From Black-box AI to Communicable AI
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By focusing on labelling and choice architecture, Communicable AI brings better results than any Black-box AI engine ever will.
The pitfall of the “Black-box AI”
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If you haven’t ever integrated any product recommendations on your e-shop you might get lured into promises of vendors like “our AI-based recommendations can increase 20% to 30% of your revenue”. Every one who tried a recommendation engine, including the ones very satisfied with it, knows that turning it off would not reduce their turnover of such a percentage.
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Our opinion on this matter is quite clear. We believe it is not.
The “what” versus the “how”, the “where” and the “why”
Until recently, all the focus of recommendations has been put on the “what”: what products should be recommended?
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One could argue, 30% (and possibly even a bit more) is quite substantial. But consider now the following aspects:
the WHERE
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Displaying these recommendations in an engaging and visually dominant way at the right moment of the visitor’s journey (for example, as an overlay after the “add-to-basket” button is clicked) can increase the impact of these recommendations 2x to 5x compared to their traditional position in the basket page
the WHY
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If you are a Netflix user, you know that their personalization system is heavily oriented towards grouping their recommendations in many sections, each with a clear and engaging label. It is not only telling you what you might be interested in watching, it tells you why these recommendations are presented to you.
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Simply generating the label of your recommendations in such a way can increase 2x to 3x their impact.
the HOW
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When scientists like Richard Thaler built the foundation of behavioral economics, in what resulted in the (relatively) simple idea of a “nudge”, their approach rooted in something deeper called “choice architecture”.
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Providing several groups of suggestions with appropriate explanations in their label and letting the visitors “see more” of each group by going to a dedicated listing page can further increase 1.5x to 2.5x the impact (on top of the prior section’s effects).
The power of “Communicable AI”
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We summarize this capacity to understand where to display recommendations visually (the WHERE), communicating why the user sees such recommendations (the WHY) and the choice architecture of what types of recommendation groups should appear where and for whom (the HOW) under the term “Communicable AI”.
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Better Self-learning
In addition to knowing what product people click and buy to improve the recommendations further (as all good AI providers do), Communicable AI enables Boxalino to learn much faster and better by identifying on what labels people engage with and which dedicated listing page are most visited and used. All these feed-back information give highly valuable information about what works or not and why.Better Personalization
By letting the user rate the suggestions and their groups (labels) by giving thumbs up/down feed-backs or adding such labels in their wish-list, Communicable AI can profile your customers not only based on what product they click on, but what are the motivating reasons are most suited for their needs.Better Reporting
How should a product recommendations report look like? This is a tough one, as you can’t analyze each context / profile performance for each product (they are too many of them) and grouping by categories or brands might hide the key information of what works or not. However, reporting on the performance of the different labels both in the context of cross/up selling recommendation, but also in the context of the visit and impact of their dedicated listing page is much more effective and useful as a report.Better Marketing
If the dedicated listing page related to any of the product recommendation labels drives many clicks and has a good conversion rate you might want to use such pages as landing page for your marketing activities?
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