Real-time Campaign Analytics Is a Game-Changer for Demand Generation

The Origins of Demand Generation

“If a tree falls in a forest and no one is around to hear it, does it make a sound?"

Have you heard of this philosophical thought experiment?

The general idea behind this experiment is that perceptions & actions are ineffective and inconsequential if no one ever knows about them.

If you think about it, companies have their own similar conundrum. It goes like this:

“If you built an amazing product or service that nobody knows about, does it even exist?”

The short answer in marketing parlance: Nope! And hence the existence of the Demand Generation function.

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The Responsibilities of Demand Generation

At the core of it, demand generation is a process of building demand for your product or services and increasing brand awareness.

The Demand Generation function's responsibility is to position your product in the market, build brand authority and increase your top of the funnel.

The latter part has evolved over the years, with Demand Gen tasked not just to bring a strong volume of leads at the top of the funnel (ToFU) but to bring in quality leads that contribute to the pipeline. Thus tying demand gen with a pipeline generation target.

What started out as “creating demand and driving leads to ToFU” has evolved into multiple activities under Demand Gen.

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Operating principles in Demand Generation

1. Know your audience

A critical part of Demand Gen involves partnering with product marketing to understand your buyer personas. These are the people that are most likely to buy your product.

2. Content

Content is the messenger, tying your potential customers’ problems with your solution, and as such Demand Gen’s expertise involves knowing which persona to target with what content, working closely with the product marketing teams. This is the part where Demand Gen decides what the first-time visitor sees versus what to promote to someone attending an event where you have a booth.

3. Channels of content distribution

Demand Generation is a patient game involving directing different prospects with different levels of engagement to diverse content. For example, they’ll target engaged users via remarketing campaigns while also trying to create net-new interest with lead generation campaigns while pushing those in the early stages of the funnel to feel the urgency to purchase. Demand Generation’s key responsibilities involve doing this complicated tango with different types of prospects.

Each step of the way invariably involves Demand Generation running multiple experiments and A/B tests to arrive at what works with different set of audiences. The ability to dissect the data and see how it all ties together through the funnel is not “a great to have” option but a need for the function's success.

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Demand Generation & the Campaign Measurement Dilemma

It takes a minimum of 8 touchpoints, filled with high-quality, relevant, and relatable content that is personalized to your target audience and aligned with each other.

As such, Demand Generation finds themselves looking at various data points and trying to balance them all, as well as the budgets for each.  The Demand Generation team is always pivoting, tweaking, and iterating on their efforts– to produce the best results.

Demand Gen teams have some degree of data from the marketing automation tools, their media platforms, and the CRM, but it is never enough in a world where data is generated faster than ever and tying context is complicated.

Here’s an exercise I encourage you to take. Ask your demand gen team the following questions and keep a tab on the time it takes:

1. In 2022, what was the best “First-Touch” campaign in generating the pipeline? What was the best “Last-Touch” campaign in generating revenue?
   
2. Which titles across the campaigns have the highest  MQL to SQL conversion rates?
   
3. What top two industries among all campaigns run in 2022 fared the best in terms of revenue generation?
   
4. Which campaign was the poorest in terms of return on investment?

5. Was it worth spending time designing, writing, and publishing the whitepapers?

The idea of the exercise is not to question the Demand Generation team's knowledge but to bring to attention how hard it is for them to analyze all these moving parts of data across dozens of disparate systems and reports.

It is an admin overhead they wish could be automated by an app or software. It does no good discovering that a specific campaign works well for a specific region a week later and published on a spreadsheet than being alerted real-time.

By their very nature, spreadsheets and dashboards are the newspapers of 2022. The data is stale by the time you have published, and it is difficult to factor in all the information and  variables affecting the metrics.

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The Power of Real-time Campaign Management

The idea of designing and launching a campaign is to capture demand from the right type of leads for sales to engage with. Ask any Demand Gen expert, and they will tell you sometimes it is as complicated as steering a ship through a storm - you need real-time analytics to course-correct your campaign and not lose money on it if it is doing poorly, to double-down on what is working to make the best use of your investments.

This is a prime use case for Artificial Intelligence to solve in marketing - to build a predictive model learning from your data on what kind of leads convert, what campaigns do well, and help you prioritize.

The beauty of a predictive AI application is that it helps to piece the campaigns together in real time and allows you to pivot based on ROI. In the realm of demand generation, letting an analytics application ingest your campaign data to help you deep-dive into your GTM strategy for the long term and act on low-hanging fruits for the short term is no longer a nice-to-have. It is an integral part of the Demand Generation arsenal.