MarTechBot is BETA software powered by AI which will make mistakes, errors, and sometimes even invent things. Please share your feedback with us so we can improve your experience.

To help get you started, here are some best practices and sample prompts.

Are you a generative AI power user? Want to share your favorite prompts with the MarTech community? Use hashtag #MarTechBot on social media!

The post Welcome to MarTechBot appeared first on MarTech.

As our colleague Nicole Farley reports, “Advertisers can submit creative content such as images, videos, and text related to a campaign, and the AI will “remix” these materials to generate ads that target specific audiences and meet objectives like sales targets.”

Dig deeper: Three essentials for writing a good ChatGPT prompt

Here is our roundup of now available AI-powered martech products, platforms and features announced this week: 

• Constant Contact’s AI Content Generator allows customers to automate the copy drafting process for email, text and social media campaigns. It was developed using the company’s proprietary data and AI algorithms, which recognize content small business customers are most likely to engage with. This feature is currently free to all new Constant Contact customers for a limited time.
• Proxima has added AI to its proprietary database of 55 million B2C shoppers to generate audiences for prospecting on Meta, Google and TikTok. Those audiences are added directly to the data intelligence company’s platform and used for targeting new, high-value customers. It also generates media buying reports based on the insights it finds.
• Catchlight, a Fidelity Labs startup, has released an AI-generated prospect email feature for financial advisors. Using data-enriched profiles created by the program, the AI captures key characteristics to make natural-sounding outreach that is unique to each lead.
• Bloomreach Content, a headless content management system, now lets users add OpenAI’s ChatGPT Text Generator to it.
• Storyteq’s Brand Portals is an AI-powered creative production hub for localizing and activating global marketing campaigns faster. It provides a centralized place for marketers to quickly adapt multichannel campaigns through a self-serve AI-enabled platform.
• Silverpush’s Mirrors provides advertisers with access to a wide range of contextual signals that can be used to enhance their ad targeting strategies. By leveraging Generative AI, Mirrors will enable advertisers to understand the nuances of user behavior and preferences, allowing for more accurate cookieless targeting.
• Mobiquity Technologies’ ATOS 2.0 now uses AI-driven ad targeting technology to optimize ad placements in real-time, ensuring greater precision and improved campaign performance across multiple platforms.

The post This week’s AI product releases: Google adds generative to ads and more appeared first on MarTech.

Research shows that businesses make up to three billion decisions annually and a recent survey by Gartner reported that 65% of decisions are more complex (involving more stakeholders or choices) than they were two years ago.

Many businesses today, and the marketers that serve them, need better insight to bridge the gap between massive amounts of data and business decisions. Only 24% of companies say they are “data-driven,” whereas others face missed opportunities, inefficiencies, and increased business risks. The average S&P company loses $250 million annually due to poor decision-making.

Decision intelligence is a framework that bridges the gap between insights and decisions. It empowers organizations to make better, consistent, and data-driven decisions. Leaders and teams can make informed decisions at every level of the company!

What is decision intelligence?

Decision intelligence (DI) is an evolving discipline that combines data, analysis, AI, automation, and experience to make better decisions. DI helps guide decision-makers with actionable insights using optimization, simulation, and decision-analysis techniques.

In contrast to traditional decision-making approaches, which rely heavily on intuition and experience, DI incorporates methodical, analytical, and data-driven approaches.

The focus of DI is not just on the technology but on how it augments human decision-making processes. It is a multidisciplinary field drawing on expertise from various arenas, including computer science, statistics, psychology, economics, and business.

According to Dr. Loren Pratt, chief science officer and co-founder of DI software provider Quantellia, and author of “How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World,” another key concept of DI is designing decisions like organizations design homes, buildings, and airplanes — by creating a blueprint first.

Much like a blueprint, a decision design helps align everyone involved in that decision — including stakeholders — around its rationale. She found that by treating decisions like a design problem, you can bring many design best practices to bear, such as ideation, documentation, rendering, refinement, QA, and design thinking.

In 2019, Google’s first Chief Decision Officer, Cassie Kozyrkov, established a new decision intelligence engineering discipline to augment data science with behavioral science, economics, and managerial science to focus on the next business advantage beyond the data.

Intelligent decisions are designed, simulated, automated, monitored, and tuned. 

Dig deeper: Why data-driven decision-making is the foundation of successful CX

What decision intelligence is not

Decision science. Decision science has usually been associated with the qualitative side of data. DS is the overarching term, while “decision intelligence” is the operational side. 

Strategic intelligence. Broadly, strategic intelligence means using BI insights to drive and support strategy. We also call this market intelligence which provides businesses with current industry trends and makes sense of consumer behavior to navigate a future course of action.

