Over the next few weeks, I will be authoring a series of blogs on data science and demand generation. In this first blog, I will make the case why we need to leverage data science for lead identification, qualification and scoring.
We are at a tipping point for the realization of value derived from data generated across social media especially in the area of Demand Generation. As the Head of Engineering at NextPrinciples, I am deeply involved in the use of Data Science to improve our understanding of social data to further help our customers in the area of Demand Generation. Prior to joining NextPrinciples, I spent over four years at Yahoo and two years at Telenav building data related platforms and products. At Yahoo my team that was responsible for building data processing pipelines that managed most of the Yahoo’s display and search advertising events. At Telenav, I helped re-architect their content management and local search platform. We applied data science for mining, classifying, and enriching local content collected from several third party vendors and crawled from the web. But leveraging data science for demand generation is a different beast.
It’s about finding more leads, better leads and faster time to revenue.
We at NextPrinciples collect publicly available structured and unstructured data from various social channels such as Twitter, Facebook, YouTube, etc. as well as from various blogs and forums to identify, score and profile leads. We use the collected data to provide us signals to identify and score leads for our customers. We call a lead as a “raw lead” when we find that the lead has some direct or implied interest in a service or product. A raw lead becomes “qualified” when it crosses certain score thresholds established by our customers. For example, these scores could measure the intensity of their interest in certain products, services or activities, unhappiness with their competitors’ products or services etc. Once leads are qualified, our customers can choose to nurture them through various channels, both traditional and social.
However, it is not that easy.
Why do we need ‘Data Science’?
The vast quantity of social data has been a gold mine for Data Scientists for the past several years. While there have been several research papers written and tools developed, use of this data for lead identification and qualification is still in infancy. It requires deeper level of understanding of not only the text of the conversation but also the context of the conversation.
Also conversations in social media are inherently ephemeral and highly influenced by “immediate” events. The conversation that is popular today may not be popular tomorrow i.e. today’s search criteria may not be relevant tomorrow further adding to the complexity.
Once the leads have been identified, the art and science of leads qualification and scoring is also a data science problem. You need to consider the lead’s demographic data, social conversations and the intent expressed in the conversation including quirkiness of the social conversations.
As we all know, Data Science is as much a science as it is an art. The key to successfully applying data science has always been ability to nail down the art part of the science. In my next two blogs, I will share with you details of how we at NextPrinciples have applied data science to the process of lead identification, and qualification and scoring.
Have you used data science for marketing and in specific demand generation? Would love to hear from you.