Leveraging artificial intelligence (AI) is now commonplace in marketing. Tools, platforms, and services put sophisticated audience targeting and segmentation tools at marketers’ fingertips, making it easier than ever to connect your products and services to customers.

As organizations grow more sophisticated in their adoption of AI, they can start to look beyond core uses cases and solutions such as the basic targeted advertising tools offered by Google and Facebook. Today, consistently finding new applications of data and smart algorithms is essential to building and maintaining a competitive advantage.

It’s important because if you’re simply using AI to do the same as everyone else then your results won’t differentiate you from your competitors in the eyes of potential customers. On top of this, core AI marketing tools (such as Adwords or Facebook advertising) are often described as “pay-to-win” – put simply, companies with bigger marketing budgets will generally get better results and take potential customers from smaller businesses. This is because they can afford to bid higher for the most important keywords.

Thinking smarter about alternative and emerging use cases for AI in marketing (or any business function) can help mitigate this. Here are a few ideas for getting ahead of the curve and ensuring your AI-driven marketing strategy is always evolving to become more efficient.

Intelligent advertising design

AI enables highly personalized design, meaning individual elements of advertising materials and marketing campaigns can be automatically tailored to specific audiences or even individuals. Individual elements of marketing campaigns – down to the style of design or the color schemes used – can be determined by algorithms to ensure they have the best likelihood of grabbing your audience’s attention and prompting further engagement. Algorithms can then assess the performance of different combinations of design elements and audiences and determine where tweaks could bring better results.

Thinking smarter about alternative and emerging use cases for AI in marketing (or any business function) can help mitigate this. Here are a few ideas for getting ahead of the curve and ensuring your AI-driven marketing strategy is always evolving to become more efficient.

Intelligent advertising design

AI enables highly personalized design, meaning individual elements of advertising materials and marketing campaigns can be automatically tailored to specific audiences or even individuals. Individual elements of marketing campaigns – down to the style of design or the color schemes used – can be determined by algorithms to ensure they have the best likelihood of grabbing your audience’s attention and prompting further engagement. Algorithms can then assess the performance of different combinations of design elements and audiences and determine where tweaks could bring better results.

Another powerful use case is trend analysis. Here AI can help you pick out changing habits and behaviors that might influence how your customers and potential customers are engaging with providers in your market. As well as your own visual messaging, you can more easily assess the effectiveness of your competitors’ campaigns and judge how customers react to different moods, color palettes, and scenery.

Image recognition (along with NLP as mentioned above) can also be used to create automated descriptions for sales copy, from pictures of items. Additionally, you can use it to protect your brand, by having it automatically alert you to anyone who might be misappropriating your creative IP, branding, or messaging for their own ends!

Try-before-you-buy with AR

Ikea lets customers view products in their own homes – to check how a new sofa or table might fit with their existing décor – by offering augmented reality (AR) tools that superimpose computer-generated graphics onto real-world images. Here, AI is used to create realistic-looking composite images, generally in real-time, as the user is looking through the camera on their phone. In the same manner, beauty brands such as L’Oreal let users try out make-up and other products and see how they will look on them, using the same technology.  While big players have been making this sort of functionality available to their customers for a while, it is increasingly offered “as-a-service” through platforms such as wearfits.com that make it usable by retailers of any size. 

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Marketing Analytics Enable Business Organizations to Transform

With an objective to evolve interminably, business organizations and companies deem marketing analytics as to the crucial slices in the realm of marketing as they are the primary drivers of successful markets. Marketing analytics promise to deliver more bottom-line impact. Especially, in times of uncertainty, which tends to inflict adversities on markets, market analytics assist to assess and evaluate the appropriate statuses of markets in order to pave ways for better planning and loss compensation.

With a paradigm shift in 2021, marketing analytics is turning virtual that comes with the backings of machine learning and artificial intelligence. It enables business organizations to improve their target advertisements and remarketing strategies to optimize their ads through advanced marketing attribution, thereby, increasing customer loyalty and customer retention.

Analytics Insight anticipates the ten best marketing analytics trends that can be leveraged for a business facelift.

The Best Marketing Analytics Trends

The Rise of Real-time Marketing Analytics

Action reciprocations in real-time is a soaring trend, induced by Covid. Business organizations and companies strive to revert back with solutions to customer queries in real-time. Organizations run low-latency customer data platforms to let marketers know about the current position and the success of their marketing campaigns and strategies.

Real-time marketing analytics also help marketers to detect underlying threats and problems. Underlying threat detection in a market is also known as SWOT analysis.

Emphasis on Data Security and Regulatory Compliance

Insulating market data against cybercrimes and cyber breaches is an important issue of address today. 2021 has witnessed a heightened increase in data breach cases that have also fractured the business infrastructures to degrees unimaginable. This has led marketers to invest more in technologies that facilitate encryption, access control, network monitoring, and physical security measures.

Customer Privacy and Data Handling

Protection of consumer and customer privacy is also imperative for marketers. To materialize consumer and customer privacy, marketers are deploying software by which users can opt-out, purging out data once a user has left a problem.

Accelerating Adoption of Predictive Analytics

Predictive analytics, as the name suggests, helps to anticipate future outcomes, based on the analyses of historical data of an organization. Predictive analyses are executed using software powered by machine learning. Predictive analytics encompasses a look-alike modeling structure, which identifies prospects that are likely to turn into high-value customers.

Enhanced Investment in First-Party Data

2020 marked the extinction of third-party cookies when Google announced the exodus of third-party cookies out of Chrome. Cookies are important to track customer behavior on a business website.

