Bain and AI

 First I had heard of AI and Bain.      Good to see it.        June 2023

Ready for Launch: How Gen AI Is Already Transforming Marketing

For CMOs, the benefits of generative artificial intelligence (if done right) will outweigh the brand risks.

Marketing is a generative AI hot spot because the new technology is so well suited to its blend of creative and data-driven work.

Chief marketing officers can draw on five golden rules as they plan how to use generative AI tools to accelerate, augment, and streamline their activities.

The rules: Generative AI needs to start and end with the customer; creative applications are only the start; quick wins and complex projects must run in parallel; marketers should keep the highest-priority use cases in-house; and the CMO is perfectly placed to be an AI change agent.

Imagine a world in which smart assistants are the common front end of digital interactions, transforming the experience of engaging with a brand’s app or website. A world in which the current mix of marketing channels has been shaken up—by a surge in text-based communication sparked by AI’s ability to personalize at scale, or by a boom in audio, video, and image-based marketing triggered by an acceleration of production speed at lower costs. Picture a corresponding disruption in the creative landscape as new sources of imagination and flair emerge and old ones lose their historic edge. Or one in which influencers become even more critical, with access to simplified or targeted versions of powerful digital tools that used to be out of their reach.  

For marketers, all of these developments are in sight because of advances in generative artificial intelligence. Marketing is helping to lead the adoption of generative AI tools that create and personalize new content, such as OpenAI’s ChatGPT and DALL-E (which was used to produce the rocket image at the top of this page), other image creation platforms like Midjourney and Stable Diffusion, and emerging audio and video creation technologies. For instance, when we surveyed nearly 600 companies across 11 industries, speeding up the development of marketing materials was one of the top seven use cases for generative AI, with 39% of respondents saying they were using or evaluating the technology for this purpose (see Figure 1).

Figure 1

Marketing has a stake in at least two of the generative AI use cases gaining most traction with companies

Across consumer-facing industries, marketing has become a particular hot spot because of generative AI’s ability to engage customers, personalize content, and reduce both cost and complexity. Meanwhile, chief marketing officers with wider responsibilities are also looking to deploy it in customer service to personalize customer experience, boost sales and retention, and support frontline employees. And for companies that operate in the marketing and advertising industry specifically, adoption and evaluation of the top generative AI use cases is notably rapid, second only to IT system integrators.

Services Alliance

Better Together: OpenAI and Bain Form an Alliance

Harness the power of generative AI to transform your business

For many CMOs, however, the buzz and expectation created by generative AI’s marketing potential come with new risks. Marketers are well placed to understand the many ways a company’s brand could suffer if the new technology’s debut is mishandled. Concerns include the safety of customer data and the threat to jobs. Copyright is a gray area too. That’s not just because the technology can reuse existing material in a way that breaches the intellectual property rights of human creators. It’s also unclear how much marketers will be able to assert copyright over content generated via the latest AI tools. Other potential hazards in AI-powered marketing include inaccurate content and unintended bias.

All this might lead some CMOs to advocate a wait-and-see approach, but that would create an even bigger threat to their company. One risk is that rivals deploy the technology first, leading to an advantage in innovation in customers’ eyes and an ability to set the “rules” of deployment in a given industry. A second is that early movers become hubs for top data and engineering talent required to compete.

Despite the concerns, AI is set to transform how marketers work. Pioneers will soon start to benefit from enhanced brand engagement, accelerated growth, time savings, a talent acquisition advantage, and lower costs, while slower-moving competitors will miss out. Coca-Cola offers a prime example of pacesetting—within a month of announcing that it was working with with OpenAI, it had launched its “Create Real Magic” campaign, which encouraged consumers to create Coke-related artwork using generative AI, conjuring up new touchpoints for the brand.

It’s a tricky tightrope for CMOs to walk. But while so much about generative AI is still in flux, some truths are already emerging.

Five golden rules of generative AI in marketing

Marketing leaders can draw on five golden rules as they plan how to embrace the technology over the coming months.

1. Start and end with the customer. Some executives may be tempted to use the technology primarily to improve efficiency, particularly given the current economic climate. CMOs need to ensure that deployment always comes back to “How can this improve the lives of our customers and employees?” Remember that this is a generational moment to redefine how marketers and brands engage with customers.

2. Creative applications are only the start. Early adopters are already using the latest AI tools to dazzling effect, generating new creative imagery at the click of a button. The winners in AI-powered marketing won’t stop there, though. They’ll take a holistic approach that also exploits AI’s ability to personalize marketing, improve behind-the-scenes processes, turbocharge measurement, enable near-real-time testing, and strengthen decision making by making sense of unstructured data (see Figure 2). Pragmatic moves include using unstructured data to inform audience targeting or updating the marketing brief with live campaign data. Another benefit of taking a holistic AI approach: improved collaboration with other departments.

