The customer acquisition funnel is the beating heart of any business, which is why all marketers know they have two primary objectives: getting as many potential customers into the top of the funnel as possible, and efficiently converting them into purchasers. To do this well, marketers prioritize their efforts by scoring each lead and analyzing all available data to select which leads, or potential customers, to focus on. Accurately scoring leads saves time and money and is a direct multiplier of revenue.
In today’s business environment, companies have a lot of data about their potential customers. However, lead scoring still comes down to human judgment and the best guesstimate by the marketing team as to what behaviors or demographic characteristics indicate purchase probability. These manually (humanly) defined scoring systems are often hard-coded into the martech stack and then rarely revisited, where they quickly become stale.
There’s a Better Way
Artificial Intelligence’s (AI) brilliance at pattern-matching makes it a natural solution for this kind of job. The best approach is to take all the information you have about a lead and compare it to the data you have about recently closed business, both won and lost. With AI, you can quickly qualify, rank, and push leads to sales teams. It’s accurate, easy to update as business conditions change, and best of all, it’s not subject to human judgment errors.
Historically, machine learning has been inaccessible to most sales and marketing teams due to the technical complexity of model training and deployment. Fortunately, that is now changing. In late 2020, predictive lead scoring became one of the top four artificial-intelligence-based technologies B2B organizations plan to deploy in the next 12 months. That’s due in part to a new revolution: the democratization of artificial intelligence (AI), making it possible for all employees to create their own AI solutions for lead scoring and much more.
Enabled by no-code tools for rapid AI development, non-technical employees can now add machine learning capabilities to their lead-scoring efforts, easily connecting those tools to their CRM and other parts of their stack. They can also leverage machine learning (ML) to extract the most value from their data, all without writing a single line of code. They can even automate the routing of leads to the appropriate sales teams, enabling those stakeholders to act on them quickly in an informed way.
As marketers add new data or change their goals, they needn’t burden their IT or data operations teams to adapt their tools. With AI, marketers are now empowered to retrain ML models on the fly themselves, incorporating thousands of new points of data and ensuring the lead-scoring system is tuned for the latest market conditions.
Why Use AI in Lead Scoring?
The bottom line: Human judgment-based lead scoring systems are by their nature inefficient and error-prone. AI-driven systems eliminate judgment bias and are incredibly efficient at surfacing patterns in complex data—delivering the most accurate lead scoring that the underlying data supports.
Marketers are under tremendous pressure to increase the quality and quantity of high-scoring leads. Sometimes this pressure results in unqualified leads receiving high scores, hurting business progress and eroding trust between the sales and marketing teams.
Determining a single lead’s interest to buy, then prioritizing that lead among thousands of others, is a difficult task. Processing all the pertinent data—demographics, firmographics, online behavior, social media activity, past sales activity, and countless other factors—is beyond what most people can reasonably accomplish. And simplistic rule-based systems ignore critical signals in that heap of data.
AI eliminates rule-based judgment errors and takes full advantage of the rich data today’s marketers possess. Combining massive amounts of data with machine learning (ML) allows the development of a wholly data-driven and continuously updating scoring system that adapts quickly to changing business conditions, competitive environments, and product line-up changes.
By automating this process, marketers can then turn their attention to focus on improving high-quality lead flow. As they add new lead data to their database, marketers can run the leads against an AI model to instantly score and stack rank them. They can even train the ML model to push messages to the right sales teams in real time, tagging the very best leads for immediate follow-up.
These benefits help sales teams reduce the time and costs to sell, bringing sales teams closer to the most profitable deals sooner, and they enable sales teams to skip the research required today. AI can determine the bottom 30% or so that aren’t even worth sales’ efforts.
Putting These Capabilities to Work at Your Organization
New AI development tools take all the complexity out of preparing and training your own lead scoring solutions. They also eliminate the need for data scientists’ hands-on work and empower marketers to translate valuable data into high-scoring leads with just a few clicks. As the sales team sees that high ratings convert, marketers will see their enthusiasm grow. After all, real sales are the ultimate measure of lead scoring success. It’s time AI did its part.
Jon Reilly is COO of Akkio, providers of an ML-based data prediction tool designed for non-technical users (like marketers).