However, in order to make up for the loss, marketers turned to invest more in first-party data that also ensured low-friction tracking of customer and consumer behavior. First-party data are also termed as ‘cookies-less’ entities.

The Emergence of Contextual Customer Experience

With the fall of third-party cookies, contextual customer experience has gained prominence. Marketing analytics has become sensitive towards contextual customer experience.

In the practice of contextual customer experience, marketers are able to employ target messaging based on inferred attitudes of their customers and where are they in their customer journey.

Enhanced Reliance of Third-party Sources

Despite the fall of third-party cookies and the rise of first-party data, marketers will continue to invest in third-party sources that lay out a robust view of customers along with the augmentation of the first-party data they collect.

According to a study conducted by IAB and Winterberry Group in 2020, marketers in the U.S. spend over US$1.19 billion on third-party sources. The numbers have witnessed a steep rise in 2021.

Vehement Adoption of AI and ML

With a rise in AI and ML culture worldwide, marketers are also deploying artificial and machine learning for the refurbishment of their business infrastructures to make them suitable for a virtual world.

A Mix of Marketing and Analyst Roles

In a modern world driven by technology, a marketer performs more than just marketing. The paradigm shift demands marketers to possess analytical skills as well. The spotlight is always on the specialists who mix analytical and marketing skills in a balanced manner.

Investments in Inbound Marketing

The dramatic shift from in-person to work from home has made inbound marketing more prominent with its proven effectiveness. Inbound marketing has emerged to be instrumental in increasing brand awareness and trust-building through refocusing strategies that drive traffic to a website.

Marketers in 2021 are investing heavily in inbound marketing tactics to ensure the evolution of their businesses.

The post TOP 10 TRENDS IN MARKETING ANALYTICS TO LOOK OUT FOR IN 2021 appeared first on Artificial Intelligence.

Women have carved a niche for themselves in almost every field one could possibly think of. Business, finance, marketing, IT, law – you name it and you’ll find women doing a great job without any difficulty. However, data science is that one field that’s still a little unexplored by women. And considering the potential that women have, this field can see remarkable changes in the days ahead.

What’s been observed so far is that the data science industry is men-dominated and the number of females who’ve made a career in this field is relatively less. Almost all the data scientists out there are men and this itself raises questions as to why is that so when the skills required to become a data scientist are gender-neutral. Be it critical thinking, structured approach, creativity, intuition, analytical ability, problem-solving techniques, or any similar skill for that matter – all these have nothing to do with whether the person is a male or a female. Women too stand the potential to inculcate all of this and emerge out to be extremely credible data scientists. Then, why aren’t the number of women data scientists shooting up?

The fact that data science as a career isn’t something that many people are aware of which is why the number of data scientists across the globe is very less. Making people aware of the same will not only open the door of opportunities for people all around but also bring in more women to the field.

No wonder, there’s a staggering difference between the number of men and women in the field. A number of factors have contributed to this like the workplace culture, the level of confidence, lack of interest, no early exposure and inherent bias. Now is the time that women are made to understand what are the opportunities available for them, what will those opportunities involve, and what the quality of life looks like for someone in this role. Women have the potential to cater to business needs and with adequate knowledge of the same can help them achieve results like never before. Additionally, being exposed to the required skills well in advance makes it easy for them to achieve the desired objective.

Yes, there are many qualified women out there who fit the eligibility criteria just right but the hurdles don’t seem to stop. Gender diversity could be unbalanced based on the way an organization recruits for a position. Also, the fact that most of the recruitments rely on referrals to determine the top candidates cannot be overlooked. No wonder, it has been observed that teams with more or less equal number of men and women are more likely to be creative, share knowledge and finish tasks faster and efficiently when compared to teams of unequal genders.

However, a point worth noting is that closing the gender gap in this field of data science shouldn’t only be about reaching a certain ratio of women to men within the workplace. Even though recruitment is still an issue, the focus should be more about empowering women. It is now time that we change how the system functions with fewer women followed by steps such as informing women interested in or entering this field that skill proficiency isn’t based on gender.

With that being said, need of the hour is to bring awareness about this current situation, highlight the growth opportunities available, and bring the benefits of gender diversity to light. Well, better late than never!!

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Traditional marketing approaches for kitchen and bath showrooms have generally involved identifying market opportunities from trends such as neighborhoods that your showroom serves, customer demographics and a healthy dose of throwing spaghetti against the wall to identify what would stick. However, this approach, while at times effective, is extremely inefficient.

Marketing today has been transformed from trial-and-error to a more sophisticated and scientific approach using data analytics to identify new and underserved markets and to develop growth strategies. Analytics were initially used by businesses to determine the effectiveness of marketing and advertising campaigns, measuring the impact of their value propositions and calls-to-action. The effectiveness of those efforts expanded the use of analytics to enable businesses to:

• Better understand market size, potential customers, customer preferences and behaviors.
• Enhance customer experiences.
• Test new products/services.
• Improve marketing spend and return on investment.
• Optimize pricing strategies.

Most kitchen and bath showroom owners are likely to believe that analytics are something that only big corporations use. They may erroneously believe that they don’t have time, the resources or the skill sets needed to employ analytics to help develop business growth strategies. But nothing could be further from the truth. The bottom line is that companies that use data – not simply gut instincts or guesswork – are going to win. Trade-area analytics could well prove a critical factor in assuring business growth.

Data can assist in isolating targetable areas for remodeling activity at different price points. It can help pinpoint where you’ll receive the greatest return on your marketing spend. It can also help you develop more effective marketing messages tailored to a specific demographic and improve the look, feel and effectiveness of your showroom by helping to assure a consistency of image between marketing and sales efforts and showroom displays.