Figure 2

Marketing use cases are at the forefront of generative AI deployment, from ideation through to measurement

3. Quick wins and complex projects must run in parallel. Some marketing teams are making early progress by deploying generative AI in manageable pilots, such as in employee-facing contexts, or in situations where employees can review AI-produced content before it reaches the customer. Rather than waiting for a solution to their thorniest deployment challenges, companies must take these small steps today to build their expertise and gain quick, confidence-building wins. But to realize the full long-term potential of AI, they also need to start working in parallel on complex projects, particularly those that connect to customer data lakes—things like personalized direct marketing, proactive engagement to retain customers, and sentiment prediction. We’d also encourage CMOs to carve out some capacity for the boldest innovations that transform experiences and value propositions, such as Spotify’s AI-powered DJ and Duolingo’s conversational role-play feature for language learning.

4. Keep the highest-priority use cases in-house. Software vendors such as Google are moving quickly to build generative AI into their products. That can streamline some forms of humdrum work without CMOs having to worry about building their own solutions. But a different approach is going to be needed in more specialized areas that offer genuine competitive advantage and differentiation in areas such as customer acquisition and engagement. These will require more bespoke capabilities that will likely have to remain in-house.

5. The CMO is perfectly placed to be an AI change agent. CMOs will need to exercise their brand guardian responsibilities carefully, managing risks and setting up guardrails (in partnership with the legal team) in areas such as intellectual property and data protection, while creating systems to respond effectively if AI–customer interactions go awry. But they must also ensure that this brand guardianship doesn’t end up stifling innovation. Marketing can be one of the earliest showcases for generative AI’s ability to reinvent work. Conversely, generative AI’s rollout can be a showcase for marketing teams eager to demonstrate their ability to contribute to adjacent functions such as product management and customer experience. 

A CMO superpower in the making

Marketing’s prominence in the first wave of generative AI brings the right kind of pressure. A wide range of stakeholders, from boards to investors to employees, are looking to the CMO to be an inspirational early adopter. That’s an opportunity to underline the strategic importance and breadth of the marketing function.

Longer term, generative AI tools can bring new power to CMOs to better balance and integrate innovation, creativity, and data-driven decisions.

Authors, Jeff Katzin

Salesforce is Playing AI too

(Will further examine) 

Salesforce doubles down on generative AI with Marketing GPT and Commerce GPT

Shubham Sharma  @shubham_719

June 7, 2023 9:07 AM

Today, CRM giant Salesforce debuted two new generative AI products. Announced at the company’s ongoing Connections conference, Marketing GPT and Commerce GPT will power Salesforce’s Marketing Cloud and Commerce Cloud, enabling enterprises to remove repetitive, time-consuming tasks from their workflows and deliver personalized campaigns and shopping experiences, at scale. 

Want must read news straight to your inbox?

The news follows last month’s launch of Slack GPT and Tableau GPT and highlights Salesforce’s growing focus on AI, where it is moving the needle to make sure generative AI sits at the heart of its core products and services. However, it must be noted that these products’ features are not available right away and will roll out in phases, starting in summer 2023.

How will Marketing GPT and Commerce GPT help?

Driven by the Salesforce Data Cloud, which hosts customer profiles comprised of data from all systems, and the Einstein GPT generative AI assistant, Marketing GPT allows enterprise users to interface with their Marketing Cloud system using natural language.

Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.

Register NowTo start off, the company said, Marketing Cloud users will be able to put in natural language prompts to query the Data Cloud profiles and identify new audience segments to target. They could also ask Einstein GPT to write or modify personalized emails — complete with subject lines and body content — for campaigns, or use Typeface within the platform to create contextual visual assets.

That’s not all.

In addition to generative functions, the marketing cloud will get AI-driven segment intelligence and rapid identity resolution capabilities.

The former will automatically connect first-party data, revenue data and paid media data from Meta and Google for a comprehensive view of a campaign’s performance, relative to the audience segment targeted.   ..... ' 

Would a TikTok Ban Cripple Influencer Marketing?

Been following this recently on Youtube, novelty key.  Much more by marketing experts at the link.

Would a TikTok Ban Cripple Influencer Marketing?

Mar 21, 2023,  by Tom Ryan

The Biden administration last week threatened to ban TikTok in the U.S. if the app’s Chinese owners refuse to sell their stakes, raising questions about if competitors Instagram, Snap and YouTube can fill the void for the influencer community.

For the third year in a row, TikTok held the record as the most downloaded app with nearly 40 percent of TikTok’s advertising audience aged between 18 and 24.

Influencer Marketing Hub’s “The State of Influencer Marketing 2023” report found that TikTok is now the most popular influencer marketing channel (utilized by 56 percent of brands using influencer marketing), surpassing Instagram (51 percent) for the first time. Facebook (42 percent) and YouTube (38 percent) follow behind.

Thomas Walters, Europe CEO and co-founder of creator agency Billion Dollar Boy, told Campaign that factors such as TikTok’s personalized algorithm, lack of emphasis on follower count and its shift towards “spontaneous, raw, unfiltered content” are all helping drive the platform’s popularity.

