Can you define Marketing AI?
Or, do you think Marketing AI is just another overhyped, technology buzzword?
The AI Reality:
You already use artificial intelligence everyday without realizing it!
Without calling attention to the technology behind the services they deliver, Facebook, Google, Netflix, and Amazon have integrated elements of AI into their offerings. This includes:
- Image recognition,
- Search results,
- Chatbots and
- Related advertising and product offerings.
More importantly, did you know:
Many organizations already have started to automate repetitive functions using Marketing AI to achieve business goals?
Don’t ask HAL 9000 from 2001: A Space Odyssey or C-3PO and R2-D2 from Star Wars to help you. Since they don’t do marketing!
Get up to speed on marketing artificial intelligence. At least, learn enough to apply it to your business.
Marketing AI: What You Need To Know
Marketing AI Defined
Every marketer faces these 5 key issues:
- How do we find optimal prospects to buy my offering?
- Which are the most effective platforms and channels to reach this audience and their purchase influencers?
- When do we deliver personalized messages to this audience and in what sequence?
- What content should these targeted messages contain, in what content format and via what devices should they be delivered?
- How do we provide the optimal customer experience to convert and retain customers?
Artificial intelligence can help you answer these questions and provide better results.
Marketing AI Definition:
At its core marketing artificial intelligence uses algorithms or a set of instructions. Based on this set of rules, the machine learns from data and past actions.
In the process the machine becomes smarter faster by understanding its mistakes and correcting them. This allows the machine to create its own algorithms that determine new paths and unlock unlimited potential.
Artificial intelligence consists of 3 main categories:
- Machine Learning Techniques use algorithms to learn from historical data sets. The machine then creates propensity models.
- Applied Propensity Models use propensity models to predict specific events or outcomes. For example, lead scoring based on likelihood to convert.
- AI applications perform tasks people currently do. For example, chatbots respond to customer questions.
BUT –To achieve success Marketing AI depends on having a sufficiently large and well defined data set. Otherwise, bad data yields bad results.
How Experts Define Marketing AI
You may be surprised to discover that in January 2015, founder and CEO of Salesforce.com, Marc Benioff, said:
“We’re in an AI spring. …[T]he revolution in data science will fundamentally change how we run our business because we’re going to have computers aiding us in how we’re interacting with our customers.
By contrast, Google owned DeepMind’s Demis Hassabis uses this short definition:
“AI is the science of making machines smarter.
Further Hassabis explains:
Specifically, AI expands human knowledge and capabilities. Because, as it works, AI develops programs that can learn to solve any complex problem without needing to be taught how.
In the process, AI becomes a multiplier for human ingenuity. As a result, it has the potential ability and applications to optimize tasks that would otherwise be overwhelmingly complex.
The State of Marketing AI
Artificial Intelligence and The Gartner Hype Cycle
Based on Gartner’s 2018 Hype Cycle for Marketing and Advertising:
Underlying AI techniques have entered Gartner’s Peak of Inflated Expectations phase. This includes natural-language processing (or NLP) which drives innovations such as conversational marketing technologies,
The Top 5 Marketing AI Types from Gartner’s 2017 Research include:
- Conversational experiences improve using text and voice.
- Real-time personalization expand based on context, intent and customer stage.
- Identity resolution help find target audiences better and faster.
- Marketing orchestration connects communications during the buyer journey.
- Augmented marketing analytics improve and predict results better.
Current Marketing Artificial Intelligence Reality
The adoption of AI among marketers increased from 43% in 2016 to 88% in 2019 according to Forrester.
But marketers using AI continued to experience similar process and MarTech stack complexities as they did before adopting AI.
Because marketers substituted one technology for another without taking advantage of AI’s ability to continuously learn and improve.
So–how do your marketing AI challenges stack up?
The top marketing challenges included:
- Wasted marketing spend (30% of respondents agree),
- Inability to operate quickly enough (30% of respondents agree),
- Hard to hire, retain and organize staff (28% of respondents agree),
- Duplicate technology and/or vendors (28% of respondents agree),
- Lack of technology integration(26% of respondents agree), and
- Difficult to translate insights into actionable outcomes (26% of respondents agree).
2030 Artificial Intelligence Projections
By 2030 artificial intelligence will deliver $13 trillion of global economic activity. This translates to an additional 1.2% of annual GDP growth over the next 10 years according to McKinsey Global Institute Report (2018).
Artificial intelligence has the ability to create value across industries (as shown McKinsey’s scatter chart.)
Across businesses AI will have the biggest impact in marketing and sales followed by supply chain management and manufacturing.
Economic growth due to AI adoption won’t be linear! Because the advantage of being an early adopter will have a multiplier effect.
Specifically, early AI adopters:
- Have stronger IT departments and infrastructure.
- Have a higher propensity to invest in AI and related technologies, and
- View the business case for AI as positive.
As a result:
AI forerunners could double their cash flow. This will yield an additional 6% in annual net cash flow growth for 10+ years. (See chart below.)
Late or non AI adopters could experience roughly a 20% decline in cash flow.