How Do Analytics Work?

Trade-area analytics identify and evaluate targeted demographics in a service area, usually within 30 minutes’ drive of a showroom’s location. The process examines demographics and consumer purchasing behavior in specific zip codes within a service area. Typical criteria used to evaluate a showroom’s service territory are number of homeowners with discretionary income, owner-occupied heads of households aged 35-64, number of houses built 20 or more years ago, level of remodeling activity in that neighborhood, and the percentage of homeowners likely to purchase kitchen cabinets or renovate their kitchens or baths.

What’s eye-opening for most showrooms that are aware of trade-area analytics is that demographic information for every neighborhood in the U.S. is readily available at no cost from the U.S. Census. Within the Census, you can search any demographic that you want by income level, zip code or other criteria. Simmons National Consumer Surveys can also assist in determining the level of kitchen and bath remodeling activity in specific zip codes and neighborhoods surveyed.

After performing a zip code analysis of a service territory, the next step is to create a map of the showroom’s job history for the past two to three years and compare that to the trade area analysis that shows neighborhoods with the highest sales and remodeling potential. Some 80% to 90% of the time, there’s a disconnect between where opportunity exists and where a showroom has been servicing, and that disconnect shines a spotlight on growth opportunities.

“I was surprised by the ability to take a map and pinpoint exactly what you want,” says BKBG member Danny McGeady, of JEM Designs, in Beavercreek, OH. “The analytics identified specific neighborhoods that are ripe for remodeling, primarily those featuring production homes built more than 10 years ago. From old listings, brochures and other data, we could accurately estimate the size and configuration of existing kitchens and the number and size of cabinets.”

Using that information, McGeady says, he developed a direct-mail campaign that features a new kitchen in the homeowners existing home and what it would cost. In contrast, past marketing efforts, McGeady notes, employed TV advertising to help build his brand.

Another reason to target specific neighborhoods using data for growth opportunities is the concept of “homophily,” which suggests that people who live next to one another share similar characteristics and circumstances, and thus can be drawn to use similar products and services. People who live in the same neighborhood not only consume similar goods and services, they share what they like, and their endorsements are the most trusted of all.

Neil Pfeister, of Englewood, CO-based Enchanted Kitchens, used the information identified in the data analytics of his service territory to develop and implement a drip marketing campaign that generated 400 leads in the last year, resulting in nine new projects.

“We never realized the depth of data that was readily available to us. Far and away, analytics have generated the best cost of leads and sales returns that we’ve ever had,” Pfeister says.

Justin Leatherman, of Leatherman Supply, in Goshen, IN, notes that “some of the zip code opportunities identified by our trade-area analytics shocked us. We were missing opportunities in a neighborhood directly across the street from our showroom.”

Leatherman not only is using trade-area analytics as a cornerstone to grow sales; he also plans to use them to identify locations for potential new showrooms.

Understanding the Market

Most showroom owners understand their market dynamics, but not many have a comprehensive grasp of the depth of their market. For example, they know how far a customer will likely travel to use their showroom and they have a fairly good idea of the cost of homes in their service area, but what they don’t often understand is the number of homeowners that are potential customers and the potential volume of business in their market. Data analytics provides that information.

The vast majority of kitchen and bath showrooms do what they do because they’ve been doing the same thing forever. They may almost exclusively serve a specific price-point customer. But if you limit your sights only on the customers and demographics that you currently serve, you may be missing 35% to 40% of your market. Trade-area analytics identify that 35% to 40% of market potential most showrooms never know about.

Big data is not the exclusive province of big businesses. There is nothing to prevent a showroom of any size from using trade-area analytics to identify new and underserved markets in their own backyards. Best of all, the data you need to make better decisions and develop more effective growth strategies is available in a few clicks at absolutely no cost. 

Tom Cohn is the exec. v.p. of the Bath & Kitchen Business Group, a Bethesda, MD-based, shareholder-owned organization of independent kitchen and bath design firms. He has more than two decades of experience providing growth strategies for kitchen and bath and decorative plumbing and hardware showrooms.

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• AI and Machine Learning are on track to generate between $1.4 Trillion to $2.6 Trillion in value by solving Marketing and Sales problems over the next three years, according to the McKinsey Global Institute. 
• Marketers’ use of AI soared between 2018 and 2020, jumping from 29% in 2018 to 84% in 2020, according to Salesforce Research’s most recent State of Marketing Study. 
• AI, Machine Learning, marketing & advertising technologies, voice/chat/digital assistants and mobile tech & apps are the five technologies that will have the greatest impact on the future of marketing, according to Drift’s 2020 Marketing Leadership Benchmark Report.

Chief Marketing Officers (CMOs) and the marketing teams they lead are expected to excel at creating customer trust, a brand that exudes empathy and data-driven strategies that deliver results. Personalizing channel experiences at scale works when CMOs strike the perfect balance between their jobs’ emotional and logical, data-driven parts. That’s what makes being a CMO today so challenging. They’ve got to have the compassion of a Captain Kirk and the cold, hard logic of a Dr. Spock and know when to use each skill set. CMOs and their teams struggle to keep the emotional and logical parts of their jobs in balance. Machine learning apps and platforms are helping to acheive goals in both side of their roles.

Asked how her team keeps them in balance, the CMO of an enterprise software company told me she always leads with empathy, safety and security for customers and results follow. “Throughout the pandemic, our message to our customers is that their health and safety come first and we’ll provide additional services at no charge if they need it.” True to her word, the company offered their latest cybersecurity release update to all customers free in 2020.  AI and machine learning tools help her and her team test, learn and excel iteratively to create an empathic brand that delivers results.