In a recent blog entry, Kolsquare, the influencer marketing platform, said that while facing challenges with conversion, TikTok is “the clear leader when it comes to driving trends and engagement amongst the youngest users of social media” and has an edge in driving awareness over other platforms.

Both the Trump and Biden administrations have said that the app poses a national security threat amid concerns China could tap user data to spread misinformation. On March 23, testimony by TikTok CEO Shou Zi Chew before the House Energy and Commerce committee may raise the rhetoric.

Some content creators are seeking to diversify to Instagram Reels or YouTube Shorts, but others doubt the TikTok ban will take effect, according to The Wall Street Journal.

In terms of potential purchasers for the app, a New York Times article speculated that many buyers could not afford TikTok (valuation at $50 billion or more) or would not want to deal with the antitrust scrutiny of an acquisition.

Many brands, according to Advertising Age, continue to put marketing dollars behind TikTok, given the likely delays or challenges that would come with enacting an outright ban. Becca Millstein, CEO of tinned fish brand Fishwife, told Adage, “We believe that TikTok can be a powerful source of organic discovery and hope that we have the opportunity to utilize it as such.”  ... '

Book: 'See, Solve, Scale' By Danny Warshay

Book:   Reading now.  Largely about building companies, getting customers and related marketing.   Nicely done, good ideas, breakthroughs and directions.  Lots of real  'Why' examples.    -  FAD

See, Solve, Scale: How Anyone Can Turn an Unsolved Problem into a Breakthrough Success Hardcover – March 22, 2022   by Danny Warshay (Author)

From Amazon:    Inspired by Brown University’s beloved course―The Entrepreneurial Process―Danny Warshay’s See, Solve, Scale is a proven and paradigm-shifting method to unlocking the power of entrepreneurship.

The Entrepreneurial Process, one of Brown University’s highest-rated courses, has empowered thousands of students to start their own ventures. You might assume these ventures started because the founders were born entrepreneurs. You might assume that these folks had technical or finance degrees, or worked at fancy consulting firms, or had some other specialized knowledge. Yet that isn’t the case. Entrepreneurship is not a spirit or a gift. It is a process that anyone can learn, and that anyone can use to turn a problem into a solution with impact.

In See, Solve, Scale, Danny Warshay, the creator of the Entrepreneurial Process course and founding Executive Director of Brown’s Center for Entrepreneurship, shares the same set of tools with aspiring entrepreneurs around the world. He overturns the common misconception that entrepreneurship is a hard-wired trait or the sole province of high-flying MBAs, and provides a proven method to identify consequential problems and an accessible process anyone can learn, master, and apply to solve them.

Combining real-world experience backed by surprising research-based insights, See, Solve, Scale guides the reader through forming a successful startup team and through the three steps of the process: find and validate a problem, develop an initial small-scale solution, and scale a long-term solution. It also details eleven common errors of judgment that entrepreneurs make when they rely on their intuition and provides instruction for how to avoid them.

Leveraging Warshay’s own entrepreneurship successes and his 15 years of experience teaching liberal arts students, See, Solve, Scale debunks common myths about entrepreneurship and empowers everyone, especially those who other entrepreneurship books have ignored and left behind. Its lasting message: Anyone can take a world-changing idea from conception to breakthrough entrepreneurial success.  ...  ' 

How AI-powered Market Research Helps Predict Success of Future Products and Advertising

 Interesting Claims made.

How AI-powered market research helps predict success of future products and advertising

Sean Michael Kerner  @TechJournalist,  December 13, 2022  in Venturebeat

Market research is a business that has always relied on data and lots of it. Making sense of all that data in a meaningful way, however, has long been a challenge, but it’s one that artificial intelligence (AI) is being tasked with solving.

Market intelligence firm, Zappi, helps organizations with research efforts around marketing advertising and product innovation. It’s a business that got started in 2012 and in recent years has deeply integrated AI and machine learning (ML) into its processes and platform to help organizations get better insights. Zappi counts PepsiCo, McDonald’s, Heineken, and Reckitt among its customer base.

Using AI to power no-code platforms and streamline work - Low-Code/No-Code Summit

“Our focus is embedding human expertise into software for the analysis, interpretation and communication of data that will help clients make better decisions,” Steve Phillips, CEO and cofounder of Zappi, told VentureBeat.

In a bid to help the company further grow its technology and go-to-market efforts, Zappi announced today that it has raised $170 million in new funding.  ... ' 

Colgate Advertising as a Space Hygiene Company

Spent a considerable amount of time working at Procter & Gamble,  often a competitor to Colgate,  so its interesting to see this product connection with arch competitor Colgate.   How well will an increased link to NASA act to promote product?: 

How Colgate-Palmolive, and NASA and an astronaut are teaming for demonstrating better space hygiene results.    Attention getting idea, but how well will this work?       

[Book Review] A Behavioral Science Road Map for Marketers

Was asked to comment on this,   here is a review,   taking a look. 

