Pick the Perfect Price

Hitting on the price that maximises profits has long been an art, but BI's ability to crunch data in short order is changing that. "We're able to put a lot more science into establishing suggested retail prices," says Mike Altendorf, vice president of IT at Ace Hardware. Although stores are encouraged to adapt pricing to their location, BI has helped Ace show store owners how much they could expect their gross margins to increase if they were to raise their prices to Ace's suggested retail. In many cases, it was more than $US50,000 annually per store. Ace had hoped to increase its wholesale margin by $US19 million in one year. It actually increased by $US175 million.

A "price to win" initiative at competitor TruServ used BI to measure the impact of lowering prices in certain segments. "We gave away gross margin hoping to increase sales," says Hastie. "It didn't increase velocity." So in a matter of three to four weeks, TruServ ended the sale. Hastie says that without BI, the company would have analysed the data at the end of the quarter, reanalysed it after the next quarter and continued giving away margin for six months instead of one.

At Fairchild Semiconductor, pricing its 50,000 products manually was such a chore that the company did it once a quarter. "It wasn't necessarily consistent. And it certainly wasn't mathematical," says Bill Hall, vice president of interface and logic. "A lot of pricing in the past had been done with gut feeling." Using a BI-powered dynamic pricing engine, prices are easily adjusted weekly, factoring in current worldwide market conditions, manufacturing cost, and capacity and current inventory. If Fairchild can't produce enough of a hot-selling item, BI helps analyse the merits of raising the price, pinpoint where the item could be sold for the most short-term profit and identify which customers clamouring for the product are likely to generate the most long-term profit. CIO John Watkins says Fairchild spent "a couple million" on BI, but the investment has already paid for itself through better pricing alone.

Whatever uses CIOs foresee for BI, Nucleus's Wettemann advises going for the easy payoff first (see "Seven Rules to Rolling Out BI", left) Quaker Chemical's Tyler concurs. "Build a simple infrastructure," he says. "You can always do more, but you can never do less. There's a tendency to do something fancy." It's better to build something simple, get it into users' hands, and let them tell you what more they need. "Don't deliver reports," he says. "Deliver tools."

SIDSEBAR: 7 Rules to Rolling Out BI

To get the most value from BI with the fewest headaches, follow this advice from Rebecca Wettemann, vice president of research at Nucleus Research

• Make sure your data is clean.

• Train users effectively.

• Deploy quickly, then adjust as you go. Don't spend a huge amount of time up front developing the "perfect" reports because needs will evolve as the business evolves. Deliver reports that provide the most value quickly, and then tweak them.

• Take an integrated approach to building your data warehouse from the beginning. Make sure you're not locking yourself into an unworkable data strategy further down the road.

• Define ROI clearly before you start. Outline the specific benefits you expect to achieve, then do a reality check every quarter or six months.

• Focus on the business objectives.

• Don't buy business intelligence software because you think you need it. Deploy BI with the idea that there are numbers out there that you need to find, and know roughly where they might be. "It can be a needle in a haystack," says Wettemann. "But you should [at least] know what field it's in."
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