The following are ten ways AI and machine learning are improving marketing in 2021:

1.    70% of high-performance marketing teams claim they have a fully defined AI strategy versus 35% of their under-performing peer marketing team counterparts. CMOs who lead high-performance marketing teams place a high value on continually learning and embracing a growth mindset, as evidenced by 56% of them planning to use AI and machine learning over the next year. Choosing to put in the work needed to develop new AI and machine learning skills pays off with improved social marketing performance and greater precision with marketing analytics. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

2.    36% of marketers predict AI will have a significant impact on marketing performance this year. 32% of marketers and agency professionals were using AI to create ads, including digital banners, social media posts and digital out-of-home ads, according to a recent study by Advertiser Perceptions. Source: Which Emerging Tech Do Marketers Think Will Most Impact Strategy This Year?, Marketing Charts, January 5, 2021.

3.    High-performing marketing teams are averaging seven different uses of AI and machine learning today and just over half (52%) plan on increasing their adoption this year. High-performing marketing teams and the CMOs lead them to invest in AI and machine learning to improve customer segmentation. They’re also focused on personalizing individual channel experiences. The following graphic underscores how quickly high-performing marketing teams learn then adopt advanced AI and machine learning techniques to their competitive advantage. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

4.    Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders. Combining all available data sources, including customer sentiment analysis using supervised machine learning algorithms, it’s possible to improve demand sensing and demand forecast accuracy. ML algorithms can correlate location-specific sentiment for a given product or brand and a given product’s regional availability. Having this insight alone can save the retail industry up to $50B a year in obsoleted inventory.  Source: AI can help retailers understand the consumer, Phys.org. January 14, 2019.

5.    Disney is applying AI modeling techniques, including machine learning algorithms, to fine-tune and optimize its media mix model. Disney’s approach to gaining new insights into its media mix model is to aggregate data from across the organization including partners, prepare the model data and then transform it for use in a model. Next, a variety of models are used to achieve budget and media mix optimization. Then compare scenarios. The result is a series of insights that are presented to senior management. The following dashboard shows the structure of how they analyze AI-based data internally. The data shown is, for example only; this does not reflect Disney’s actual operations.   Source: How Disney uses Tableau to visualize its media mix model (https://www.tableau.com/best-marketing-dashboards)

6.    41% of marketers say that AI and machine learning make their greatest contributions to accelerating revenue growth and improving performance. Marketers say that getting more actionable insights from marketing data (40%) and creating personalized consumer experiences at scale (38%) round out the top three uses today. The study also found that most marketers, 77%, have less than a quarter of all marketing tasks intelligently automated and 18% say they haven’t intelligently automated any tasks at all. Marketers need to look to AI and machine learning to automated remote, routine tasks to free up more time to create new campaigns. Source: Drift and Marketing Artificial Intelligence Institute, 2021 State of Marketing AI Report.

7.    Starbucks set the ambitious goal of being the world’s most personalized brand by relying on predictive analytics and machine learning to create a real-time personalization experience. The global coffee chain faced several challenges starting with how difficult it was to target individual customers with their existing IT infrastructure. They were also heavily reliant on manual operations across their thousands of stores, which made personalization at scale a formidable challenge to overcome. Starbucks created a real-time personalization engine that integrated with customers’ account information, the mobile app, customer preferences, 3^rd party data and contextual data. They achieved a 150% increase in user interaction using predictive analytics and AI, a 3X improvement in per-customer net incremental revenues. The following is a diagram of how DigitalBCG (Boston Consulting Group) was able to assist them. Source: Becoming The World’s Most Personalized Brand, DigitalBCG. 

8.    Getting personalization-at-scale right starts with a unified Customer Data Platform (CDP) that can use machine learning algorithms to discover new customer data patterns and “learn” over time.  For high-achieving marketing organizations, achieving personalization-at-scale is their highest and most urgent priority based on Salesforce Research’s most recent State of Marketing survey. And McKinsey predicts personalization-at-scale can create $1.7 trillion to $3 trillion in new value. For marketers to capture a part of this value, changes to the mar-tech stack (shown below) must be supported by clear accountability and ownership of channel and customer results. Combining a modified mar-tech stack with clear accountability delivers results.   Source: McKinsey & Company, A technology blueprint for personalization at scale. May 20, 2019. By Sean Flavin and Jason Heller.

9.    Campaign management, mobile app technology and testing/optimization are the leading three plans for a B2C company’s personalization technologies. Just 19% of enterprises have adopted AI and machine learning for B2C personalization today. The Forrester Study commissioned by IBM also found that 55% of enterprises believe the technology limitations inhibit their ability to execute personalization strategies. Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

10. Successful AI-driven personalization strategies deliver results beyond marketing, delivering strong results enterprise-wide, including lifting sales revenue, Net Promoter Scores and customer retention rates. When personalization-at-scale is done right, enterprises achieve a net 5.63% increase in sales revenue, 10.26% increase in order frequency, uplifts in average order value and an impressive 13.25% improvement in cross-sell/up-sell opportunities. The benefits transcend marketing alone and drive higher customer satisfaction metrics as well.   Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

CMOs and their teams rely on AI and machine learning to iteratively test and improve every aspect of their marketing campaigns and strategies. Striking the perfect balance between empathy and data-driven results takes a new level of data quality which isn’t possible to achieve using Microsoft Excel or personal productivity tools today. The most popular use of AI and machine learning in organizations is delivering personalization at scale across all digital channels. There’s also increasing adoption of predictive analytics based on machine learning to fine-tune propensity models to improve up-sell and cross-sell results. 

