You need to get started using programmatic buying tools, but you’ve never done it before, and you don’t know where to begin. Join the club. We get it. It’s the same reason we haven’t learned to cook for ourselves yet.
Here’s the good news: We’ve got experts at Basis Technologies who know the ins and outs of programmatic advertising. All you have to do is ask the right questions. Lucky for you, we’ve asked the basic questions and we’ve come equipped with answers (and, lucky for us, everyone assures us there is no such thing as a dumb question).
Understanding Programmatic Ad Buying
To start: Programmatic is a very broad term. Simply put, it’s technology that automates digital media buying. This can include automating anything from rate negotiation and campaign set up to optimizations and actualizations. One of the primary buying tools you have at your disposal is a DSP.
If there really are no dumb questions, then can I ask: What is a DSP?
A demand side platform (DSP) is an automated ad buying platform, where advertisers and agencies go to purchase digital ad inventory. Examples of ad inventory include banner ads on websites, mobile ads on apps and the mobile web, and in-stream video. DSPs are integrated into multiple ad exchanges.
I’ve heard of a SSP. Is that the same acronym and I’ve been typing it wrong into Google this whole time?
Nope, it’s not the same thing, but it is similar in concept. Supply-side platforms, or sell-side platforms (SSPs), facilitate the sale of publisher inventory through an ad exchange. SSPs offer services such as minimum bid requirements in order for the publisher to maximize how much their ad space sells for. The difference is that DSPs are for marketers and SSPs are for publishers. SSPs (like DSPs) are plugged into multiple ad exchanges.
You keep mentioning ad exchanges. What are those? And why do DSPs and SSPs both need to be connected to ad exchanges?
Think of the ad exchange as the “go-between” in the automated buying world. An ad exchange is a digital marketplace that enables advertisers and publishers to buy and sell advertising space via real-time bidding (RTB). Meaning the ad exchange announces each impression—with the inventory flowing through DSPs and SSPs—in real time and asks buyers if they are interested in buying said impression and at which price.
All of this makes sense now, but I still don’t understand why I should use a DSP!
In order to understand why DSPs matter, it’s important to remember where the need came from and how the ad industry operated before automated buying. Traditionally, if you were a media buyer at an ad agency, the buying process was facilitated through human beings—it was you (the advertisers), the publishers (website where ad will appear), an audience (the viewer of the ad), and a bunch of spreadsheets and emails going back and forth negotiating prices. This process was complicated, time-consuming, and often error-prone. DSPs allow advertisers and agencies to buy across a lot of sites at the same time—and all of this is done instantly and efficiently, usually before the webpage loads.
How do I know which Demand side platform is right for me?
There are many DSPs in the programmatic world to choose from. Choosing the right DSP for you depends on a number of factors, like what type of data you need (first-party or third-party) and how many ad exchanges the DSP is plugged into, because that can affect reach. DSPs like Basis DSP give you access to over 40 billion daily impressions across all devices and channels. Other things to consider include cost, how much training and hands-on support you prefer, and ease of use—many DSPs have multiple, clunky or confusing user interfaces, which require a lot of education.
Speaking of training, I’m glad you mentioned that. What if I sign up for a Demand Side Platform and find out I have no idea what I’m doing?
Some DSPs come with a full team of experts, offering you everything from full-service to self-service and everything in between. With Basis DSP, you’ll start with a three-month platform training program, offering you an overview of programmatic, a walk-through of the interface, and best practices for campaign creation and optimization. Ongoing support is available in the form of a customer success manager and resources to keep you informed—like new feature webinars, best practice guides, and new quarterly business reviews.
Customer data platforms (CDP) and data management platforms (DMP) are marketing and advertising tools (in that order). They have similar sounding acronyms and, in some ways, work in the same way. For example, they both capture and organize data, use existing data, generate analysis and reports, and help to create a single customer view. With a CDP and a DMP, digital marketers can personalize their marketing campaigns, see how effective those campaigns were, and drive leads.
But to maximize your marketing results, there are distinct differences between the two platforms that you need to understand.
How to use a customer data platform and a data management platform
Data management platform
A data management platform collects, segments, analyzes, and stores anonymous customer data from various sources. Advertisers (mainly) use this unified, segmented data to effectively target (and retarget) advertising campaigns to their intended audience. Designed primarily as an adtech tool, a DMP can also drive product recommendations on your website for each unique visitor.
Examples of how to use a DMP effectively include:
Leveraging audience data to identify any new customer segments and reach those target audiences through various paid media channels
Using this audience data to personalize interactions
Learn more about DMPs
Customer data platforms
A customer data platform is a marketing solution that collects data from your existing customer database, website, mobile app, and CRM to customize marketing and content for current customers. It’s an ideal solution for any/all remarketing efforts.
Both platforms handle first-party data (direct from the customer, CRM and/or marketing automation database, or purchase transactions), second-party data (data provided from other companies, such as partners, resellers, etc.), and third-party data (data from multiple sources).
