I continue to see countless articles on how the U.S. economy is affecting everyday purchases – especially groceries. But very little is written about how shopping patterns are tied directly to paychecks. This started me thinking: Do paydays at the start and end of each month create spikes in supermarket sales? How much of a lift can retailers expect on the first week of each month? Which categories are most likely to spike at the beginning of the month? Has the average first-of-the-month spike increased in recent years?
Fortunately, I have access to Nielsen’s Strategic Planner database, where was able to measure weekly sales over time. Although many months begin in the middle of the week, it was clear see the supermarket sales spikes at the start of each month. Of course Thanksgiving and Christmas drove their own sales spikes, but nearly all other months started stronger than they finished.
The chart clearly shows a first-week-of-the-month lifts, but the next step was to quantify these sales spikes. By calculating the average weekly sales for the first week of the month vs. other weeks (excluding November & December) I measured a lift of 4.3%. It was sometimes higher, sometimes lower, but there is clearly a spike for each non-holiday month.
I wanted to be able to show how this first-week-of-the-month effect has grown as the economy struggles, but the year-ago 52-week period shows a slightly higher lift at 5.2%. The lift was only 3.5% two years ago. These differences may be attributed to months starting on different days of the week each year, making it difficult to measure this sales lift over time.
So how does this vary by category? I found that by tracking weekly category sales, several Nielsen categories showed much higher sales spikes. Categories with the strongest first-week-of-the-month spike include food staples and ingredients for cooking from scratch. Many of these skew to lower income or older or Hispanic households.
See supermarket products most likely to sell first week of month at right:
*Index reflects average weekly sales for first week of the month vs. remaining weeks. Excludes last 8 weeks of 2008 (holiday season). Download First-Week-of-the-Month
So now what? Both retailers and manufacturers can use this information to fine tune their promotion strategies. Some brands may want to adjust their timing for FSI drops, advertising, and in-store promotions. This average 4.3% lift may not seem like much, but smart CPG leaders are looking for any advantage they can find as our industry continues to get more competitive.
- Tom Pirovano, TheShopperWonk
-email: tom.pirovano@nielsen.com
While sitting on a flight to Chicago from London yearning for the old boom days of business class transatlantic travel, I started thinking about Loyalty, as you do when you've tired of the too few decent movie.
So many airlines could do so much better if they captured all of the flights I could make with them, but don't know (or it seems don't even care) how loyal I really am and how much business I take to their competitors. Airlines know that their real money comes from a relatively small number of business travelers, particularly those who can fly in Business or First. Yet they don't appear to do too much to encourage or reward greater loyalty beyond free miles, that you can never seem to redeem due to blackouts when you want to use them, or provision of lounge facilities full of curled up sandwiches and potato chips.
You'd think in the current economic climate they'd be fighting much harder for customer loyalty and share of wallet - it could amount to $10's or even $100s of thousands of dollars per customer for them. If they knew how much each of their customers spent with competitors they would be able target their offers and rewards more efficiently.
When looking at grocery retailers, most tend to lose out on business to any number of competitors. The opportunity for them is that they could get a much better picture of customer loyalty than by just relying on their loyalty card data and the commonly used Loyalty segmentations (such as Recency Frequency Value) built from it.
Although a grocery retailers top 30% of customers typically account for 70% or so of its overall sales, the majority of its customers are spreading their grocery purchasing across a number of other retailers. The potential $ opportunity they miss is huge. With better insight on where they're losing business and to who, they would be better placed to win a greater share of their customers total spend.
The irony is that Loyalty data alone cannot actually define a customer’s loyalty to the retailer. There are varying ways of estimating it through modeling/inferring with Household data, however this only works to a degree when trying to estimate a cardholders total grocery spend.
When you want to know what customers spend on different products or Categories (coffee, detergent, beer etc) or know which competitors they are buying these products from, you really need to ask them. This is where market research and consumer panels like Nielsen Homescan add value and deliver a more complete picture on real customer loyalty, than with loyalty data alone.
As an example, on examining a major European retailer and their customers using Nielsen Homescan panel data, it was found that over half of their total grocery spend was with other competitor retailers. This represents a huge opportunity to build custom from in a planned, targeted, and measurable way. Category level opportunities are even more significant. If the retailer knew that certain customers were buying the majority of their coffee or produce from a competitor down the road, they could develop and target relevant actions to win this business (e.g. promotions, targeted discounts, samples, or even introduce new products).
It is true that it is easier to retain and develop an existing customer than acquire a new one. But it would be more effective and profitable if you knew which of your customers you're losing sales from and to whom.
As David Ogilvy said "A blind pig can sometimes find truffles, but it helps to know that they are found in oak forests."
-Nigel Paice, Co-Founder TheShopperWonk
-email: nigel.paice@nielsen.com