The post 10 Ways AI And Machine Learning Are Improving Marketing In 2021 appeared first on Artificial Intelligence.

Speaking at DIGIT’s inaugural MarTech Virtual Summit, Daniel Winterstein, CTO at Good-Loop, discussed the ‘evolution’ of creative AI, and how the tech can aid your marketing strategy.

In recent years we have seen a series of breakthroughs in the capabilities of artificial intelligence (AI) technology.

We are now adopting AI tech across industries worldwide and using it to power thousands of products in our lives, including computers, cars and even our kettles.

Business leaders, too, are beginning to see the value of AI integration within their organisations.

Research carried out in 2019 by Accenture revealed that that four out of five UK executives understand the need to scale AI across their firm to survive and remain competitive.

But how can AI be utilised for marketing purposes? In his talk at DIGIT’s inaugural MarTech Virtual Summit, Good-Loop CTO Daniel Winterstein covered a “realistic look” at what AI can do today in advertising.

Touching on the power of “unlocking smart personalisation and dynamic creation,” Winterstein also discussed the problems with AI and the creation of ‘bland’ advertising.

AI can allow marketers to create ‘on the fly’ and personalise campaigns for users. “What could possibly go wrong with that?” Winterstein asked.

What can AI do?

Using examples of historic versus current marketing logos and products, Winterstein suggested that the move to more modern advertising means that marketing campaigns and products have “lost a lot of identity”.

Winterstein identified that identity loss has been happening across advertising and marketing, driven by market testing and a push towards the mainstream; something which could be now accelerated by AI.

But to understand how AI can impact your marketing, you must first understand what AI itself is. Winterstein said that AI can cover a lot of areas, and means different things to different people.

“There are deep learning neural networks behind much of the cutting-edge AI. A lot of practical AI, though, is often driven by simplest things like flowcharts,” he said.

AI can also mean automation. Tasks that were once carried out in factories are now being done in offices around email and signup flows. However, Winterstein said that AI might not always be as it seems.

He commented: “It can sometimes mean people in disguise. More than once, a service that has been presented as AI has actually been powered at the back end by humans. And it can be a buzzword. Sometimes something masquerading as AI is just common software wearing shiny clothes.”

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Despite these potential issues, he said that all the tasks that AI can carry out are valuable and “have their place in our businesses”.

Current tools using AI software to generate images are useful for marketing purposes. While searching for or creating an image to use for marketing, you would have to consider copyright and privacy concerns. In the case of AI-generated images, this is not the case.

Winterstein said: “These tools have the ability for you to set up photos that have never existed. You can do Photoshop from your office computer [to produce these images].

“This technology is getting better year on year; it’s hard to say where the limits are. I am not sure if there are limits.”

Don’t be bland, and consider the ethics

However, Winterstein brought the talk back to identity loss, and the more common issue of marketers simply using AI to create blank advertising simply because it ‘works’.

“When people start looking at deploying AI in brand-safe spaces, there will be this push for bland, because it’s safe,” he said.

“Just because we have these tools which can create and personalise, it doesn’t mean that they are always giving value. Personalisation often isn’t actually that personal.”

So how can we avoid the creation of bland marketing campaigns? Winterstein suggests that, by injecting purpose and identity into the heart of your campaign, purpose-driven AI can deliver real results.

However, Winterstein also raised an important point on the ethical issues surrounding AI, mainly that every AI project should implement an ethics plan. Failing to do so can have a hugely negative impact on any project.

“If you roll out an AI without thinking about what you’re doing and what can go wrong, then it is all too easy to have unintended consequences, like systems which are sexist or systems that are racist,” he said.

Winterstein’s talk ended with a comment from AI software on the importance of ethics and the consideration of the purpose of your marketing campaign: “Design is a way to communicate your values to your audience. In order to do that, you have to really know who your audience is and what they care about.

“Brand purpose is the single most important way to drive the identity of your brand. The best brands are the ones that aren’t just trying to win customers but are also trying to make the world a better place.

“AI projects should be purposeful by design. One of the most important things to remember about projects that involve AI is that they should be inherently useful and help you achieve something that you couldn’t otherwise achieve without.”

The post MarTech 2021 | Enhancing a Marketing Strategy with Artificial Intelligence appeared first on Artificial Intelligence.

• AI and Machine Learning are on track to generate between $1.4 Trillion to $2.6 Trillion in value by solving Marketing and Sales problems over the next three years, according to the McKinsey Global Institute. 
• Marketers’ use of AI soared between 2018 and 2020, jumping from 29% in 2018 to 84% in 2020, according to Salesforce Research’s most recent State of Marketing Study. 
• AI, Machine Learning, marketing & advertising technologies, voice/chat/digital assistants and mobile tech & apps are the five technologies that will have the greatest impact on the future of marketing, according to Drift’s 2020 Marketing Leadership Benchmark Report.

Chief Marketing Officers (CMOs) and the marketing teams they lead are expected to excel at creating customer trust, a brand that exudes empathy and data-driven strategies that deliver results. Personalizing channel experiences at scale works when CMOs strike the perfect balance between their jobs’ emotional and logical, data-driven parts. That’s what makes being a CMO today so challenging. They’ve got to have the compassion of a Captain Kirk and the cold, hard logic of a Dr. Spock and know when to use each skill set. CMOs and their teams struggle to keep the emotional and logical parts of their jobs in balance.

