If you are still manually optimizing campaigns the same way it was done half a decade ago, you may be a quickly disappearing breed in the user acquisition space. Remember that machine learning is artificial intelligence that allows computer systems to progressively improve performance on a task by “learning” through statistical approaches. Put another way; machine learning is the development of algorithms that allow for more accurate predictions with incremental data collection. That is why Facebook, Google, and all the major media platforms are perfectly ripe for automation—the bigger your paid customer acquisition budget, the more data you can deliver into these machines to enable them to train and learn faster to help you hit your desired success goals. The critical question is: why are you looking to automate something? Remember that the two biggest challenges for startups are hiring people and acquiring new customers. The best way to tackle these challenges is to figure out how to run a Lean growth team without compromising on driving results. The stage has now been set for the following transformation: to move digital media and marketing beyond the purely tactical into a world that’s more intuitive, highly automated, and more strategic than ever before. Given all the promise and possibilities, what can automation and artificial intelligence accomplish when applied to digital marketing efforts? There are several areas ripe for innovation. Automated Media Buying Machine learning is particularly well suited to making predictions when given a large amount of data. Major marketing platform providers like Google and Facebook use machine learning to deliver more relevant ad experiences to consumers and improve the performance of their offerings to get advertisers to spend more. Advances in machine learning have given rise to independent software providers building out packaged solutions to save time for media buyers on everyday gruntwork and free you up to work on strategy, creatives, segmentation, and more. Cross-Channel Marketing Orchestration The next step up from automated media buying involves more complex systems that can work across multiple digital marketing platforms. Each major marketing platform (Google, Facebook, Twitter, Snapchat, etc.) offers different capabilities, APIs, and relative strengths and weaknesses, given your specific marketing objectives. Orchestration is a critical concept in this class of autonomous marketing solutions. It goes beyond automated bid management to consider your marketing funnel, customer journey, or life cycle. Systems that orchestrate marketing efforts with a full view of the customer journey encompass a host of benefits driven by AI and automation, particularly in the following areas: Segmenting and targeting customers Predicting behaviors across various channels with a unified view of the customer journey Fine-tuning and perfecting cross-sell and upsell opportunities. Identifying the right channel to drive engagement based on reach and frequency modeling Improving attribution accuracy Fraud detection and protection Virtual Marketing Assistants Voice-based interfaces to intelligent assistants like Amazon Alexa represent an area of growth and exploration in the marketing world. While most “assistants” are designed for consumer use or customer service applications, these intuitive voice interfaces hold promise in helping marketers better understand what’s happening in their digital media efforts, uncover trends, make adjustments, and take advantage of opportunities in new ways that would have been too laborious or complex using traditional user interfaces (UIs) or reporting dashboards. Content Curation The amount of digital content available today is staggering. So what kind of content do your customers like? What resonates with your prospective customers? Artificial intelligence can help marketers sift through vast amounts of content to help them find out what their customers spend their time consuming or engaging with. This can lead to ideas about what media outlets might be fruitful places to advertise. These insights can also fuel your content development, content marketing, and advertising efforts from a creative perspective. Customer Support and Service Today, chatbots are the first line of contact for routine customer support requests. According to Gartner research, 85% of all customer interactions will be handled without a human agent. The increased adoption of chat-based interfaces for customer service, marketing, shopping, and more serves both business and consumer interests. The volume, structure, and repetitive nature of routine service requests make automation highly approachable with off-the-shelf solutions that plug into standard chat interfaces ranging from text messaging to Facebook Messenger. AI-powered customer service has many incredible benefits for businesses and consumers alike. For consumers, chat-based interfaces are accessible, feel familiar, and provide immediate responses to the most common queries. It saves consumers time compared to wading through support lines, phone trees, support queues, or waiting for customer support emails to get addressed and answered by a support agent. Businesses can improve customer experience by integrating chat-based customer service and support channels with their CRM systems and data management platforms. This type of integration allows a company to escalate its most valuable customers to the top of the service queue, for example, to present retention-based offers to customers on the verge of lapsing or poised for an upgrade or new purchase based on their behavioral patterns. Segmentation Development and Management The amount of data coursing through the global internet at any given moment is nearly unfathomable. The big four alone—Amazon, Microsoft, Google, and Facebook—store 1.2 petabytes of data. That’s 1.2 million terabytes (a terabyte is 1,000 gigabytes). Trillions upon trillions of customer data points exist within this primordial data soup, ready to be accessed and pumped into the modern digital customer