Calculated decisions. Not every output or recommendation is a decision, Kozyrkov says. In decision analysis terminology, a decision is only made after an irrevocable allocation of resources takes place. If you can change your mind for free, no decision has yet been made.

Applications of decision intelligence

DI applies to various decision-making problems, such as resource allocation, risk management, strategic planning, and, yes, marketing. I’ve used it in developing systems and platforms for complex energy, finance, policy, and marketing decisions.

Our last startup platform supported DI for go-to-market executives reducing the decision-making process from nine months to a fraction of time with greater visibility, training, and impacts.

DI has been applied in credit applications or fraud detection in financial services.  It has been used in retail to determine how much inventory to purchase, optimal stock levels, or price forecasts. According to Dr. Loren Pratt, employing decision intelligence can positively impact evidence-based decisions in a healthcare crisis.

Other use cases include customer satisfaction, marketing attribution, and competitive and go-to-market strategies. Designs of the framework of these decisions were standard for GTM; however, implementation required building an enterprise platform, training, and data support. But in the end, this decision-making time dropped from nine to one-to-three months. The average impact was over $10 million, including an apparel company discovering a new $90 million revenue stream embracing the platform. 

Dig deeper: Automating decisions with real-time situational context

Benefits of decision intelligence

McKinsey senior partner Kate Smaje states that organizations are now accomplishing in 10 days what used to take 10 months. Having DI supports the continually increasing pace of decisions required to stay competitive.

The first benefit is DI aids leaders in navigating complex decisions with more focused and comprehensive information. As you design the decisions, you can structure cross-organizational information toward specific goals or objectives. Having this kind of visibility facilitates navigating trade-offs between competing objectives. It eliminates more of the analysis paralysis found in most strategic and high-level tactical decisions. 

Next, DI reduces risk and uncertainty. Decision-makers with real-time data and insights can leverage DI to identify and proactively mitigate potential risks. With the visibility in trade-offs, organizations can better apply risk/reward plans to avoid costly mistakes hindering a competitive edge.

Decision Intelligence enhances efficiency and productivity. By automating specific decision-making processes and providing decision-makers with real-time data and insights, DI can help streamline decision-making and improve productivity. You are reducing decision latency. These processes can be built or programmed into systems to free up time and resources to explore more options or allocate to other important tasks and initiatives.

Finally, organizations leveraging DI gain a more potent competitive edge by leveraging data and technology by evaluating, then acting on, more intelligent and faster complex decisions which typically cripple momentum or transformation.

Limits and challenges of decision intelligence

With data, AI, and automation involved, it’s not surprising that there are some challenges and limitations that are also present with DI.

Ethics/bias. DI can methodically help reduce bias and reinforce ethical decisions. At the same time, with any data-driven and automated system, decisions leveraging DI built by humans still risk being developed based on biased or discriminatory data or algorithms. Awareness training, along with all other organizational data-driven efforts, is a must.

Data availability. Leaders and project managers must be aware of data access and availability limitations. Decision effectiveness is often challenging to find on smaller datasets. Sometimes things go wrong, but it’s more based on luck than data. For complex and infrequent decisions, an organization may need help to define an approach for measuring decisions. In such cases, technology limitations may prevent a solution. Organizations need to formalize such decision-making processes and can only use technology. Also, it’s worth highlighting what could be missing or the scope of what’s possible.

Resistance. An important part of DI is ensuring more transparency, consistency, and training in the decision-making process. The traditional culture of decision-makers will initially be resistant as it feels that it dismisses their experience or instinct or runs against their specific agendas. Those in charge of DI efforts need to communicate how DI benefits their efforts and leads to better outcomes for individuals and organizations.

Leaders can overcome these challenges and limitations through clear communication and a well-defined scope of its application. Each new initiative can grow and enhance an organization’s decision-making culture.

Tips and factors

• Choose a focused decision. Begin by implementing DI in functions where business-critical decision-making needs improvement (e.g., data-driven, AI-powered). Alternatives include large complex decisions or ones that can be scaled and accelerated through automation.
  
• Begin with outcomes. There’s a flood of data in your organization, but you should only gather relevant data to that outcome to design a decision model. Add additional data or test theories of additional information once you’ve started with your early set.

• Map out decisions. Document assumptions, thoughts, emotions, concerns, and fears involved in your decisions. Review them quarterly or semi-annually. It will increase your organization’s decision-making muscle.

• Don’t automate everything. Humans, especially when it comes to complex and sensitive decisions, are necessary.

• Authority should be to the decision. Provide authority to make decisions to the people closest to the point of impact of that decision. Ownership will incentivize effective decision-making.
  
• Develop new decision-making habits. Teach decision-makers to apply systematic best practices, such as critical thinking, trade-off analysis, recognizing bias, and listening to opposing views.

