Generating leads is an important part of marketing, but it doesn’t end there. Those leads need to end up driving revenue before you can confidently say you’ve met your goals.
So, how do you map out a strategy that will hit revenue targets (and remove some of the anxiety)?
Jason Widup, VP of Marketing, and Mark Huber, Head of Brand & Product Marketing at Metadata, discuss how you can build a demand model that’ll ultimately connect your leads to revenue.
Check out the full episode for practical takeaways that’ll help you build a demand model and improve marketing performance.
Watch the full episode
Or keep reading for three main takeaways from the episode.
Three top takeaways:
Takeaway 1: Be conservative when setting goals in your demand model
Optimism has its perks, but when building a demand model, it’s advisable to take a more conservative approach. The best way to do that is to plan for the current numbers and historical rates you’re getting.
If you can actually increase your conversion rate or average selling price, that’s great news! But it isn’t something you should always plan for right off the bat. Ease yourself in and have it at the back of your mind instead.
It’s not a case of being pessimistic, you’re simply bracing yourself in order to get realistic results.
Takeaway 2: Your relationship with sales is important
Seeing as they deliver the data, your sales team plays a huge role in building a functional demand model. So, you need them on your side.
A huge part of maintaining that relationship involves plain, old honesty. As much as possible, try not to cry wolf with your sales team by making things seem more urgent than they are. Simply be realistic about timelines.
Also, it’s important to explain to them exactly why you need accurate data. Let them know the benefits and the consequences. That way, you can put things into perspective and ensure you’re all on the same page.
Takeaway 3: Don’t build a demand model without (accurate) historical data
There are a couple of moving parts involved in building a functional demand model. Historical data is one of the more important ones.
So, if you don’t have historical data, or what you have isn’t particularly airtight, then it’s best not to build a demand model. For one, it’ll come out all wrong, and you’ll end up aiming higher or lower than you really need to.
But, don’t totally give up on the idea of a demand model altogether. Revisit your historical data, fix and tweak it where you can. Or, if it’s a case where you have no data at all, simply start tracking it. With good baseline information, you’ll be able to get something started and improve over time.