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Founders looking forward: Our 2020 predictions

Being able to accurately predict the future is understandably difficult: the Danish physicist Niels Bohr arguably summed it up perfectly when he said, “predictions are hazardous, especially about the future". However, with the end of December fast approaching, the time has come for the inevitable annual reflections on the 12 months to date and predictions for what the future will hold. Read on to see what our co-founders think will be happening in 2020 and beyond from each of their unique perspectives:

 

Matt Hayes - CEO

 

1. You might call me an optimist, but I hope and expect there to be a greater focus on ‘honest’ marketing, with brands increasingly moving towards removing emotive and emotional anxiety-inducing messaging. The days of Boohoo’s language and branding – with deadline countdowns for sales that don’t actually end – are surely numbered.

 

2. Brands that successfully deliver engaging and effective 'interactivity' within a customer’s inbox will see real success with it. Expect to see more forms, more ‘click on images’ to reveal, more AMP based stuff (Gmail) and maybe add to cart direct from email. However, one thing I think we’ll see less of is scratch and reveal or video, because as a medium in email, it’s dead.

 

3. More brands will (finally) get to grips with better personalization, threading more individualised content into all email programs, especially BAU. Tech companies have helped them overcome the barriers in the last few years, but brands need encouraging to step up and get it done, so now it is about implementation and actually doing it! 

 

Gabri Corti - CPO

 

1. From a product perspective, I expect 2020 to introduce a wiser use of AI in e-commerce and usher in more widespread adoption. It’s an exciting time for AI and this trend will surely continue in the coming year. Advancements in technology are providing increasingly powerful machines, removing many of the computational barriers that we had in AI until a couple of years ago.

 

2. I also expect to see progressively more tech companies offering AI-based solutions and more retail businesses willing to experiment with them. The new AI-related feature I most anticipate seeing will be purchase intent prediction. I believe an increasing number of pioneering brands begin deploying solutions to accurately predict users’ behaviour on e-commerce applications, using information obtained in real-time by analysing customers’ actions and behaviours. For example, at Kickdynamic we’ve already noticed some customers browse the product comments for a long time before they place an order. By acknowledging this and other patterns, businesses will be able to better understand users’ actions, intents – and ultimately – to offer better services to their customers.

 

3. Email will benefit even more from AI with real-time product recommendations. Current solutions for email provide product recommendations at the time of send, i.e. the products to show to each recipient are determined before the email campaign gets sent. In the coming year these recommendations will be generated on-the-fly at the time recipients read their emails. The real-time generation is crucial to improve the quality and effectiveness of the recommendations because an AI system will be able to include more variables like stock availability, latest price changes and offers, the time of the day and users’ approximate locations as well as weather conditions to tailor the recommendations even more precisely.

 

Ricky Saccomandi - CTO

 

1. Every decade has had its database and 2020 will mark the definitive start of the graph database era with massive adoption of this technology. In e-commerce, the surge of graph database technologies will contribute to more AI, in particular, to more real-time predictions, not only about what a user may like but also about what a user's intentions are (buying or browsing). This is because graphs unlock the immense potential AI and machine learning hold because the technology incorporates all of the context and connections needed to make AI more broadly applicable.

 

2. The dramatic success in machine learning has led to a torrent of AI applications. AI systems are taking over a vast array of tasks that previously depended on human expertise and judgment. Often, however, the "reasoning" behind how these systems make decisions is not clear and can cause a lack of trust. As AI is applied more broadly, it will be crucial to understand how it reaches its conclusions. XAI—especially explainable machine learning—will be essential to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent "machines". Especially in fields such as health-care or finance, where mistakes can have severe consequences, the black box aspect of AI makes it difficult to trust. Furthermore, data protection regulations that have recently entered into force emphasise the “principle of transparency” of intelligent algorithms and imply the “right to explanation” of algorithmic decisions.

 

It should be noted that some (perhaps naïve) explainable AI has long been familiar in e-commerce as part of online recommender systems. Some types of recommender systems adopt intuitive yet easily explainable models to generate recommendations, such as memory-based collaborative filtering, which provides recommendations based on ‘similar users’ or items.

 

However, as the machine learning algorithm adopted to making predictions increases its accuracy, the explainability of the result often becomes harder. State-of-the-art recommendation and search models extensively rely on complex machine learning and latent factor models (such as matrix factorisation or even deep neural networks) and they work with various types of information sources. The complex nature of these state-of-the-art models makes search and recommender systems black-boxes for end-users and the lack of explainability arguably weakens the persuasiveness and trustworthiness of the system. As such, with the growth in availability and utilisation of AI and machine learning in e-commerce and retail marketing, there will be a parallel development of XAI to complement it.

 

 

 

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