Is Your Big Data Hot or Not?

Example #1 – A retailer is looking at week-by-week sales of a new fashion line to understand how each product line is performing as items get continually discounted throughout the season. Data is gathered from thousands  of stores across the country and then entered into a heat grid graph that includes:

• X axis – week 1 through 12, beginning from the launch of a new campaign (e.g. Nordstrom’s Summer Looks)
• Y axis – product line (e.g. shoes, dresses, skirts, tops, accessories)
• Color of the squares – % of discount (e.g. dark red = 70%, red = 60%, orange = 50%, yellow = 30%, green = full price)
• Size of the squares – # of units sold

Looking at this graph, the retailer can easily see that most shoes sell at the beginning of the season – even without heavy discounts. This helps the retailer predict inventory levels to keep up with the demand for shoes.

It also shows that accessories almost never sell at regular prices, nor do they sell well when the discount levels are higher than 70%. Knowing this, the retailer can control its capital spending by not overstocking on this item. The retailer can also increase profit per square footage of their store by reselling its accessories earlier in the season to avoid high markdowns and inventory overstocks at the end of the season.

Example # 2 – A digital music streaming service provider is using analytics to assess the performance of its sales channels (direct vs. sales through different social media sites such as Facebook and Twitter) to guide future marketing and development spend. For that, the company uses a heat grid to map out:

• X axis – various devices (iPhone, iPad, Android Smartphone, Android Tablet, Blackberry)
• Y axis – various channels (direct site, Facebook, Twitter, …)
• Color of the circles – # of downloads (0-100 = red, 100-1000=orange, 1000-10000 = yellow, 10000+ = green)
• Size of the circles – app usage hours per day – the bigger the size, the more usage