• Beware narrow framing. In the book “Decisive” by Chip and Dan Heath, the authors explain that a straightforward way to improve decision-making is to avoid limiting the scope of the frame. A decision is rarely just a “yes” or “no.” There are always multiple options, so have at least three available for any decision.

Conclusion

Decision-makers frequently need more information, time, and experience to make complex decisions. A study by Bain found that business performance seems 95% correlated to the effectiveness of decisions. Decision intelligence systems improve efficacy by explaining and justifying the decisions, learning from past decisions’ feedback, and comparing the impact to improve decision effectiveness.

Decision intelligence is a crucial tool that can help you make better decisions. By combining data science, AI, and human expertise, DI can help reduce uncertainty and improve effectiveness. However, DI has its challenges and limitations. You must be aware of these risks and take steps to mitigate them.

The post How to use decision intelligence to tackle complex business challenges appeared first on MarTech.

That said, I’m surprised how many CMOs still keep their distance from the tech part of their function. The days of a CMO delegating the martech strategy to their MOps team are at an end. The emergence of generative AI, particularly ChatGPT, has changed everything. 

This article explores why today’s CMOs should prioritize marketing technology strategy and create internal policies for using generative AI.

Why CMOs need to be technology aware

The case for CMO engagement in technology was already compelling:

• Technology enables everything that marketing does. Bad technology choices will impact marketing performance.
• Technology accounts for ~25% of the marketing budget and significantly contributes to customer acquisition costs (CAC). There is a direct relationship between spending and CAC and, ultimately customer lifetime value (CLTV).
• Technology is the source of business intelligence about customers and their behavior, critical information in formulating a marketing strategy. Inaccurate or incomplete information will make strategy formulation difficult.

Dig deeprer: Why CMOs must be the company’s biggest advocates for digitalization

The impact of ChatGPT on marketing

There is no longer a clear demarcation line between technology and content. It’s now feasible to leverage AI to create text, video and animations. More importantly, ChatGPT’s simple user interface has the potential to completely transform how companies engage with their customers and deliver information. 

For me, the simple video that HubSpot produced to introduce Chatspot.ai drove this home. In 19 minutes, Dharmesh Shah, Founder and CTO at HubSpot, demonstrates the power and potential of AI visually and understandably. Every marketer should watch this video and think about how this type of interface and technology might augment and improve the customer journey and experience. 

As vendors introduce new AI-enabled capabilities, it will require reevaluating the components of the martech stack and potentially replacing anchor systems that have been in place for a long time. It’s worth noting that historically, in times of great innovation, new technology leaders emerge and that’s likely to be the case with AI. 

What today might look like mature and stable technology categories (e.g., CRM, marketing automation) may look entirely different within the next five years. Today’s market leaders may not be the leaders of tomorrow. It’s often much easier to start building from the ground up than deal with the technology legacy (a.k.a., technology tax) of an existing platform when trying to provide a leap in innovation. It’s important not to be complacent and to stay on top of shifts and changes. 

Complicating the evolution of the martech stack is the normal cycle of marketing hype associated with “shiny new things.” Companies are adding AI to their product descriptions without actually delivering anything that leverages AI. 

The challenge is to separate legitimate from non-legitimate claims. A straightforward way to test is to look at the size of the data set a vendor is using to deliver their capabilities. Regardless of product category, if a vendor is not working with a large dataset, they aren’t leveraging AI. To be effective, AI needs a large training set of data in order to learn. 

In addition, if a product leverages AI, it doesn’t always mean it is a better choice than an equivalent product that doesn’t leverage AI. It’s important to understand what differentiation their use of AI actually delivers.

A further complication is that a lot of venture capital is going into technology companies focused on leveraging AI to deliver a broad range of new capabilities. There is already an extensive category of generative AI products and an equally robust category of AI detection tools. 

Not all of these companies will make it. At some point, the market will become saturated with investment, the venture capitalists will move on and some companies will implode. 

Dig deeper: AI in marketing: 7 areas where it shines and struggles

The CMO and ChatGPT  

The CMO’s job is to understand where AI, particularly generative AI, fits within the marketing strategy. 

Making that decision requires an understanding of the capabilities and limitations of the technology, the products that deliver those capabilities and the associated ethical and compliance issues that surround AI. 

Italy, for example, has banned ChatGPT (at least temporarily) over concerns that the training data OpenAI used contravened GDPR. Copyrighting AI-generated content is a grey area at the moment. The U.S. Copyright Office has said it will consider copyrights on a case-by-case basis. This is something that is going to evolve over time.

CMOs should consider internal policies around the use of generative AI. I’m more than happy for my team to leverage generative AI for inspiration around email flows and to create multiple versions of content for A/B testing. At the same time, I want to ensure that we proceed cautiously with AI-generated articles, blog posts and other content that may get flagged and penalized by the search engines for being AI-generated. 

