Ongoing Challenges With Artificial Intelligence in Growth Marketing by@lomitpatel
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Ongoing Challenges With Artificial Intelligence in Growth Marketing

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A fundamental challenge for startups using AI is acquiring enough first-party customer data from their users. Data capture comes even before the algorithms, and AI is not useful until enough people are using your product or service for you to capture a critical mass of data events. It is critical to build trust and a strong relationship quickly when you’re asking for data from new customers, and the way to do that is to reiterate value consistently. Data is a commodity that will continue to increase in value as more regulations like GDPR and GDPR are adopted worldwide.

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Lomit Patel

Lomit Patel is a growth executive and author of Lean...

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Artificial intelligence is starting to hit its stride, and many obstacles along the way have been addressed—the availability of computing power, data management systems, and so on. However, there are still many challenges ahead for the top industry trends for AI marketing to be successful.

Here are some of the most significant challenges you’re likely to encounter and how you can mitigate them.

Data Acquisition

A fundamental challenge for startups using AI is acquiring enough first-party customer data from their users. It is hard to be explicit about how much data a company needs to truly leverage AI—that depends on what use cases they want to start with. This could be based on conversion goals or historical conversion rates; for example, this could be for analytics or segmentation and targeting.

I like to think about artificial intelligence and machine learning as a tool chest to help get the benefits of marketing automation. With things like deep learning, the tool chest just got deeper and has more powerful tools.

Therefore, all companies, from early-stage startups to big multibillion-dollar businesses, can leverage AI provided they have the correct data acquisition strategy. I think it’s fair to say that startups have to be even more strategic in what data they collect and how they collect it; for example, in some cases, they can bootstrap their algorithms by licensing third-party data sets to start with—and improve the performance of their models with their first-party data.

Most of the friction in data collection results from customers’ lack of trust in startups that lack brand awareness or do not provide a compelling value proposition for solving their problems. It is critical to building trust and a strong relationship quickly when you’re asking for data from new customers, and the way to do that is to reiterate value consistently. Data capture comes even before the algorithms to establish long-term success in leveraging AI and machine learning in growth marketing.

The value of first-party customer data is priceless, and no publicly accessible data will ever provide the same competitive edge.

Access to unique data isn’t a problem for major platforms like Google, Facebook, and Amazon because they are well-established brands that continue to provide clear value in return for capturing user data. However, most startups are beginning at ground zero to convince people to share their data with them. Unfortunately, AI is not useful until enough people are using your product or service for you to capture a critical mass of data events. Only with this flow of information can the AI intelligent machine help with your acquisition, retention, and monetization efforts across the entire customer journey.

Getting the data flywheel process started and keeping it going is something all businesses face with AI, but this is true even more for startups. However, startups should not give up on AI because, in the long run, it offers many opportunities to disrupt, innovate, and solve problems faster. The key is to ensure you have a good product/market t that compels people to give up their data before you have reached the scale to provide optimal value from the data flywheel kicking in.

The value of first-party customer data is priceless, and no publicly accessible data will ever provide the same competitive edge.

This means you need to invest the effort to develop a robust data acquisition strategy that provides value or incentives to customers for giving up their data. An example is Amazon Prime, which offers much more than free two-day shipping to members, all because Amazon knows that Prime members have significantly higher LTV than non-Prime members. Because of the value add, Amazon captures a wealth of customer data that enables it to better target new users and make product recommendations to current customers. The key to their success was investing in a data acquisition strategy from the beginning, then using that data to give value back to their customers consistently.

Privacy Controls

Data is a commodity that will continue to increase in value as more data regulations like GDPR and privacy controls are adopted worldwide. I would expect more major media platforms like Google, Facebook, and others to use proactive “privacy” measures to give users more transparency about what data they want to share with advertisers.

It’s hard to predict how this will impact these companies regarding their customer data policies, but it will surely be a major distraction for them. It could lead to more challenges for growth if the companies end up sharing less data to identify and track users coming from their platforms, where the bulk of the paid acquisition budget is spent.

There is a big risk that Apple and Google may completely stop sharing mobile device app IDs with the attribution platforms, which play a collection in tracking customers on mobile devices. This could lead to AI intelligent machines being forced to make decisions with less accuracy and transparency in the attribution data they depend on.

Cross-platform attribution about the customer journey is already challenging, but it will get even more so shortly as these major technology companies are scrutinized on all fronts regarding user privacy.

Facebook's privacy policies and all the other major media partners will be an ongoing challenge as they evolve. They all want to empower users to influence the regulatory debate around data privacy. The truth is, by requiring users to make many changes to opt out, most people will ignore it and end up using the Facebook and Google default permission settings (which will let the companies scoop up their information).