[Book Review] A Behavioral Science Road Map for Marketers  in Customerthink

David Dodd -October 17, 2022

Data science and behavioral science have emerged as the twin pillars of marketing success in the twenty-first century. They have become, in essence, the yin and yang of consistent, high-performance marketing.

These disciplines are both essential because together they enable marketers to develop a more complete understanding of their customers and potential buyers. Data science (which includes the technologies that enable the collection and processing of data) can give marketers a rich picture of buyer behaviors. Behavioral science (primarily in the form of behavioral economics) provides a set of principles that enable marketers to better understand how people process information and make decisions. 

Data science has received a huge amount of attention in marketing circles over the past several years. For example, the use of artificial intelligence in marketing has recently been one of the hottest topics in the industry. The use of behavioral science in marketing has received somewhat less attention even though it has a longer history of use by marketers.

The reality is, marketers have been using principles of behavioral economics for years, albeit largely unwittingly. A 2010 article in McKinsey Quarterly put it this way:  "Long before behavioral economics had a name, marketers were using it. 'Three for the price of two' offers and extended-payment layaway plans became widespread because they worked - not because marketers had run scientific studies . . ."

A new book by Nancy Harhut - Using Behavioral Science in Marketing:  Drive Customer Action and Loyalty by Prompting Instinctive Responses (Kogan Page, 2022) - is a timely and much needed resource for marketers who want to leverage the power of human psychology in their marketing efforts.

Nancy Harhut is a seasoned marketing professional who has extensive experience with using behavioral science in marketing. In 2017, she cofounded HBT Marketing, a consultancy that specializes in applying principles of behavioral science to marketing. Prior to HBT, Nancy held senior creative management positions at several agencies, including Hill Holiday, Mullen and Digitas.

What's In the Book

Nancy Harhut refers to Using Behavioral Science in Marketing as a "hands-on handbook," and that is an apt description of her book. She clearly wrote Using Behavioral Science in Marketing primarily for hands-on marketing practitioners.

While Ms. Harhut provides clear and concise descriptions of the behavioral science principles covered in the book and includes ample citations to the research relating to those principles, her primary focus is on how marketers can apply those principles. She wrote, "In fact, you'll find I go short on the scientific research and longer on the way to use it." ... ' 

Multi Channel Digital Merchant

Brought to my attention: 

Martech and Digital Experience Management: The New Frontier, by John Schneider

Martech is experiencing unprecedented growth. In just the last decade, it grew 5,233%. Over the next few years, global spending on digital transformation is expected to reach $2.8 trillion. What’s driving this surge in digital strategy investment?

The majority of C-suite leaders say an improved customer experience is a top factor driving their digital transformation. More specifically, marketers’ focus has shifted from basic content management to identifying, creating and publishing personalized content at scale.

The new marketing paradigm requires real-time, 1:1 personalization, changing the messaging, content and channel at any moment to serve any individual based on their needs, while predicting future outcomes. To offer this level of personalization, marketers are investing in connected digital experience platforms, with integrated solutions for deploying immersive content experiences at scale, representing the new digital frontier.

Rise of AI and the New Frontier of Experience Management

Today, you must develop a holistic, technology-enabled content strategy in order to succeed. Consumers expect you to anticipate their needs and offer relevant suggestions before their initial contact. Fortunately, the majority of marketers understand this, with 77% acknowledging that real-time personalization is crucial to their company’s success, according to Adobe. And it certainly doesn’t hurt that personalization delivers 5x to 8x the ROI on marketing spend.

To achieve the level of personalization consumers demand, it’s imperative that marketers stay ahead of the digital evolution. Powerful martech tools, such as customer data platforms, AI-based personalization and digital asset management (DAM), help you make “next best action” decisions that future-proof your customer experience strategy.

Before we dive into the new customer experiences and marketing capabilities these platforms provide, let’s take a quick trip back in time to discover how we got here.

1990s: Modern internet formed, websites grow

Websites are built in a painstaking manner by internal IT departments over a period of years. Updates are rare and sites offer limited information and experience, mostly brochureware.

2000s: Websites go from nice-to-have to essential marketing channel

Content Management Systems (CMS) like Interwoven, Vignette and WordPress emerge with WYSIWYG capabilities, enabling marketers to create and edit content without the help of IT. But changing the experience requires developer intervention

2010s: Basis for the Digital Experience Platform (DXP) emerges

The focus begins to shift from managing content to managing experiences. Platforms like Adobe and Sitecore emerge, enabling marketers to build webpages using composable libraries of marketing assets, thanks to integrated DAM and content systems. Still, scale is limited by the output of any single worker and their ability to manually create content and set personalization rules for combining different assets together, based on assumptions about different personas.

Also, commerce and content remain disconnected, managed through disparate experience management and commerce solutions. This leaves customers with a disjointed, clunky experience and hinders marketers from using content to support their entire funnel.

Today: Holistic martech solutions power connected customer experiences

The marriage of omnichannel content and commerce is realized through DXPs like Episerver (now Optimizely), Sitecore and Adobe that seamlessly integrate the content and commerce sides of the funnel. Moreover, AI-powered CDPs, and DAMs allow marketers to deliver individualized customer experiences at scale. Finally, marketing goals and technological capabilities work in lockstep to deliver the future of experience management.  .... ' 

On Intent Data

 Exploring Intent marketing data. 

