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3 burning AI questions facing ecommerce marketers

5th Oct 2017
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As marketers, we’re in for an exciting few years. In the run up to 2020, we’re set to see the real impact of Artificial Intelligence (AI) technology on the world of digital commerce. Gartner predicts that by then, 30% of digital commerce revenue growth will be attributable to artificial intelligence technologies, while at least 60% of businesses in the sector will be making use of the technology.

Such growth should come as no surprise as AI development increases and businesses become more receptive to the benefits it may bring them. This is especially true for digital commerce marketers who view AI as a valuable tool to support new marketing disciplines such as personalisation, predictive bidding and voice search, all of which require analysing reams of data and extracting valuable insights.

With all the promise of AI technology in marketing, understanding where and how it can have the most impact is essential to the modern marketer:

What should AI be able to achieve?

Marketers need to consider AI’s capabilities before buying into the hype. Effective digital commerce marketing requires analysing massive amounts of data and identifying underlying relationships between these datasets to inform decisions and approaches. Many brands and digital commerce technologies have been doing this successfully for years and AI solutions are not intended to replace these wholesale; rather they can be used to enhance current systems by continually refining and improving the analysis process over time.

Improved efficiency is always welcome, especially when marketers are having to deal with the increasingly complex and large amounts of data that is becoming available to them. Machine learning is a double-edged sword in this respect; not only does it save time in analysing this ever-increasing number of attributes, but it also provides marketers with a much more granular view of this data than previous systems could without extensive modelling and reworking.

Hand in hand with these benefits is the agility that AI can provide to data analysis. In the age of the ever-changing online marketplace, where retailers expect a real-time, omnichannel view of the customer, it is vital to be able to respond to this rapidly. After all, having this insight is no good if it can’t be acted upon. AI can allow for regular algorithm changes to track and capture this behaviour, instead of the manual retooling of systems which can miss out on key insights.

But how can AI actually achieve this?

Once the uses and advantages of AI have been identified, marketers need to decide on how to manage its implementation. AI is not just an ‘on switch’ that can be flicked and left to fix everything. To unlock its potential it needs good, clean data to work with in the first place, or the system may start throwing out inaccurate suggestions.

Even when AI is able to identify relationships that influence commerce marketing, it doesn’t suddenly render previous technology and practices obsolete. It should be used to quickly extract actionable factors from data which are then plugged in to existing scoring and weighting systems to ensure everything remains aligned to business objectives. Remember, it’s not a fix-all solution!

Fundamentally, people are still a marketer’s best resource. At its heart, marketing needs to resonate with shoppers and be driven by business objectives and strategies which can still really only be understood and interpreted by humans. AI can’t account for factors defined by business logic, and should be used alongside other systems to enhance and inform employees’ decision making rather than to replace it wholesale.

So what does AI mean in practice for marketers?

Once implemented, AI solutions can allow commerce marketers to improve their strategies in a number of ways. Perhaps most beneficial to the here and now is its enablement of mass personalisation. An increasingly complex area but one which has never been more important as the industry comes to realise that terms such as ‘millennial’ are far too vague to base campaigns on.

AI, through its ability to distil data granularly, can be used to efficiently break down shoppers into even more specific groups (sometimes even groups of one!) to allow for significantly more accurate audience segmentation that resonates.

AI can also assist marketers in addressing the looming challenge of understanding cross-channel consumer habits. Thiry six percent of online transactions now take place across more than one device, a critical factor which can cloud marketers’ strategies, making it harder to identify users who are constantly flitting between devices. With AI assistance, it can become much simpler to identify these trends and assess what roles different channels play in the marketing journey by monitoring consumer patterns that might not otherwise be identified.

Both of these can factor into improved prediction systems based on input models and target outcome. AI analysis of customer behaviour can be used to develop models predicting how likely a consumer is to open an email, purchase a product or register online, all of which can be used to help tailor marketing or even implement a dynamic pricing structure which adapts based on a constant data flow.

AI truly can benefit commerce marketers, and with 63% of respondents to Gartner’s annual enterprise survey suggesting that they had plans to invest in AI before the end of 2017, the AI boom is truly under way. However, if it’s to live up to its promise, marketers must prepare their existing systems and expectations now, or risk being caught out by the hype and failing due to poor implementation or an over-reliance on AI.

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