Both CDPs and DMPs collect the same types of data, but what they target differs. DMPs primarily pursue third-party data (cookies and segmented customer IDs) and then store that data for a short time. CDPs focus on structured, semistructured, and unstructured PII first-party data.
A CDP stores this data over long periods of time so marketers can build in-depth, accurate customer profiles and nurture customer relationships. And a CDP can share and draw data with any system (CRM or DMP) that needs it (and has it) to influence all types of marketing.
CDP vs. DMP—User profiles, data selection, and data capture
User profiles for DMPs segment and categorize people tied to a cookie’s lifespan to capture their anonymous behavioral data.
Data selection involves several field values to collect the necessary data. Yet, as part of the field data, DMPs can gather important insights, including when people visited a website, how long they spent there, and what type of information they read on it. But to get the most out of DMPs, you need to turn to analytics tools to extract more patterns.
CDPs avoid anonymous data and focus on specific data that identifies individual customers. An email address is one example of the type of customer identifiers used by CDPs.
The role of CDPs and DMPs in your marketing strategy
Data management platforms Each platform can play a role in your marketing strategy. Through access to historical data, both platforms can illuminate and inform your digital marketing strategy, but in very different ways. For example, DMPs are effective for digital channels and audience segmentation.
CDPs, on the other hand, are beneficial for social media websites, offline interactions, and insights into customer needs and purchase behavior. With a CDP system that manages data, you’ll better understand customer needs and expectations based on their purchase behavior and past interactions with your brand.
Knowing when to use or choose a data platform
Deciding on whether to use a CDP, DMP, or both comes down to:
Understanding the differences between the two platforms
Determining how each platform can help you achieve your marketing objectives.
Knowing how you want to use your data
Establishing if you can dedicate enough resources to using these platforms to optimize their potential
Not CDP vs. DMP, but CDP and DMP
A CDP and DMP can work together. However, if you need third-party data for short-term customer leads and conversion, you should work with a DMP. If you seek long-term customer engagement that requires first-party data, you should work with a CDP. Both platforms offer ways to enhance the customer experience (CX) and can help you create, provide value, and maximize return on investment (ROI).
Depending on the type of CDP you select, there are also opportunities to combine these platforms to take advantage of more marketing opportunities. For example, you can use DMP data in real time to personalize the interaction with first-time site (anonymous) visitors to establish and maintain trust. You can also deepen your customer profiles with the third-party data that a DMP delivers.
CDPs draw data from DMPs and share information back with them. The two systems work well together, with DMPs driving in new prospects and leads and CDPs helping brands connect and engage with them. So when a DMP is integrated with a CDP, you can gain access to first-party data that shows what customers are doing beyond their interactions with you. This insight helps you find out more details about what they want or need.
Customer relationship management (CRM) is a complete software system that manages customer relationships, but it isn’t a single solution. To effectively manage, analyze, and improve your customer relationships, you need a comprehensive set of cloud solutions that supports your organization at every customer interaction point.
Which is why your CRM solution should include a sales cloud, service cloud, ecommerce cloud, and marketing cloud, as well as a customer data platform (CDP) that can combine online, offline, and third-party data sources for an always up-to-date customer 360 view.
What is a CRM system?
A CRM system gathers, links, and analyzes all collected customer data, including contact information, interactions with company representatives, purchases, service requests, assets, and quotes/proposals. The system then lets users access that data and understand what happened at each touchpoint. Through this understanding, a complete customer profile is developed, and a solid customer relationship is built.
Customer data can also be aggregated to populate incentive compensation modeling, sales forecasting, territory segmentation, campaign design, product innovation, and other sales, marketing, and customer service activities. CRM tools and software help you streamline the customer engagement process, close more sales deals, establish strong customer relationships, build customer loyalty, and ultimately increase sales and profits.
CRM tools have almost always been seen as sales tools. However, over time, these solutions have extended their reach and become integral to marketing, ecommerce, and customer service functions.
The power of customer relationship management is derived by constantly gathering customer data, analyzing that data, and then using those insights to deepen relationships and improve business results. It allows any customer-facing employee to convey, “We know you, and we value you.”
A set of data-driven CRM tools supports you beyond the sales process, which is crucial to business performance. With the in-depth knowledge of your customers, you can:
Offer and sell new, add-on products—at the right time in the right way at the right price
Help customer service teams resolve issues faster
Help development teams create better products and services
CRM software supports strong, productive, loyal customer relationships through informed and superior customer experiences. The goal? To improve customer acquisition and retention by providing experiences that keep your customers coming back. Customer relationship management is both a strategy and a tool that supports those experiences in five key ways.
1
Answer the most basic customer questions
Customer relationship management helps you find new customers, sell to them, and develop a loyal customer relationship with them. These systems collect many different types of customer data and organize it so you understand your customers/prospects better and can answer (or even anticipate) their questions.
2
Manage customer data
Bad decisions come from a lack of access to and inability to interpret customer data. Being able to store, track, and validate customer data within an automated system will allow sales and marketing teams to optimize customer engagement strategies and build better relationships.
