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Big data’s impact on traditional demand gen agencies

Agencies focused on demand generation must change tack if they’re to float in this data drenched era, Raffaele Apostoliti, president and CEO at Expandi says

The rise of big data and artificial intelligence has meant a significant quantity of data is available to companies, providing far deeper insights into prospects and clients than ever thought possible. At the same time, pressure for results has never been stronger.

So, how is the role of a

demand generation

agency changing to meet this scenario? How can agencies help customers take advantage of this ‘gold mine’ of information?

Martech and big data have been around for a while. Clients and agencies had to update their skill sets to maintain course and not fall behind their competition. For demand generation agencies the goals never change – clients demand better ROI. What changes is how you get it.

Data and tools need to be allies in this evolutionary process. The first step is for agencies to start delivering real results and understanding the different types of data.


Standard segmentation based on industry, size, geo location, or job title etc, does not provide enough information to efficiently target the right audience. Applications of firmographic are needed to help marketers better identify their target audience and increase campaign efficiency.

Marketing scoring:

Since marketing automation became mainstream, marketers have added prospect and client scoring to standard data. Although a positive step forward, we’re still noticing weaknesses. Due to insufficient knowledge and systems integration at the final stages of the customer journey, scoring is outweighing the importance of digital and social. This generates serious frustration for marketers who are experiencing very limited opportunities and sales, despite huge volumes of digital leads.

Buyer (or purchase) intent:

Advanced analytics are being used to acquire, process and interpret vast volumes of the target audience’s information and online behaviour by tracking identified signals. 

Buyer intent

represents a major step forward in overall ROI achievement, but too often the information gathered is mistakenly kept separate from the main CRM data. This process can be much more effective if matched with the known audience and profiles available in systems.

Propensity models: Companies have a high amount of

data on their leads

– either as opportunities or sales-generated. It’s essential to use models and algorithms that provide reasonable expectations of how many leads, opportunities and sales you can generate by any chosen factor i.e. industry or job title etc.  These models are crucial in setting up campaign targets, optimising efforts and resources by further prioritising actions that are more likely to generate stronger results. This is where predictive analytics, machine learning and artificial intelligence are going to play a crucial role in the future of demand generation.

However, this is very much in its early stages. There are still questions regarding who is really using it and how. Not to mention what their success-rate is. Moving forward, agencies will play a pivotal role in understanding its full potential. To take full advantage of data, companies will need to carefully select the right partners and implement the right marketing strategy.

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