experience—personalized ads, offers, content, services, and more based on the newfound ability to anticipate customers’ needs and desires. Marketers can tap their vast data stores to create unlimited customer segmentation models powered by artificial intelligence. Companies can already tap third-party data sources to enhance customer records with thousands of attributes, like household income, zip code, behaviors, and more. AI allows companies to take this to the next level by combining these conventional data attributes to a live stream of customer interactions, transaction data, product usage data, support and service data, and beyond. Segmentation vendors use AI to generate and update dynamic customer segments to feed into their execution systems to run targeted campaigns across the customer’s user journey. Insight Generation Artificial intelligence can look through a mountain of behavioral data on a hyper-granular level to accurately predict what a consumer will do next, based on their past behaviors and actions. This is how ad platforms create lookalike audiences. Leveraging vast amounts of data and machine learning, these systems can easily cluster people based on behavioral attributes (or other factors) to anticipate their next move, motivations, and desires. Suppose everyone in Cluster 1 takes actions A, B, and C. In that case, we can predict that customers who take steps A and B will likely follow that with action C. AI systems discover the interests, context, and hedonistic activities around users and products by looking at audience behavior. And the system automatically adapts to evolving consumer behaviors and interests. This can lead to new insights that inform your strategy, creative approaches, offers, and more. Creative Generation Perhaps one of the fascinating aspects of artificial intelligence today intersects with marketing technology in potentially problematic (even dangerous) ways. Natural language generation—coding computers to write or generate written or spoken words in a way that can pass as human—has been a field of academic study for decades. Advances in recent years, notably around recurring neural networks and their offshoots, have led to rapid improvements in natural language generation. Regarding marketing, applications around natural language generation could be used to analyze your marketing copy and create variations using your brand’s “voice.” This requires some training but is well within the realm of possibility these days. Natural language generation models have become so powerful that Open AI, a research company working on artificial general intelligence or AGI, refused to release the code related to its large-scale language model known as GPT2. The company cited concerns over misuse and abuse related to the “fake news” problem. This brings us to “deepfakes”—AI-generated video content nearly indistinguishable from the original content. The technique uses human image synthesis to combine and superimpose existing images and videos onto source images or videos using machine learning. Technology marketing applications revolve around creative generation, personalization, and more. Still, fears of misuse related to fake news and even “revenge porn” make deepfake technology concerning its ability to be weaponized by rogue actors and nation-states. Given the breadth of applications for artificial intelligence in growth marketing, it’s important to assess the ability of each to impact your most important outcomes. For example, training a neural network to generate copy variations based on a catalog of ads and customer service interactions might be interesting, but the cost, complexity, and time involved may outweigh any lift you might achieve with some fancy new AI-generated ad creatives. Given the breadth of applications for artificial intelligence in growth marketing, it’s essential to assess the ability of each to impact your most important outcomes. When it comes to immediate impact with the AI-powered marketing technology just outlined, the following are your best bet: Segmentation development and management Automated media buying Cross-channel marketing orchestration Insight generation These four interrelated disciplines all play a role in how you approach optimization today—with or without AI. They also fit nicely into a broader customer life cycle framework. And for any of the four disciplines just listed, AI offers a high enough “risk to reward” ratio to make the potential benefits of time and resources involved worth the cost and distraction factor. Building a Business Case for Automation Most startups are resource-constrained and therefore need to develop a business case to determine which projects to prioritize based on their cost/benefit analysis compared to the success goals of the business. It’s essential to clearly articulate the problems that leveraging AI and Machine Learning to automation would help solve. For example, figure out the ROI of how much money you would save based on lowering the cost of acquiring new customers and hiring fewer people to manage these campaigns. It is highly recommended to determine longer-term ROI projections over three to five years. The goal should be achieving an ROI that remains positive over time and compounds that value by continuing to enrich growth team efficiencies and results. To turbocharge your performance, embrace marketing automation to help you achieve data-driven results far beyond manual capabilities. About the Author is a forward-thinking leader with 20 years of experience helping startups grow into successful businesses. Lomit has played a critical role in scaling growth at startups, including Roku (IPO), TrustedID (acquired by Equifax), Texture (acquired. by Apple), and IMVU (#2 top-grossing gaming app). Lomit is a public speaker, author, and advisor, with numerous accolades and awards throughout his career, including being recognized as a Mobile Hero by Liftoff. Lomit's book is part of Eric Ries' best-selling "The Lean Startup" series. Lomit Patel Lean AI

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