3
Automate the sales process
Sales force automation makes selling more efficient, helping you sell more quickly. The best CRM systems use artificial intelligence (AI) and unified customer data to automate the sales process by prompting sellers with recommended next-best actions.
4
Personalize marketing campaigns
Customers and potential customers arrive through various channels, including websites, social media, email, online/offline events, etc. Unfortunately, many businesses struggle to connect marketing efforts across all these channels. Marketing teams can improve conversions, strengthen customer relationships, and align messaging across their digital customer channels by leveraging CRM systems.
5
Align sales and marketing
With customer relationship management, marketing and sales work better together to drive sales and increase revenue. When sales and marketing are in sync, sales productivity goes up along with marketing ROI.
CRM features and benefits
Customer relationship management solutions are one of the largest and fastest-growing enterprise application software categories. The CRM market size was valued at $41.93 billion in 2019 and is projected to reach $96.39 billion by 2027, growing at a CAGR of 11.1% from 2020 to 2027.
More and more companies are using CRM solutions to acquire more sales leads, improve the sales pipeline, boost productivity, and improve customer satisfaction. However, many have encountered problems ranging from cost overruns and CRM integration challenges to system limitations. These are avoidable problems, and you can help ensure success by focusing on a customer-first strategy.
It’s critical for businesses to have integrated, customizable, and comprehensive views into their customers’ and potential customers’ solution/product interests, customer service needs, and purchase history. A good CRM system should provide that view. All data is in a single location, viewable through optimized dashboards.
Additionally, your marketing team can leverage CRM solutions to orchestrate personalized marketing and lead generation campaigns. These systems can help track all cross-channel interactions—from engagement to purchase. Mature cloud CRM solutions do more. They are fully integrated with back-office solutions to successfully support the entire customer journey.
Because it manages prospect and customer engagement points across all channels, your CRM system can inform all your communications and marketing activities, delivering the 360-degree customer view needed for a truly connected omnichannel experience.
Many different vendors have many different types of solutions. However, a few capabilities are must-haves.
Provide accurate, consistent data for that much-needed, complete customer 360-degree view
Types of CRM
CRM software solutions, at their core, are used to manage customer relationships and sales interactions. Still, many businesses leverage these systems simply as a sales force automation tool. But these solutions, such as Oracle’s, offer many more valuable capabilities that span a wide range of marketing and sales functions, including marketing, customer service, sales, and partner channel management.
Today’s CRM software can support the entire customer journey. But what one company may need from a CRM system can be vastly different from what another company might require. To help you select the right CRM for your organization, it’s helpful to know that there are three main types of CRM solutions: collaborative, operational, and analytical.
Data is the most critical part of any CRM software solution. In fact, customer data is the starting point for all marketing and sales activities. Successful customer engagement and relationship strategies hinge on accurate, complete, and accessible customer profiles. Bad data comes from several places, including:
Fraudulently entered data
Keystroke errors
Duplicate customer information
Natural changes (company bankruptcy, job changes)
Incomplete and inaccurate data can increase quickly to degrade the value of your CRM tools, resulting in unnecessary expenses. Conversely, when customer data is complete and accurate, businesses stand a better chance of reaching their target customers and prospects. In short, your data is a valuable asset. So it’s important to focus on collecting and optimizing these four CRM data types:
Identity data
Identity data includes descriptive details to identify customers, leads, and contacts. This data should be used for marketing segmentation.
Descriptive data
Descriptive data includes lifestyle details relevant to your contacts. It is what completes that all-important 360-degree view of leads and contacts.
Quantitative data
Quantitative data includes measurable data points that can help you interpret how your leads and contacts have interacted with you.
Qualitative data
Qualitative data can help you better understand your contacts’ intent, including search behaviors related to buying decisions.
CRM vs. marketing automation
Both CRM and marketing automation systems are data-driven. They focus on gathering, storing, and using data. For example, marketing automation systems gather leads by communicating with potential and current customers.
Specifically, marketing automation looks to gather enough customer data points to show intent and then hands that person off to the sales team as a marketing-qualified lead (MQL). A CRM solution picks up where the marketing automation solution left off and works to convert those marketing-qualified leads into contacts.
AI in CRM
Discover the next generation of CRM (0:38)
The best CRM systems offer robust analytics coupled with AI and machine learning. AI is the future of customer relationship management, going beyond contact management and sales force automation to truly helping you sell.
AI in CRM can guide you toward the next-best actions and provide smart talking points—specific to each customer opportunity. AI also delivers timely customer intelligence that helps you optimize customer experience (CX) across marketing, sales, and customer service.
CRM vs. CX
When customer relationship management first arrived on the scene, businesses would capture data but not know what to do with it. Today, CRM systems are integrated with AI, which helps interpret and predict what that data means.
CRM AI capabilities are the foundation to using a 360-degree view of the customer that will start them on their way to becoming your customer. As these AI enhancements continue to evolve, CX will continue to improve—and in turn, customer expectations will continue to increase.
