When pulling trended data (across multiple periods) you will see a pattern.
Over time we can see the sales cycle of a product and what impacts it based on
merchandising, price adjustment, velocity, and distribution changes.
The way the sales data moves from period to period develops into a "trended pattern".
The chart above depicts and interprets the types of trend patterns we might see when
pulling in sales data.
Upward Trend: A collection of consistent periods where sales increases.
Downward Trend: A collection of consistent periods where sales declines.
Flattening Trend: A collection of consistent periods where sales are flat, does not
gain or lose, and maintains similar level of sales.
Sideways Trend: Where sales are more unstable and might spontaneously gain and
lose over a few short periods.
Long Term Trends: Sales performance over a long collection of periods. We might see
various trends along the way, but are sales truly gaining or
declining over a longer period of time. One way is to look at the
first period or starting to see where sales are, and compare to
the ending or last period. Or you can use Excel's trend lines that
plot the sales pattern in the data over time, and we can
determine if sales are gaining or losing long term.
Short Term Trends: Sales performance gains or losses over a shorter collection ofperiods. If we used the latest 13 quad periods, and see sales peak
or decline over three of those periods then drop down again,
then those three periods are considered a short term trend.
Another term that can be used for trends are "Peaks & Valleys“ or “Highs & Low’s”.
You might see this for a product that consistently promotes every other week or
month. One example might be ramen noodles. One week they may sell 10/$10
and another week they go off promotion and sell for $1.99 instead.
Peaks are the high points in sales during a time period whereas valleys are the low point in sales. Of course, it does not need to be as unstable as the chart depicted as this is a visual example. Sales peaks and valleys can come during any time in the trend line and will not as dynamic, depending on the product or category where it may have more or less seasonality to it.
In this example, we pulled dollars across 13 quad (4wk) periods for a product. We see that beginning sales trends are in a declining /downward phase, then bottoms out with a short term flat trend, then rebounds in an upward or gaining trend. But one method that is practical would be to look at same sales vs. Year Ago to see if there is a consistent pattern or if something is happening to this product to cause the downward and upward sways in the data.
We have now added 13 quad (4wk) periods for the same time span Year Ago in order to see if there is a pattern of it current year's trends are an anomaly? What we see here is a fairly consistent sales pattern in terms of downward and upward trends across the same periods. BUT...current year's sales are shallow compared to year ago sales. This is where you would begin to dive deeper into the sales data to determine why sales performed lower this year vs. YAG?
Just a few example to look at as to why sales performed less this year:
-Loss of breadth (ACV) or depth (TDP) of distribution?
-Did promotional frequency change (decline or shift)?
-Competitive brands or new entrants erode our share?
-Channel leakage (competitive retailers offered better incentives for consumers to
shop there instead)?
-Everyday & dealing price go up?
-Change to promotional tactics (more TPRs this year vs. Features last year)?
-Was schematic changed (less shelf space and more frequent out of stocks during
-Less consumer marketing?
-New sub-segment drawing excitement and innovation this year (some consumer
shifting to this new sub-segment away from the product) and or less consumer
I would also look at the category sales trends too, to see if a similar pattern is also present. This might help address a couple of key points made above.