By Janet Dorenkott, VP and Co-owner, Relational Solutions, Inc
During the first day of IE Group's Consumer Packaged Goods Forecasting and Planning Summit last week, almost every speaker reiterated that historical trends and year-over-year comparisons do not carry the same weight that they have in the past. And although these metrics are still valuable for forecasting, many other factors now need to be considered due to the rapidly changing economy and buying behaviors of consumers.
One of the main thoughts behind this reconsideration of traditional methods is that consumers are saving again and, thereby, taking money out of the market. Pat Conroy of Deloitte Consulting estimated that if consumers save just seven percent more than they have in the past - not an unlikely scenario, according to his research - they will be removing $500 billion from the economy. He also made an excellent point that, as consumer spending goes down, a greater percentage of the money that they do have to spend may be allocated toward services vs. products - his theory being that their expendable income will go toward fixing products they already own to stretch their investments.
We also heard in the presentations that there is a new attitude - "Frugal is cool." People are buying fewer luxury items. Even those consumers who can afford them are leaning toward purchasing less conspicuous and more "everyday" products. Another factor affecting purchasing is the general fear in the market coming about due to the government's explosive spending, the housing crisis, the banking and auto crisis and more.
Tim Weidenhaft of General Mills echoed the sentiment that shifts in demand patterns due to economics also makes purchase history less relevant. He pointed out that this is causing higher demand volatility and a resurgence of coupon redemption.
A key sentiment in many presentations was that automating the integration of POS scan and forecast data and integrating it with things like promotions, shipments, forecasts and orders through applications like POSmart, is a critical factor. Also, leveraging panel data, weather trend data, syndicated data and even economic data that can be purchased through third party sources are several more ways to improve forecasts.
Discussion Questions
for the BrainTrust Panel: Given the current trends in consumer spending (and saving), what factors in addition to historical trends should be used to help determine your forecasts? What other impacts from the economy should be considered?
RetailWire BrainTrust Comments:
Projections based on forward-looking shopper insights are the way to go. This recession has shocked shoppers into a totally new mindset that will take years (if not decades) to evolve. Historic models need to be re-evaluated. Historic analysis has its place, but can no longer be counted on to predict sales in the short or medium term.
The smart retailers and marketers will use this time to invest in getting to know their customers and testing new propositions to win their loyalty with new, more cautious, savings and environmentally-oriented American shoppers.
Alison Chaltas, Principal, Interscope
One challenge comes from trying to tease apart a number of overlapping and interacting trends: 1) shoppers spending less overall; 2) shoppers diversifying channels to include discount stores, dollar stores, club stores, mass drug, etc.; 3) shoppers cherry-picking deals; 4) shoppers trading down within categories (from national brand to discount brand) and across categories (from prepared foods to basic ingredients); 5) shoppers spending more in stores as they eat out less.
Retailers can look to panel data and industry studies and surveys, but that only gets you so far. Each chain is different, each region is different, each store is different. Retailers with loyalty data have a great opportunity to dig into the data and observe where the trends are taking *their* shoppers and where the demand is heading in *their* stores. It's hard work, and many retailers don't have the tools to make it easier, but the payoff can be huge.
Ben Sprecher, Founder and President, Incentive Targeting, Inc.
| Jane Sarasohn-Kahn, Health Economist, THINK-Health, Says: |
 New data points and qualitative factors will be instrumental in helping us better forecast and respond to consumer demands. 
What do you say? Send us your comments here |
In times like this, we need a large dose of scenario planning. In scenarios, we consider what we know we know (e.g., that consumer spending is down), what we know we don't know (e.g., that consumers are saving, but how long will this persist and at what rate?), and what we don't know we don't know.
This last category we refer to as "wild cards." In my work in health forecasting, we often refer to acts of bioterrorism or pandemics (think: swine flu). Wild cards are the low probability events that blow our 'reasonable' forecasts to smithereens.
When it comes to saving, who would have thought? New data points and qualitative factors will be instrumental in helping us better forecast and respond to consumer demands.
Jane Sarasohn-Kahn, Health Economist, THINK-Health
Consumer data is the most interesting to me in terms of adjusting demand forecasts, especially some of the interesting things that panel companies have been doing around purchase intent, and tying demographics to saving and spending behavior changes to get a better profile of who's buying and who isn't.
Whether that data comes from a big consumer data company, from a retailer's direct research into its customer base, from a manufacturer's direct research into the market, or from somewhere else, it comes down to this: you can't be customer-centric if you're not using customer data, especially in how you plan.
Nikki Baird, Managing Partner, Retail Systems Research
Pattern Recognition during good "stable" times is difficult to do correctly. Forecasting from these patterns is even more difficult.
If you are forecasting what the next 5-6 months will entail (Demand, Trends, Pricing...) the current trend data is appropriate. You would not build a 5-year strategic plan based on current (previous years) data alone.
The fundamentals still hold true: Incorporate a blending of data types/sources and provide appropriate weighting to each variable; POS scanning market basket data, credit card data, loyalty/membership data, geo-demographics data, syndicated data, GDP, etc.
Each of these provides particular insight into consumer behavioral changes. Targeting BEST customers and keeping them does not change. Converting "Near" best customers to best does not change.
Still, like steering a big ship, frequent corrections are required, not infrequent wild changes. More frequent analytics measurements and near-term tactics are more prudent than 4-5 year strategic plans during economic turmoil.
Emmett Cox, Global Retail Analytics Leader, GE Money
Read the entire story and RetailWire discussion at:
http://www.retailwire.com/Discussions/Sngl_Discussion.cfm/13781
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