or loyalty rewards, tailored to each segment’s preferences and behavioral drivers. This targeted approach can make customers feel more valued and more likely to stay when they feel that their wants and needs are being proactively met.

Cross-sell and Upsell Opportunities

Customer retention encompasses more than just keeping the user engaged with the existing product or service — it’s also about being able to grow the brand-customer relationship and build long-term loyalty to the brand the will evolve with and outlast the current offerings.This means that predictive analytics can also be used to identify the best opportunities to cross-sell and upsell new products at different stages of their lifecycle. For example, if a customer has been engaged with your brand for a certain period of time and has shown interest in specific product categories, a targeted upsell offer for a new, upgraded product in their category of interest can be framed to enhance their experience and increase their commitment to your brand.

The common theme across all churn reduction strategies is proactivity. Predictive analytics enables a level of foresight that you wouldn’t otherwise have, which opens up many opportunities to preemptively engage with key customers that are most at-risk of churning.

The Future of Predictive Analytics for Customer Retention

As AI technologies evolve, they will enable marketers to anticipate customer behavior with even greater precision, moving beyond basic predictions to uncover deep, nuanced patterns in consumer data. This next generation of predictive analytics will harness real-time data streams and sophisticated machine learning models to forecast customer needs almost before they arise.

Marketers will be equipped with AI-driven tools that not only predict churn but also suggest the most effective personalized retention strategies for each customer. With AI’s continuous learning capabilities, these models will adapt to changing consumer behaviors, ensuring that retention efforts are always one step ahead. In the future, customer retention will no longer be about simply reacting to trends — it will involve strategically shaping them through advanced predictive insights, leading to more proactive, efficient, and personalized marketing efforts. If you need help creating predictive analytics strategies to reduce churn, our growth experts can help. Talk to us.

The post Using Predictive Analytics to Drive Customer Retention: 5 Strategies to Reduce Churn appeared first on NoGood™: Growth Marketing Agency.

Whether it’s by building niche communities or plugging into existing ones, marketing in the age of Web3 is and should be defined by a heightened focus on leveraging community in a new decentralized internet landscape. This is where Web3 marketing agencies come into the picture.

Meet Your Customers Where They Are with Crypto and Web3 Marketing

The 7 Best Web3 and Crypto Marketing Agencies

1. NoGood

NoGood is one of the most reputable marketing and growth agencies in the Web3, crypto, and NFT space. As a TechCrunch-verified growth expert, we work with numerous companies in the crypto space, performing everything from rapid experimentations to channel assessments to market research.

New York, NY

SEM, Content Marketing, CRO

SaaS, Healthcare, Consumer

50-100 employees

2. Coinbound

Coinbound is a cutting-edge crypto marketing agency that specializes in helping blockchain-based businesses achieve their marketing goals.

New York, NY

NFT Marketing, Influencer marketing, SEO

NFT, Web3, Crypto

11-50 employees

3. Rehab

Rehab is a web3 and digital sustainability agency that helps disruptive businesses accelerate innovation and reduce speed to market.

London, UK

Web3 Platform Discovery, UX & UI Design

B2B, B2C, DTC

51-200 employees

4. Tenten

BBDO has long established itself as a prominent player in the industry. With a rich history dating back to 1891, BBDO has consistently demonstrated its ability to deliver compelling campaigns. At the heart of BBDO’s approach lies their mastery of storytelling and engagement.

Taipei City

Martech, Web3, Blockchain, Headless CMS

B2B, B2C, DTC

11-50 employees

5. Serotonin

Serotonin is a breakthrough marketing firm and product studio for transformative technologies.

New York, Los Angeles

Brand positioning, Content Marketing, Community Growth

B2B, B2C, DTC

50-100 employees

6. Bemeir

Bemeir is an e-commerce agency specializing in helping brands design, launch, support, maintain, and grow web3 websites and blockchain and cryptocurrency projects.

New York, NY

Progressive Web Apps (PWA), Growth Marketing, Email Marketing

B2B, B2C, DTC

0-10 employees

7. Noir

★★★★★

Noir harnesses the power of branding and will position your new or existing initiatives into a Web3 powerhouse.

London, Dubai

App Development, Augmented Reality, User Experience

B2B, B2C, DTC

11-50 employees

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Noir harnesses the power of branding and will position your new or existing initiatives into a Web3 powerhouse.

Noir builds powerful web3 brands & digital experiences by focussing on community, technology & the market to launch brands’ vision for an optimistic new internet. They have helped some of the biggest names in Web3 raise capital for their project, from launching IDOs to NFT memberships to advising on investor relations.

