MarTech Investments in 2021 and beyond. The share of brands who choose the Customer Experience MarTech category as their main area of investment over the next 18 months grew by more than 150% compared to our 2020 survey.
Our Portada Insights report “What Brand Marketers Need from MarTech in 2021 and Beyond” includes the aggregated results of brand marketers preferences in MarTech investment categories over the next 18 months. Results are broken down by the 5 Top MarTech investment categories and geographically (U.S and Latin America). Additionally, results for the top two categories (Advertising & Promotion and Customer Experience) are broken down.
200 brand marketers in the Portada network throughout the Americas were polled. The survey took place in December 2020 and January 2021.
The 20 page report also includes qualitative statements of brand marketers interviewed by Portada as well as advice and best practices from a select group of marketing service providers on how to best leverage marketing technologies. Below are the key results of the report:
The share of brands who chose the Customer Experience MarTech category as their main area of investment over the next 18 months grew by more than 150% compared to our 2020 survey. This increase is related to the acceleration of digitization and e-commerce propelled by the COVID-19 pandemic.
Regionally, the increase of the expected investment in MarTech related to Customer Experience is more pronounced in Latin America compared to the U.S.
Advertising & Promotion continues to be the leading category in the U.S. and is the second one in Latin America. The advent of Internet privacy regulations has increased the need of brands to invest in technologies that foster first-party data capture-maintenance and analysis as well as in technologies that are viable in a cookieless world including contextual targeting.
MMA Global introduced an analysis showing that outcome-based marketing plans can outperform traditional reach-based marketing plans by more than 50% on return on ad spend (ROAS).
The growth strategy, Outcome-Based Marketing 2.0 (OBM2), represents a major step forward in validating how marketing organizations can achieve profitable growth by targeting more responsive audiences.
The OBM2 strategy enables brands to align budgets, channel allocation, and audience targeting directly with ROAS tied to those consumers who are more likely to respond to and be impacted by a brand’s advertising.
MMA found that a marketing campaign organized around the most responsive target audience for a brand — dubbed the “movable middles” — will yield far better outcomes than traditional media plans optimized for reach. Movable middles are defined by having a mid-range probability of buying an advertised brand; they are proven mathematically to have five times the responsiveness to that brand’s advertising and are unique to each brand. While they overlap somewhat with heavy buyers, they also include medium, light, and non-buyers. This helps marketers not only achieve good returns, but also expose their brand to a larger group of receptive consumers for long-term growth.
Joel Rubinson, the former Chief Research Officer for the Advertising Research Foundation, conceptualized the methodology behind OBM2 strategy (in part based on work conducted two years ago) on MMA’s behalf. Rubinson worked in conjunction with global information services and technology company Neustar to leverage their agent-based simulation models that incorporate media consumption and purchase behavior data from their targeting platform. These models uncover the individual consumer-level activities and market-level factors that are predictive of purchase intent with a high degree of accuracy. This approach helped find non-buyers with similar purchase probabilities and predicts where and how to target these important audiences.
“We are incredibly grateful to Neustar for working with us by providing their team’s amazing support and advanced innovative modeling techniques to predict media consumption and purchase behavior across 100% of U.S. households. This capability was invaluable to proving Outcome-Based Marketing 2.0’s value,” said Greg Stuart, MMA CEO. “MMA has been on a decade-long pursuit of a more disciplined understanding and implementation of ‘modern marketing’ for CMOs responsible for higher levels of business growth and profitability for their businesses. This new strategy is one more leap forward to achieve this as well as to raise the stature and gravitas of marketing as a whole.”
In a proof-of-concept study, MMA and Neustar deployed agent-based simulation methodology to target the movable middles on behalf of a brand of frozen pizza in the U.S. market. The analysis proved:
An Outcome-Based Marketing 2.0 plan optimized to target the movable middles outperforms a traditional reach plan by more than 50% on ROAS
Targeting the movable middles helps marketers increase marketing outcomes across the board versus traditional reach-based approaches, including attracting more non-buyers, increasing total consumer reach, and increasing buyer-penetration across light, medium, and heavy buying groups
Movable middles can be predicted with 99% accuracy for each brand, regardless of industry vertical.
