There’s growing excitement – admittedly at times borderline hype – about what artificial intelligence can and will do for businesses. While speculation abounds among pundits, journalists and ‘thought leaders’ surrounding the impact that AI will have on jobs (CBInsights predicts 10m jobs are at risk in the next 5-10 years) there’s relatively little analysis of the tangible effect AI will have on marketer’s day-to-day work, and the opportunity to ‘upskill’ us all.
Today’s marketers will benefit by navigating an increasingly AI-centric (and AI-literate) world where bots, intelligent software and machine learning play an increased role in the marketing function. To help you cut through the noise, here are some tangible examples of where AI is likely to become a relevant part of the modern marketers’ workflow, as well as ideas on how to better understand and qualify the impact that AI can have on your business.
Data analysis and processing is becoming far less laborious, and much more effective. In the past, brands and agencies have needed to employ teams of data analysts whose job was to build segments based on observed patterns in first-party data, and often merge second and third party data.
Building segments is time-consuming, error-prone, with segments often out of date by the time they’ve been created. Challenges compound when the half-life of a cookie is short, many marketers still lack a single customer view, and data match rates between marketing and ad platforms comes into play. To conduct data analysis and run intelligent campaigns at scale, marketers and their analysts often rely, with mixed success, on merging data from numerous silos. The result; marketers still lack visibility into how reliable their data is, and few quantitative tools to benchmark data quality against.
Thanks to open source machine learning libraries and developer tools for data scientists, along with the cloud computing infrastructure that supports AI and machine learning – think Amazon AWS plus Apache Spark, or Google Cloud’s Machine Learning Engine and Microsoft’s Azure Machine Learning Studio – data science is becoming democratised. This means data scientists spend less time piping and cleaning data, and more time solving meaningful problems with data. Advice: get to know, love, and empower, your data scientists. The more they understand your marketing and business needs, and can access the right data, the more they will make you shine.
Machine learning is increasingly helping marketers to understand and anticipate human behaviour, delivering value for the customer at the moment they want it. That said, there’s still a lot of guesswork and manual data processing involved in delivering personalised marketing campaigns.
When marketing campaigns are augmented with artificial intelligence, however, they are capable of much more. For example, a campaign can analyse whether a customer had responded well to a particular piece of creative, and determine the correct creative to show on the next engagement. Or a customer who had visited your bricks and mortar store recently might be shown creative related to the product they picked in-store using geo-targeting technology. Mapping customer identity with machine learning will enable marketers to be much more precise and personalised with their marketing efforts.
Improve Customer Experience
Artificial intelligence is already having a significant impact on customer experience. From Google Assistant to Amazon’s Alexa and Apple’s Siri, digital assistants have become a big part of our lives. That’s only going to increase.
Marketers are particularly well positioned to understand and anticipate how consumers are interacting with machines. Critical questions must be asked, such as how are these new technologies shaping consumer behaviour, and how does this impact my brand awareness or experience? What is the role of search and product discovery when voice is the primary internet access point? What role should automated chat-bots or digital assistants play in the traditional marketing funnel and how do we solve for this new paradigm? Where are consumers in the technology adoption curve, and how do I optimise to get the timing right?
Anytime there’s an opportunity to anticipate a customer need – from helping order movie tickets, to providing the answer to frequent customer service questions, there’s a chance to put AI to work. Voice recognition technologies, remember, all rely on AI to work. But understanding where to invest and why will mean the difference between hype chasing, and bottom line impact.
When a technology is as highly anticipated as AI, everyone wants to get in on the act. Some marketing technology vendors are also less than honest about what their AI powered tools can do, and no one solution offers a panacea. More often than not, people and business logic is just as critical to AI success. Just as technical literacy has always been an important part of a marketer’s skillset, AI literacy will become critical. It behoves all marketers to understand the basics of how machine learning algorithms work, the difference between supervised and unsupervised machine learning, and how to manage AI-assisted marketing and ad-tech tools. The closer marketers become to their in-house data science teams, and the easier the communication and collaboration, the greater the measurable impact will be.
Marketers today can harness AI to improve data processing, map the customer journey, optimise customer offers and improve the overall customer experience. One way to do this is to reach outside of the marketing organisation into data science and the supporting engineering and product groups of your business, as well as trusted technology partners and advisors. There are numerous guides to machine learning and artificial intelligence out there, many of which can be understood by those without a technical background. To help build a common language between marketers and data scientists, here is some reading to satisfy your hungry, and soon to be augmented, human minds. Good things will follow.
- Machine Learning is Fun!, an excellent 15min detailed primer on machine learning from Adam Geitgey
- Machine Intelligence Landscape (think AI ‘Lumascape’) by Shivon Zillis, founder of Bloomberg Beta, to understand the vendor and partner ecosystem better
- If you really want to go deep on the foundations, Robbie Allen’s deeply curated list of AI/ML resources is excellent
And for the really brave, why not enroll in world renowned Andrew Ng’s Stanford online course on machine learning and get hands on yourself!