Data is the most strategic asset for any business. However, massive volumes and variety of data has made catching it at the right time and right place, discovering what’s hot – and needs more attention – and what’s not, a bit trickier these days.
Heat grids are ideal for seeing a range of values in data as they provide a gradient scale, showing a change in data intensity through the use of colors. For example, you can see what’s hot in red and what’s normal in green; and everything else in various shades of color in between. Let me give you two examples of how companies have used heat grids to see if their data is 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
This graph helps the music service provider analyze data from millions of records to quickly understand the popularity and usage patterns of their application on different devices, sold through different channels.
Heat grids can be use in variety of other forms, such as survey scales, product rating analysis, customer satisfaction studies, risk analysis and more. Are you are ready to find out whether your big data is hot or not? Check out this 3 minute video to learn how heat grids can help you.
Understanding buyers/users and their behavior is helping many companies including ideeli – one of the most popular online retailers - and Travian Games – top German MMO (massively multiplayer online) game publisher – gain better insight from their hottest asset – their big data!
What is your hottest business asset?
- Farnaz Erfan, Product Marketing, Pentaho
This post originally appeared on Smart Data Collective on July 13,2012