dataset on restaurants to perform market basket analysis

I’m sharing this story so that it sticks in your mind. I had performed Association Rule Learning by hand, when there are off-the-shelf algorithms that could have done the work for me. It was only after I delivered and presented the report that I released what a colossal mistake I made.

I ascribed numbers to the rules such as support (the number of records that match the rule out of all record) and lift (the % increase in predictive accuracy in using the rule to predict a conversion). I came up with rules like: “ User Creates x widgets in y days and logged in n times then they will convert“. I came up with a bunch of rules that indicated outcomes and presented ideas for possible interventions to influence those outcomes. I spent weeks pouring over the data, looking at correlations and plots.

We were interested in patterns of behavior that indicated churn or conversion from free to paid accounts. I once did some consulting work for a start-up looking into customer behavior in a SaaS app. As market leaders continue to capitalize on this technique, a new predictive model of market basket analysis is making in-roads across industries in an effort to identify consecutive purchase patterns.Photo by HealthGauge, some rights reserved. Today more and more businesses are discovering new ways of using market basket analysis to gain meaningful insights into hidden customer purchase patterns. Hence, they approached Quantzig to help them develop a new data model that would support the analysis. When analyzed it was found that they lacked the necessary warehouse architecture to support market basket analysis. The client- a leading retailer of organic food products found itself struggling to improve sales. By incorporating market basket data in BI tools businesses can develop precise strategies to meet their business goals. This turns out to be a major roadblock for an industry whose sole purpose is to fulfill the needs of its customers. Surprisingly, only a fraction of the retail industry players and decision makers make decisions based on the information regarding their customers’ purchasing behavior. Market basket analysis empowers retailers to identify product groups that a customer is more likely to buy, given a previous purchase or a contemplated purchase history. One of these tools is market basket analysis or affinity analysis. To thrive in a complex business landscape retailers require specialized tools at their disposal to cater to the dynamic shifts in customer needs and preferences. The retail industry as a whole is highly fragmented and complex owing to the rapid pace of technological developments and the ongoing digital shift.

Dataset on restaurants to perform market basket analysis