- In Deloitte’s 2019 CMO Survey Report, 56% of participants anticipated using AI for content personalization. 33% of respondents foresee applying AI for improving marketing ROI by optimizing marketing content and timing.
- Mckinsey’s survey found that 44% of companies reduced operational costs and increased business revenue by using AI in marketing.
- The Bain and Company found that marketing insights gathered through new machine-learning capabilities helped gain 10x in marketing performance. The company also saw a 25% increase in additional revenue through cross-selling.
- An EY study of CEOs and business leaders reveals that 62% of respondents believe artificial intelligence will have a significant impact on creating efficiencies at their company.
- In a survey conducted by BCG, 90% of respondents agreed that artificial intelligence represents a business opportunity for their company. However, 70% of companies report minimal or no impact from AI so far.
Marketers have started using artificial intelligence to enhance every step of the customer journey. AI is used to forecast sales, deliver personalized website experience, offer 24/7 customer service via chatbots, and optimize ROI via programmatic ad targeting.
Leading consultants like Deloitte, McKinsey and Company, Bain and Company, EY LLP, and Boston Consulting Group have been researching to identify the role of artificial intelligence in marketing.
Let’s look at what these leading consultants say about the usage of AI in marketing.
Deloitte: “Deliver personalized experience at enterprise scale using AI”
In Deloitte’s 2019 CMO Survey Report, 56% of participants anticipated using AI for content personalization. 33% of respondents foresee applying AI for improving marketing ROI by optimizing marketing content and timing.
Marketers cannot process tons of data about all their customers at one time. That’s why they have been dividing customers into different segments (based on characteristics, browsing behavior, and purchase history).
However, that might not be enough. Customers now demand brands to act on their needs, just like they are treated by their favorite staff at their go-to restaurant.
It is challenging to offer this type of personalization at an enterprise level. That’s where artificial intelligence can help businesses deliver those experiences to millions of customers at a time.
56% of marketers believe AI can help yield better engagement with customers and prospects. Companies like Omniconvert are already using artificial intelligence to deploy more than 500 new automated experiments every 4 hours.
Deloitte says, “Artificial intelligence and machine learning can make decisions in the moment based on hundreds or thousands of data points far more than we humans can consider and do so at enterprise scale.”
This doesn’t mean relying entirely on AI for your business’s marketing. It’s about using the superpowers of artificial intelligence to make lightning-quick decisions and nurture millions of individual customers simultaneously.
To use AI effectively in marketing, you need to have an appropriate level of transparency and interpretability. Without interpretability, it would be difficult to determine how artificial intelligence is contributing to your business.
By proactively and strategically finding ways to integrate AI and expanding machine-human collaboration across your organization, you can build more-human bonds with millions of your customers.
McKinsey and Company: “Implement core practices to boost artificial intelligence results”
Mckinsey’s survey found that 44% of companies reduced operational costs and increased business revenue by using AI in marketing.
Artificial Intelligence is expected to have around $2.6 Trillion worth of business impact in sales and marketing annually.
Some of the companies, from various sectors, are attaining outsize business results from AI. These high-performing companies saw both higher revenue increases, and more significant cost decreases than other companies that use AI.
It was found that 20% of organizations implemented AI in their company in 2019. The number is expected to grow in 2020.
According to Mckinsey, these companies are more likely to apply AI core practices to drive value and mitigate risks associated with technology.
The core practices applied by artificial intelligence high-performers include:
- Investing in AI talent and training.
- Ensuring business staff and technical staff have the necessary skills.
- Aligning business, analytics, and IT leaders to work together on specific problems.
- Adjusting AI tactics with their corporate strategy.
- Having an AI strategy with a clear enterprise-level road map of use cases.
- Creating well-defined governance processes for critical data-related decisions.
- Updating AI models frequently.
- Using AI insights in real-time to enable daily decision making.
- Tracking a comprehensive set of distinct AI performance indicators.
Implementing these core practices can increase your chances of improving results generated via artificial intelligence in your marketing.
Bain and Company: “Leverage the power of AI to boost cross-selling”
Online retailers using machine learning algorithms to generate customer intelligence and detailed shopper profiles have seen average order values increase from 5% to 10% and experienced ROIs of 6x-7x.
