Before launching a new business, most entrepreneurs develop business models and projections.  They do this in order to estimate the types of returns their business is capable of producing.  One segment of those projections is represented in the marketing plan.  Stakeholders read this document to understand how the business will market their services and what they hope to achieve.  Marketing teams should treat their activities like a business by planning, measuring, optimizing…repeating.  They should also be held, and hold themselves, accountable for the success of those plans.

Ultimately, your online success is represented by quantifiable metrics like sales, leads generated, registrations, customer acquisition and/or a specific type of user behavior.  As an online marketer, in order to optimize your campaigns to achieve those metrics, it’s important to understand the equation for success.  For example, if you are trying to achieve a certain number of leads each month, you need to how you will convert those leads and how much it will cost.  You will need to make certain assumptions based on as much fact as possible.  If you know how well your inbound traffic converts today, or how effectively similar businesses convert, you can use this data to project your expected returns:  “Based on historical data, we will have to spend X dollars to produce Y # of leads for our business, which will result in Z revenue”.

However, what happens when you don’t convert business in-line with your projections or assumptions?  Where do you look for answers to optimization?  Answer:  Your equation needs to be expanded.  This equation should be built as your business model and it is better to have more measurable points of conversion.  For example, each medium, geography, conversion method should have their own assumptions and points of optimization review.  The more precise you can be with a weakness in your campaigns, the more capable you will be to make an effective optimization.

In addition, each point of measurement in your model needs to be analyzed accurately.  Effective measurement builds confidence in optimization activities, model projections and assumptions development.  The analytics tools we have available to use are quite robust these days.  My suggestion is to build your model and wish list of conversions for measurement.  Then build your systems around measuring these metrics.

The more you measure, the better you can optimize.