Be Nostradamus for Your Business

How many times have you heard the statement, "the market is unpredictable"? Every marketer understands that each aspect of marketing can carry multiple anomalies. It is impossible to avoid them all and get the exact desired results.

But what if we could predict these anomalies? What if we could reduce the degree of variance and narrow down on expected results?

If you consider the larger picture — the entire marketing and sales cycle — it looks like it is based on probability. So how do we reduce these probabilities and predict the anomalies? The answer lies in breaking the cycle down into flows, and analysing those flows with respect to individual channels.

Doing that brings clarity by stabilizing the whole picture. Once we divide business development into marketing and sales, we know business development is directly proportional to the marketing and sales cycles.

Let us take the digital marketing cycle first. To understand it, we'll divide the flow into three major instances:

• Reach — the total volume of audience we are able to reach.
• Potential Lead — the volume that is engaging with our efforts.
• MQL — a marketing qualified lead, where a potential lead takes an action that shows genuine interest in the company.

Now we can apply each instance to the different channels of digital marketing. There may be any number of tools, sites, and processes, but they all fall under the following channels:

• Email
• Social
• Paid Social
• Paid Ads
• Organic

Once we are able to organize our efforts into these channels in the form of numbers, the rest can be estimated with minimal anomalies.

For example: say we put in the effort to reach 'N' audience in each campaign, and our target is 'n' MQLs (in this case, form submissions). All we have to do is run smaller-volume campaigns a reliable number of times and then assume n ∝ N. Now we have an idea of how much audience volume we need to reach in order to hit our goal.

There will, of course, be uncertainty in the numbers — but this gives us a quantified way to start, and as we go we can refine it with further A/B testing. With this approach we can also calculate the percentage of conversion from one instance to the next, which clearly shows us the gaps in our efforts.