Making Sense of Intent Data for Sales and Marketing Pros

Fiona O'Connor, Content Marketing Manager    in TechTarget

Intent data promises different benefits depending on how it’s made, how it’s sourced and how you put it to use. Companies are adopting it for different reasons and solution providers are touting it heavily. Some forms of intent may only help to increase your advertising efficiency. Others can give you a suggestion of who might be considering a purchase because of an upcoming contract renewal, a change in personnel, or similar indirect indicators. And a few can provide meaningful access to significant pockets of undiscovered demand that can even drive more real opportunities into your pipeline. While many users have adopted one or more of these signal types, many organizations haven’t yet realized the major differences that could be important to their getting the most bang for their buck.

We’ve created this guide to help accelerate your own journey with intent data – whatever stage you’re at. It walks through how purchase intent data can help support every area of your organization, from Sales to ABM and Marketing – and we’ll provide additional resources to further expand your knowledge.

What is real intent data?

As originally coined, “purchase intent data” was a category of behavioral data (data created by analyzing the behaviors of people) that provided a strong indication of an impending product or service purchase. Recently however, not only has the term been abbreviated to simply the word “intent” alone, but some vendor players have worked hard to broaden its meaning to include any “signal,” regardless of whether it can be reasonably associated to an impending purchase.

As we use the term, purchase intent data must be able to guide go-to-market (GTM) teams first to significantly more active demand than they could otherwise see, and then, very precisely to specific people whose behaviors have signaled an interest in buying a given product. In a nutshell, it’s about maximizing visibility into the totality of current market demand and then optimizing the focus and precision of your actions on the available insights. Among the many different types of data or data sources now being labeled as “intent,” our definition is quite specific. Depending on the sourcing and methods involved, a given resource will have very different characteristics, and therefore, very different utility in supporting a GTM.

For a clear framework you can use to evaluate intent data sources and types, read Making Sense of B2B Purchase Intent Data and Putting It to Use.

Like any data source, intent adds more value to your GTM if you can effectively put it to use in support of multiple use cases and user groups. Next, we’ll take a look at how real purchase intent data can be useful in various ways for specific GTM teams. From strategy optimization through to one-to-one sales personalization, the right intent source can quickly provide essential guidance that yields meaningful competitive advantages.

Intent data for Sales Development

Though Sales Development has exploded in popularity especially at enterprise tech companies, it’s still a relatively immature discipline at many of them. Sales Development organizations are often assembled in a rush with the expectation that they will mature themselves as they go along. But the reality at many companies appears to be that the build process is leaving a lot of important gaps which can easily become chronic points of failure. Today, we still see many Sales Development organizations where such gaps continue to cause underperformance in their companies’ sales development capability. While the majority of these teams’ time is intended to be spent on better qualifying demand for more effective sales follow-up, in a recent survey of Sales Development organizations, most companies reported that their teams still fail to hit their objectives. While a range of issues contribute to this, the quality of data resourcing stands out as a key point of failure.

While intent data has not yet become a staple for Sales Development teams, it is growing in usage. (Of course, as discussed above, the utility of intent for these use cases very much depends on the data source and its available insights). At the time of the research, less than 25% of all tech Sales Development teams were using intent data in a rigorous fashion despite the fact that the right type of intent source has been shown to yield substantial improvements in conversations, meetings and opportunities. When Sales Development teams are trained and enabled to use an appropriate source of purchase intent data, they are able to execute better outreach and more effectively deliver meetings that progress through the pipeline.

For more insights on the challenges facing Sales Development teams and how purchase intent data can help, read How Does Your Sales Development Program Measure Up?   ....' 

Has Science-based Marketing taken a Pandemic hit?

 Surprised at the suggestion, but interesting. 

Has science-based marketing taken a pandemic hit?

May 20, 2022    In Retailwire  by Tom Ryan

Surveys show that trust in scientists took a hit during the pandemic and it may be impacting science-based marketing claims.

Recent research from John Costello, a marketing professor at Notre Dame’s Mendoza College of Business, along with researchers at Simon Fraser University and Ohio State University, explored how invoking science in the marketing of consumer products can backfire.

The research generally found science-based claims are effective when marketers try to sell the practicality of the product. When trying to sell based on sensory pleasure, consumers are less likely to buy it when it is described as developed using science.

“The reason this occurs is because people stereotype the scientific process as being competent but cold, similar to how they stereotype scientists,” said Prof. Costello in a press release. If consumers become aware that science is necessary in making the product, the backlash against the science claim doesn’t occur, according to the findings.

The research further found those who work in STEM industries (science, technology, engineering and math) don’t exhibit the “science backfire effect.” Researchers, however, also cited public polling concluding that a growing number of Americans have lower trust in science, suggesting that segmentation strategies may be beneficial for marketing managers.