Your business needs to fully understand your customers (and how they buy) to not only meet their expectations but to provide them with compelling experiences. This is the future of CX and should serve as your guide to selecting the best CRM solution.
How CRM improves customer experience
A complete customer view is necessary for business success and growth. Without a CRM system, you’ll struggle to develop that much-needed 360-degree view of the customer that you need to:
Personalize customer interactions
Automate business processes (with appropriate CX integrations)
CRM software solutions help sales reps organize their leads, automate follow-ups, and manage their opportunities and pipeline. But sales isn’t the only department within your organization that can benefit from your CRM platform. Marketing, customer support, product development, content management, and HR can all achieve high ROI from a CRM solution.
For example, since your solution holds essential information about every customer, your customer support teams can put that customer data to good use. With CRM data, your customer support reps (CSRs) have more insight into who your customer are, their needs and motivations, and what type of relationship they’ve had with your brand in the past. This information gives your customer service reps context when interacting with those customers.
As with any other business application, the decision to host your CRM on-premises, in the cloud, or as a hybrid model depends on your business needs.
On-premises CRM
On-premises CRM gives you complete control over your system, but there is a trade-off. These systems must be purchased, installed and deployed, monitored, maintained, and upgraded. As a result, they can be costly, involve time-intensive installations and upgrades, and require in-house IT resources for ongoing maintenance.
With an on-premises CRM solution, access to new functionality can be a long, drawn-out, and expensive process. Also, sophisticated AI-based technology—to support virtual assistants, chatbots, next-best recommendations, and predictive analytics—will not be available.
Cloud CRM
Software-as-a-service (SaaS) options offer simple interfaces that are easy to use and require less IT involvement and investment than on-premises CRM tools. Because upgrades are pushed through automatically, you always have the most up-to-date functionality without significant IT effort.
This includes new advanced technology, such as AI and machine learning that can help you turn your customer data into relevant customer experiences. Cloud-based CRM also offers the convenience of anytime, anywhere access through mobile devices.
Hybrid CRM deployment
A hybrid CRM deployment requires trade-offs in all the areas mentioned above, but this deployment model can also deliver the best of both worlds. However, it’s important to recognize that IT technology is increasingly moving to the cloud. Companies that remain heavily invested in on-premises CRM risk being left behind as competitors advance to the cloud. Your ability to provide mobile access will also be limited.
There are so many things to take into consideration when selecting a CRM system for your organization. But in the end, the CRM model most suitable for you is the one that allows you to interact with your customers in meaningful ways to drive exceptional customer experiences.
Marketing has only become more challenging as the number of channels and touchpoints to integrate continues to grow. Successful brands need to build a cohesive and seamless cross-channel marketing strategy to bring all the aspects of their marketing plan together. That’s where marketing intelligence andmarketing attribution tools come in.
Brands can use marketing intelligence and attribution tools to track marketing strategies in real time, providing data that helps drive smart decisions and marketing mix choices. Recently,Salesforce and Mevo joined forces to create a unified solution for marketers looking to streamline their marketing efforts.
Let’s explore how this partnership can help you achieve your marketing goals and conquer the challenge of cross-channel marketing.
The Challenge: Creating Effective Cross-channel Marketing Campaigns
Marketers today face the challenge of cross-channel marketing, which includes integrating and normalizing data across many channels. Ensuring a consistent customer experience with various touchpoints like social media, email marketing, and search engine ads can be tough, if not impossible.
A recent survey revealed that most marketers crave a centralized platform to access cross-channel marketing data. Yet, less than 30% actually have such a system. Even among those with a plan, 60% still rely on manual data assembly.
Accurate data is necessary for marketers to know how well their marketing campaigns are doing across different channels. To get this data, they need to understand how customers move through multiple platforms and map their behavior between all of them concurrently. Although a fully-integrated, cross-channel marketing strategy might still be a ways off for some brands, marketers can still make great strides by using data and the right technology to bridge the gap in the meantime.
The Solution: Unification of All Channels and Touchpoints
Effectivemarketing mix modeling and accurate attribution are crucial elements in any successful marketing strategy. Attribution is a vital aspect because it shows how each step affects a customer’s decision-making, and ultimately informs marketers and their C-suite about which marketing channels and messages are most effective.
To achieve complete optimization, brands should strive to make all marketing decisions data-driven while also deploying insights gleaned from attribution and modeling programs as quickly as possible to business leaders who can drive real change. This knowledge-sharing reduces useless silos while optimizing the allocation of marketing resources and driving both return on investment (ROI) and return on ad spend (ROAS).
Tips for Implementing Cross-Channel Marketing Effectively
Implementing a cross-channel marketing strategy is a powerful tool that can improve brand recognition and increase sales, but it can also be overwhelming if not done correctly. Some tips for implementing an effective cross-channel marketing strategy simply and easily are:
Create a Consistent Brand Message Across All Channels: Ensure that your brand message is consistent and clear across all marketing channels.