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Industries

Locations

•  Brand Identity
• User Experience
• Augmented Reality
• App Development

• B2B
• B2C
• DTC

• London
• Dubai

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Frequently Asked Questions

Ready to discover how our expertise can drive your brand’s growth and success?

The post Top Web3 and Crypto Marketing Agencies in 2025 appeared first on NoGood™: Growth Marketing Agency.

How Is AI Used for Email Personalization?

There are two main approaches to using AI for email marketing. The first is straightforward: using AI to write the emails themselves as a way to reduce time and effort in copywriting. The second, which is the focus of this guide, is a more complex method of analyzing and leveraging large amounts of user data to drive email personalization at scale.

Here are a few ways in which AI can be used for email personalization:

1. User Segmentation and Profiling

AI algorithms analyze large datasets to identify patterns and segment users based on various attributes such as demographics, past behavior, purchase history, and preferences. This segmentation enables marketers to target specific groups with personalized content that resonates with their interests.

2. Predictive Analytics

AI uses predictive analytics to forecast future user behavior based on historical data. This allows marketers to anticipate needs and send proactive emails that guide users along the customer journey, such as suggesting replenishment products before they run out.

3. Optimized Send Times

AI can determine the optimal time to send emails to each recipient based on their past interactions. By analyzing when users are most likely to open and engage with emails, AI ensures that messages are sent at the most effective times, improving open and click-through rates.

4. Subject Line and Content Optimization

AI can test and optimize subject lines, email copy, and call-to-action buttons. Through techniques like A/B testing and natural language processing, AI can determine which variations perform best and adapt future emails accordingly.

Can AI Write My Emails?

Yes, but that’s not the end of the story — writing an email is only one part of the email marketing process. Even the most well-written email will fall flat if it’s not strategically optimized for the end user. AI copywriting tools can automate, speed up, and streamline their content writing process, but there’s still a need for skilled copywriters to spend time on revision, refinement, and strategy — all of which are crucial in ensuring both high quality and quantity of content.

Instead of just asking if AI can write your emails, ask yourself if AI can help you determine what to write for each email and each user or audience segment. AI can be used to analyze user data, including browsing history, purchase behavior, and engagement metrics, to generate insights about what content will resonate most with each segment of your audience. This data-driven approach ensures that the content you create is highly relevant and personalized, increasing the chances of engagement and conversion.

AI can also analyze the results of previous A/B tests to determine what types of subject lines, content, and calls-to-action have performed best with different segments of your audience. These insights can then be used to guide future email content, ensuring that you continually optimize your messaging based on real-world performance data.

So the answer is yes: AI can write your emails, but it shouldn’t stop there.

What Is the Best AI Tool for Email Marketing?

There are hundreds of AI tools that can be used for email personalization, but the most useful ones are likely to be features that are integrated into existing email marketing platforms like Klaviyo, Braze, Mailchimp, Iterable, etc. Here’s an overview of the best AI tools that you should be using to enhance the impact of your marketing emails.

1. Klaviyo Predictive Analytics

Klaviyo predictive analytics leverages advanced data science to forecast customer behavior and trends, enabling businesses to optimize their email marketing strategies. By analyzing historical data, Klaviyo can predict key metrics such as customer lifetime value, churn probability, and future purchasing behavior. This predictive power allows marketers to tailor their email campaigns with a high degree of personalization. For instance, they can send targeted promotions to likely repeat buyers or re-engage customers at risk of churning. The benefit of this approach is twofold: it enhances the customer experience by delivering relevant content and boosts business outcomes through more effective and efficient marketing efforts.

To access Klaviyo’s predictive analytics, users must meet the following conditions:

• Minimum Order Volume: At least 500 customers must have placed an order. This count refers specifically to unique customers who have made purchases, not just total profiles.
• Ecommerce Integration: Users need to have an integration with an ecommerce platform such as Shopify, BigCommerce, or Magento or utilize Klaviyo’s API to track placed orders.
• Order History: There must be at least 180 days of order history, with some orders placed within the last 30 days.
• Repeat Customers: A subset of customers must have placed three or more orders

2. Iterable Brand Affinity

Brand Affinity™ is a feature of Iterable AI that labels users based on their historical engagement with your brand. These labels—loyal, positive, neutral, negative, and unscored—are generated weekly and are based on user interactions across all communication channels, including emails, push notifications, and in-app messages.

Brand Affinity scores are calculated using the user’s history of interactions with your messages. Recent interactions are weighted more heavily. Each user is assigned one of the following labels: loyal, highly engaged with your messages; positive, generally engaged but less frequently than loyal users; neutral, occasionally engaged; negative, usually disengaged; and unscored, not enough data to generate a label.