“Our studies suggest that many consumers have mixed feelings about science in product development, despite the fact that societally, we increasingly rely on products produced by science,” Prof. Costello said. “As a result, marketers need to exercise caution when discussing the scientific process used to create products that consumers are buying for taste, enjoyment and other types of pleasure.”

Researchers assessed trust in scientists based on a Pew Research Center December 2021 survey that found only 29 percent of U.S. adults have a great deal of confidence in scientists to act in the best interests of the public, down from 39 percent from a November 2020 survey.  .... ' 

Levis using AI

Note interesting links to marketing. 

Levi's AI Chief Says Algorithms Have Helped Boost Revenue

By The Wall Street Journal, December 28, 2021

Artificial intelligence and a data repository built on Google Cloud have helped Levi Strauss & Co. improve revenue and profitability, says chief strategy and AI officer Katia Walsh.

The repository houses data that shoppers share with the company, as well as public and private information on consumer buying habits, weather and climate forecasts, and economic trends. Analyzing the data via machine learning and automation has helped Levi augment personalized consumer marketing, make informed pricing decisions, forecast demand, and optimize fulfillment, Walsh says.

Applying AI to pricing "enabled us to not discount broadly and as deeply as has been the practice in the past," and has allowed the company to precisely target customers which has helped increase revenues, Walsh says.

From The Wall Street Journal   

HBR IdeaCast on AI and Marketing

Brought to my attention, the podcast below, and ongoing ...

HBR IdeaCast

A weekly podcast featuring the leading thinkers in business and management.... 

What We Still Need to Learn about AI in Marketing — and Beyond

Eva Ascarza, professor at Harvard Business School, studies customer analytics and finds that many companies investing in artificial intelligence fail to improve their marketing decisions. Why is AI falling flat when it comes to this key lever for profit? She says the main reasons are that organizations neglect to ask the right questions, weigh the value of being right with the cost of being wrong, and leverage the improving abilities of AI to change how companies make decisions overall. With London Business School’s Bruce G.S. Hardie and Michael Ross, Ascarza wrote the HBR article "Why You Aren’t Getting More from Your Marketing AI."   ... 

Neuromarketing

 We touched on this years ago, as part of a broader cognitive science analysis of marketing,   it seems to have slipped behind the curtain since then.

A relook at the definition, there were a half dozen companies companies pushing it at the time. We tested some scenarios, mostly to test ad results.  It was common at the time to analyze Superbowl ads in context.

What is Neuromarketing?

It's time for an updated definition of "neuromarketing."  By Roger Dooley

Neuromarketing is the application of neuroscience and cognitive science to marketing.

Neuromarketing is the application of neuroscience and cognitive science to marketing. This can include market research that tries to discover customer needs, motivations, and preferences that traditional methods like surveys and focus groups can’t reveal. 

Neuromarketing can include the evaluation of specific advertising, marketing, packaging, content etc. to more accurately understand how customers react at the non-conscious level. And, it can include applying the knowledge obtained from neuroscience and cognitive science research to make marketing more effective without testing specific ads or other materials.

“Consumer neuroscience” is sometimes used as a synonym for neuromarketing.  ..."

L'Oreal Taking Cosmetics Digital, Virtual

Worked with a large Cosmetics Company, so of interest

L’Oréal’s New Digital Chief Takes Cosmetics Virtual  By The Wall Street Journal, August 6, 2021

L'Oréal Group's newly appointed digital executive is exploring how online games, augmented reality and social media can help the French cosmetics company reach new demographics amid an expected increase in e-commerce sales.

"The pandemic has been a catalyst for digital adoption," and L'Oréal is "constantly reimagining" its digital strategies, said Asmita Dubey, the company's chief digital and marketing officer.

Before her appointment as digital and marketing chief, Ms. Dubey held digital and marketing management positions at L'Oréal for eight years.  Ms. Dubey succeeded former Chief Digital Officer Lubomira Rochet in April and reports to L'Oréal Group Chief Executive Nicolas Hieronimus, who took the reins in May.

L'Oréal's brands include Lancôme, Kiehl's, La Roche-Posay, Garnier and Maybelline New York. The cosmetics company has about 3,000 employees who are focused on digital initiatives. It employs about 85,400 people total.

From The Wall Street Journal

Food & Beverage Consumer Research and Prospective Marketing

 Mady new kinds of prospective marketing enabled by AI methods that 

How PepsiCo uses AI to create products consumers don’t know they want  in Venturebeat

By Sage Lazzaro  @sagelazzaro

If you imagine how a food and beverage company creates new offerings, your mind likely fills with images of white-coated researchers pipetting flavors and taste-testing like mad scientists. This isn’t wrong, but it’s only part of the picture today. More and more, companies in the space are tapping AI for product development and every subsequent step of the product journey.

At PepsiCo, for example, multiple teams tap AI and data analytics in their own ways to bring each product to life. It starts with using AI to collect intel on potential flavors and product categories, allowing the R&D team to glean the types of insights consumers don’t report in focus groups. It ends with using AI to analyze how those data-driven decisions played out. 

