Traditionally, companies relied on a limited set of customer demographics and consumer behavior metrics for lead scoring, using these sources to assess the likelihood of a purchase. Lead scoring is a more complex process today. Data sources have increased exponentially and must now be incorporated into the lead scoring process, which adds a layer of complexity.
New lead generation strategies resulting from the rise of the Software-as-a-Service (SaaS) industry are another factor that adds to the complexity. SaaS-related lead generation approaches include freemium models and other business drivers that capture more data and enhance lead profiles with new dimensions. They make lead scoring more complex by generating large amounts of new data, including precise information on popular features and how customers use products.
The mountains of data available today present companies with wonderful opportunities, but the downside is that sorting all of this newly available information can be daunting. However, when marketers and salespeople understand how lead scoring has evolved, they can take advantage of company data and accurately assess customer value. So how has lead scoring changed? Here are three ways it’s evolved in the SaaS era:
1. More data — and more data types: Before big data came along, lead scoring typically involved data from a small number of sources, including customer demographics and behavior such as product inquiries. Now, B-to-B salespeople have access to huge data sets from multiple sources, including CRM, social media and e-commerce. Companies that are able to effectively process all of the data sources can generate a nuanced picture of the lead.
2. Sophisticated lead scoring technologies: Today’s sales and marketing professionals not only have more data sources, they have new lead scoring technologies that integrate processes such as machine learning and predictive analytics. This enables companies to more accurately identify the signals that indicate a conversion opportunity, such as freemium usage data that signals users’ likelihood of being converted to premium status. Since lead scoring models are no longer static, marketing and sales teams can dynamically predict activities customers may perform next — and prompt them to move down the sales funnel.
3. Fresh strategies to handle messaging, channels and timing: The more precise lead scoring methodologies used by today’s marketing and sales teams give companies a competitive advantage when it comes to messaging, channel selection and timing. Using predictive analytics, companies can target consumers more effectively and generate conversions using channels like social media. More refined information on product usage translates into actionable data, which companies are using now to drive conversions.
The evolution in lead scoring that was made possible by big data as well as SaaS and freemium models definitely presents challenges. The volume of the data can be overwhelming, and lead scoring complexity can open up a gap between data and insight. Marketing and sales teams have to overcome these challenges to drive return on investment.
The most effective way to bridge the gap is to choose a platform that enables efficient lead scoring and facilitates analytics-driven marketing campaigns. Companies that find the right platform can gain an unbeatable competitive edge.
Anil Kaul is the CEO of Absolutdata, an analytics and research firm.
Dr. Anil Kaul is the co-founder and CEO of Absolutdata, a company specializing in big data analytics, marketing analytics and customer analytics.
Anil has over 22 years of experience in advanced analytics, market research, and management consulting. He is very passionate about analytics and leveraging technology to improve business decision-making. Prior to founding Absolutdata, Anil worked at McKinsey & Co. and Personify. He is also on the board of Edutopia, an innovative start-up in the language learning space. Anil holds a Ph.D. and a Master of Marketing degree, both from Cornell University.