### OMS: Optimizing Paid Search And Customer Acquisition

Let's tackle a question that we all eventually have to deal with. Somebody is requiring us to cut back on an advertising channel. Maybe the CFO did an analysis, and thinks that the ad-to-sales ratio in paid search is too high. She demands that we cut our paid search program by 50%.

The online marketer and web analyst work together to cobble conversion rate reporting, and if available, profitability reporting, attempting to make a case to continue to spend money. And this is a good thing. Your web analytics reporting helps you see what happened in the past. Your reporting shows you that you're losing a lot of money at the margin in your paid search program. It sounds like your CFO is right.

Your CEO, however, will want to know what will happen in the future if the CFO gets her way and cuts your paid search program in half. Your job is to make a case to the CEO to keep spending the money. You have to prove that there is a long-term return-on-investment that must be protected.

This is a perfect application of the Online Marketing Simulation, the "OMS" as I call it. Why not simulate what happens over the next five years if your paid search budget is cut by 50%?

First up, the current plan.
• Year 1 Demand = \$19.1 million, Profit = \$0.6 million.
• Year 2 Demand = \$19.3 million, Profit = \$0.7 million.
• Year 3 Demand = \$19.5 million, Profit = \$0.7 million.
• Year 4 Demand = \$19.6 million, Profit = \$0.8 million.
• Year 5 Demand = \$19.6 million, Profit = \$0.8 million.

This information alone would be good for your CFO and CEO to see. When is the last time you showed your Sr. Management team where your online business is heading, from a sales and profit standpoint, over the next five years? In the old days, you'd put your finger in the air and guess that a 35% sales increase would happen, and then you'd be praised when you had a 45% sales increase. Unfortunately, for most of us, those days are gone.

Ok, now we plug the 50% reduction in the paid search budget into the OMS.

• Year 1 Demand = \$18.2 million, Profit = \$1.0 million.
• Year 2 Demand = \$17.5 million, Profit = \$0.8 million.
• Year 3 Demand = \$16.8 million, Profit = \$0.6 million.
• Year 4 Demand = \$16.1 million, Profit = \$0.4 million.
• Year 5 Demand = \$15.4 million, Profit = \$0.1 million.

Which business would you rather be part of?

So often, our customer acquisition activities are unprofitable, and are the first area that the CFO wants to cut. If you're using your standard web analytics platform, you're going to look at conversion rates and average order values and you'll end up agreeing with your CFO.

If you run your customers through the OMS, simulating the long-term migration of your customer base, you'll arrive at a different answer. The example I illustrate above repeats itself across the data I analyze.

Short-term optimization frequently results in a long-term drain on the business. It is here that the web analytics community are sometimes sold the wrong message. We're told about these glorious optimization tests, we even read about how offline data is integrated into the multivariate tests that result in an optimized outcome. It all sounds really good.

Until we do a better job of simulating the long-term impact of our decisions, we won't know if we're actually optimizing our business, or if we're hurting the future of our business. It is time for the online marketing and web analytics community to take the next step!