While it’s not clear how well search engines can detect AI content today, it’s definitely an area of focus for them, particularly given the concerns related to spreading disinformation and deep fake imagery.

As generative AI gets more sophisticated, so will the technology for detecting it. There’s no point in investing in AI-generated content if it will be buried by a search engine. 

Dig deeper: How CMOs should respond to ChatGPT’s marketing impact

This is not the time to question the value of your content marketing team

One thing that ChatGPT will not eliminate is the need for human content creators — at least not yet. ChatGPT and other generative AI tools can be great content assistants and, at some level, content creators. But they are not a replacement for your content team. 

These tools look back at the data they have. Without data about your new product, they can’t effectively write about it or deliver the nuances of your value proposition and positioning. Marketing strategy, creative direction and decisions need a human touch and will for the foreseeable future. 

The need to articulate an AI strategy

At some point, in an executive or board meeting, CMOs will be asked to articulate how AI fits into the marketing strategy. While the CMO should be able to call on their technology head to talk about technical details, products and implementation, they should be the ones to talk about the overall strategy. 

To be clear, AI and ChatGPT are not synonymous. ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with the chatbot. It exists under the category of generative AI. AI technology as an enabler exists in many different algorithms. You can have a ChatGPT strategy, and that may be your starting point for embracing AI, but it shouldn’t be characterized as your overall AI strategy. 

AI has tremendous potential across many marketing categories. For example, it can be leveraged to define micro-market segments, adapt the customer journey and deliver a high degree of personalization. Many of the tools in your stack today may already be leveraging AI. 

Where does the CMO go from here?

Today’s CMOs should prioritize marketing technology strategy and work hand in hand with their technology lead. Advances in AI will serve as a forcing function in this regard. 

If the CMO does not own the AI and technology strategy, they cannot effectively create an overall marketing strategy. It is imperative that a CMO be able to articulate how they will achieve their marketing strategy, and technology is very much a critical piece of that.

CMOs do not have to be deeply technical. They do, however, need to understand the following:

• What’s in their marketing technology stack.
• How each technology product contributes to the marketing function and supports their marketing strategy.
• The relationship between their technology expenditures and customer acquisition costs.
• Where AI (in all forms) has the potential to add value and/or disrupt their marketing stack.
• Know enough about martech stack to manage the technology team and its performance effectively.

At the end of the day, it is the CMO who is measured on marketing’s success. If success is impeded by poor technology choices, the CMO is and should be accountable. 

For those CMOs who have not yet crossed the divide and immersed themselves in marketing technology, I recommend the following:

• Subscribe to the Marketing AI Institute newsletter to keep abreast of the latest in AI and its implications for marketing. The Marketing AI Institute also hosts valuable events and provides training on marketing-related AI applications.
• Read the CabinetM MarTech Innovation report. Each quarter CabinetM publishes a roundup of new MarTech products announced in the quarter. (Note: This is not a lead collector for CabinetM, it is provided as a non-gated resource.) Alternatively, task your technology team with giving you a quarterly update on technology advances and new products they believe will add value to your marketing plan.
• Reach out to your key vendors, request information on their AI strategy and plans, and then monitor progress against that.
• Ensure that your organization has a single source of truth for all the technology being used to support marketing and sales in a form that you can review and assess. You may need to request that additional data fields be added to their tracking and reporting to get at the information that is most meaningful to you.
• Establish regular review meetings with your technology head and use these meetings to:
  • Collaborate on developing a technology strategy.
  • Discuss current technology utilization, performance and spend.
  • Identify gaps and opportunities.
• Don’t be afraid to dig in and ask questions. As a CMO, you are not expected to know the details of the technology architecture and integrations. Your job is to translate that architecture and strategy into a plan your C-suite colleagues and the company board will understand.
• And of course, keep reading MarTech.

The post Why CMOs must cross the technical divide appeared first on MarTech.

OpenAI has until April 30 to inform Italian users of “the methods and logic” behind the processing of data and provide a way for them to exercise their existing personal data rights. Longer-term deadlines were also given for age-gating to the app and launching an awareness campaign so Italians know what their rights are.

Why we care. Italy is the first EU nation to take any regulatory action on ChatGPT. In the past most EU restrictions around privacy have come from France and Germany. Spain has also requested the EU’s privacy data protection board to look into ChatGPT’s practices.

As generative AI technology continues to evolve, regulators are taking a hard look at its use of user data across the web, especially when large language models are trained on that data without the consent that GDPR law requires.

Conditions for ChatGPT’s return. OpenAI has until April 30 to inform users in Italy about how data is used in its algorithms to run its AI models. 

