How Human is Your Lead Generation?
If there is one word that’s permeating industry discussions more than any other - it’s “Artificial Intelligence.” What was once something companies hoarded on servers and the cloud has become a valuable asset for the generation of leads.
Lead Gen is now firmly on the agenda for most B2B marketers – and with a good reason. According to the annual IAB / PwC Value of the Online Performance Marketing Industry study, in 2018, online lead generation accounted for ￡5 billion worth of sales via 80 million leads generated and returning an average ROI of ￡12 for every ￡1 spent. Usually, marketers use a combination of email campaigns, blogs, and social media content to reach a global audience – at a fraction of traditional advertising costs. However, in the age of technological evolution-revolution, what is the future of lead generation in terms of Artificial Intelligence?
I believe, the future will be defined by the unprecedented ability for Artificial Intelligence to find high-precision targets then qualify them at machine speed and scale. Right now, nearly 50% of sales time is wasted on prospects that will never turn into a sale. In a 2018 Hubspot survey, where they evaluated 1,070 companies, it was found that only 4% of them used a data-driven approach to generate leads. Yet, these 4 % outperformed their competitors by more than 500% in sales productivity and efficiency. The power of AI is rapidly democratizing the ability of all companies to understand the uniqueness of each prospect in such ways:
Lead Scoring: A lead generator manager with a rich pipeline of qualified potential clients has to make decisions on a daily, or even hourly, basis as to where to focus their time when it comes to closing deals. Often, this decision-making process is based on gut instinct and incomplete information. With AI, the algorithm can compile historical information about a client, along with social media postings and the salesperson’s customer interaction and rank the opportunities or leads in the pipeline according to their chances of closing successfully.
Managing for Performance: Every month, leadgens have to assess the revenue pipelines with an eye towards nurturing deals that might stall, or even worse, fall through. Using AI, we can use dashboards to visually see which contracts stand a good chance of being started.
Forecasting: Using an AI algorithm, leadgen managers will be able to predict with a high degree of accuracy revenue, which in turn would help a company to better manage their resources.
Upselling and Cross-Selling: AI algorithm will help to identify which of your existing clients are more likely to buy a better version of what they currently own (up-sell) and/or which are most likely to want a new product offering altogether (cross-sell). The net effect is an increase in revenue and a drop in marketing costs.
To cut a long story short, in each of these examples above, the quantity of gathered data used will increase the algorithm’s ability to personalize the approach to any client. But, the real question, how human is your lead generation process now?