You can use these labels in segmentation, campaigns, journeys, data feeds, and Catalog collections to send personalized, relevant messages to your customers. For example, you might use Brand Affinity labels to reward loyal users by adding them to a reward or perk journey, boost revenue by testing offers on positive users, offer different discounts to different audience types, improve open rates or deliverability by suppressing negative users, or add users to a reactivation journey when they switch to negative.

3. Braze AI-Powered Testing

Braze’s AI-powered testing transforms how brands optimize their customer journeys by applying machine learning-driven experimentation at every stage. This advanced approach allows you to test and refine every element of your marketing strategy—from timing and copy to channels, images, and entire customer journeys.

By leveraging AI to conduct these experiments, you can gain deeper insights into what works best for your audience, ensuring that each interaction is tailored for maximum impact. This iterative process helps create more engaging and effective experiences, ultimately driving better results and higher satisfaction across your customer base. With Braze’s AI-powered testing, you can move beyond basic A/B testing to continually evolve and perfect your customer engagement strategies.

These are just a few examples of how brands and marketers can be using AI to personalize email campaigns and journeys. The key is less so in what tools you use, but more about being aware of the vast variety of tools that are available for use — just so long as you know to take advantage of them to supercharge your email marketing initiatives.

What Role Does AI Play in the Future of Email Marketing?

AI is already becoming a ubiquitous part of marketing functions, from content writing to paid advertising, and email marketing is no exception. Through advanced algorithms and machine learning, AI can analyze vast amounts of data to understand customer behaviors and preferences, allowing marketers to craft highly targeted and relevant content.

AI-powered tools can automate email scheduling, segment audiences with precision, and even predict the best times to send emails for maximum engagement. Furthermore, AI can enhance A/B testing processes, providing insights into what types of subject lines, copy, and visuals resonate best with different segments.

In the future, AI will likely become more and more integrated with email marketing by enabling hyper-personalization at scale. Predictive analytics will evolve to not only recommend the best content but also to dynamically generate it, tailoring each email to individual recipient’s preferences and behaviors in real-time. AI will integrate with advanced sentiment analysis, allowing marketers to gauge the emotional tone of their audience and adjust messaging accordingly.

Moreover, AI will anticipate customer needs before they even articulate them, using sophisticated algorithms to predict lifecycle stages and sending proactive, value-driven content. This anticipatory approach will transform email marketing from a reactive to a proactive strategy, fostering deeper customer relationships and significantly boosting loyalty and retention. If you need help understanding how to leverage current AI technologies for strategic email personalization, drop us a line and let’s work together.

The post AI Email Personalization 2.0: The Innovations Shaping Lifecycle Marketing appeared first on NoGood™: Growth Marketing Agency.

Zero-party data is immensely valuable for email marketing, particularly amidst the death of third-party cookies and the growing consumer demand for hyper-personalization. 

Need more help creating a zero-party data strategy for your email marketing initiatives?

What is zero-party data?

Zero-party data is information that a customer intentionally and willingly shares with a brand. This type of data is collected directly from customers through interactions such as surveys, quizzes, preference centers, and personalized shopping experiences. Unlike first-party data, which is gathered through customer behavior and interactions with a brand’s website or app, zero-party data is explicitly provided by the customer, ensuring its accuracy and relevance.

Here are some key characteristics of zero-party data:

1. Voluntarily Shared: Customers willingly provide zero-party data, often in exchange for a more personalized experience. This creates a level of trust and transparency between the customer and the brand.
2. Highly Accurate: Since customers are the source of zero-party data, it tends to be more precise and reliable compared to inferred data from other sources.
3. Directly Collected: Zero-party data is obtained straight from the customer, eliminating the need for guesswork or assumptions about their preferences and needs.
4. Privacy-Friendly: With increasing concerns about data privacy and regulations like GDPR and CCPA, zero-party data stands out because it is collected with the customer’s explicit consent, making it a privacy-compliant way to gather information.

Here are some examples of zero-party data:

• Preference Centers: Where customers specify their interests, preferred communication channels, and product preferences.

• Surveys and Quizzes: Tools that engage customers and collect insights on their preferences, habits, and needs.

• Personalized Account Settings: Features that allow customers to customize their experiences, such as wishlists, saved items, and preferred shopping categories.

The difference between zero-party, first-party, second-party, and third-party data

Zero-party data is information that customers intentionally share with a brand. Since customers provide this data willingly, often in exchange for a more personalized experience, it is highly accurate and relevant.