OpenAI will also have to provide tools that allow those whose data is used — including users and non-users — to request a correction of data inaccuracies or to have the data removed if a correction isn’t possible.

Age verification. OpenAI has until the end of September to install a robust age verification system that keeps out users under age 13. And they must submit a plan for this system by the end of May.

Additionally, users aged 13 to 18 who don’t have parental permission will also need to be restricted from using the app.

Campaign. As another condition for lifting the ban, OpenAI will need to launch a local awareness campaign on radio, TV and digital to educate Italians about ChatGPT’s processing methods and data usage. The company has until May 15 to launch the campaign.

The post Italy lays out requirements for ChatGPT’s return appeared first on MarTech.

This is the profile of your typical smart phone user, surfing the net, looking for the next thing. As they flip from page to page and scroll up and down, they may experience one of six basic emotions: fear, anger, joy, sadness, disgust and surprise.

If the page view sparks the right emotion, then that viewer could be turned into a lead. But which emotion can do that? Can this be done in a loud, distracting environment (like in real life)? And can you score the interaction for ad effectiveness use it to optimize a campaign?

First, some background

The hypothesis that all humans feel one of six basic emotions was proposed by psychologist Paul Ekman. His work also inspired others working the intersection of psychology and marketing, looking for ways to measure emotional response so they can sharpen their approach to consumers.

Machine learning and AI modeling have been used by various businesses, all taking different approaches to the reading of emotions through human facial expressions. Some of these approaches were limited by technology, requiring the subject to sit in front of a desktop PC, either in a lab or at home, so that the digital camera could scan their faces and calibrate these images with the software, Max Kalehoff, VP of growth and marketing at Realeyes told us.

With people using smartphones, staying still long enough to be calibrated was not going to work.

Dig deeper: You smiled, so we think you like this product

Cue the face

Realeyes built its facial recognition app for mobile on previous work. It’s AI had been trained on close to one billion frames. Those images were then annotated by psychologists in different countries to take account of cultural nuances. The algorithm in turn was trained by using these annotations, Kalehoff explained, yielding over 90% accuracy.

The potential for Realeyes to work on the mobile platform intersects with the explosion of social media, and in this realm the app is agnostic. It does not matter what the user is looking at — TikTok, YouTube, Facebook, Instagram. The Realeyes app is gauging their reaction.

“To (the best) of our knowledge, this is the first time it’s been done,” Kalehoff said “We are answering a demand to provide detection of attention to creatives in a mobile environment.”

To put Realeyes on the smartphone, users have to opt-in, and are then directed to an environment where they can look at some ads. They are told to scroll through some screens, “doing what they normally do,” Kalehoff said. A small app will reside on the phone helping measure visual attention data and clickstream interaction data. “Our definition (of attention) focuses on a stimulus while ignoring all other stimuli,” he said. “The experience for the participants is under three minutes.”

Looking for data in the right places

What Realeyes looks for depends on the media the consumer is viewing. One outcome sought is what they call a “breakthrough.” “Real people try to avoid ads,” Kalehoff noted, so breakthrough occurs when an ad successfully gets someone’s attention despite a naturally distracting environment.

This matters as people “swipe, skip or scroll” past ads to get to content. They will swipe on TikTok, scroll through Facebook or Instagram, or skip in YouTube, Kalehoff observed. Did the ad get through?

Then there is the type of viewing, like Netflix or Hulu, where the consumer’s involvement is passive. Here Realeyes is looking for “in focus reaction.” Is the viewer paying attention to the ad? What are they seeing, second by second, and is that creating a positive or negative impression?

Then there is online shopping, for example on Amazon. Here validating visual data gets a four-question follow-up, testing for brand recognition, ad recall, trust in the brand and likability of an ad.

The simplicity of Realeyes’ approach is that scanning for facial expression will work anywhere with anything. As two-thirds of the digital media spend goes to three or four major platforms, “you only have to go to a few places to get where the attention resides,” Kalehoff said.

Room for improvement

The foundation of Realeyes is the training database that informs the AI of the meaning of a facial expression. Porting the app to the handheld means being able to spot smiles and frowns, then using that information to correct a bad impression or improve on a good one.

Still Realeyes is aware there is room for improvement. It has had to work on adjusting its face-reading app to work in low-light conditions while remaining accurate, Kalehoff pointed out. The AI has also received additional training recognizing different skin tones and again delivering accurate readouts.

There are also some upsides. Realeyes can tell if the same face appears more than once. This can be an issue with paid surveys, where a subject may want to participate more than once to score a little extra cash, Kalehoff noted.