In the process, competitive profit pressure may shift market share to leading AI businesses. (McKinsey)
Marketing Models: How To Apply Artificial Intelligence To Your Business
Marketing AI can be used at every point during the customer journey for B2B, B2C and not-for-profit organizations.
When applying AI to your specific marketing programs, use either a customer driven or marketing driven approach.
Customer Driven AI Approach
When taking a customer-focused perspective, use marketing artificial intelligence at each critical step or action during the buyer journey.
For example, the RACE Model developed by SmartInsights organizes activities by Reach, Act, Convert and Engage.
Customer drive artificial intelligence options include:
Expands REACH to Attract Visitors and Prospects:
- AI-Generated Content develops content from regular, data driven events such as earnings calls.
- Smart Content Curation provides targeted content to visitors.
- Voice Search responds to verbal commands such as Amazon’s Alexa or Apple’s Siri.
- Programmatic Media Buying improves digital advertising results while supporting brand safety such as your ad appearing next to adult content.
Persuades Visitors and Prospects to ACT:
- Propensity Modeling teaches the machine to make better predictions based on real world data.
- Predictiveness Analysis improves conversions, pricing and repeat purchases to reach prospects at the best time.
- Lead Scoring accelerates this process through machine learning.
- Ad Targeting finds the best ads, audiences and stages to deliver them.
CONVERTS prospects into customers:
- Dynamic Pricing determines the best price and/or offer needed to persuade customers to buy. As a result, it maximizes profitability.
- Web and App Personalization serve appropriate information based on visitor buying stage.
- Chatbots answer visitor questions.
- Re-targeting serves ads to people who have visited your site or landing pages.
ENGAGES Customers To Retain Them:
- Predictive Customer Service supports customer retention.
- Marketing Automation improves content personalization.
- Dynamic Email tailors and personalizes communication.
Marketing Driven AI Approach
Alternatively and possibly more intuitive, apply artificial intelligence across your marketing processes.
For example, Marketing Artificial Intelligence Institute’s Paul Roetzer used this method to develop his 5Ps Marketing Focused AI Framework.
PLANNING Builds Optimized Strategies:
- Constructs marketing personas.
- Determines editorial calendar topics by analyzing existing content for gaps and opportunities.
- Captures competitive intelligence.
- Segments contact databases and identifies companies and contacts most likely to convert. Also, predicts conversion paths, highlights key points along the buyer journey, and determines churn rates.
- Allocates paid digital advertising budget by channel and audience.
PRODUCTION Develops Content:
- Creates data-driven content. This includes social media updates and nurture and sales email flows.
- Optimizes content for search including recognizing, categorizing and auto-tagging images.
- Analyzes and scores text for grammar, sentiment, tone and style.
- Converts voice-to-text, and text-to- voice.
- Curates content.
PERSONALIZATION Tailors Customer Experiences:
- Tailors content, predictive product recommendations, offers, emailings and web experiences. This includes images, text and CTAs.
- Engage users with chatbots. Answers both voice and text questions.
- Serves contextually relevant advertising based on user history and look-a-like data.
PROMOTION Manages Communications Across Channel and Devices:
- Adjusts digital ad spend in real-time by channel and audience. (Note: This may reduce the need for digital advertising and/or media buying services.)
- Optimizes email, cross-channel and re-targeted campaigns.
- Tests different marketing promotion elements. This includes headlines, landing pages, images and creative.
PERFORMANCE Converts Results Into Actionable Information:
- Scores leads while continually adapting the lead scoring system.
- Monitors marketing activities and outcomes to find areas for improvement and to forecast performance.
- Develops performance reports.
Not sure where to start with your marketing?
Use Marketing AI Institute’s Marketing AI Scoring Tool to assess your organization’s competitive level.
What You Need To Know About Marketing AI Job Skills
Artificial intelligence will have the biggest impact on jobs involving repetitive activities and/or low digital skills. McKinsey predicts these positions will decrease by about one-fourth to 30%.
Jobs requiring non-repetitive skills and higher levels of digital skills will increase about one-fifth to 50%.
87% of marketers believe AI and machine learning are already important to business success. But the May 2019 DMA data reveals a significant gap between current skill levels and the skills businesses need to leverage AI.
How To Apply AI To Your Marketing Programs
Wonder how you should start using artificial intelligence in your marketing?
Founder of Marketing Artificial Intelligence Institute Paul Roetzer recommends:
Further Roetzer suggests testing artificial intelligence on your marketing by:
- Pick a manual task that takes a lot of time for your staff.
- Find an AI technology created to solve that specific use case.
- Try it to get a sense of the size of the impact AI can make on your specific marketing programs.
Marketing AI Conclusion
The current reality:
Businesses already use AI to support their marketing programs to yield better results faster.
While AI remains in Gartner’s hype phase, start testing how to integrate these emerging technologies into your marketing. This will help you to at least remain level with your business peers and competitors.
You risk that your competitors gains will accelerate at a higher rate than yours. Even worse, over time, this will translate to lower relative margins and possibly lower market share for your business.
So don’t be afraid to take your first AI steps!
As companies like PR20/20 have discovered, when applied to your marketing, AI yields positive results for both your business and your employees!
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Photo Credit: (c) 2019 Heidi Cohen – Free to use with link back to this article.