First-party data is information that a company collects directly from its own customers through their interactions with the brand’s digital properties, such as websites, mobile apps, and social media. This data is directly collected from customer interactions with the brand, making it accurate and reliable as it reflects real customer behaviors and preferences. The brand owns this data, giving it full control over its use. An example of first-party data is website analytics that show which products a customer views most often or purchase history from an online store.

Second-party data is essentially someone else’s first-party data that is shared with another organization. This data is obtained through a partnership or agreement between two companies, ensuring it comes from a known and reliable source. Second-party data maintains the accuracy and relevance of first-party data and is shared based on mutual agreements, often enhancing its utility without compromising privacy. For instance, a car rental company might share its customer travel preferences with a hotel chain to offer tailored travel packages.

Third-party data is information collected by an entity that does not have a direct relationship with the customer. This data is typically aggregated from various sources and sold to companies for advertising and marketing purposes. Third-party data offers broad reach, providing insights on a wide range of customers, but it may lack precision and relevance compared to data obtained directly from customers. Additionally, third-party data is increasingly scrutinized due to privacy regulations and consumer concerns. An example of third-party data is information collected from various websites and sold to an advertising network to target ads based on user behavior across the internet.

Comparing these types of data, zero-party and first-party data are generally more accurate and relevant since they come directly from customer interactions or preferences. Second-party data can also be reliable, depending on the quality of the partnership, while third-party data, although broad, may lack precision.

In terms of ownership and control, zero-party and first-party data are fully owned and controlled by the brand, allowing for better data governance. Second-party data involves shared control, whereas third-party data is typically owned by external aggregators.

Regarding privacy and trust, zero-party data excels as it is shared voluntarily by customers, and first-party data is also trusted as it is collected directly by the brand. Second-party data maintains trust if partnerships are transparent, but third-party data faces the most privacy challenges and trust issues due to its aggregated nature and lack of direct customer consent.

Why is zero-party data important for email marketing?

Zero-party data is particularly important for email marketing because it serves as the prerequisite and enabler of highly personalized and impactful email campaigns. While consumers are typically (reasonably) protective of their personal data, they are willing to share their data in exchange for an improved experience that caters to their wants and needs.

Zero-party data is the perfect way to strike that balance because it allows customers to be in control of what information they choose to share in order to get that personalized brand experience they’re looking for. 

Here are just a few of the key benefits of using zero-party data for email marketing.

1. Enhanced personalization and engagement

With zero-party data, marketers can tailor their email content to meet the specific preferences and needs of their audience. This level of personalization goes beyond simply addressing the recipient by name; it involves crafting messages that resonate with their interests, preferences, and stages in the customer journey. Emails that are relevant and personalized based on zero-party data are more likely to be opened, read, and acted upon.

Customers appreciate when brands take the time to understand their preferences, leading to higher engagement rates and, ultimately, better conversion rates.

2. Compliance with privacy regulations

In an era where data privacy is becoming increasingly important, zero-party data provides a compliant way to gather customer information. Since this data is voluntarily provided by the customer, it aligns with privacy regulations such as GDPR and CCPA, reducing the risk of legal issues associated with data collection.

3. Improved data accuracy and strategic insights

Zero-party data is highly accurate because it comes directly from the customer. Unlike first, second or third-party data, which can sometimes be inaccurate or outdated, zero-party data reflects the customer’s current preferences and intentions, ensuring that your email marketing efforts are based on reliable information.

The insights gained from zero-party data can also inform broader marketing strategies. Understanding customer preferences and intentions can guide product development, content creation, and overall marketing messaging, ensuring that all aspects of your marketing strategy are aligned with customer expectations.

How to collect zero-party data

Unlike other data types, zero-party data is willingly provided by customers, making it highly accurate and valuable. Here’s a guide on how to effectively collect zero-party data:

1. Surveys and Questionnaires

Surveys and questionnaires are straightforward tools for gathering zero-party data. By asking customers specific questions about their preferences, interests, and intentions, you can gain valuable insights.

• Incentivize participation: Offer discounts, freebies, or entry into a giveaway to encourage customers to complete surveys.
• Keep it short and simple: Long surveys can be off-putting. Focus on asking essential questions that provide meaningful insights.
• Personalize the approach: Tailor the survey questions based on previous interactions to make them relevant and engaging.

2. Preference Centers

A preference center allows customers to choose what kind of communications they want to receive and how frequently. This self-service option empowers customers to share their preferences willingly.

• Make it accessible: Ensure that the preference center is easy to find on your website or in email communications.
• Offer multiple options: Provide a variety of preferences, such as product interests, content types, and communication frequency.
• Regular updates: Prompt customers periodically to update their preferences to keep the data current.