Team Downsizing

There are plenty of scary headlines in the news about AI killing off jobs. A new report by McKinsey Global Institute predicts that by 2030 as many as 800 million jobs could be most customers highly value personalization recommendations, content, and offers most customers highly valued personalization recommendations, content, and offers that affect everyday working lives, comparable to the shift away from agricultural societies during the Industrial Revolution. In the United States alone, between 39 and 73 million jobs are expected to be automated—making up around a third of the total workforce.

Businesses will see transformational change in all areas of their organizational structure. This will require them to retool their business processes and reevaluate their talent strategies and workforce needs. They will have to carefully consider which workers are needed, which can be redeployed to other jobs, and where new talent is required. There is a danger of a political backlash if unemployment goes up. Many companies are finding it is in their self-interest and part of their societal responsibility to train and prepare workers for a new world of work.

There will be an impact on the growth team as more roles and tasks get automated with AI. This will be especially evident on bigger growth teams where many campaigns, media buying, and data scientist roles become obsolete as machines prove they can handle that work better, smarter, and more efficiently than humans. If you know AI will be a part of your future growth strategy; you have to make sure your team is aware of this and willing to learn how it will improve their jobs. It’s essential to invest time and resources into training employees who will be key to the success of the AI intelligent machine.

Future growth teams will be much leaner as the organizational structure evolves for humans and machines to coexist.

Machines will become much smarter, more productive, and achieve better results leveraging artificial intelligence, with humans playing the supporting role to empower them. This process will start with the automation of small tasks but will scale up over time.

Future growth teams will be much leaner as the organizational structure evolves for humans and machines to coexist.

The best safeguard for workers is to take proactive action to build up their skillset so they are relevant in the future of work. The growth marketing team has many roles with rudimentary tasks that are ripe for automation. Instead of worrying about job losses, spend time acquiring new and relevant skills that will allow you to perform higher-level tasks in the technical, strategic, creative, problem-solving, communication, and leadership arenas. AI will create a demand for new jobs that will benefit workers if they stay open to developing the needed talents and capabilities.

New Channels and Opportunities

There is an old saying that is very appropriate for this dawning age of AI— “don’t put all your eggs in one basket.” In other words, a business should use multiple channels and diversify across different paid and organic platforms to maximize its ability to acquire customers. The key is to reduce risk by not being highly dependent on any one source of traffic. A mistake many startups make is to use paid user acquisition teams that focus on a few highly dependable channels like Google and Facebook. Both are good-quality traffic sources, but your startup paid customer acquisition risk is tied to them not imploding, or else you’re done as well.

The challenge is to ensure that the different channels you test are easily integrated into your AI intelligent machine. The best way to do that is to make sure that they have APIs and are already set up within your attribution measurement partner, so you can easily manage new campaigns by adjusting the business inputs (bids, budgets, creative, and goals) to test, learn, and iterate at scale using AI to achieve your desired business outcomes.

Staying on Top of Fraud

In our modern world, nothing is certain except death, taxes, and fraud. As soon as advertisers caught up with incentivized traffic and bot farm schemes years ago, fraudsters quickly devised new mechanisms to swindle advertisers out of their budgets. According to the eMarketer Digital Ad Fraud 2019 report, estimates of fraud vary widely, but even the most conservative estimates

put the money involved well into the billions annually worldwide. Recent estimates vary from $6.5 billion to as high as $19 billion, a range that points to the difficulty of measuring fraud’s true impact.

Plenty of co-conspirators in the ecosystem are incentivized to keep fraud alive because they are personally benefiting from it. Unfortunately, there is no consistent definition of fraud and no alignment among the key stakeholders in the attribution platforms, ad networks, ad agencies, and media buyers. Ultimately no one is motivated to solve this problem because it would impact their future compensation and revenue.

One approach many high-volume advertisers use is to work with a third-party fraud detection tool from their attribution provider to monitor and filter traffic for anomalies. This can be effective because of the sophistication of detection algorithms and their multi-advertiser view of fraudulent traffic.

However, the simplest way to minimize fraud is to avoid ambiguous, non-transparent channels and to buy media directly from reputable sources. The ongoing challenge is that fraud impacts attribution by injecting bad data signals into the AI intelligent machine, which plays havoc with the algorithms. Despite technological advances, ad fraud will continue to be a big problem due to the growth of digital ad spending—or, at least, until there isn’t enough easy money to make by the fraudsters.

Facing Challenges

These challenges are ongoing and ever-changing because AI is still in the infancy stage with a lot of untapped growth in marketing. However, going all in to tackle these ongoing challenges presents many opportunities for learning and growing individually and collectively as a team. At the end of the day, it’s always better to learn to be the disruptor than to be disrupted.

About the Author

Lomit Patel is the Chief Growth Officer of Tynker, with 20 years of experience helping startups grow into successful businesses. Lomit has previously 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 Lean AI is part of Eric Ries' best-selling "The Lean Startup" series.

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