Asked how her team keeps them in balance, the CMO of an enterprise software company told me she always leads with empathy, safety and security for customers and results follow. “Throughout the pandemic, our message to our customers is that their health and safety come first and we’ll provide additional services at no charge if they need it.” True to her word, the company offered their latest cybersecurity release update to all customers free in 2020.  AI and machine learning tools help her and her team test, learn and excel iteratively to create an empathic brand that delivers results.

The following are ten ways AI and machine learning are improving marketing in 2021:

1.    70% of high-performance marketing teams claim they have a fully defined AI strategy versus 35% of their under-performing peer marketing team counterparts. CMOs who lead high-performance marketing teams place a high value on continually learning and embracing a growth mindset, as evidenced by 56% of them planning to use AI and machine learning over the next year. Choosing to put in the work needed to develop new AI and machine learning skills pays off with improved social marketing performance and greater precision with marketing analytics. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

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2.    36% of marketers predict AI will have a significant impact on marketing performance this year. 32% of marketers and agency professionals were using AI to create ads, including digital banners, social media posts and digital out-of-home ads, according to a recent study by Advertiser Perceptions. Source: Which Emerging Tech Do Marketers Think Will Most Impact Strategy This Year?, Marketing Charts, January 5, 2021.

3.    High-performing marketing teams are averaging seven different uses of AI and machine learning today and just over half (52%) plan on increasing their adoption this year. High-performing marketing teams and the CMOs lead them to invest in AI and machine learning to improve customer segmentation. They’re also focused on personalizing individual channel experiences. The following graphic underscores how quickly high-performing marketing teams learn then adopt advanced AI and machine learning techniques to their competitive advantage. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

4.    Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders. Combining all available data sources, including customer sentiment analysis using supervised machine learning algorithms, it’s possible to improve demand sensing and demand forecast accuracy. ML algorithms can correlate location-specific sentiment for a given product or brand and a given product’s regional availability. Having this insight alone can save the retail industry up to $50B a year in obsoleted inventory.  Source: AI can help retailers understand the consumer, Phys.org. January 14, 2019.

5.    Disney is applying AI modeling techniques, including machine learning algorithms, to fine-tune and optimize its media mix model. Disney’s approach to gaining new insights into its media mix model is to aggregate data from across the organization including partners, prepare the model data and then transform it for use in a model. Next, a variety of models are used to achieve budget and media mix optimization. Then compare scenarios. The result is a series of insights that are presented to senior management. The following dashboard shows the structure of how they analyze AI-based data internally. The data shown is, for example only; this does not reflect Disney’s actual operations.   Source: How Disney uses Tableau to visualize its media mix model (https://www.tableau.com/best-marketing-dashboards)

6.    41% of marketers say that AI and machine learning make their greatest contributions to accelerating revenue growth and improving performance. Marketers say that getting more actionable insights from marketing data (40%) and creating personalized consumer experiences at scale (38%) round out the top three uses today. The study also found that most marketers, 77%, have less than a quarter of all marketing tasks intelligently automated and 18% say they haven’t intelligently automated any tasks at all. Marketers need to look to AI and machine learning to automated remote, routine tasks to free up more time to create new campaigns. Source: Drift and Marketing Artificial Intelligence Institute, 2021 State of Marketing AI Report.

7.    Starbucks set the ambitious goal of being the world’s most personalized brand by relying on predictive analytics and machine learning to create a real-time personalization experience. The global coffee chain faced several challenges starting with how difficult it was to target individual customers with their existing IT infrastructure. They were also heavily reliant on manual operations across their thousands of stores, which made personalization at scale a formidable challenge to overcome. Starbucks created a real-time personalization engine that integrated with customers’ account information, the mobile app, customer preferences, 3^rd party data and contextual data. They achieved a 150% increase in user interaction using predictive analytics and AI, a 3X improvement in per-customer net incremental revenues. The following is a diagram of how DigitalBCG (Boston Consulting Group) was able to assist them. Source: Becoming The World’s Most Personalized Brand, DigitalBCG.  

8.    Getting personalization-at-scale right starts with a unified Customer Data Platform (CDP) that can use machine learning algorithms to discover new customer data patterns and “learn” over time.  For high-achieving marketing organizations, achieving personalization-at-scale is their highest and most urgent priority based on Salesforce Research’s most recent State of Marketing survey. And McKinsey predicts personalization-at-scale can create $1.7 trillion to $3 trillion in new value. For marketers to capture a part of this value, changes to the mar-tech stack (shown below) must be supported by clear accountability and ownership of channel and customer results. Combining a modified mar-tech stack with clear accountability delivers results.   Source: McKinsey & Company, A technology blueprint for personalization at scale. May 20, 2019. By Sean Flavin and Jason Heller.

9.    Campaign management, mobile app technology and testing/optimization are the leading three plans for a B2C company’s personalization technologies. Just 19% of enterprises have adopted AI and machine learning for B2C personalization today. The Forrester Study commissioned by IBM also found that 55% of enterprises believe the technology limitations inhibit their ability to execute personalization strategies. Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

10. Successful AI-driven personalization strategies deliver results beyond marketing, delivering strong results enterprise-wide, including lifting sales revenue, Net Promoter Scores and customer retention rates. When personalization-at-scale is done right, enterprises achieve a net 5.63% increase in sales revenue, 10.26% increase in order frequency, uplifts in average order value and an impressive 13.25% improvement in cross-sell/up-sell opportunities. The benefits transcend marketing alone and drive higher customer satisfaction metrics as well.   Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

CMOs and their teams rely on AI and machine learning to iteratively test and improve every aspect of their marketing campaigns and strategies. Striking the perfect balance between empathy and data-driven results takes a new level of data quality which isn’t possible to achieve using Microsoft Excel or personal productivity tools today. The most popular use of AI and machine learning in organizations is delivering personalization at scale across all digital channels. There’s also increasing adoption of predictive analytics based on machine learning to fine-tune propensity models to improve up-sell and cross-sell results. 

