Generating sales leads is no small task in an age when information flows through abundant channels at breathtaking rates. Yet while today’s generous data supply is a rich resource for intelligence gathering, it does not readily yield insights through conventional means. Therefore, sales teams are increasingly relying on sophisticated analytical approaches to help them gain a deeper understanding of consumers and define the profiles and persona of ideal prospects.
Automating this cumbersome work helps sales professionals reduce the time and money spent on laborious research and futile pursuits. Lead generation—a critical part of the sales process—typically demands more than its share of company resources, both human and financial. According to a 2022 survey of marketers, 61% of respondents reported lead generation to be the top challenge in their work. In addition, 53% of these respondents indicated that lead generation expenses accounted for at least half of their budget.
With a growing presence in the lead-generation space, AI-driven tools help sales teams capture insights from more diverse and current digital sources—such as social media and news APIs—and filter them down to the most relevant prospects. Thus guided, sales representatives can focus their efforts on leads most likely to convert into sales. These technologies can nimbly mine pertinent data from the steady current of digital information and, leveraging Natural Language Processing (NLP) capabilities, extract the insights that form meaningful, actionable intelligence. Beyond data mining, AI tools can filter, score, and rank identified leads according to customizable metrics that track relevance. With a dense wilderness of unruly text condensed into an ordered list of qualified and validated prospects, sales teams have a clear map to follow on the road to sales conversions.
These search results can provide guidance not limited to identifying and prioritizing prospects. AI algorithms can also enable predictive analytics that gives sales representatives insights into when and how to approach potential buyers. They can even identify the most appropriate decision-maker in an organization to contact. Firmographic data, organizational details that are of particular import in B2B marketing, as well as intent data, which can signal that a prospect has a relevant need and is searching for solutions, aid in market segmentation and are easily accessible to sales teams who are trained in data science and machine learning technologies.
Empowering sales forces with the ability to mine information about prospects from this broad source of information is a significant boost, as indicated in a survey of marketing professionals cited in an article exploring lead conversion statistics. With 43% of marketers surveyed showing that collecting sufficient data was their biggest lead conversion challenge, it is clear that technologies that automate this process are considered beneficial. These tools can dramatically enhance not only the generation of leads but also the effective nurturing of leads.
As an example of how automation can reduce the rigors of lead generation, Data Society developed an AI-based tool to achieve just that goal for SAFEbuilt. This organization, a network of companies offering construction and development services to municipal governments, recognized its sales team’s time and effort invested in identifying and qualifying sales prospects. The process was further complicated in SAFEbuilt’s case because the company’s client base is communities, whose profiles—in terms of need, resources, and intent—can be challenging to capture through digital media sources.
When SAFEbuilt approached Data Society with this dilemma, the Data Society team designed an AI-powered tool. This tool used NLP to mine data from social media, news API, and job listings across the US to locate municipalities that would be likely candidates for SAFEbuilt’s services. The team worked closely with SAFEbuilt’s subject matter experts to determine the criteria that most accurately define their ideal customer personae and establish a suitable scoring system to rank leads. The result is a customized, continuously updated lead-generation dashboard that displays qualified leads—scored and ranked by relevance—along with pertinent details to guide sales representatives in approaching the prospects.
Enterprises that have heeded the call of lead generation technologies are reaping the rewards. According to an AIbees article about today’s best AI lead generation tools, organizations that have adopted such tools have seen:
In addition, the benefits of streamlining these efforts are compounded by their power to boost the bottom line. For example, according to a 2022 report, B2B companies taking sophisticated approaches to lead generation enjoy 133% greater revenue than their less technologically-forward counterparts. Further, a Venture Harbour article examining the importance of lead nurturing reports that 77% of marketers surveyed credited marketing automation with increased conversion rates.
Despite the solid case for implementing AI tools to optimize lead generation, companies should be prepared for certain challenges as they adopt these solutions. According to a Teradata “State of Artificial Intelligence for Enterprises” report, decision-makers from large organizations cited a dearth of talent and understanding of such technologies as the second biggest barrier to implementing AI solutions in their companies. Concerns about whether these internal skills gaps could limit employees’ ability to use advanced technologies effectively inhibit organizational embrace of such tools. However, with ongoing workforce training in data science skills and a commitment to cultivating enterprise-wide data maturity, companies can develop the internal expertise necessary to enjoy the advantages of technologically-driven sales and marketing tools.
SAFEbuilt—a family of companies that provide community development and municipal infrastructure services—sought a solution to the onerous process of identifying promising sales prospects. This 30-year-old enterprise, which serves over 1,800 clients in 30 states, understood why its existing clients chose to work with them. However, recognizing new candidates for the company’s services required arduous searches through volumes of text from disparate sources. SAFEbuilt wanted to find a way to streamline the process of distilling oceans of content down to high-potential leads with the following priorities: