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Did you know there are over 3,874 companies offering marketing technology? That’s how many companies are featured on Scott Brinker’s behemoth 2016 Marketing Technology Landscape Supergraphic, which drives home the challenge of navigating the marketing industry.
“Marketing has the unique challenge of not having a typical stack or process. If you look into any Fortune 500 company, they will have hundreds of products that they are stitching together,” says Eric Stahl, an SVP of Product Marketing at Salesforce.
Leading marketing experts agree that the plethora of tools available to marketers and advertisers 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, but a professional uses 20 to 50 different tools to manage all their tasks.
The result is more time spent managing software than managing strategy. Every marketing executive we spoke to also emphasized that vendor variation and incompatibility drive the number one challenge of modern marketing: sourcing and centralizing reliable data.
To understand how AI can address modern marketing challenges, you first need to understand the tools marketers use every day. If you aren’t a professional marketer, read our brief introduction to marketing technology covering the typical enterprise “stack” and various add-on solutions.
Challenge #1: Lack of reliable, centralized data
Every function in a corporation struggles with data collection, cleansing, and centralization, but inconsistent standards and nonexistent integrations make consistent data flow especially difficult for marketers.
Along with tool variation, marketing tech (martech) and advertising tech (adtech) are often siloed from one another. Ritchie Hale, CIO at TouchCR, warns that “the separation of these two stacks creates an identity gap, where we are trying to uniquely identify the person we are advertising to before we have an identifiable piece of information from them.”
One key driver of the lack of interoperability between tools is the breakneck speed of technical development and acquisitions in MarTech. “Most DMPs (data management platforms) and marketing automation tools possessed by companies like Oracle, Adobe or Facebook were acquired recently and haven’t even been integrated with each other,” explains Monika Ambrozowicz, global marketing manager at Synerise.
Another problem, pointed out by Mark Kovscek, president of Velocidi, lies in the nuances of preparing data for different marketing purposes. “As the data is cleansed and prepared, it is optimized for a specific use case (e.g., programmatic, campaign reporting, media attribution) and therefore creates competing versions of the data.”
Without reliable, centralized data, marketers are doomed to suffer many inefficiencies and lost opportunities. Many key decisions, such as creative, messaging, and campaign parameters, are essential to the success of marketing, yet driven by gut or laziness rather than science. Even with the haphazard decision-making, marketers are still spending over three hours on average every week just analyzing disparate data sources.
Another insidious effect of not coordinating between tools is the risk of undermining your own marketing campaigns. Mark Torrence, CTO of RocketFuel, warns, “If multiple partners are used to buy traffic based on similar data or targeting ideas, they may bid against each other, driving up the cost (and driving down the efficiency) of the buy.”
The effects of poor data centralization 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 AI addresses this challenge
Many martech companies aim to be an AI layer that centralizes and manages communication and data across marketing tools. The players best positioned to win are major enterprises like Salesforce, Oracle, and Adobe, which already offer end-to-end solutions within their own ecosystems and can afford to aggressively acquire and integrate smaller players.
Salesforce CEO Marc Benioff spent over $4 billion in 2016 buying AI companies to roll into Einstein, an AI layer that optimizes results across all of Salesforce’s Clouds. One client, Fanatics, is a sports merchandise retailer using Marketing Cloud Einstein to personalize product recommendations.
Sports fans come in different flavors, such as hardcore fans, gear fanatics, fans of a school or local team, and fans of fans, i.e. customers purchasing sports gear for friends and family. According to Stahl, an SVP at Salesforce Marketing Cloud, Einstein segmented customers into these large buckets, but also provided “sub-segment targeting” that led to 15-20 percent clickthroughs on Fanatic’s email campaigns.
The same personalization can be ported to Salesforce Commerce Cloud, where Einstein has helped some customers achieve 28 percent more revenue and 11 percent increase in average order value (AOV) due to better recommendations.
Similarly, Adobe controls a vast suite of creative tools as well as a popular DMP and web analytics for enterprises. Vice president Amit Ahuja oversees Sensei, Adobe’s AI 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 on a consumer.”