As for practical application Realeyes worked with Mars Inc. on a project to boost sales using increased attention metrics. The experience yielded an 18% sales increase across 19 markets, optimizing the ad spend by about $30 million, Kalehoff said. Even a five percent increase in “creative attention” can lead to a 40% increase in brand awareness.

The post Scanning the faces that scan the mobile screens appeared first on MarTech.

Dig deeper: Three essentials for writing a good ChatGPT prompt

Here is a roundup of actual AI-powered martech products, platforms and features announced this week. 

• Mailchimp’s Email Content Generator is aimed at providing generative AI to small- and medium-sized businesses. With it marketers can generate email content and customize it, creating personalized email campaigns tailored to their brand, tone and marketing intent. It responds to natural language prompts and delivers three versions of the content for users to work with. Mailchimp’s AI tools can also analyze campaign performance, providing detailed insights and suggestions for improving both campaign and content. 
• Sprout Social has new AI features across its social media management platform, including an integration with OpenAI adding new GPT-powered features and functionalities. One is Smart Query Suggestions which adds GPT-powered query keyword recommendations and can create more tailored topics. These features build on Sprout’s existing proprietary ML and automation capabilities. 
• Inuovo’s IntentKey is designed to understand why CTV viewers are engaging with content. Its AI is a map of the connections between more than 25 million different WHY-based signals, which can be about people, places, things, products, ideas or emotions. It automatically finds and dynamically modifies audience segments every five minutes and uses data from that to provide advertisers with actionable household-level insights.
• Evocalize’s EVOLVE is part of its collaborative marketing platform allowing franchisees to tailor content and campaigns. EVOLVE uses each user’s data to make highly localized versions of marketing copy. It also delivers campaign insights, plans for optimizing spend across channels for local conditions and detailed results predictions for each location in real-time.  

The post Mailchimp, Sprout Social lead this week’s AI-powered martech releases appeared first on MarTech.

I was hunting down a story on one of the biggest and most notable businessmen in the virtual world. I remember the meeting as if it were yesterday, walking through his expansive, modern, glass building, taking an elevator up to the top floor and sitting around a huge boardroom table. 

My interview uncovered that he had built this entire virtual island and a flourishing business while embedded in Iraq. He explained that the meaningful connections he made in the virtual world helped ease the stress and anxiety of his real-world chaos. I was amazed, and it cemented my belief in the power of this technology. 

This story is not unique. Reporting on Second Life business I heard dozens of similar stories of people getting real-life needs met by virtual connections. Second Life was full of virtual humans — some even run by computers — who immensely influenced others in the community even though there was never a physical meeting.

I quickly learned not to care if the avatar accurately represented the human. Our digital representations afforded us the freedom we couldn’t find anywhere else and a safe place to explore ourselves and our relationships with others. I believe this freedom and the willingness of consumers to let go of the stigma surrounding digital connections are helping to fuel the current trend of virtual influencers. 

Today’s virtual influencer

Fast forward 20 years, and we now have brands and agencies creating virtual humans to help build connections between their organizations and the hearts of their consumers. Wikipedia defines a virtual influencer as “a computer-generated fictional character that can be used for a variety of marketing-related purposes, but most frequently for social media marketing, in lieu of human influencers. Most virtual influencers are designed using computer graphics and motion capture technology to resemble real people in realistic situations.” 

“Virtual influencers are the most human reflection of a brand, in that the brand is required to take ownership of their identity and apply their creativity to bring it to life in the form of a virtual being,” says the leading virtual human expert and “Forbes 30 Under 30 Entrepreneur” Christopher Travers. “For fans, a virtual influencer created by a team who cares about message, craft and creativity is a valuable media experience that can entertain and provide fulfillment in the form of insight, friendship, or just fun.” 

Benefits of virtual influencers to an organization include not paying the exorbitant fees of real influencers, owning the face (in fact owning the whole thing!) of the influencer or spokesperson and removing much of the risk associated with having a mistake-prone human represent your brand.  

Virtual influencers solve the challenge brands encounter when building meaningful connections across social platforms because like it or not, brands and corporations are not people. Crafting a values-driven, virtual influencer offers audiences a relatable and human-centered experience that can help grow brand affinity and relevance. 

Meet the new breed of influencers

There are almost 300 virtual influencers on record today, and the number is growing. Arguably the most successful and popular virtual influencer is Lu. She has a following of over 30 million users across her social platforms and is a new revenue stream for her creator, the Brazilian retailer Magalu. 

Described as the Amazon of South America with brick-and-mortar stores, Magalu has turned Lu into an influencer that can charge advertisers a high premium. Anyone selling a product on Magalu can pay to have their product creatively placed within the storytelling content that Lu is sharing with her 30 million followers.

She is active on all the major platforms, including YouTube, Instagram, Facebook, TikTok and Twitter. Stay tuned for a more comprehensive case study on Lu and Magalu, as I believe this is the future of advertising. 

