Using Data Science to Power Demand Generation in the Digital Focused Economy

Using Data Science to Power Demand Generation in the Digital Focused Economy

The change in business models is driving more organizations to adopt digital in their business strategies. With the move to digital, marketing teams are also making a pivot in the strategies that they use for Demand Generation. With increase in as a service business models, the customer engagement models are also evolving and this impacts the customer lifecycle.

The organizations don’t view the sales cycle as a funnel, but as an infinite loop as depicted in the image below.

The infinite loop of Sales cycle
Image 1: The infinite loop of Sales cycle

So, while funnels have been focused on driving customers through a well-defined linear flow across stages, the next-gen demand generation teams have to focus on this non-linear cycle of customer engagement to drive not only new business but also, cross-sell and up-sell.

As the conversation moves towards digital, the new customer journeys create large volumes of data throughout the loop. Marketers need an additional set of skills to skim through the data and provide them with actionable data-driven decision making support.

The data, if analyzed well, can prove to be the biggest trump card for marketers in understanding buying behavior and influencing the customers at the right moment in the decision-making process.

In the evolving demand generation process, marketers’ focus will shift beyond traditional digital marketing from the areas of campaigns to delivering exceptional digital experiences.

New focus areas for marketers

1. Content | 2. Personalized Experiences | 3. Intent Marketing

The new focus areas will be dependent on marketers knowing their digital visitors well, thus needing adoption of Customer Data Platforms (CDP) at an enterprise level. This also means an increase in adoption of 3rd party intent tools like Bambora. COVID-19 has accelerated the adoption of capabilities across organizations as they grapple with the new ways of navigating the fast-evolving business landscape.

Post-covid the onus is on digital marketers to be strategic, agile, flexible, collaborative, and data-driven and this they’re trying to do through increased personalization. Read more in our blog.

The following sections will help in exploring each of the concepts and how analytics can help new-age marketers with each of the new focus areas.


Below are some of the content marketing trends

  1. 70% of marketers are actively investing in content marketing. (HubSpot, 2020)
  2. 69% of the most successful marketing organizations have a documented content strategy while only 16% of the least successful have the same. (Content Marketing Institute)

The need to drive user engagement throughout the customer journey is the driving force behind the increasing focus on content. After getting the user to visit the digital platform, marketers want them to remain engaged using the right content depending on their stage in the engagement journey. To know this, the marketing team has to know the content consumption patterns and what gaps exist in the available content.

Analytics platforms can act as the key enablers for the content teams to get these insights. The indicative insights derived can be as below:

1. Content consumption

  • Who is consuming the content?
  • What content is being consumed
  • Which content is linked to which stage of the customer journey?
  • Which content is being consumed in what percentage?
  • Which content is driving engagement?

2. Content Gap

  • Identify the content searches which are not being fulfilled on the platform
  • Identify which customers are dropping from the website without consuming content
  • Identify the content which are having maximum bounce rates

To drive all the insights, an organization will need to have not just good data engineering and reporting capability. Still, a good data science engine deriving insight from the unstructured data. The evolution of the content field will lead to a rise in content scientists who will help the content marketers drive the strategy for developing highly engaging content fulfilling the organization’s marketing objectives.

Personalized Experiences

According to Salesforce

  • 82% of business buyers want the same experience as when they’re buying for themselves. But only 27% say companies generally excel at meeting their standards for an overall B2B experience, signalling ample room to improve.
  • 72% of business buyers expect vendors to personalize engagement to their needs

This highlights how personalized experiences will be a driving force for business buying decisions. Marketers need to know about the platform visitors and also their relationship with the organizations to make personalization happen.

Customer Data Platforms (CDPs) are the best way to store customer information across multiple silos and build analytics use cases on top. Having customer data available at a single place can enable organizations to build segments of customers based on the latter’s online behavior, install base/purchase history, and interaction history etc. Each of these segments can be further divided based on preferences, and each user can get personal digital experience on the platform as well as personalized messaging based on the campaigns targeting them.

For unknown users who visit the platform for the first time, personalization can be done based on the data that such users generate as they engage with the platform and do a look-alike modelling to find similar customers and their personal preferences. As unknown users engage more with the platform, the platform gets more intelligent about their requirements.

Hence the marketers will not just need a CDP but also an analytics platform to enable real-time decision making for platform visitors.

Intent Marketing

Intent Marketing has been around since 2007, but the big step forward came in the year 2015 when Google started capturing intent of the searcher. The intent available can be of two kinds:

  1. First-Party intent: Captured from the organization’s own digital and other properties
  2. Third-Party intent: Purchased from external sources.

Availability of the intent information provides the organization behavioral information for a platform visitor or a group of visitors. This intent data lets the organizations know the focus area for the visitor and where they stand in their purchase decision making process.

If the marketers augment the customer data with the intent data, they will be able to make improved decisions regarding the prospective customers. Below are the use cases that can be augmented with the use of intent data:

  1. Campaigns: Marketers can drive targeted campaigns post knowing the intent, resulting in improvement in the campaign conversions boosting the return on investment.
  2. Personalization: The platform personalization is based on knowledge about the visitor and their behavior. If the visit intent is known, the customers can be taken along a path to the right information that they will find useful. This personalization can boost the digital experience leading to improved sales.
  3. Lead Scoring: Lead scoring is done by all marketers to help sales prioritize their efforts. Most of the lead scoring methods are rule-based or machine learning-based using the information captured at the moment of lead identification. Intent data augments the in-moment knowledge and provides more accurate results, leading to better utilization of sales efforts.

    With the known advantages of these methods, marketers need to revamp the analytics models already in place. The new business scenarios will be intent driven to generate more value for the customer.


The Digital evolution for marketing is seeing increasing adoption of data science and analytics capabilities by marketers. This adoption is driven by a need to be more customer-centric and also due to improvement in the kind of information available with the marketers. The as a service economy is changing the customer journey from a funnel to a loop where customer journey post-purchase is also important. This means that demand generation is an ever-existing process, and the marketing team is always engaging with the customers. Supported by new information sources and technologies, data science will keep adding value to marketers and will be the game changer on how an effective marketing team function.

Authors: Nishant Khanna

For delving deeper into how new engagement models are shifting data management, read our earlier blog on CDP in this series.