“It’s that whole journey, from innovation to marketing campaign development to deciding where to put it on shelf,” Stephan Gans, chief consumer insights and analytics officer at PepsiCo, told VentureBeat. “And not just like, ‘Yeah, let’s launch this at the A&P.’ But what A&P. Where on the shelf in that particular neighborhood A&P.”  ... ' 

AI Contributing to Marketing

 Some of our earliest work touched on this, but its hard.  

How Much Can AI Contribute to Increasing Efficiency for the Marketing Department?  by 7wData

Many businesses — and their marketing teams — are increasingly adopting intelligent technology solutions to boost operational efficiency while enhancing the customer experience (CX). With these platforms, marketers can obtain a more nuanced, comprehensive understanding of their target audiences.

The insights collected through this process can then be employed to drive conversions while lessening the marketing teams' workload.

In a nutshell, artificial Intelligence (AI) is a technology that is meant to imitate human psychology and Intelligence. It is a computer science field focused on creating machines that seem like they possess human intelligence. We call these machines’ intelligence “artificial” because humans create it, and it does not exist naturally.

Machine learning (ML) is a popular subset of AI. ML algorithms are computer-implementable instructions. They take data as input and perform computations to discover patterns within that data and use those patterns to predict the future.

An ML model improves its performance over time as it encounters more and more data and self-corrects on making mistakes to reduce the chance of repeating them in the future. ML is mostly used in systems that capture huge volumes of data. In marketing, this data is of your customers.

A successful marketing campaign’s mark is a great user experience. There’s a higher chance of prospects’ conversion when they can resonate with the content. It is what turns loyal customers into brand evangelists. And this is where AI can assist in improving customer experience.

Marketers can determine which form of content is most relevant for their target audience by analyzing AI-generated data. Factors such as historical data, past behavior, and location can recommend the most useful content for the users.

You can observe an instance of this capability in your online shopping experiences. Everyone knows how Amazon shares relevant products to buyers according to their views, purchases, and previous searchers. That is AI at work!  ... .' 

Optimize Your Omnichannel Marketing Strategy

Useful look at the space via Wharton. 

How to Optimize Your Omnichannel Marketing Strategy

 LISTEN TO THE PODCAST:

Wharton’s Raghuram Iyengar talks about his research on how firms can harness the full benefits of omnichannel marketing.

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Omnichannel marketing seems like a simple enough concept. Consumers like to shop online, offline, and across different channels, so firms need to meet them wherever they are. But coming up with an omnichannel marketing strategy is a lot more complicated than just collecting cookies and tracking purchases. A new study that appears in a special issue of the Journal of Marketing in collaboration with the Marketing Science Institute explains why omnichannel is not a panacea.

There are three big challenges to making it work. Those challenges are outlined in the study, along with some solutions that include using machine learning and blockchain technology to harness the full benefits of omnichannel marketing. Wharton marketing professor Raghuram Iyengar is a co-author of the paper, titled “Informational Challenges in Omnichannel Marketing: Remedies and Future Research.” The other co-authors are: Tony Haitao Cui, marketing professor at the University of Minnesota’s Carlson School of Management; Anindya Ghose, marketing professor at New York University’s Stern School of Business; Hanna Halaburda, technology, operations and statistics professor also at NYU Stern; Koen Pauwels, marketing professor at Northeastern University’s D’Amore-McKim School of Business; S. Sriram, marketing professor at Michigan University’s Stephen M. Ross School of Business; Catherine Tucker, management and marketing professor at MIT Sloan School of Management; and Sriram Venkataraman, marketing professor at the University of North Carolina’s Kenan-Flagler Business School. 

Iyengar joined Knowledge@Wharton to talk about the findings. Listen to the full podcast at the top of this page or keep reading for an edited transcript of the conversation.

Knowledge@Wharton: Not only are firms trying to execute omnichannel marketing better, but researchers like you are trying to understand it better, even while the rapid evolution of technology makes that a moving target. What does this study add to the literature?

Raghuram Iyengar: Omnichannel certainly is a very hot topic. When companies are thinking about omnichannel, they sometimes want to think about distinguishing from multichannel. The big distinguishing aspect of it is multichannel has different ways in which you’re reaching the customer. Omnichannel is that as well, but it should be in synergy.

If you are, for example, a customer of REI, you might have a mobile application, you might have emails coming in. And if they are pursuing an omnichannel strategy, they are hoping that the customer is seeing different pieces of information in conjunction with each other and, in some sense, are complementary to each other.

Carrying that out is not that easy because you need to have a good sense of what the data is like — all the different touchpoints that the customer has had with REI or any other company — and then be able to execute it on the back end. Putting it all together is not as simple as it seems. ... ' 

Salesforce and Marketing AI

 Overview of Salesforce and Marketing AI

Using AI to Automate Marketing Processes Using Salesforce Tools

Salesforce relies heavily on AI technology to help companies dramatically expand their market share. By Diana Hope

Artificial intelligence has become incredibly important in the field of marketing. The massive applications of big data in the field of marketing is one of the reasons that the market for AI technology is growing at a rate of 39% a year.