Use Data to Personalize Your Campaigns: Use data to personalize your campaigns and provide your customers with relevant and timely content.
Optimize Your Campaigns for Mobile: Ensure that your campaigns are optimized for mobile devices, to capture the huge percentage of consumers who use their phones for shopping and research almost exclusively.
Measure and Analyze Your Campaigns: Measure and analyze your campaigns on a regular basis to find areas for improvement and optimization across all aspects of your cross-channel marketing strategy.
Examples of Brands with Successful Cross-Channel Marketing Campaigns
Many brands have successfully used cross-channel marketing to reach their target audience. Some used social media and email marketing to expand their reach, while others focused on in-store experiences and personalized recommendations to boost sales. Examples of brands that used effective cross-channel marketing include:
Nike: Utilizes social media, email marketing, and in-store experiences to create a seamless customer journey.
Sephora: Offers personalized recommendations through its mobile app, email marketing, and in-store consultations.
Coca-Cola: Utilizes TV commercials, social media, and experiential marketing to create a cohesive brand message.
Amazon: Uses email marketing, mobile app notifications, and personalized recommendations to drive sales across multiple channels.
The advertising industry has undergone significant change, with new technologies and strategies constantly emerging. One recent innovation that is causing a stir is generative AI advertising. This groundbreaking approach is revolutionizing the creation and delivery of ads, resulting in more personalized and captivating consumer experiences.
Generative AIis a groundbreaking tool that enables marketers and advertisers to create dynamic and interactive campaigns. These campaigns can adapt in real time according to user data and preferences. By leveraging artificial intelligence, brands can deliver highly targeted and relevant content. This content captures attention and generates meaningful results.
This post will examine how generative AI advertising is revolutionizing the ad industry and explore the benefits it offers to advertisers and consumers alike.
What is Generative AI Advertising?
This tool uses AI technology to create and personalize ads. It employs machine learning algorithms to generate highly personalized ad content for individual users. This approach enables advertisers to create more engaging and relevant ads, increasing the likelihood of attracting and converting customers. Generative AI also helps optimize ad campaigns by automatically generating and testing variations of ads to find the most effective ones. With AI, advertisers can enhance their targeting capabilities and deliver impactful ads to their target audience.
Why Generative AI is Important in the Ad Industry
Generative AI presents a ground-breaking method for developing tailored advertisements based on unique tastes and interests. Using algorithms to evaluate data quality, advertisers can create advertising that appeals to specific target demographics. This technology increases the chances of converting consumers into customers by delivering more relevant and engaging content. Generative AI also helps companies save time and money while increasing the efficacy and efficiency of ad development. This innovative technology has the potential to transform the advertising sector by improving ad targeting and raising the overall effectiveness of marketing initiatives.
What is the Role of AI Targeted Advertising in Marketing?
AI-targeted advertising is crucial in modern marketing strategies. It uses artificial intelligence algorithms to analyze data and identify the most relevant audience for products or services. AI gathers information on consumer preferences, behavior, and demographics, allowing marketers to tailor advertisements to specific target groups. Generative AI advertising is essential but not meant to replace humans; it enhances their capabilities. AI provides reliable and accurate insights in real-time, improving return on advertising spend. This collaboration between humans and AI allows marketers to focus on strategic tasks requiring creativity and intuition while AI handles data analysis and marketing optimization.
What are the Benefits of Generative AI Advertising?
Here are some key benefits of this cutting-edge technology and how it can drive business success.
Improved Accuracy
Unlike humans, who can make mistakes when handling data, generative AI algorithms process and analyze vast amounts of information with precision and consistency. By relying on AI technology, advertisers can minimize errors and ensure that their data-driven marketing and advertising decisions are based on accurate and reliable insights. Additionally, humans have inherent biases that can unintentionally influence advertising strategies. Generative AI advertising eliminates this bias, allowing for a more objective and unbiased approach to targeting and engaging with the audience.
Increased Efficiency
Artificial intelligence allows marketers and advertisers to automate and optimize advertising campaigns, saving time and resources. Generative AI algorithms analyze large amounts of data and create personalized and targeted ads that resonate with the target audience. It improves advertising effectiveness, leading to higher conversion rates and return on investment. With this amazing tool, marketers and advertisers can streamline processes, reach customers at the right time, and achieve better results.
Enhanced Creativity
Generative AI advertising offers enhanced creativity through machine learning and data analysis. This technology allows marketers to explore endless creative possibilities and stand out in a competitive market. By leveraging Generative AI, advertisers can create tailored ads that engage their target audience, leading to higher conversion rates and overall business success.
Personalization
Advertising can be tailored to each consumer’s tastes, interests, and behaviors with the help of generative AI. With this level of personalization, marketers may give their target audience more exciting experiences. Marketers and advertisers can improve their chances of grabbing the attention and interest of potential customers, which will result in improved conversion rates and sales, by presenting tailored adverts. Additionally, since customers value brands that recognize their particular wants and preferences, tailored advertising can increase customer satisfaction and loyalty.
