Last week, Jessica Kao, Senior Manager, Demand Generation at GuideSpark was featured in a Marketo Webinar – Revenue Reporting: Your Genie in a Bottle. She showed a cool interactive Excel spreadsheet we use here at Digital Pi with clients to help them forecast the forward flow of net new leads at top of the funnel through a demand planning through to customer. Now you can get your copy of the demand planning model to do your own demand forecasting. The model answers the burning question every marketer should build his or her demand gen plans on:
If my programs generate [this many] leads, when will they convert to [how many] customers and [how much] revenue?
The variables for the model are:
When you change a variable (yellow cells), the model updates all the dependent values. The model has a nice visualization feature that moves the entire dataset to any change in the time duration for a stage. For example, when you change a stage from, say two months to six the data magically cascades down to reflect the flow of leads to customers on the adjusted timeframe.
This is a simplified version of the demand planner we use with our clients that can help you engage others in your organization to start the conversation around demand planning. I have used this simple version of the model to help drive home the reality that a lead created today doesn’t equate to a deal today – or maybe even this month – or this year depending on your marketing/sales funnel.
Try this experiment. Gather input from your historical data (Salesforce.com, Marketo, whatever) to estimate conversions and timeframes. If you can’t get to data for this, make an estimate. Set the time frames to all zero with the exception of SQL (opportunity created), which you want to set to one month. Under these settings, the marketing funnel is running in real-time (zero duration) and the sales cycle one month – producing customers in February 2015 from leads created in January 2015. Plug in your historical or estimated conversion data now and watch – and show what your demand forecast really looks like. I use this approach to get cross-function stakeholders to speak the same language for the impact that time has on the relationship between marketing spend and revenue. Don’t forget to reference the funnel chart below the data. It shows the lead flow in the form of a funnel, and it too updates its shape in real-time to the changing data.
This is a simple tool to share and use to help you get demand planning into your marketing life. In practice, the model becomes valuable when compared with actual data over time. For example, you might plug in data for a monthly demand forecast for three months, then use Marketo RCE to compare actual conversion and timeframes with the model inputs to see how you performed against the forecast, and determine if you need to adjust your assumptions. Yes, it would be awesome if Marketo had a way to do this inside Marketo/RCE – maybe some day. How cool would it be if Marketo could tell you what your forecast should be based on monitoring historical data? Okay, let’s dream big and imagine that Marketo could even advise you on a marketing plan using historical performance by channel to suggest where to invest more or less in order to hit a revenue goal?
In the real world, there are many more variables that define a demand funnel. For example, marketing channels (content syndication, trade shows, etc.) have different conversions and timeframes that should be factored in. There is a point of diminishing returns where time invested to make the model cover every possible factor that influences demand isn’t worth the effort. Too much tweaking and the model becomes too hard to validate and explain; keep that in mind if you decide to make the model represent your marketing world.
I hope you enjoy experimenting with our simple demand planning model. Email me at firstname.lastname@example.org if you find any bugs or have any good stories to tell of how you used it.
Get it here.