May 9, 2021 • 3 MIN READ
Meet Patrick, our first-ever Data Analyst & Product Research Lead. In his role, Patrick leads the team that fine-tunes our data channels — both big and small — to unlock actionable insights about our customers, our products and our four-sided marketplace.
These channels help us understand what's on the restaurant menus, recommend the most affordable nutritious meal kits, normalize catalogue data, make personalized basket recommendations, serve coupons, and get food to your door within those tight 60-minute windows.
We caught up with Patrick to talk about R&D's practical applications and (of course) the future!
Did you know that The Harvard Business Review calls Data Science "the hottest job of the 21st century"?
Data science is not a new term in the world of technology today. It has taken over the corporate world in the last few years. The demand for skilled data science professionals has seen an upsurge, as organizations are on the constant lookout for data science professionals to resolve business complexities with efficient data analysis.
New sources of data, from log files and transaction information to sensor data and social media metrics, present new opportunities for any Data Science team out there to achieve unprecedented value and competitive advantage in the expanding industry space. From a business standpoint, businesses will need to empower people across their organization to make decisions swiftly, accurately and with confidence. The only way to achieve this is to harness big data and behaviour retail analytics to make the best plans and decisions, understand customers more deeply, uncover hidden trends that reveal new opportunities and more.
At Ayazona, I believe that deeper, data-driven customer insights are critical to tackling challenges like improving customer conversion rates, personalizing campaigns to increase revenue, predicting and avoiding customer churn, and lowering customer acquisition costs alongside improving our logistics. On the other hand, consumers today interact with companies through multiple interaction points — mobile, social media, stores, e-commerce sites and more. These dramatically increases the complexity and variety of data types we have to aggregate and analyze.
When all of these data is aggregated and analyzed together, it yields insights we never had before — for example, our high-value customers, what motivates them to buy more, how they behave, how and when is it best to reach them? Armed with these insights, we can improve customer acquisition and drive customer loyalty. Thus, Data Engineering is the key to unlocking the insights from our customer behaviour data — structured and unstructured — because we can combine, integrate and analyze all of our data at once to generate the insights needed to drive more value to our customers and partners.
Well, I have a fun fact...there are nearly as many pieces of digital information as there are stars in the universe 🤓.
Want to explore new practical uses for Data Science with Patrick? See our current openings below;
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