# Recommend · Data Trends 2019 · Zorraquino > Suggesting what interests the customer most. URL: https://www.zorraquino.com/en/trends/data-trends-2019/recommend ![Recommend: Suggesting what interests the customer most.](https://cdn.sanity.io/images/0w1142l8/production/c4e2d7e99f48e57a6b9e61bb6e00d39ccfec3bcc-1600x1199.webp/recommend-suggesting-what-interests-the-customer-most.webp?w=1600&fit=max&auto=format&q=85) Trend 01 # Recommend 1. [Data Trends 2019Home](https://www.zorraquino.com/en/trends/data-trends-2019) 2. [Trend 01Recommend](https://www.zorraquino.com/en/trends/data-trends-2019/recommend) 3. [Trend 02Design products](https://www.zorraquino.com/en/trends/data-trends-2019/design-products) 4. [Trend 03Fine-tune campaigns](https://www.zorraquino.com/en/trends/data-trends-2019/fine-tune-campaigns) 5. [Trend 04Automate](https://www.zorraquino.com/en/trends/data-trends-2019/automate) 6. [Trend 05Improve logistics](https://www.zorraquino.com/en/trends/data-trends-2019/improve-logistics) 7. [Trend 06Cluster](https://www.zorraquino.com/en/trends/data-trends-2019/cluster) 8. [Trend 07Understand reality](https://www.zorraquino.com/en/trends/data-trends-2019/understand-reality) 9. [Trend 08More data sources](https://www.zorraquino.com/en/trends/data-trends-2019/more-data-sources) 10. [Trend 09More types of data](https://www.zorraquino.com/en/trends/data-trends-2019/more-data-types) 11. [Trend 10A-commerce arrives](https://www.zorraquino.com/en/trends/data-trends-2019/a-commerce-arrives) 12. [Trend 11Integration & Open Data](https://www.zorraquino.com/en/trends/data-trends-2019/integration-and-open-data) 13. [Trend 12New players](https://www.zorraquino.com/en/trends/data-trends-2019/new-players) 14. [Trend 13Visual Data & Storytelling](https://www.zorraquino.com/en/trends/data-trends-2019/visual-data-and-storytelling) Recommend Suggesting what interests the customer most. ## In short Data to recommend better and personalise the sale. Recommending, the best way to personalise the sale. Back to basics: the best salespeople are great recommenders. Increasingly used by all kinds of companies. Different types of algorithms, which evolve according to the goal and the type of company. Based on previous purchases, purchases by customers like you or the best sellers in each category, etc. Popularised by Amazon, Google, Netflix, Spotify and the like. ## Cases 01 / 02 ## More on the trend ### References - [5 lessons you can learn from Amazon’s recommendation engine](http://altitudelabs.com/blog/amazon-product-recommendation-engine/) - [What are recommendation systems and how do they work?](https://medium.com/mediarithmics-what-is/what-are-recommendation-systems-and-howdo-they-work-91aa8bf3ec11) Pages 5–14 of the report [![Design products: Data to know what people want to buy.](https://cdn.sanity.io/images/0w1142l8/production/8d69b45810d7992744a00bcca84b5656acb243d9-1600x945.webp/design-products-data-to-know-what-people-want-to-buy.webp?w=1600&fit=max&auto=format&q=85)](https://www.zorraquino.com/en/trends/data-trends-2019/design-products) Next trend · 02 [Design products](https://www.zorraquino.com/en/trends/data-trends-2019/design-products)