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Anyone looking for evidence that insurance advertisements have become a mainstay of popular culture need look no further than Geico’s YouTube channel, which currently boasts 1.88 million subscribers and is home to videos with tens of millions of views. Geico’s competitors might not have as robust a presence on the platform, but their brand representatives (Jake from State Farm, Allstate’s Mayhem, Progressive’s Flo) are as familiar to the public as the Geico Gecko.

For these insurance companies, spending vast amounts of money on advertising on both traditional and digital channels is a must. However, having such universal brand recognition also makes it difficult for brands to know whether a particular person requires more exposure to advertising messaging in order to convert or whether that person has already been shown sufficient messaging to make a decision.

At the moment, most insurance companies are erring on the side of caution, preferring to inundate audiences with additional digital advertising instead of strategically focusing their spending on the people with whom it would make the most difference. With deep learning, insurance companies can drive down the costs of customer acquisition while improving the effectiveness of their advertising, enabling them to win away more new customers in a highly competitive market.

Optimizing digital advertising

What factors might induce someone to choose one insurance provider over another? The amount of the quote and the quality of the services provided absolutely play a part, but in the crowded world of insurance, where individuals have a multitude of companies to choose from, each of which offers a relatively similar roster of products for more or less the same price point, advertising and branding can play a crucial role in swaying someone to choose one provider over another.

Insurance companies understand this, hence the plethora of witty, exciting, and just plain weird ads that inundate the airwaves regularly. But what they lack is the ability to tell which type of person is more likely to pick their product over another. Considering that anybody possessing a home, car, motorcycle, etc. is a potential target for an insurance provider, being able to hone in on the specific traits that differentiate a future Progressive customer from an Allstate devotee is an incredibly valuable skill that helps brands save money while improving the likelihood of a successful conversion.

Thanks to technological advances, insurance companies can now recruit deep learning’s analytical capabilities — its ability to find discrete patterns within customer data that might hitherto have remained undetected by human marketers — to optimize their digital advertising and targeting. Deep learning allows insurance companies to differentiate between people who are already customers, people who are not yet customers but are amenable to conversion, and people who will not be swayed regardless of how many Limu Emu ads they are shown, thus enabling brands to focus their advertising spend on the most likely prospects and avoid wasting money on fruitless pursuits.

In other words, insurance providers can use deep learning to focus their advertising on the types of people who are most likely to be attracted by a brand’s unique proposition, whether this happens to be the particular value an insurance company is able to offer or the brand ethos the company has carefully cultivated.

Insurance providers can enlist a deep learning-enabled algorithm to examine all of the existing customer data they have on hand and combine it with available third-party data like demographics. From there, the algorithm will find similarities between existing customers, then identify people in the general population whose characteristics mark them out as good candidates for conversion. The benefit of relying on deep learning to carry out this search, as opposed to more manual marketing methods, is that the deep learning algorithm is not limited to a single understanding of what a customer looks like; instead, the algorithm is capable of identifying infinite combinations of characteristics that might distinguish someone as a potential customer. In some cases, what the algorithm identifies as a valuable audience cluster might run counter to what marketers believe to be their core consumer base, thus presenting brands with the opportunity to reach a previously untapped audience.

A solution for common insurer problems

As has already been mentioned, deep learning offers insurance companies the ability to hone in on those who are still on the fence and figure out the best way to convert them. Cognitiv’s research has found that the implementation of deep learning, in addition to delivering higher rates of incremental lift than other solutions currently on the market, is also capable of increasing conversion rates and improving ROI on incremental customers. For the insurance industry in particular, which has long struggled with incrementality and cost of acquisition, the existence of such a solution will help providers map out more effective, sophisticated marketing strategies that focus on reaching the right audiences in the manner most likely to lead to an efficient conversion.

Insurers spend so much money on advertising, often without the assurance that their blanket of ads is having the desired effect of swaying undecided insurance seekers. Many companies have tried for years without success to accurately measure incrementality and attain incremental lift on the scale they require. Deep learning finally offers a solution to those problems. By training a deep learning algorithm on insurance providers’ own first-party data, marketers can gain a better understanding of what their customers look like, and target others with similar characteristics while avoiding sending ads to existing customers or people who have already made their mind up. Diehard Geico fans need not fear, though: the Geico YouTube channel will always be there when they need it.

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“We want to expand what Vodafone stands for in consumers; minds,” says Pamela Brown, chief marketing officer, Vodafone Smart Tech. “It’s a significant step because we’re traditionally a mobile phone company.”

Despite years spent dabbling in the Internet of Things (IoT) sector, she admits there is a lot of ground to make up in brand perception. “I’d love this to have higher awareness than it does today. Analysts talk about these massive numbers like there will be 41bn smart devices by 2027. This is significant growth, and I believe Vodafone has a role to play in it.”

Last week, Vodafone introduced the first of its ‘Designed & Connected by Vodafone’ range of smart tech products – Curve, a multi-tracking device that uses GPS, Wi-Fi, Cellular and Bluetooth to help its users to track inanimate valuables such as keys, laptops, bags, as well as keeping connected to loved ones or pets.

It’s the kind of brand extension Vodafone has been gravitating towards for some time.

