Category Archives for "Analytics Use Cases"

Oct 14

Data Analysis for Retail Industry

By ganpati | Analytics Use Cases

Crunch Numbers to Manage Product Assortment

Crunch sales numbers to identify key products that are missing, and make decisions about assortment: An example of a home-run product was the story about an auto-parts retailer that sold many parts for Honda Accords — and also sold lots of brake pads, but, for some reason, he didn’t specifically carry Honda Accord brake pads. Thanks to the data analysis, he enjoyed greatly increased sales and profit when he simply added Accord brake pads to his assortment.

Don’t leave the decision of Adding Products to chance

Fisher rightly notes that retailers will periodically update their assortment, getting rid of products that are not selling so well and adding new products that they think will sell better. When retailers think about updating their product assortment, they have two decisions — get rid of the worst sellers and add some new products. The decision about which products to get rid of is easier because you have sales data.

Don’t Throw Away Low Seller That Customers Want

At times some low selling products may become key to customer retention and attracting them to the store. Fisher’s analytics show that even if that product is not selling very well, it may well be the favorite product of some of your best customers! And if you get rid of it, you not only lose the sales on that product, but you lose the customer and everything else they are buying. This happened to Walmart. In 2008, Walmart made an effort to “declutter” stores by removing 15% of the SKUs they carried. What happened was an immediate decline in sales, and Walmart eventually had to roll back most of the changes.

Learn from the latest successful innovations happening in the retail supply chains

One lesson retailers can learn from others as well as themselves is to track the spike of product demand. For example, at Christmas, notes Fisher, the demand is for fashion apparel, toys, and many consumer electronics products, in particular. “These are products that have a gross margin of 50% or more, so missing a sale is extremely expensive,” Fisher notes. For those products, you need flexibility. Your data analysis will allow you to identify and plan better for these high times, and low times as well.

Many retailers believe that during a recession, people become more price-conscious. The impact of the recession on retailers, for the vast majority, maybe 90%, was bad news, says Fisher. Revenue went down. However, he notes that some “discount” retailers (like Walmart) are counter-cyclic and do as well or better in a downturn. Follow trends. Keep track and see what’s happening on a longitudinal scale. Again, pricing intelligence and tracking and analyzing your data in an ongoing way allows you to have both the facts and the feel for what’s happening, which will make better prepared to deal with what’s coming.

Retailer Scanned point of sale data and Retailer Measurement data from 3rd parties like Nielsen and IRI (key account data) are derived from scanned point of sale data. Together, these 2 variations of POS data are the primary data sources with which we do category management work.

These data sources provide a clear picture of sales, movement and tactical performance. They also give a real-time view of category performance and trends. Sales and marketing professionals should have strong competencies with POS data, well beyond pulling and reading POS data.

Sep 11

Data Storytelling: The Essential Data Science Skill Everyone Needs

By ganpati | Analytics Use Cases

businessman drawing an arrow on a wall standing on graph chart

By Brent Dykes

Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google’s Chief Economist Dr. Hal R.Varian stated, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.” Fast forward to 2016 and many businesses would agree with Varian’s astute assessment.

Google’s Chief Economist, Dr. Hal R. Varian predicted the growing importance of data skills back in 2009. (Photo by Imeh Akpanudosen/Getty Images for Variety)
As data becomes increasingly ubiquitous, companies are desperately searching for talent with these data skills. LinkedIn recently reported data analysis is one of the hottest skill categories over the past two years for recruiters, and it was the only category that consistently ranked in the top 4 across all of the countries they analyzed.

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