“When the AI hype dies down, the differentiation will be at the data layer,” predicts Ahuja. “Outside of Google, we’re sitting on the biggest system of record for any behavioral digital data. No one else can do what we do.”
Behemoths like Salesforce and Adobe are not the only companies tackling the data challenge in marketing. 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.
“Customers in commerce reap the benefits in obvious ways (when a search for ‘spoon’ also serves up search results for items labeled ‘ladle,’ for example), while publisher customers have more effective indexing of stories and can organize content by relevancy and date,” explains Praveena Khatri, Swiftype’s VP of marketing.
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. “To cut corners, employees often build campaigns around only one or two parameters,” complains Mark Shore, cofounder of Strike Social, “which leads to the lowest common denominator and weaker performance.”
Another challenge with larger enterprise is dependency on external talent for essential work. Torrence of Rocket Fuel explains that most Fortune 500 companies rely on marketing partners to buy media in the “walled gardens” of Google, YouTube, and Facebook. Additionally, most of them “still have the bulk of their media spending controlled and managed by ad agency partners, who have their hands on the keyboards of the platforms.”
If mastering marketing tools is difficult, mastering AI research and development is even harder. 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.
“Fortune 500 companies simply do not have the inclination or resources internally and they routinely, if not universally, fail at the BUILD. So the question is how to BUY,” says Tasso Argyros, CEO of ActionIQ. Even companies with teams of data scientists and engineers on hand often find their employees lack the 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 AI research, not the other way around. “The most important thing is not advanced math models or complicated neural networks,” Yulia Khansvyarova of marketing intelligence agency SEMrush asserts. “The most important part is feature engineering. The more domain knowledge you have, the better. Constantly verify your hypothesis with your end users.”
How AI 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.
Shore of Strike Social claims its 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 percent while reducing execution time by 75 percent.
RocketFuel is another AI-driven marketing company replacing manual optimization with smarter automation. “We do this using a variety of machine learning and optimization techniques, including neural networks, logistic regression, multi-armed bandit, performance-aware pacing, bid multipliers, and more. We frequently balance scores from 2 or more models to achieve multi-objective optimization,” explains RocketFuel’s Torrence.
One agency held a four-way competition with RocketFuel and three other vendors, all of which were doing manual optimization. RocketFuel’s AI-powered optimization bested the competitors by 8 times on cost per acquisition (CPA).
Aside from invisible automation layers, innovative marketing firms have also built conversational approaches. Equals 3 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 in beta tests to achieve a “75% reduction in vendor cost and 7x faster campaign deployment.”
Challenge #3: Inability to calculate ROI
Not all data is 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 AI 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.
Plomion of GumGum explains why such breakthrough technologies revolutionize brand marketing: “Without technology, analyzing a 3 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.” Suddenly, computing ROI on sponsorships or advertising campaigns becomes tractable.
Visual intelligence also enables brands to drive engagement through personalized customer experience. StackLa helps brands “discover the best user-generated content (UGC) around their brand, categorize it around customer personas, and recommend the right content for the right marketing channel,” explains CTO Peter Cassidy.
Using user content found on StackLa’s platform, Virgin Holidays increase bookings by 260 percent from the previous year, and Topshop increased sales of online products by 75 percent. Cloudsight, another visual intelligence company, helped customers achieve a 4 times growth in time-on-site by showing the most relevant images for each user, according to cofounder Brad Folkens.
AI 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 create improved understanding and handling of customer and lead behavior, such as predicted purchases or churn. TouchCR grew its own business by 20 percent while reducing ad send by 60 percent by using AI to match marketing efforts to demographic and psychographic indicators.
Kazuhiro Takiguchi, CEO of ReFuel4, produced similar results for clients like Spotify, which saw a 40 percent increase in clickthrough rates and 3 times app installs over previous campaigns by using AI-powered marketing. According to Takiguchi, such results are achieved by “taking existing and past ad performance to predict future performance of creative.”
“The holy grail for marketers has always been personalization at scale with affordable customer acquisition costs,” says Pascal Bensoussan, CPO at Reputation.com. “AI 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, but the rise of artificial intelligence and machine learning offers a clear way to chip away at these previously insurmountable obstacles in modern marketing.
This story originally appeared on Www.topbots.com. Copyright 2017
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