Cross-selling can improve your average order value and bottom-line profit. But, what products to cross-sell and when is a challenge for many marketers.
In an interesting case study, the company focused on cross-selling using artificial intelligence.
The company had everything (good-quality data, the right technology, and internal talent) it takes to generate better results. However, the analytics team was not aligned with business-unit and functional experts.
The company created a new team of both coders and analytics experts. The new squad changed the old approach to analytics to produce meaningful results.
The team assessed the current cross-selling performance and explored the events that triggered the sale of additional products.
They then leveraged artificial intelligence algorithms to determine which product a customer was likely to buy next. The team trained the AI by integrating 20 databases into a system that contained a 10-year history of the client and external data.
They used Agile development methodologies to break the project into small parts that covered every core task, such as data prep and loading, test and implementation, and knowledge transfer.
They also conducted weekly meetings with the top management to address roadblocks affecting the cross-selling.
The marketing insights gathered through new machine-learning capabilities helped gain 10x in marketing performance. The company also saw a 25% increase in additional revenue through cross-selling.
By strategically implementing AI to understand customer behavior and recommend additional products can significantly boost your cross-selling capabilities.
EY (Ernst and Young) LLP: “Assess the risks and overcome the barriers of implementing artificial intelligence”
An EY study of CEOs and business leaders reveals that 62% of respondents believe artificial intelligence will have a significant impact on creating efficiencies at their company.
Another 62% say AI will have a substantial role in their company staying competitive. Besides, 60% of respondents believe artificial intelligence will help them gain a better understanding of customers.
However, the biggest barrier to implementing artificial intelligence is the lack of skilled professionals. In a survey, EY found some restrictions affecting AI adoption. These barriers include:
- Lack of experts needed to implement AI.
- Absence of trust in the quality of data.
- Concerns about data privacy and use.
- Lack of required infrastructure and interoperability.
The two factors to overcome these barriers (as cited by CEOs and business leaders) are:
- Having a compelling business case for AI.
- Having a strategic vision and commitment to AI from C-level executives.
For AI implementation, you can either take a top-down or bottom-up approach, both of which are great.
A top-down approach begins with identifying a business problem and goes down to accessing the technical feasibility. A bottom-up approach begins with identifying an AI technology and goes down to determining what value can be provided.
The biggest risk artificial intelligence possesses to businesses is the bias in data. AI will amplify the bias unless you accurately put in checks to prevent this from happening. If you fail to recognize the preference, you will end up spending hundreds of dollars in marketing campaigns and see no result.
The regulators’ lack of understanding of AI could also cause issues. Therefore, it is essential for organizations to invest in learning and development of internal controls, so that they can make informed, data-driven decisions.
Boston Consulting Group: “Combine AI and human elements of the business”
Boston says, “Organizations that combine the capabilities of humans and machines will develop superior customer experiences and relationships, more productive operations, and dramatically increased rates of innovation.”
A global FMCG company used AI to optimize its process for allocating marketing spending. Advanced analytics modeling compared ROI across brands, markets, and media channels and created a dynamic model to analyze different potential scenarios for allocating that spending.
As a result, the company made better spending decisions and saw a 10% increase in marketing ROI during the first 12 months.
But, is every company that implements AI is winning?
In a survey conducted by BCG, 90% of respondents agreed that artificial intelligence represents a business opportunity for their company. However, 70% of companies report minimal or no impact from AI so far.
For AI to deliver exceptional results, companies need to integrate it into the individual processes that power the core of their business. Companies need to develop the right feedback loops so that artificial intelligence can get better with time.
According to Boston, companies that combine human (organization, talent, and ways of working) with technology (data and digital platforms) see better outcomes. The outcomes include:
- Personalized customer experiences and relationships
- Bionic operations
- New offers, services, and business models
Businesses that use AI in marketing see higher growth than those who don’t. Research and studies by top consulting firms have also proved it. However, to get the maximum benefit of AI, it is crucial to determine the problem you want to solve and hire the best talent to manage the technology.