How A.I. Can Solve the Top 3 Pain Points in Marketing

Leading marketing experts agree that the plethora of tools available to marketers is both a blessing and a curse. The variety of options enables marketers to keep up with an ever-evolving digital landscape of consumer and customer behavior. A professional can use between 20-50 different tools to manage all their tasks. More time is spent managing software than strategy. How can artificial intelligence (A.I.) address top marketing challenges?

Challenge #1: Lack of Reliable, Centralized Data

Companies struggle with data collection, cleansing, and centralization. Inconsistent standards and lack of integration make consistent data flow especially difficult for marketers. Along with tool variation, marketing tech and advertising tech are often siloed from one another.

Without reliable, centralized data, marketers are doomed to suffer many inefficiencies and lost opportunities. Key decisions such as creative, messaging, and campaign parameters are essential to the success of marketing. Yet many of those decisions are driven by gut or laziness rather than science. Even with haphazard decision-making, marketers still spend over three hours on average every week just analyzing disparate data sources.

The effects of poor data centralization can also lead to a poor consumer experience. Retailers employing ad retargeting often don’t realize when you’ve already made a purchase and send you tons of irrelevant, useless, and annoying ads.

How A.I. Addresses This Challenge

Many MarTech companies aim to be an A.I. layer that centralizes and manages communication and data across marketing tools. Players like Salesforce, Oracle, and Adobe are best positioned to win. They already offer end-to-end solutions within their own ecosystems and can afford to aggressively acquire and integrate smaller players.

Salesforce spent over $4 Billion in 2016 buying A.I. companies to roll into Einstein, an A.I. layer that optimizes results across all of Salesforce’s Clouds. One client, Fanatics, is a sports merchandise retailer using Einstein to personalize product recommendations. Einstein provided “sub-segment targeting” that led to 15-20% of clickthroughs on Fanatic’s email campaigns. Einstein has also helped other customers achieve 28% more revenue and 11% increase in average order value (AOV) from better recommendations.

Similarly, Adobe controls a vast suite of creative tools as well as a popular data management platform (DMP) and web analytics for enterprises. Vice President Amit Ahuja oversees Sensei, Adobe’s A.I. layer which unifies data across their cloud solutions. Since Adobe owns the underlying video and content creation tools, Ajuha explains that Sensei is able to “collect rich data across all creatives and all metadata to inform a brand owner which creative to put in front of a consumer.”

Smaller companies like Swiftype address the common bottleneck of coordinating assets and documents in marketing workflows. Waiting on the creative department to send you copy and graphics while analysts pull the latest campaign performance metrics is painful and inefficient. Swiftype consolidates knowledge and data from multiple sources, like Marketo and Salesforce campaigns, task management tools, and repositories like Dropbox and Google Drive.

Challenge #2: Talent Bottleneck

Mastering a myriad of tools also presents training challenges for marketing teams and creates expertise bottlenecks. Training junior employees to navigate complex enterprise software is tedious and error-prone.

Mastering A.I. research and development is even more difficult. Very few organizations are positioned to succeed on this front. Even if a company miraculously centralized reliable and high-volume data in a single system, specialized talent is still required to design and operationalize working models.

Companies with teams of data scientists and engineers on hand often find their employees lack the requisite advanced mathematics background to truly innovate in modern artificial intelligence. Engineers alone also do not ensure success. Marketing executives agree that domain expertise and business needs should drive A.I. research, not the other way around.

How A.I. Addresses This Challenge

Automation of tedious marketing tasks improves accuracy and reduces workload, allowing marketing teams to be more efficient and effective. While the landscape of providers offering automated solutions is still crowded, many marketing executives are seeing early traction.

Strike Social claims their technology “automates the tedious process of campaign setup, spots nuanced patterns undetectable to humans, and breaks ad campaigns into several micro-campaigns and shifts ad dollars to the best-performing targets in real time.” The company was able to improve YouTube view rates by 25% while reducing execution time by 75%.

Aside from invisible automation layers, innovative marketing firms are also exploring conversational approaches. Equals3 built Lucy, a “cognitive companion for marketers” as described by Managing Partner Scott Litman. Lucy acts as your trusted marketing analyst, helping you with research, segmentation, and planning. Only she works 24/7 and gets smarter with more data. Litman claims that Havas Media, one of the world’s largest media agencies, has successfully used Lucy to achieve a “75% reduction in vendor cost and 7x faster campaign deployment.”

Challenge #3: Inability to Calculate ROI

Data is not created equal. Marketers have a hard time turning data into insights, much less calculating the ROI of their decisions.

Ben Plomion, CMO of GumGum, highlights the pervasive pain point where “brands spend more than $60BN globally on sports sponsorships but are unable to capture the full value of their logo impressions on broadcast TV and also social media.”

Politics may also be interfering with true ROI calculations. Many marketing departments fear accountability and deliberately cherry-pick metrics to present to executives rather than analyze the hard truth of what is really working.

How A.I. Addresses This Challenge

New neural network approaches like deep learning have the superhuman ability to detect patterns, leading to many recent breakthroughs in image recognition and computer vision. Computers can not only reliably classify objects in photos and videos, but also identify specific brands and products.

“Without technology, analyzing a three-hour game for different logo placements can take days. By leveraging computer vision technology, a machine can simultaneously analyze every sponsor and location within every frame of the video in a matter of seconds, allowing a full game to be analyzed in a few hours or less,” explains Plomion of GumGum. Suddenly, computing ROI on sponsorships or ad campaigns becomes trackable.

Visual intelligence also enables brands to drive engagement through personalized customer experience. Using user content found on Stackla’s platform, Virgin Holidays increased bookings by 260% from the previous year and TOPSHOP increased sales of online products by 75%. Cloudsight, another visual intelligence company, helped customers achieve a 4x growth in time-on-site by showing the most relevant images for each user, according to co-founder Brad Folkens.

A.I. can also replace older methods to better value different data sources and turn them into more accurate business insights. Superior analysis of audience segmentation and responses lead to improved understanding and handling of customer and lead behavior, such as predicted purchases or churn. TouchCR grew their own business by 20% while reducing ad spend by 60% by using A.I. to match marketing efforts to demographic and psychographic indicators.


“The holy grail for marketers has always been personalization at scale with affordable customer acquisition costs,” states Pascal Bensoussan, CPO at “A.I. and machine learning are becoming table stakes in all the core components of the marketing/advertising technology stack.”

Challenges with data capture and centralization, as well as recruiting and training, will continue to plague marketing teams. However, the rise of artificial intelligence and machine learning offers a clear way to chip away at these previously insurmountable obstacles in modern marketing.

Editor’s Note: This article was originally published in full on TOPBOTS.

About Adelyn Zhou

Adelyn is the CMO of TOPBOTS, a leading research and advisory firm focused on artificial intelligence strategies for Fortune 500 companies. She is a keynote speaker and internationally recognized expert in applied artificial intelligence. Find her on LinkedIn or follow her on Twitter. View all posts by Adelyn Zhou
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One Response to How A.I. Can Solve the Top 3 Pain Points in Marketing

  1. Suzy DeLine says:

    Great article Adelyn! Do you have any particular favorite solution from the above that you’d recommend a marketer take a look at?

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