“The MMA always tackles the toughest marketing challenges, particularly when it comes to data and marketing optimization. Empirical evidence and analytics validate OBM2 as the most effective approach to improving return on advertising spend.” said Norman de Greve, CMO, CVS Health, and Chair of MMA’s North American Board.
The MMA always tackles the toughest marketing challenges, particularly when it comes to data and marketing optimization. Empirical evidence and analytics validate OBM2 as the most effective approach to improving ROAS.
“We want to ensure the brands we sell in our stores have the best chance of being considered for purchase — and MMA’s work is a welcome addition to any brand’s planning toolkit to help deliver results in an efficient and effective way,” noted Kay Vizon, Director of Media Services for Kroger. “I support this advancement in planning and applaud the MMA for their ongoing push to uncover ground-breaking and data-driven approaches to further brand growth.”
“We cracked the code on ad responsiveness when we realized that using a beta distribution to model consumer probabilities of purchase was the connection between ad responsiveness and targeting,” added Rubinson. “We are, in essence, arbitraging the opportunity to push harder on the groups of consumers that will predictably buy more of a product when exposed to advertising.”
“This research uncovers why audience targeting recommendations based on ROAS and purchase probability work,” said Marc Vermut, Vice President, Marketing Solutions at Neustar. “It found that a marketing campaign organized around those ‘movable middles’ — the most responsive target audience for a brand — will yield far better outcomes than standard media plans optimized for reach.”
Marketing campaign organized around those ‘movable middles’ will yield far better outcomes than standard media plans optimized for reach.
Additional categories, including detergents, nutrition bars, and margarine, will be run through the same analysis in the coming months. The MMA has raised funds and expects to validate this planning approach in real-world, in-market experiments that will last a full year called “Brand as Performance.”
Noted marketing academics, including University of Oxford’s Dr. Felipe Thomaz and Dr. Andrew Stephen, as well as UCLA Graduate School of Management’s Dr. Dominique “Mike” Hanssens, have conducted a review of the methodology used in creating this new growth strategy and support MMA and Neustar’s model. Both universities are developing other complementary assessments and research that validate the findings. Meanwhile, a white paper on OBM2 can be found here.
More importantly, it allows brands to keep up with customers’ increasingly high standards and expectations. Customers want their interactions with brands to feel personal and relevant, and AI enables a level of targeting and tracking that any marketer should get excited about.
But distinguishing between what can be implemented now and what will be possible in the near future is important, as the field of AI is in constant evolution. Here, we break down the different ways marketers can use AI to streamline operations, deliver better customer experiences and channel data into insight.
AI can be defined as a subset of computer science through which machines display “intelligence” by making predictions and decisions. AI acquires intelligence based on the analysis of data sets, a process enabled by algorithms that tell the machine how to complete tasks and interpret information.
The most basic form of this is machine learning, which uses historical data to predict future outcomes. As the machine acquires more data, it becomes better at making predictions.
AI already driving marketing budgets, data-driven insights
Recent studies reveal the important role that AI plays in driving marketing budgets and business growth strategies. Marketers clearly believe that AI is a valuable tool: 72% of marketers surveyed in a PWC study view AI as a “business advantage.” By 2021, organizations are projected to spend $57 billion on AI platforms for marketing.
And organizations are already seeing the results of implementing AI: 3 of 4 companies using AI have reported a boost in sales of at least 10%. 75% of organizations in another study say AI has driven customer satisfaction by at least 10%.
In terms of how CMOs are currently implementing AI, another recent survey found that many are using it for content personalization (56.5%), predictive analytics (56.5%), and targeting decisions (49.6%). But those are just a few of the ways AI can support marketing efforts today.
AI has 8 broad applications in marketing today
Marketers are accumulating data at an astonishing pace with the intention of harnessing it into better targeting. But sometimes the mere volume of data that organizations acquire makes it difficult for them to know how to make use of it. AI is incredibly helpful in this respect, as it enables real-time analysis of large volumes of data, automate tasks, and generate insight.