Real-Time Optimization
Unlike traditional advertising, generative AI continuously analyzes and adjusts based on insights from user behavior, allowing for the dynamic optimization of ad campaigns in real time. Advertisers can customize their messages and creatives based on their target audience’s preferences and interests at any moment. This level of customization increases the chances of engaging potential customers and maximizes the efficiency of advertising campaigns. With real-time optimization, generative AI ensures every impression counts and drives desired results, making it a powerful tool for advertisers looking to optimize their advertising efforts.
Cost-Effectiveness
This tool allows marketers and advertisers to create personalized and targeted ads at a lower cost. AI algorithms analyze consumer data and behavior to generate relevant advertisements, ensuring effective reach to the target audience. Furthermore, generative AI advertising enables real-time optimization, allowing marketers to refine and enhance ad campaigns based on performance data continuously. This increases the chances of success and minimizes the risk of wasting money on ineffective advertising strategies.
Improved Customer Experience
With Generative AI, it can change how advertisers and marketers connect with customers. One significant benefit is the improved customer experience. Generative AI analyzes customer data and preferences through AI algorithms and machine learning to create personalized ads. It improves client satisfaction, boosts conversion rates, and encourages repeat business. Advertisers can customize their adverts for specific consumers by offering pertinent and appealing material that speaks to their needs and interests. Long-term success and increased customer satisfaction result from this.
Public Perception
Generative AI positively impacts public perception. AI algorithms generate ads that resonate with the target audience, improving brand perception. Consumers feel connected to the brand when ads are tailored to their preferences. This fosters a favorable view of the brand and increases loyalty, engagement, sales, and revenue. This tool delivers highly relevant and engaging ads, enhancing the user experience and positively influencing public perception.
What are the Potential Risks in AI-Powered Advertising?
As AI-powered advertising continues to gain prominence in the digital marketing landscape, it is essential to be aware of the potential risks associated with this technology. Here are some of the potential risks in AI-powered advertising.
Ensuring Data Used for Model Training is Representative
Ensuring that the training data used for AI-powered advertising represents the real world. Results may be distorted, and prejudice may occur if the data is biased or unrepresentative. It may have detrimental effects on targeting particular demographics and the precision of the marketing and advertising strategy. To mitigate these risks, advertisers and AI developers must carefully curate and validate their training data to accurately reflect the diverse population and avoid perpetuating biases.
Legal and Ethical Concerns
AI algorithms can collect large amounts of personal data, raising concerns about its usage and consent. Discrimination and bias are also risks in AI-powered advertising. If algorithms are not adequately trained, they may perpetuate stereotypes or exclude certain groups. Transparency and accountability are also concerns. It is difficult to understand the decision-making processes of AI algorithms, raising questions about responsibility in case of legal or ethical problems. These risks emphasize the need for regulation and oversight to ensure responsible and ethical use of AI-powered advertising.
Lack of Control
With AI in charge of targeting and delivering ads, advertisers may need more control over ad placement and audience targeting. This lack of control can lead to ads appearing on irrelevant or inappropriate platforms, harming a brand’s reputation and wasting ad budget. Furthermore, AI algorithms continually evolve, posing risks of unintended consequences or biases in targeting.
Brand Inconsistency
There is a risk of brand inconsistency in AI-powered advertising. AI algorithms analyze data and make decisions based on patterns and trends. However, they may need to accurately interpret the brand’s values, tone, and messaging. This can lead to advertisements that are consistently unclear or misleading for consumers. AI-powered advertising can also lack the human touch and creativity a brand needs to communicate its message effectively. Investing time in training the AI model to learn about appropriate branding and positioning is essential.
The marketing industry constantly changes, and new techniques and technologies are being developed daily. One promising development is the growth of generative AI in marketing. Traditionally, marketing relies on machine analysis and human prediction. However, generative AI allows for greater creativity and effectiveness by leveraging the speed of a machine with how about saying ‘super-human’ instead of ‘human-like’ predictive abilities. This rapidly growing technology can automate content generation, design, and strategy with algorithms and machine learning, providing a window into the evolving future of marketing.
With technologies like generative AI and generative attribution reshaping marketing aspects ranging from personalized content production to predictive analytics, staying on top of the latest advances is critical to keep your brand competitive. This blog will help to do that by exploring the impact of generative AI on the marketing industry and the future of marketing.
What is Generative AI?
Generative AI focuses on producing unique and fresh content. It consists of a generator and a discriminator that work together to create realistic and distinctive outputs. The discriminator evaluates the generated samples and distinguishes them from accurate data, while the generator produces new pieces like images, sounds, or texts. This process is repeated until the generator can produce outputs indistinguishable from real data. Generative AI has various applications, including text creation, virtual characters, images, and videos. It can revolutionize entertainment, design, and marketing by offering original and innovative ideas.
How Does Generative AI Work?
