Use of Daily Data

I’ve often been asked if daily data should be used for trade promotion analysis.  My experience is that daily data doesn’t add to a trade promotion analysis since promotions rarely cover a few days and daily data requires 7 times the data storage and processing of weekly data.  There are some occasions where daily data is helpful in specific detailed analysis but that should be done on a one off basis.  But there are situations where daily data is more helpful than weekly data. 

Many brands (typically brand leaders) do not get their fair share of shelf space.  There are many reasons for this as any brand in a retail store needs a minimum of 1 facing and plan-o-grams are set up to balance the case pack out, the available shelf space, variety and replacement/logistical concerns.  The most common factor is that retail buyers order and set POG’s based on relative sales to the category as they measure consumer take away.  If a brand sells 50% of the volume in a category a buyer will try to provide 50% of the shelf space to that brand.  A material factor that impacts sales are promotions which can generate anywhere from 2 to 10 times normal weekly sales.  This means that 6 promotions in a year could sell the equivalent of 12 to 60 normal weeks of sales while the remaining 46 weeks will sell 46 weeks of normal volume.  If a leading brand runs a promotion it cannot sell more than the product on the shelf.  This is a particular problem for products that are in cooler or freezer where space is finite. 

 Let us take a simple example.  The leading brand of orange juice has a 50% national share but in a large chain only get 40% of the shelf space based on their sales in that chain only being 40% of the category sales.  When daily sales data is reviewed, it is seen that on Friday the leading brand when on promotion sells 80% of category volume and on Saturday sells 30% of the category volume and on Sunday 5% of the category volume which when averaged in over the year results in a 40% share of category sales.  The daily data shows that the leading brand sells 80% during a promotion when in stock and the share of category sales drops materially indicating out-of-stock (OOS).  The OOS is occurring due to insufficient facings in the cooler; inventory is depleted on Friday, and those buying orange juice have to buy competing brands or private label. 

This is a self-fulfilling situation.  The leading brand was only getting 40% of the shelf space due to having sold only 40% of the category sales in this chain.  But with only 40% of the shelf space the brand was OOS before the end of the promotion holding its share of category sales down to the 40% level which resulted in 40% of the planogram (POG) given to the brand.  An analysis of the daily sales data (perhaps supplemented with in store audits during the promotion), will allow for an estimate of what the real share potential is and when reviewing that type of data, most retailers will make adjustments to the POG.

 Daily data has a role in special situations in evaluating the impact of Trade Promotions.  But the situations where it has an incremental value over weekly data is very limited and should be handled on a one off basis.  Since the situations where daily data is helpful usually has a significant impact, the extra cost and effort involved is justified (e.g. increasing the share of the POG from 40% to 50% in a chain).  For building an ongoing Trade Promotion Analysis it is better to build a database using weekly data and not daily data.