Step by Step Guide: Digging in with Multiple Metrics

Askuity Customer Success
By Askuity Customer Success
Oct 07 2016

You probably spend a lot of time looking at overall sales performance. How are you tracking to plan, how are you tracking to last year, what is your YTD trend? But then the unexpected happens – there is big dip or peak in your data and you want to know why. Here is a step-by-step guide how to drill into data on Askuity. 

 

Is it retailer specific?

Typically if you spot an overall trend, the first step is determining whether the trend is retailer specific. Pull up all your retailers on sales overview and check to see if all retailers have the same trend. A lot of the dips/peaks that we see in data are specific to one retailer.

 


In the example above, we focus on one retailer. You can see that the unit sales are quite steady until April, when they start to climb, almost doubling by August.

Could inventory levels or average price have influenced this?

With the new multiple metrics feature, it’s easy to plot last year data, turns or average price against your sales.

1. Add LY data as a second metric to sales units. See if you saw a similar increase/dip at this time last year, which could be an indication of a seasonality trend.

 

It’s clear in this case that the same sort of break out did not occur last year and that this increase is probably not due to seasonality.

 

2. Add inventory turns as a second metric to sales units. See if any increase or decreases in turns align with your dip or increase in sales. If your turns have jumped up while your sales are dropping – this is a good indication of some stocking issues. In terms of overall trend, we recommend using turns vs units on hand. If you have an overall increase in sales units, you would expect to see an increase in units on hand but it is difficult to see if the increases are proportional by eye. If your inventory has steadily increased with sales, you can expect to see turns stay flat while sales increase.

 

In this example – we see that turns drop a little as sales spike. This would indicate that shipments have kept up with the increased demand.

 

3. Add average price as a second metric to sales units. Check and see if a price increase or decrease has coincided with the drop or increase in sales. Note that a lower average price is not always an indication of a discount but could also be the introduction of a lower value item with high sales.

 

In this example, the average price decrease at first and then increased with the influx of sales. This would lead me to believe that two or more things are occurring. If we focus on the big spike in average price during the last 8 weeks, we could guess that a new product line at a higher price point was introduced to the market.

 

If you identify that inventory or price could be the culprit – the next step is to try and narrow down the locations and products that influenced this.

If neither turns nor price seem to have a coinciding trend to the sales (as is the case in the example), the next steps are still the same.

Is this trend specific to a certain set of products or groups of stores?

Now that you’ve found a trend, you will want to know:

  1. Is this national level or specific to a region?
  2. Is this specific to a certain product or group of products?

To narrow down to a specific region, we like to look at all products by state. When you add all states to sales overview, it can take a minute to process due to the amount of data and creates quite a confusing graph. However – if one or a few states significantly contributes to the trend, it should be obvious over all other states which remain level. This example is a perfect portrayal of that – where the state of Texas definitely stands out. Going forward, we know we can focus on that specific state or group of states. Drop all other states out of sales overview. If you don’t see anything specific, just go back to the retailer level tag, or try looking at trends at a higher level (region) if there are too many confusing lines with states to draw a conclusion.

 

 

We typically recommend starting at the top level of your product hierarchy, for example at the category level. Eyeball the data, similarly to how you did for the states and see if one category stands out. If it does, then break it down into sub-categories. If you do not have a hierarchy or have a very large child tag – jump into reports and run a product report. By scrolling through the product report and using the percent change metrics, you can spot a trend at the product level. (Ask your CS manager if you need a bit more help with this step). The smaller the tag you identify, the easier it is to find a specific product in the reports.

 

In this case – it is clear that there are two categories contributing to this: Sealant and Tooling.

 

Validate the Assumption

Tackle these categories one at a time, starting with Tooling. It looks like tooling is a new product line, breaking the category down into subcategories returns a similar result. We now want to find out if these products were only released in Texas or if they are new everywhere. Add the states back into sales overview.

 

It’s clear that these are newly released products across the country but seem to have really taken off in Texas. If we considering the tooling category against all products – we can see that this new product line is the big impact to the increase since April (in the screenshot, I have added an annotation to emphasize the time period we are considering).

 

However, it is clear that the tooling category did not contribute to the change in average price (see below).

 

Conclusion:

  1. The new tooling product line has massively increased the bottom line at this retailer, especially in Texas.
  2. (If we had dug in a little further) The big increase in tooling in Texas was accompanied by a huge spike in sealants due to the products often being purchased together. This presents an opportunity to offer a deal with products of these two categories in other states.
  3. The average price increase we saw at the end of the year did not contribute to 1 or 2. The user would have to repeat the above drill down process to identify the products responsible for the price increase.

Share

Other Posts