3. Interactive Content

Interactive content like quizzes, polls, and assessments can be engaging ways to collect zero-party data. These formats are not only fun for the user but also provide insightful data for marketers.

• Design engaging content: Ensure that the interactive content is entertaining and relevant to your audience.
• Clear value proposition: Explain what the user will gain from participating, such as personalized recommendations or insights.
• Follow-up: Use the results from interactive content to follow up with personalized email campaigns or product suggestions.

4. Contests and Giveaways

Running contests and giveaways can be an effective way to collect zero-party data. Participants are often willing to share their information for a chance to win a prize.

• Set clear rules: Make sure the terms of participation are transparent and easy to understand.
• Collect relevant data: Focus on collecting data that will provide insights into customer preferences and behaviors.
• Thank participants: Show appreciation by sending a thank you email and possibly offering a small reward to all participants.

5. Onboarding Processes

The onboarding process is a critical time for collecting zero-party data. When customers sign up for your service or newsletter, use this opportunity to gather relevant information.

• Step-by-step process: Break down the data collection into manageable steps to avoid overwhelming new customers.
• Explain the benefits: Let customers know how sharing their information will enhance their experience.
• Follow up: After onboarding, use the collected data to send personalized welcome emails and offers.

6. Feedback Requests

Asking for feedback after a purchase or interaction can provide valuable zero-party data. Customers are often willing to share their thoughts and preferences when asked directly.

• Timing is key: Request feedback soon after the customer interaction to ensure the experience is fresh in their mind.
• Make it easy: Simplify the feedback process with easy-to-use forms or quick survey links.
• Show appreciation: Thank customers for their feedback and let them know how their input will be used to improve their experience.

How to use zero-party data for email personalization

Zero-party data is a goldmine for creating highly personalized email campaigns. Here’s how to effectively leverage zero-party data to enhance your email personalization efforts:

1. Segment Your Audience

Segmentation is the foundation of personalized email marketing. Using zero-party data, you can create highly specific audience segments based on various criteria such as preferences, interests, and behaviors.

2. Personalized Content and Recommendations

Zero-party data allows you to deliver content and product recommendations that resonate with individual customers.

• Dynamic content: Use dynamic content blocks in your emails to show different content to different segments based on their preferences. For example, if a customer has shown interest in a specific product category, highlight those products in your email.
• Product recommendations: Leverage zero-party data to suggest products that align with a customer’s expressed interests. Personalized recommendations can significantly increase the likelihood of conversion.
• Relevant offers: Tailor promotions and discounts based on customer preferences and purchase intentions. Personalized offers are more likely to drive engagement and sales.

3. Customized Email Journeys

Creating customized email journeys based on zero-party data ensures that each customer receives relevant and timely communications throughout their relationship with your brand.

• Welcome series: Develop a personalized welcome email series that introduces new subscribers to your brand and highlights products or content that match their interests.
• Behavioral triggers: Set up automated email triggers based on specific actions or behaviors, such as cart abandonment, product browsing, or purchase anniversaries. Tailor the content of these emails to the individual’s preferences.
• Re-engagement campaigns: Use zero-party data to identify inactive subscribers and send personalized re-engagement emails that reignite their interest with relevant content or exclusive offers.

5. Personalized Subject Lines and Sender Names

The subject line and sender name are critical elements that influence whether an email gets opened. Personalize these elements using zero-party data to increase open rates.

• Personalized subject lines: Incorporate the recipient’s name, interests, or previous interactions into your subject lines. For example, “John, don’t miss out on our new arrivals in electronics!”
• Customized sender names: Use a sender name that resonates with the recipient, such as a specific customer service representative or a name associated with their favorite product category.

Best practices for leveraging zero-party data

Leveraging zero-party data effectively requires a strategic approach to ensure that the data collected is both valuable and actionable. First and foremost, always prioritize transparency and trust. Clearly communicate to customers why you are collecting their data and how it will enhance their experience.

Simplify the process of data sharing by using concise and user-friendly forms, surveys, or preference centers. It’s crucial to ask only for information that you will genuinely use, avoiding any unnecessary or intrusive questions. Once you have the data, integrate it seamlessly into your marketing platforms to create highly personalized and relevant communications. Regularly update and validate your data to maintain its accuracy and relevance. Additionally, continuously monitor and analyze the performance of your personalized campaigns to refine and optimize your strategies. 

Here are some do’s and don’ts when it comes to collecting and leveraging zero-party data:

By following these do’s and don’ts, you can effectively use zero-party data to enhance your marketing efforts, build trust, and provide value to your customers.

The post How to Collect and Use Zero-Party Data for Email Personalization appeared first on NoGood™: Growth Marketing Agency.