Not all these virtual influencers are B2C-focused. Maarten Reijgersberg, CEO of creative agency RAUWcc, crafted Esther Olofsson, a self-described “virtual human created with AI.” 

You can follow Esther’s adventures on Instagram as she explores top tech conferences such as SXSW and The Next Web. Esther got her start as part of a campaign for a Dutch hotel chain, and Reijgersberg decided to keep her alive as an innovation engine and testing ground for new projects. 

Dig deeper: How to become a B2B influencer on LinkedIn

When asked about how he is measuring her success, Reijgersberg quips:

“[She is] successful if my colleagues continue to challenge their generative AI knowledge and if she generates press coverage for RAUWcc. Of course, we are working on more reach and engagement and she will eventually have to earn her own money.” 

Poor thing. Even virtual influencers need to prove their ROI. 

To AI or not to AI? That is the question

The improvements in AI and ML technologies have vastly increased the capabilities of automated avatars since my days in Second Life. At that time, virtual avatars or “bots” could do simple tasks like engage in a one-way conversation, share text-based chat messages and make simple, repeatable movements.

Today tools like ChatGPT, Cinema 4D, Stable Diffusion and a host of others allow teams to create much more life-like human interactions and content with their virtual humans. 

But just because the technology exists doesn’t mean all successful virtual influencers are AI-driven. Lu is crafted by a large team of content creators who don’t use AI tools. 

The benefit is that the brand has 100% control over creative content and output. The downside is that producing social content for millions of followers across multiple channels is hugely time-consuming and resource intensive. AI tools can ease the burden of cranking out content and reduce the required resources, but it increases risk and unpredictability in exchange. 

You can experience this unpredictability by engaging with AI-driven influencers such as Kuki, described as “the world’s most advanced AI virtual model,” or Kitt, an AI-driven vTuber. 

vTubers have taken over Twitch and YouTube and are essentially human-run, animated avatars that stream live content onto streaming platforms. Kitt, however, is completely run by AI technologies and engages her following in animated and unpredictable conversation. You can even pay $25 to have her sing your favorite song, a very interesting monetization model. 

While the unpredictability and newness of this technology are extremely captivating, it’s like watching a train wreck.

At one point, Kuki was correcting the grammar of one of her followers, educating him on the difference between “you are” and “your” — not the way to build affinity for a brand. 

Fast forward to the future

Despite the clunkiness and unpredictability of AI-run influencers, tools and platforms are being developed to perfect these technologies and use cases. One day, brands can spin up a virtual influencer that can produce their content, monitor their feeds and build relationships with fans 24/7. In the meantime, brands and creative teams will continue exploring and innovating how virtual influencers can bring value to consumers. 

“Tomorrow, virtual influencers will be as abundant as the JPG, the GIF, or the MP4. In this landscape, the novelty of the medium will fully wane and the most value-adding, artful implementations will reign,: says Travers. “Perhaps some leaders will be entirely generative, perhaps other leaders will be creator-led, but ultimately the barrier to entry will be made [zero] in time, there will be an abundance of virtual influencers and creativity will win at the margin.”

The post Meet 3D virtual influencers, the new breed of marketing influencers appeared first on MarTech.

“Best of breed has jumped the shark. The concept that a CMO has to buy 250 different technologies and try to figure out which is actually giving them the intent signal that they need — that ship has sailed.”

Rob Tarkoff, Oracle EVP and general manager of CX, knew he was mixing his metaphors. The message was nonetheless clear. Marketers don’t need more standalone tools, they need a platform.

This is not a new message from Oracle. Back in 2015 the late Mark Hurd, then Oracle CEO, predicted that by 2027 two marketing suites would command 80% of the market. He clearly thought Oracle was one of them.

Since then, however, we’ve been through several phases, including the proliferation of “Frankenstacks,” — poorly integrated custom stacks patched together from multiple sources — as well as the model of a central marketing solution with countless partner apps available to plug in and play.

For Tarkoff, none of this works.

Dig deeper: Marketing attribution: What it is, and how it identifies vital customer touchpoints

One streamlined process

“What needs to happen today,” he said, “is that all of those [marketing] flows need to be unified into one streamlined process, one data model, one set of interactions, one clear end-to-end process to build a campaign that has multichannel touch.”

Oracle claims to have built precisely that through the development of Oracle Fusion Marketing, a solution that supports the execution of multiple campaigns across advertising, email and other channels. “We’ve built this system,” he explained, “to take out this crazy concept of continuing to add point applications.” Another breakthrough, Tarkoff said, was integrating the Oracle Unity CDP with the marketing orchestration, content and advertising platforms.