The company first hit the telecoms scene in the early 90s, as a mobile network provider, and with the proliferation of the internet, it soon got into broadband. Like its mobile competitor EE, Vodafone recognises ‘connectivity’ as its selling point. However, while EE was busy with 5G deals at the Baftas or its sponsorship of Wembley stadium, Vodafone was diving into IoT.

“From the B2B perspective, we’ve been offering smart IoT connections for over 10 years. We have 15 million connections on that side,” shares Brown. And then in 2017, it entered the consumer market with the launch of ‘V by Vodafone’ – billed as a simple system for consumers to connect and manage IoT devices, alongside a product range that included a car dongle, a 4G security camera and a pet location and activity tracker.

The problem is that the term IoT belongs more in a B2B book of buzzwords than in the consumer vocabulary. “Consumers don’t know what IoT is,” Brown says. “But they do know what smart tech is. We learnt a lot with the V by Vodafone range. The category is more embedded in consumers’ mindsets, especially with the explosion of smart speakers.”

Brought on more than two years ago from the team that built Hive, British Gas’ smart home offering, Brown knows a thing or two about changing consumer habits and how to convince people to get on board. She has since pulled the brand out of the ‘IoT space’, as such, and hired a creative director, created an in-house studio and given the brand a full refurb that both aligns with Vodafone’s distinctive red, but also distinguishes itself apart from that side of the business.

“When you think of Vodafone, you probably think of a very red brand. Whereas the new visual identity is a more darker colour palette. We’re trying to bring to life more of the desirability credentials of the device in the imagery and the marketing assets that we create. So we’re using red in a more subtle way.”

The first to be introduced this year, Curve will be followed by two more products which will utilise the same functionalities.

Beyond that, where does Brown expect Vodafone to be in five years’ time? “I hope these products will become every day to people. So whether that’s in-home devices or out of home, we want to use connectivity to help protect people and things that they love.”

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They’re called salespeople, but they spend shockingly little of their time selling. Instead, their days consist of administrative work, manual data entry, looking for potential customers and communicating with those prospects: cold calling, sending emails, scheduling meetings and having conversations — many of which lead to nothing.

This is both time-consuming and expensive for companies. Lost sales productivity and wasted marketing budgets cost organizations $1 trillion dollars a year, according to industry estimates.

Things are starting to shift, however. New tools, including ones that incorporate artificial intelligence (AI) — mostly machine learning and natural language processing — are changing the ways in which salespeople do their day-to-day jobs.

For all the talk about how AI is revolutionizing the business-to-business (B2B) sales process, its biggest advantage is more mundane: It is helping salespeople better manage their time.

AI is a transformative technology. Its power lies in its ability to extract, parse and analyze massive amounts of data almost instantly. In a sales context, AI-enabled tools help B2B marketers gain a more nuanced understanding of their customers’ wants and needs and improve efficiencies like never before.

According to research from McKinsey & Company, organizations that use AI in sales cite an increase in leads and appointments of more than 50per cent, cost reductions of 40–60 percent and call time reductions of 60–70 percent.

Interestingly, the most innovative tools are also the most practical. Today’s AI-powered software does everything from write emails to schedule meetings to detect sales behavior. This helps salespeople identify viable leads and close more deals.

Take, for instance, chatbots. Chatbots use AI to conduct conversations via video, audio or text. Chatbots begin the sales process by asking customers basic questions related to their role, industry and company size.

These low-level questions weed out those who may not meet the minimum qualifications of a company’s prospect profile. No salesperson wants to waste time in a conversation that’s not going anywhere.

Chatbots and lead scoring machine learning tools further improve and enhance the sales process. Lead scoring determines the worthiness of potential customers by attaching values to them based on their interest in a given set of products or services.

Scores not only help salespeople spot potential customers more quickly, they also help salespeople customize their pitches by mapping out an individual prospect’s needs.

That’s not all. Virtual sales assistants and chatbots can arrange meetings and calls, which drastically reduce the amount of back-and-forth scheduling details that often swamp salespeople.

Intelligent predictive engagement tools are another illustration. These AI-powered tools remind salespeople to reach out to prospective customers and even recommend what to say during that follow-up call or email. After all, the key to any follow-up with a client is relevant and meaningful content.

Finally, there’s process automation. Automation, along with predictive analytics, helps identify relationships between pieces of data. This allows salespeople to develop a deeper understanding of customer behavior and helps them forecast future sales more accurately.

Prescriptive analytics, meanwhile, helps salespeople navigate the sales process by uncovering the best path to value for customers, according to a recent research report by Gartner. Put simply, it enables salespeople to adapt and customize their product recommendations based on their customers’ individual needs.

These AI-powered tools are geared at improving sales productivity. They eliminate tedious busywork, such as logging calls or taking notes, and allow salespeople to focus on the essential parts of their job.

Importantly, they empower salespeople to engage with customers who are most apt to buy their products and services.

To be sure, just because these new tools exist does not mean that salespeople will embrace them. Case in point: A large number of customer relationship management (CRM) implementations fail because salespeople do not want to use their time doing data entry. (They, understandably, prefer to spend their time selling.)

However, these data inputs are vital to helping salespeople derive value from CRM by generating the kinds of insights necessary to improve their product’s attractiveness in the market and increase sales. To realize the value of CRM systems, salespeople need to be persuaded that adopting them is worthwhile.

There’s the rub: Given the biggest constraint for salespeople is the availability of “precious selling time,” organizations need to show salespeople that AI implementations can help reallocate their time toward selling. This provides the best opportunity to prevent AI tools from going the way of CRM systems.

But it’s likely that salespeople will need convincing. The value proposition is clear: Adopting AI tools will give them their time back.

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