Generative AI has been in existence since the 1960s. While many people associate AI with GPT-based models, it goes beyond that and focuses on using computers to create new and unique pieces of data autonomously. It can include sentences, images, consumer touchpoint journeys, and more. The discussion around generative AI today tends to focus on GPT-based applications, but it is important to recognize this technology’s broader scope and potential.
What are the Uses Cases of Generative AI?
Generative AI has a wide range of applications across various industries. Some of the most prominent applications of generative AI include:
Efficient Data Analysis and Predictive Modeling
Generative AI algorithms can process and analyze these extensive datasets much faster and more accurately than traditional methods, enabling marketers to uncover patterns, trends, and correlations that would be otherwise difficult, if not impossible, to identify. Moreover, it can help in predictive modeling, using huge datasets, including historical data, to anticipate future trends and outcomes, enabling organizations to address potential challenges and seize opportunities proactively.
Recovered Consumer Journeys
Generative AI has revolutionized data analysis, enabling the recovery of consumer journeys that were previously unavailable due to missing data. These customer journeys provide valuable insights into consumer behavior and preferences. It uncovers hidden patterns and trends in consumer journeys that were previously inaccessible.
Personalized Content Creation
With generative AI, marketers can effortlessly create content tailored to individual preferences, interests, and needs. Whether it is personalized product recommendations, customized emails, or targeted advertisements, it helps them deliver content that resonates with their audience on a personal level. It not only enhances user engagement but also increases conversions and brand loyalty.
Improved Customer Experience Through Chatbots
Generative AI-powered chatbots enhance customer experience by providing instant and personalized assistance. These bots understand natural language and context, engaging in human-like conversations and offering accurate responses. Marketers can use it to provide 24/7 customer support, efficiently resolve queries, and ensure seamless interactions.
Targeted Advertising and Recommendation Systems
Generative AI algorithms enable advertisers to analyze vast amounts of data quickly. This analysis helps them understand consumer preferences, interests, and behavior patterns. As a result, they can create tailored advertisements that resonate with their target audience. It leads to higher conversion rates and better marketing ROI. Generative AI also powers recommendation systems that analyze user data and provide personalized suggestions based on preferences and past behavior. It enhances the user experience, increases engagement, and drives sales.
Democratized Data
A lack of technical expertise can significantly challenge marketing teams in handling and analyzing data quality. However, generative AI has eliminated this barrier. Marketing teams no longer need data scientists or IT professionals to access and interpret data. Generative AI tools empower marketers to quickly understand and use data for informed decision-making. This data democratization improves marketing campaign efficiency and effectiveness, allowing teams to target the right audience, optimize content, and personalize customer experiences.
What are the Benefits of Generative AI in Marketing?
In the field of marketing, generative AI offers several advantages that can greatly enhance advertising and promotional efforts. Some advantages of Generative AI in marketing include the following:
Increased Efficiency and Productivity
Marketers utilize generative AI to automate tedious tasks and procedures, enabling them to concentrate on strategic and creative aspects of their business while eliminating human error that impacts data quality. This technology simplifies and enhances the creation of tailored and targeted content on a large scale. With generative AI, companies can optimize their marketing efforts, improve campaigns, and successfully reach their intended audience efficiently.
Enhanced Customer Engagement and Satisfaction
Generative AI can analyze data to understand customer behavior and preferences better. It allows marketers to tailor their strategies accordingly. The technology enables personalized recommendations, targeted advertising, and interactive experiences, which resonate with customers on a deeper level.
Cost-effective Marketing Strategies
By utilizing generative AI technologies, marketers can automate and optimize various aspects of their campaigns, reducing the need for manual labor and streamlining processes. This automation saves time and reduces costs associated with traditional marketing methods. It analyzes data, identifies patterns, and generates personalized marketing content for more targeted and effective marketing campaigns. Due to this cost-effectiveness, marketers can allocate resources efficiently and achieve better returns on marketing investments.
Ability to Learn Constantly
With access to more data over time, generative AI algorithms improve their understanding and ability to cater to a customer’s specific needs. As they learn from user interactions and feedback, these AI systems continuously enhance their capabilities, allowing them to provide more accurate and personalized solutions.
What are the Best Strategies for Implementing Generative AI for Marketing?
Implementing generative AI in marketing requires careful planning and consideration of the best strategies. Let’s look at the best strategies for implementing generative AI for marketing.
Clearly Define Marketing Goals
Defining your marketing goals includes identifying specific objectives, like increasing brand awareness, improving customer engagement, or driving sales. Having clear goals helps align generative AI strategies with your overall data-driven marketing strategy. This clarity guides implementation and ensures the effective use of AI technology to meet marketing objectives.
Identify Relevant Data Sources
Identifying relevant data sources involves determining the types of data that will be most useful. These data sources could include customer demographics, purchasing behavior, social media interactions, website analytics, and more. Marketers should utilize these relevant data sources to train the generative AI on accurate and valuable information, ensuring more effective marketing initiatives.
