OMS: Keyword Optimization

Any online marketer and web analytics practitioner looks to maximize the performance of her business. Typically, this is done by analyzing campaign performance. The marketer and web analytics practitioner work together to make sure that current activities are optimized.

The Advanced Web Analytics Practitioner knows that all current activities cause changes in future activities.

For instance, a set of keywords may yield lower costs, higher conversion, and as a result, an increase in short-term profit. From an SEO, Online Marketing, and Web Analytics perspective, this is all good.

From an Online Marketing Simulation (OMS) standpoint, it may be good, it may not be good!

In my dataset, Merchandise Division #4 represents a product line with more expensive price points. Merchandise Division #3 is an extension of Merchandise Division #4, with less expensive price points. Not surprisingly, customers flock to Merchandise Division #3!

So, let's run simulations of future performance on 1,000 customers. Remember, we categorize customers based on combinations of future value, advertising channel preference, physical channel preference, and merchandise preference. This yields anywhere between maybe 80 and several thousand segments. Once done, we apply twelve-month sales and profit value to each segment, we migrate customers to their new segment, then we replicate the process four more times, with the ultimate destination being the segments that these 1,000 customers will reside in five years from now.

The first simulation is for a paid search customer buying from Merchandise Division #3, the division with lower price points.

• Annual Repurchase Rate = 24%.
• Demand: Year 1 = $51,000, Year 2 = $35,000, Year 3 = $28,000, Year 4 = $25,000, Year 5 = $23,000.
• Merchandise Division #3 Buyers: Year 1 = 128, Year 2 = 72, Year 3 = 52, Year 4 = 45, Year 5 = 42.
• Merchandise Division #4 Buyers: Year 1 = 113, Year 2 = 65, Year 3 = 48, Year 4 = 41, Year 5 = 38.

Ok, now let's run simulations of future performance on 1,000 customers buying from Merchandise Division #4 via paid search --- this division has similar product, but higher price points.

• Annual Repurchase Rate = 42%.
  Demand: Year 1= $123,000, Year 2 = $76,000, Year 3 = $52,000, Year 4 = $41,000, Year 5 = $35,000.
• Merchandise Division #3 Buyers: Year 1 = 169, Year 2 = 103, Year 3 = 70, Year 4 = 54, Year 5 = 46.
• Merchandise Division #4 Buyers: Year 1 = 258, Year 2 = 137, Year 3 = 87, Year 4 = 66, Year 5 = 55.

The typical web analytics practitioner partners with the online marketer, seeking to optimize conversion rates and transaction profitability. Once the transaction is complete, the typical web analytics practitioner and online marketer moves on to the next conversion.

In this case, which customer are you willing to pay more to acquire? I'm willing to pay a fortune for the customer who purchases from Merchandise Division #4. I'll gladly sub-optimize my short-term business in order to acquire a customer that will spend more in the future in both Merchandise Divisions!

This is what Advanced Web Analytics via the Online Marketing Simulation (OMS) is all about. We want to understand how our optimized short-term decisions impact the long-term health of our business.

Keyword Optimization requires a view of long-term performance, in order to be successful. The OMS environment can point you in the right direction.