Although Oracle does indeed fuse CX and advertising, Tarkoff acknowledged that the work they’ve done serves primarily B2B marketers. “We’ve written a lot of code over the past few years,” he said.

Dig deeper: Oracle Fusion Marketing reduces the role of traditional CRMs

App marketplaces don’t solve the problem

Some obvious competitors like Salesforce and HubSpot seek the best of both worlds, offering extensive proprietary suites of solutions, but also running huge app marketplaces featuring best of breed solutions configured to integrate with their platforms. Tarkoff, however, thinks of this approach as less the best of both worlds than a way of hedging bets.

“I think that’s a way of hedging bets that doesn’t really solve the problem,” he told us. “Sure, we have partners — but just bringing a marketplace and saying it’s your job to orchestrate the marketplace, that’s not solving the problem. Make it simpler.”

Simplicity and efficiency are his watchwords. “I haven’t seen a model where having an app marketplace actually improves the effectiveness of marketers. It sounds good on paper. We want people to see the power of the unified suite. It doesn’t mean that we’re closed; it means we’re complete.”

Doesn’t it also mean that it forces an Oracle customer to become, in effect, an “Oracle shop,” locking them into the Oracle suite rather than allowing “composability”?

“Truthfully, in SaaS, we’re providing it as a service. We’re not deploying any software on premise, so you’re not locked in. As long as the service provides value for you, you’ll keep it; if it doesn’t, you’ll switch.”

Machine learning is baked in

Another differentiator between Oracle and prominent competitors like Adobe and Salesforce is that it doesn’t have a tag — Sensei or Einstein — for its AI capabilities. Nor has it made any splashy announcements about its adoption of generative AI; no equivalent to Einstein GPT or Sensei GenAI.

Tarkoff says there’s a reason for this. “Oracle has always taken the approach in development that AI and machine learning are built into all of our applications. It’s always been a deliberate difference in how we market AI — rather than having a Sensei or an Einstein or some extra layer of AI, we build machine learning into all the core flows.”

One example, he said, is in the “completely revamped” conversational UI called Redwood. “In that UI we have enabled a lot of machine learning flows to be captured in a conversational fashion.” I think the big difference with large language models is that you get a response in the form of a written statement or some narrative as opposed to a set of directions.”

This doesn’t mean Oracle isn’t paying attention to generative AI. “Like a lot of people, we are experimenting with what that means across marketing, sales and service concepts. How good is it at helping you to optimize the right kind of marketing message? How good is it at helping you figure out the right interaction for a chatbot? We’re doing all of the same experimentation. The difference with Oracle is that we just don’t believe in hyping things we don’t think are true innovation. It’s just a different orientation altogether.”

He also points out that, at an enterprise level especially, good governance is needed. He referred to the inadvertent leak by Samsung engineers. “Some of the engineers put their code into ChatGPT to try to debug it — and it was proprietary code.” Feeding proprietary customer information to a large language model would also be a significant problem.

“It’s in the hype phase,” he concluded. “It’s a new toy for everyone and it will have productivity enhancements but I think there’s a lot that has to get figured out.”

The post Marketers need a unified platform, not more standalone tools appeared first on MarTech.

Dig deeper: Three essentials for writing a good ChatGPT prompt

Here is a roundup of actual AI-powered martech products, platforms and features announced this week. 

• Botify Assist is a collection of ChatGPT-powered tools for Botify’s enterprise SEO platform. One is a personalized search assistant that helps people find information using plain text. The other provides content optimization recommendations to assist writers. 
• SE Ranking’s AI Writer generates various types of written content “in accordance with the highest SEO copywriting standards.” It can help marketers generate topics, structure and optimize text, and adjust tone, based on 11 different writing styles.
• Searchie’s Wisdom AI, powered by GPT-4, uses audio, video and written content to create answers to natural language questions. This makes it easy for users to create a conversational assistant for digital products, including courses, memberships, coaching programs, and podcasts.
• PDFgear’s PDF Chatbot “allows users to interact with PDF documents as if they were human.” The AI-powered chatbot can discover knowledge from lengthy PDF documents, such as books, textbooks, essays, manuals, etc. An AI chatbot pulls information from PDF documents of any length — including books, textbooks, essays and manuals — and summarizes it in short sentences.
• ToqueAI has added GPT-4 to its content creation platform. The generative AI increases content coherence and improved context understanding in 37 different languages. It also allows users to generate images.
• Big Purple Dot’s BPD AI Assistant lets people use natural language to query the firm’s mortgage- and real-estate-focused CRM. 
• Widewail has added ChatGPT to its customer review and reputation management systems. This will allow clients to use natural language prompts for managing review responses and provide new topic and sentiment analysis reporting.

The post This week’s new AI/ChatGPT-powered martech products appeared first on MarTech.