Ch-Ch-Ch-Changes: Notes From SAS Technology’s Annual Analyst Summit

I’ve recently returned from SAS Institute’s Annual Analyst Event held June 23-24, 2008 in Monte Carlo. At this event, SAS leadership revealed a roadmap to amplify, with the most effective decision science yet developed, the judgment of professionals in a wide range of industries including retail and consumer goods. Forrester noted specific new science based processes, deployable without restrictions about legacy transaction applications for:

  • Merchandise planning. The most critical decision in any consumer goods value chain is which merchandise to stock. But this decision, although ultimately driven in retail by the buyer’s judgment, must draw on data and analytics that evaluate, based on historic demand) the relative likely revenue and margin resulting from different merchandise portfolios, and test the feasibility of the portfolios against constraints such as store space or labor availability or the firm’s available working capital.
  • Size optimization. For retailers selling footwear or apparel a statistical understanding of the distribution of sales by size by store is vital in order to meet consumers’ needs and avoid mark downs and stock outs. It’s well known that consumers’ sizes vary from one region or country to another, with Norwegians for example in general being taller for example than Greeks but retailers need powerful sparse data analytics to plan for the differences in populations that visit urban and out of town stores.
  • Space optimization. Retailers provide space in stores in proportion to their expect sales and margin for each merchandise item. But the complex tradeoffs between affinity items, with different margins and attracting different promotional funds simply demand an enterprise analytic approach rather than single user planning tools.
  • Revenue optimization. For retail and CPG, decisions about pricing and mark downs are critical to the achievement of financial goals and while analytics are widely deployed to understand the likely overall impact for example of a promotion on volume and profit the real challenge is to solve simultaneously the merchandise assortment, pricing optimization and space management for each store within the retailers’ supply chain and working capital constraints
  • Pack optimization. Pack optimization balances the complex cost and velocity trade offs at different points in the supply chain between moving only highly aggregated quantities, for example pallets and shipping highly disaggregated units. For example single SKUs or Stock Keeping Units.

What does this mean for Process and Applications Professionals?

Process and Applications Professionals in retail and consumer goods industries have to deal with more data than ever. Hidden in tons of data are nuggets of information that hold the key to retail and CPG profit and survival — which merchandise (and which sizes) to place in which channels and stores at which prices and times. Yet most firms make these most business critical decisions, either with a tangle of planning spreadsheets or with the analytics provided by their transaction applications vendor of choice.

Forrester’s take? Forrester believes that consumer goods planning processes, for the most part heterogeneous and ageing, are about to be transformed to take advantage of cheaper processing power, more accessible embedded analytics and more process standardization. Forrester invites its readers to take the confidential survey Forrester Research: Merchandizing Makeover, Q2 2008 to benchmark their “merchandizing makeover” plans against those of their peers.  We are planning to publish the aggregate results of responses to the survey, together with an overview of “merchandizing makeover” technology options.

George Lawrie, Principal Analyst
Business Process & Applications
Forrester Research

Comments

re: Ch-Ch-Ch-Changes: Notes From SAS Technology’s Annual Analys

George,Thanks for the great summary. I couldn't agree with you more. As a consultant in digital media strategy, I've been working with several companies whose core business is about solving the data overload with some type of transformation technology, from data to information to knowledge to understanding to wisdom.Two of my clients are now focusing on the CPG space, and it's clear that these folks are deeply under the gun -- too much info too fast creates a real challenge to good analysis, rendering "BI" a bad metaphor for "black eye."I'm really interested in data visualization -- and in particular mapping -- to provide useful metaphors, and draw signal from the noise.I'd be interested in your perspective on something I wrote about one application for category managers in CPG companies -- http://blog.awhere.com/item/210591 -- which allows them to map their data, and review it in relation to updated demographic data, weather data and other custom info.I'll keep track of your blog, and see what else your team is saying about CPG.