SwiftIQ Blog

Data, APIs, Predictive Insights and More

SwiftIQ Awarded 2016 Vendor of the Year by RaceTrac

Posted by Lea El Hage

May 22, 2017 4:17:45 PM

Atlanta - 4/26/2017


Topics: Category Management, retail analytics, baske-level data, SwiftIQ, NACS


Posted by Lea El Hage

May 2, 2017 11:18:19 AM

Leveraging the power of basket-level insights to drive sales and profitability.

By Tom McDonald & Lea El Hage


Topics: SwiftIQ, Awards

Topics: Category Management, data analysis, transaction data, advanced analytics, future, retailer, retail collaboration, supplier, CPG

What You Need To Know To Partner With The Retailer Of The Future

Posted by Stacy Klimkowski

Aug 11, 2016 4:30:00 PM

Retailers, more so than ever, are changing the way that they conduct business with consumer packaged good suppliers (CPGs). According to a McKinsey report Playing catch-up: How to partner with the retailer of the future, in order to effectively maximize retail partnerships and boost sales, consumer packaged goods suppliers need to adapt the way they approach retail support. The top three trends influencing the way retailers work with CPG suppliers from the report are summarized as follows:  


Topics: Big Data, Category Management, Merchandising, CPG Retailer Collaboration, promotion, retail technology, Real-Time Analytics, vendor portal, dayparts, basket-level, coca-cola, Anheuser-Busch, advanced analytics, leaders

Consumer Good Technology Visionaries and the top 3 data trends on their minds

Posted by Stacy Klimkowski

Jul 26, 2016 12:00:00 PM

Working with two leading multi-billion dollar global brands, Coca-Cola and Anheuser-Busch has validated the cutting edge work that SwiftIQ is doing around retail data analytics. So what is it like working with these CPG giants? The 2015 and 2016 Consumer Goods Technology (CGT) Visionary list named two of our champion partners at Anheuser-Busch, and Coca-Cola as forward thinking visionaries, and gave insights into what is on the top of their minds when it comes to data analytics. The following are the top three takeaways from these leaders, but also coincide with what we at SwiftIQ are seeing across vendors in this space:


Topics: Big Data, Category Management, POS Data, retail analytics, transaction data, data management, cloud computing, security, cloud

Why Retailers and CPG's should harness the power of the cloud

Posted by Stacy Klimkowski

Jun 21, 2016 7:30:00 AM

“You have got your head up in the clouds,” used to seem like an insult, however, these days, whether you are a supplier or a retailer, if you are not thinking about how the cloud can help your business take analytics to the next level, then your head may truly be up in the clouds.  We tend to hear a lot about how digital companies are using cloud computing, but if you work in retail analytics, you may be relatively unfamiliar with what the cloud may actually represent for high scale data analysis, business agility, and data security. This article will outline what exactly the cloud is, how it is ideal for large scale data processing and machine learning from high velocity transaction data, as well as how the security compares to inefficient on premise based systems.


Topics: POS Data, Data Sharing, retail analytics, retail technology, grocery, Millenials, basket-level

How I Hit the Jackpot with SwiftIQ

Posted by Victoria Conlon

Jun 2, 2016 8:00:00 AM

This is the story of how I won big in Las Vegas, but it might not be in the way you would expect. In a city full of casino floors and world-class shows, my weekend trip consisted mostly of groceries; to elaborate, it was at the 2015 National Grocers Association (NGA) Show last year when myself and a team of marketing students had the opportunity to present a case competition on the topic of technology in the grocery industry. Admittedly, my first thoughts on attending were “Wow, that would look great on my resume,” and “Free trip to Vegas, why not?” Little did I know how much impact that event would have beyond a simple resume boost or weekend getaway.