Train and Fine-tune Generative AI Model Using Collected Data
One effective strategy is to train and fine-tune it using collected data, which includes gathering relevant data from customer interactions, market trends, and historical marketing campaigns. Marketers can create more personalized and effective marketing materials by continuously refining the model through iterations. The trained model can generate content such as ads, social media posts, and email campaigns that resonate with the target audience.
Evaluate and Optimize Generated Marketing Content
Generative AI can benefit marketers by automating marketing content creation. This can be done by analyzing engagement, click-through rates, and conversion rates. Ongoing evaluation and optimization maximize the impact of generative AI in marketing campaigns.
Find the Ideal Generative AI Tool
New technologies are often adopted in the marketing industry without assessing if they genuinely address a particular problem. Marketers must exercise caution when embracing it to follow trends. Instead, they should evaluate whether this technology aligns with their goals and enhances their product or campaign.
Prioritize Security Measures
Marketers must guarantee that the AI tool has the necessary security measures to protect sensitive data. For example, allowing anyone to upload data to platforms like ChatGPT or other open-source tools can potentially expose confidential information. Therefore, it is crucial to thoroughly evaluate the security protocols of the AI tool to ensure the secure handling of proprietary data.
What are the Challenges and Potential Risks of Generative AI?
As AI systems become more advanced and capable of generating increasingly realistic content, challenges arise regarding the potential misuse of this technology for other purposes. Some of the challenges and risks associated with generative AI are as follows:
Representative Data is Used for Model Training
One of the challenges is the reliance on representative data for model training. Just like humans, AI models learn from their environment, and if they are trained on biased or limited data, they will reflect those biases and limitations in their output. If the training data does not accurately represent the real world, the AI model cannot build an accurate real-world model, which can lead to skewed or inaccurate results. An AI model possesses limited knowledge and understands only what it has been programmed to learn.
Organizational Readiness
Although some applications of Gen AI are relatively simple and require low levels of readiness, more complex problems demand careful consideration of various factors. Privacy, legal, regulatory, and ethical concerns all come into play when dealing with these advanced applications.
Generative AI has gained significant attention for its ability to mimic human intelligence and generate seemingly independent responses. However, it is important to understand that despite its impressive capabilities, it functions more like a highly skilled parrot. It reads the context and responds accordingly, but it is not truly independently intelligent. Instead of replacing humans, it serves as a tool to amplify human abilities.
Privacy and Data Security Concerns
As generative AI models learn from large datasets, there is a concern in data privacy that these models may inadvertently incorporate sensitive or private information into the generated content. This could potentially lead to the exposure of personal data or the creation of misleading or harmful content.
Legal and Regulatory Challenges
Many companies operating in highly regulated industries are hesitant to fully embrace AI due to concerns about the lack of established legislation and regulations governing its use. These companies are adopting a cautious approach, waiting for clear guidelines to be established before fully implementing Generative AI technologies.
Ethical Considerations
One of the main challenges is the potential for misuse or malicious intent. Since Generative AI can create realistic and convincing content, there is a risk of using it to spread misinformation, generate fake news, or even create fake videos that can be used to deceive or manipulate people. There are also concerns about copyright infringement when using it to create content that may be similar to existing works.
What is the Future of Marketing with Generative AI?
As AI marketing continues to advance, marketers can build and recover models more effectively. They can understand consumer journeys with greater accuracy and recover valuable insights from them. By utilizing generative AI, marketers can close the loop more efficiently, taking faster and more informed actions based on each consumer’s specific interests and actions. This goes beyond traditional targeting and retargeting methods, as it allows for creating custom content tailored to each client’s unique preferences and probabilities.
Moreover, it holds immense potential for marketers with access to large CRM datasets. Marketers can go beyond traditional segmentation and personalize promotions and pricing for each individual consumer. This level of customization allows marketers to target their audience more effectively and deliver personalized experiences that resonate with consumers.
Commonly Asked Questions about Generative AI
What is generative AI vs. AI?
Generative AI refers to artificial intelligence systems that can generate new and original recommendations based on complex analysis, like media plans and content, such as images, music, or text, based on patterns and data it has learned. On the other hand, AI generally refers to the broader field of computer systems that can perform tasks or make decisions that typically require human intelligence.
What is generative AI good for?
Generative AI is good for generating new and unique content, such as artwork, music, and writing. It can also be used for data augmentation and for creating realistic simulations for forecasting or scenario building.
How does generative AI affect digital marketing?
Generative AI significantly impacts digital marketing by automating and enhancing various tasks such as complex marketing analysis like media mix modeling, attribution, media scenario planning, content creation, personalized advertising, and customer segmentation. It enables marketers to access powerful, robust marketing insights for decision-making and deliver more targeted and relevant messages to their audience, improving engagement and conversion rates.
How does generative AI affect marketing analytics?
Generative AI greatly enhances marketing analytics by generating more reliable, representative unbiased insights and predictions based on large amounts of data. It automates the analysis process, improves data quality and representativeness, identifies patterns and trends, and provides valuable recommendations for marketing strategies.
