What does couture have to do with cured Italian meats? Almost nothing. Which means: something! Both are produced by small companies who know something about taste.
Being a tastemaker is all about knowing what people want before they want it, then using that information to predict trends. In the business world, that means connecting people with data. Fashion house Adrianna Papell discovered as much. Founded in 1979, the New York City-based company grew from a simple dressmaking outfit specializing in silk garments to a fashion house devoted to outfitting women for all occasions. Clothing the elegant masses and keeping up with fickle consumer sensibilities means tracking products and sales figures through time and by geographic location. As the company grew, it had a hard time keeping up with all that data.
Columbus Foods, on the other hand, isn’t clothing people, but feeding them. The California-based company makes salame, soppressata, and your other favorite deli meats every single day since 1917. In fact, they have 250 different products they sell across the US. But making good meat is time- and resource-sensitive. For instance, some types of salame take 90 days to mature. Knowing how much of it to make ahead of time to meet customer demand is vitally important — making too little means missing out on sales, while making too much increases the chance of some food going bad. For a long time, there was no good way to quickly predict what move to make.
To better serve their customers, Adrianna Papell and Columbus Foods needed a means of making connections within the data they collected in order to predict trends in consumer demand. Both companies started using business analytics to get a grasp on that data, giving them a clearer picture of what their customers want ahead of time. And in both cases, analytics ultimately helped them increase sales.
Adrianna Papell partnered with IBM business partner Sky IT Group to connect the dots. Using point-of-sale data, retail partner location, and time, they were able to get a sense of what their customers were buying and when they bought it. Armed with that insight, they were able to predict what would sell well, reduce production of what wouldn’t, and increase sales by fifteen percent.
Meanwhile, Columbus Foods turned to IBM business partner Applied Analytix to predict purchases and prevent inventory shortfall. By tracking historical sales data for their meats, they could more accurately predict buying patterns and make better-informed decisions about how much meat to make. So no matter how long it takes to make a batch of pepperoni, they don’t have to worry about spoilage.
Understanding trends isn’t about predicting the future. It’s about the ability to help convert data into revenue. Check out IBM’s wide array of business analytics products to start making those connections.