Topics: Category Management, Analytics, Basket Analysis, shopper mission, convenience store, item affinities, consumer packaged goods, consumer insights, data analysis, dayparts, breakfast purchase data, cross-purchase, shopper marketing, product bundles, co-occurance

Affinities and Promotions-Part 2

Posted by Stacy Klimkowski

Apr 1, 2016 8:51:03 AM



Topics: Big Data, Category Management, retail analytics, item affinities, consumer insights, dayparts, breakfast purchase data, cross-purchase, basket-level, bundles, promotions, energy drinks

Using Affinities to Optimize Promotions- Part 1 of 2

Posted by Stacy Klimkowski

Mar 23, 2016 7:00:00 AM


Whether you are a category manager, merchandiser, retail buyer, or shopper marketer in the fast moving consumer goods industry, chances are you use a variety of data sources to make important business decisions. When it comes to promotion planning, deciding what items are best to drive dollar sales and co-promote together can feel like a guessing game. The lack of insight surrounding item cross-purchase and incremental sales impact often results in promotions being run solely to use trade funds. By using full-store basket-level transaction data to identify item relationships, retailers and suppliers can make more informed recommendations. Here we examine how category and item affinities can be analyzed to plan and maximize your promotions.

For instance, if I am a category manager for the fast-growing high margin energy category, and my retailer wants to partner with me to run a bundled promotion/event, I would start by understanding what categories and items are sold with energy drinks and the respective relevancies they have together. The measure of these interrelationships are what we will call “affinities” and calculates the likelihood of a purchase of a category, item, or group of items, based on the respective co-purchases in the basket. The key affinity metrics that we will use to conduct a market basket analysis are as follows:

  • Occurrences: the number of times that an item was purchased with an energy drink
  • Support: the % of total transactions that have an energy drink and affinity item in the basket
  • Confidence: of energy baskets, it is the % of transactions that have energy drink and the affinity item purchased together
  • Lift: determines the likelihood of the transaction for an item/category vs. any other item/category (i.e. breakfast sandwiches are 50% more likely to be bought with energy than any other category)
  • Conviction: describes the strength and directionality of the product correlation; a higher positive number indicates energy strongly drives the affinity item, while a lower negative number shows that the affinity item more strongly drives the energy drink purchase.


The chart below illustrates that even though juices and tonics are the most often purchased item with energy drinks, occurring in 1.6 million transactions, the negative lift indicates that it is 7% less likely to be purchased with an energy drink than a random category. On the other hand, breakfast sandwiches, while occurring less frequently, are 50% more likely to be bought with energy drinks than a random category. Now that we know there is a connection between those categories, we look to the conviction which shows a negative number, indicating that without a breakfast sandwich in the basket, the energy drink purchase may not occur.


So we have learned that breakfast sandwiches are likely to be purchased with energy drinks and drive the sales of energy drinks, therefore, we can conclude that a bundled breakfast promotion with energy drinks is most likely to have success. The next step is to examine which items would be most relevant to promote together.


Below we can see that Monster and Redbull are fairly equally likely to be purchased since the lifts and convictions are in the same range and are directionally the same, however, Monster pairs slightly better with breakfast sandwich #1.


STEP 3: 

Extracting this same data by SKU level, also shows that Monster 16 oz. cans are the leading energy item purchased with breakfast sandwiches, occurring in 44% of baskets vs, the second leading item Red Bull 12 oz. found in 35%. Therefore, it makes sense to run the promotion with the better selling and higher price-point Monster 16 oz. drink.



When time context is added using a daypart analysis, we can see the frequency of energy drink purchases broken down by hour of the week. The below graph shows that energy drinks are most often bought from 5 a.m. to 9 a.m. which validates why breakfast is so relevant in the affinities analysis. Perhaps McDonalds is on to something in their decision to test selling Monster 16 oz. energy drink along with all day breakfast. 



Putting it all together, these insights can be used by category, sales and marketing teams to improve promotions, optimize store layouts, understand shopper behaviors, and even enhance media targeting.

SwiftIQ’s recent launch of its Affinity application can conduct on-demand market basket analyses in seconds and provide purchase behaviors across, items, brand, and categories, as well as time of day, day of week, and average dollars and items in a basket. If you want to optimize retail execution, or learn more about SwiftIQ’s product affinities request a demo by clicking here.



Subscribe to Email Updates

Posts by Topic

See all topics >