Machines are getting better at thinking for themselves. Microsoft’s DeepCoder, for example, has learnt to write its own code. And Google has built Federated Learning — a tool that automatically personalizes apps directly on users’ devices.
For marketers, this evolution in machine learning has enormous potential: almost all (98%) think it will benefit their marketing efforts. It’s easy to see why confidence is high – smart technology can help brands in multiple areas, from boosting targeting precision to improving product suggestions.
But this enthusiasm is still tempered by apprehension.
We live in very privacy-conscious times, with the new EU-US Privacy Shield and General Data Protection Regulation (GDPR) putting data usage in the spotlight, globally. And as the machines we create become more autonomous, many marketers are starting to ask the question: how do we ensure intelligent tech processes data in way that safeguards privacy and trust?
The machine learning opportunity
To start with, let’s take a look at what machine learning offers. First and foremost, there’s efficiency. Not only can machine learning analyze data on a scale that humans and existing programmatic tools can’t achieve, but it can learn how to action data without being programmed.
Secondly, it can be applied for a myriad of purposes. For example, a brand may use machine learning to ensure the overall customer experience is seamless: connecting data across different departments and providing fast responses via tools like chat bots. Or, marketers might use it to optimize advertising impact by matching ads with individual needs and attributes, such as their location, instead of targeting broad audience segments.
Machine learning can even be used to predict which techniques and creative will work best; assessing behavioral data over time to build tailored and relevant messages that are likely to be well received. This ensures marketers are maximizing engagement while minimizing wasted ad spend. But marketers will only benefit from AI-based technology if their data and processes are in optimal shape – they must guarantee compliance with privacy laws. After all, machine learning will only ever be as good as the data and programming powering the technology.
Keeping a hold on privacy
Overcoming consumer privacy concerns is undoubtedly a key challenge for machine learning. Following a record-breaking number of data breaches in 2016, it’s not surprising that consumers are anxious about how companies use their data and digital privacy in general.
To maintain consumer trust and loyalty, marketers must ensure privacy is prioritized at every stage of data processing and machine learning is supervised. After all, machines that are able to program themselves – free from human guidance – are at risk of producing unpredictable outcomes, and unknown usage can put privacy at risk. Marketers therefore need to check that the input, procedures, and output of autonomous machines are scrutinized for accuracy and privacy protection, paying particular attention to the transparency of algorithms.
They must also comply with new data privacy regulations. The EU-US Privacy Shield and the GDPR are transforming how businesses collate, process, and store European consumer data, which means they are both having a significant impact on international brands.
Effectively a replacement of the Safe Harbor agreement, Privacy Shield is a framework that governs the transfer of EU citizens’ data to the US. The GDPR has a broader reach, applying to any business processing data that makes EU citizens personally identifiable. To meet the requirements of both, marketers will have to make all data processing transparent, including that of smart machines, especially if they want to avoid the GDPR’s costly fines.
Taking control of data
The most effective way to guarantee data stays safe is to place it where its creation, usage, and storage can be tightly controlled: a centralized hub.
By unifying consumer data from disparate sources — including first-party customer data and third-party insight — marketers can analyze and manage insight before it is shared with smart machines. This will allow them to ensure that any risks to consumer privacy are mitigated and data output is accurate. And that’s not to mention producing a complete view of consumer journeys on all channels that will come in handy for enhancing personalization.
Yet they must select the right tools if data is to truly be protected. To maintain a high level of security, systems should have the capacity to assess data from multiple data sets and filter it accordingly. And if systems are to meet transparency regulations, they need to allow instant data transfer and accessibility at all times, from every area of the organization. Only with a precise and privacy-assured view of consumer data can machine learning fulfill its potential as a tool that improves and redefines the customer experience.
Machine learning in the future…
Of course, machine learning is not restricted to marketing and in the next few years we are due to see it make waves in a wide range of sectors. For instance, computer scientists at MIT are experimenting with neural networks that can provide evidence for healthcare decisions and improve understanding of diagnoses.
Clarity and privacy are due to remain center stage too: earlier this year LinkedIn founder, Reid Hoffman and eBay founder, Pierre Omidyar each invested $10 million in the Ethics and Governance of Artificial Intelligence (AI) Fund, which is intended to support research into the ethical issues of AI and how to solve them.
There is a long road ahead for machine learning in its many guises — be it smart tech, AI or automated tech. To keep their creations under control, marketers need to balance security with creativity; centralize data management to make things simple, and adhere to data regulations, all the while never losing sight of the vast and exciting opportunities machine learning can promise.
About the Author
Adam Corey is VP Marketing at Tealium