In recent years, marketing automation has been a topic of utmost interest. It is one of the biggest gamechangers for the marketing profession brought on by AI.

How is AI Changing the Future of Marketing?

This magic tool promises to attract qualified leads to your product and increase revenue exponentially. But what lies behind this AI-driven technology? What does it offer its users? And how can companies implement this sought-after solution into their workflows?

Marketing automation is a form of artificial intelligence technology that automates repetitive marketing activities. It helps marketing and sales departments to run campaigns across various channels: email, text messaging, social networks, and websites. In addition, the platform provides an individual approach to each client, based on the data of their purchasing habits. This is why AI is particularly useful in the field of e-commerce.

There are various providers of marketing automation solutions that rely on complex advances in AI and machine learning. Products of these providers differ in scope, price, and the size and goals of user companies. In this article, we are going to take a closer look at one of the world’s largest platforms for marketing automation – Salesforce.

What is Salesforce Marketing Cloud and What AI Features Does It Bring?

Salesforce has a lot of AI features embedded in it. One of the biggest is Salesforce Einstein, which the company describes on this page:

“Bring the power of artificial intelligence to everyone with Salesforce Einstein.

Salesforce Einstein is a layer of intelligence within the Salesforce Platform that brings powerful AI technologies to everyone, right where they work. And with the Einstein Platform, admins and developers have a rich set of platform services to build smarter apps and customize AI for their businesses.” .... ' 

Use of Digital Humans Expand

 Recall our own experiments in the space.   Interesting to see these expanding ...... would like to see more data on how effective these are in varying contexts.     Are humans naturally warmer in these contexts

Cookie, Candy Companies Among Those Fielding Digital Humans in Marketing

May 20, 2021 

Ruth the Cookie Coach is a “digital human” incorporating AI to help Nestle connect to customers around its Toll House brand, offering recipes and support. (Credit: Nestle)

By AI Trends Staff

Ruth the Cookie Coach is a digital human being introduced by the Toll House brand of Nestle Global to provide baking assistance on a 24-7 basis, using an avatar incorporating AI that exhibits a degree of emotional intelligence, according to the company.

Ruth is named after the creator of the Nestle Toll House original chocolate chip cookie, Ruth Wakefield. Customers have the option to see, speak, and chat with Ruth while following the dynamic, on-screen content, according to an account on the website of Soul Machines,

The avatar is the culmination of two years of effort between Soul Machines, which offers a Human OS platform with a Digital Brain, and Nestle. The effort leveraged data from customer questions through the call center, social channels, multiple recipes across the web, and with the expertise of Nestle Corporate Pastry Chef Meredith Tomason.

Founded in 2016 in Auckland, New Zealand, Soul Machines has raised $65 million to date, according to Crunchbase. The company was spun out of the University of Auckland by Mark Sagar, CEO and Greg Cross, chief business officer. The company combines AI researchers, neuroscientists, psychologists and artists to create lifelike, emotionally responsive digital humans it calls Digital Heroes, with personality and character.   ... ' 

Signal Analytics and Baby Care

I see some of my former employers are involved.   Platform for predictive demand forecasting based on marketing decisions is a good thing. 

Signals Analytics Launches AI-Driven Intelligence Platform for Booming Baby Care Market  in PRNewswire

Platform allows baby care brands to leverage wide dataset of consumer and market connected insights in near real time.

NEW YORK, May 18, 2021 /PRNewswire/ -- Today, Signals Analytics, an AI-powered consumer and market intelligence platform, is adding Baby Care to its rapidly growing list of industry-specific offerings. Fresh off the heels of its successful Consumer Electronics and Apparel category launches earlier this year, the Baby Care expansion comes as demand for connected, external data and predictive analytics spikes in helping support cross-functional, insight-driven decision making.

Signals Analytics' new Baby Care solution offers a view into cross-market trends as well as category-specific insights for Baby Wipes & Diapers and Baby Personal Care, with a focus on Skin Care, Hair Care, and Body Hygiene - helping brands compete in this crowded sector. Pulling from over 13,000 external data sources -- spanning Instagram, Amazon, Target, and LexisNexis -- the platform will help fuel actionable go-to-market decisions in areas such as product innovation, pipeline prioritization, brand messaging, and marketing execution, to enable brand relevance and category share growth. 

Valued at $67 billion globally in 2020, the baby care market is expected to reach $88.7 billion by 2026, in part due to a rise in disposable parental income, older parental ages, and increased consumer awareness of baby hygiene. Historically, the market has been dominated by a few powerplayers, yet smaller, high-growth challenger brands are gaining momentum and market share. Ecommerce availability has leveled the playing field giving newer, more niche baby brands previously untapped exposure. With competition heating up and new products rolling out, it is imperative that brands have insight into what will drive new parents to purchase and understand the concerns they have top of mind. ... '