1. Market intelligence and insight: With the help of algorithms, machine learning enables in-depth analysis of complex data sets from data management platforms (DPMs), data warehouses, or other repositories, connecting the dots to support marketing intelligence and forecasting in a way that humans cannot.
2. Customer profiles and personas: Through the analysis of on-site interactions, purchasing history, referral sources and geo-specific behavior, AI can help brands form a 360-view of their customers and match them with personalized content and promotions.
3.Lead generation and sales:Machine learning and predictive analytics can help marketers automate the process of generating and scoring leads. They also help brands keep customers engaged through predicting turn: through analyzing users’ engagements with brands, they can tell when someone is about to drop off. Brands can then attempt to re-engage these users with notifications and emails.
4. Media buying: AI automates the laborious process of media buying and ensuring that ads are seen by relevant audiences through programmatic advertising and optimization and measurement platforms. With almost no human input, AI helps marketers analyze, manage, and measure the performance of ad campaigns.
5. Customer experience: According to Gartner, 85% of customer service inquiries will be handled via AI by the end of 2020. AI is increasingly being implemented in the customer experience space to support improved call centers and automated customer service via chat bots and digital assistants.
6. User experience: AI helps marketers optimize user experience on websites through analyzing data about single users’ behavior to personalize content, promotions, and notifications. A study from Evergage found that 33% of marketers are using AI for more personalized website experiences, and that 63% of them noted increased conversion rates, while 61% assert that customer experiences have improved.
7. Natural language generation and content creation: There are a variety of applications for AI in the realm of content. Using simple rules and formats, AI-enabled tools and platforms can author content such as business reports, product descriptions, stock market reports, and sports recaps without human input. Through setting the rules and formats, marketers can dictate the tone and style that the content takes.
AI-enabled content platforms can also make suggestions about what kinds of content formats and topics a brand’s target audience is likely to engage with through tracking users’ online activity.
8. Chatbots: While chatbots technically fall under customer experience, they have changed the marketing world in such a way that they deserve their own dedicated text. AI-enabled bots are successfully delivering customer service for thousands of global brands through natural language processing and machine learning.
Natural language processing allows machines to interpret the meaning of written and spoken speech and respond accordingly, all without human intervention. The machine can track the effectiveness of its responses and adapt accordingly, improving as it has more conversations.
Marketers must self-educate before selecting vendors
Marketers considering ways to implement AI in their organization have to be careful when evaluating different products and platforms. Many use the term “AI” loosely, mislabeling tools that implement data processing and analytics as “AI.” Smart organizations can bring in experts to educate and advise them as they consider the alternatives.
Ask questions about the data sets they use and pay attention to whether they have data scientists on staff. Request a demo and confirm what deliverables and KPIs will be included in their activities.
Make sure your data is clean and high-quality
While AI might seem like magic, it still depends on effective human inputs: namely high-quality data that it can learn from. If marketers don’t format data in a way it can be processed, or you do not have the infrastructure to process it correctly, it will not produce an “intelligent” machine.
To this end, marketers must innovate and collect more annotated data that can be tagged to train AI systems. Measuring only clicks is not going to create a rich enough data set to use for impactful AI.
Remember the human touch
AI will be able to replace humans in many, but not all, of the brand interactions customers expect. Consumers are excited about AI – an Acquia study found that 53% of consumers say they are “looking forward to artificial intelligence making interacting with brands a better experience.” At the same time, the study found that 85% percent claim that “a human touch is needed, in addition to technology, for a positive customer experience.”
Marketers should only use AI where it will enhance customer experiences, and it turns out there are plenty of situations in which people prefer to speak to a human than a machine. 75% of the respondents to the Acquia survey agreed that “the problem with automated experiences – interacting with technology instead of a real human – with brands is they are too impersonal.”
The future of AI in marketing depends on smart investments
Implemented correctly, AI will offer us tools that make our work better, easier, and more enjoyable. Marketers will be able to focus on the strategic, creative elements of their work and leave the tedious and time-consuming tasks to a well-trained machine.
All of this, though, depends on marketers educating themselves so that they can help their organizations invest in smart solutions. As AI evolves at a rapid pace, marketers will face increasing pressure to keep up.a