Would you like to know who is most likely going to purchase your product or service, thus making your marketing and sales efforts more efficient? With predictive lead scoring, AI technologies can be used to predict purchase intent automatically.
Infer from Ignite Technologies is a predictive lead scoring solution that does this, and I was fortunate enough to interview Rob Franks, Vice President or Professional Services at Ignite Technologies and learn more on their predictive lead scoring solution.
His background is a combination of marketing and technology leadership, starting his corporate career as a classically trained marketer and evolved into a marketer with a deep understanding of technology. The last 5 years he has led teams that use computational and database technology to enable better marketing performance, and worked with some great technologists in areas of computational simulation, data management platforms and now AI/ML.
Now, over to the interview on predictive lead scoring with Rob Franks!
What is Your Company’s Background?
Ignite Technologies buys great companies with great customers and makes them sustainable for the long run. Not every great technology company can be a billion dollar unicorn, some grow at a moderate pace.
If the owners, founding team or investors aren’t happy with a moderate growth company they can sell to Ignite. Infer was one of those companies.
Founded in 2010 with a top tier founding team and investors, they built a great product with great customers, but the growth rate wasn’t where they wanted it to be. We purchased the company and intend to structure it in a way that it can run forever.
What Problem Does Infer Solve?
Half of B2B marketing and sales investment is wasted on leads that won’t buy, Infer tells you who they are with predictive lead scoring. (Apologies to John Wanamaker)
What Does Infer do, and why is it Important to Your Customers?
Infer streamlines marketing investments with predictive scoring that helps the sales team focus on opportunities with the highest probability of closing. Infer uses a combination of innovative data gathering and machine learning to classify and rank leads and accounts on the likelihood that they will convert.
When marketing and sales teams in the B2B space struggle with how to target and close leads they often look at additional resources as a way to improve results. Too many resources and acquisition costs for a new customer can kill profit margins. Competitive advantage goes to the company with the best product and an efficient acquisition strategy for that investment.
Sales automation tools like Salesforce have emerged to drive better efficiency through workflow, process and reporting but they don’t provide any real intelligence about the quality of the opportunities in the pipeline.
The emergence of AI and ML driven predictive tools like Infer created a new dimension for efficiency. These tools gather data, create a profile of the lead and compare the profile to leads that have converted. Leads with a high probability of closing receive high scores.
The Infer platform integrates with leading salesforce automation tools to provide up to the minute scores for leads as they are created, allowing it to effectively support sales and marketing workflows without adding new process.
The scores are updated in the lead record itself, so the marketers can develop automated workflows to send the lead to a nurture campaign, mark it as a marketing qualified lead or promote it to a sales qualified lead immediately.
The scores can also be used to improve digital marketing by integrating the scores into DMP’s or creating look alike profiles for targeting. Finally, they can be used to evaluate the potential and performance of lead generation investments by the quality of leads created.
Infer has both predictive and behavioral models so a high potential lead could be identified by a predictive profile and indicate high levels of imminent purchase interest by behavior. Behavior might include email opens or accessing product information on a website.
Infer works by gathering data, called signals, about the lead from over 4000 different sources. The sources include syndicated data providers, proprietary data gathered from web crawling and the inputs and behaviors of the customer. Initial models are built by taking a history of data, classifying the signals and using win loss history to train a predictive model.
The models are reviewed and optimized in Infer’s proprietary workbench tool by experienced data analysts and scientists. When the model is ready it is deployed to score all active leads and new leads as they are created. Scores can change as signals evolve so the models are periodically re-built with a more recent history of wins.
Infer’s key advantages over competition are a proprietary data classification model and the powerful workbench tool. Data classification allows for use of a wide range of signals to develop meaningful features for the model.
The workbench enables the model to be tailored to the needs of the client through precise refinement, filters and exceptions that improve model validity and usability.
How Does Infer use AI?
Infer is a predictive modeling solution that uses historical data combined with signals about the companies to make a prediction about likelihood that the company is a good sales prospect.
The model uses functional approximation to map input variables to output variables. We create a prediction that a lead is good or bad based on the model using classification data modeling.
The model is trained with actual historic data that includes both negative and positive results using a technique called supervised machine learning. The outputs are evaluated against a holdout sample to evaluate accuracy.
Infer has a proprietary workbench that gives the data scientist flexibility to apply multiple types of algorithms to create the most accurate forecasts. Signals are gathered and classified to create a score for each lead.
In Your Mind, What is the Future of AI in Business and Marketing?
Artificial Intelligence and Machine Learning will help marketing become more relevant and less dependent on agencies.
Marketing has struggled for relevance in a world where classic media channels and brand work have been marginalized by sexy shiny new technology services that require legions of agencies and consultants.
Marketing has a difficult time proving ROI and creating meaningful competitive differentiation. With AI/ML competency in house, marketing becomes a source of competitive advantage by using AI and ML to understand, identify and communicate with customers more effectively.
The AI enabled marketing team evolves from “classic marketing” into three key areas of importance;
- Brand/Image/PR remain important for strategy and message,
- Performance Marketing emerges as a huge and driving force to marketing teams and
- Capable marketing technologists bring much of the work in house and hold it all together with a mar tech stack.
The issue that holds marketing back is lack of on-team expertise and resources. Today, too many marketing team resources are spent on agency and vendor coordination.
Additionally, a bunch of new roles have emerged to manage the mar tech stack and those roles have been assigned to technical non-marketing resources placed in the marketing department.
In some sense marketers have become integrators of tools and tech and outsourced innovation and creativity. In many ways they lost the marketing, customer and product depth that allows marketing to create unique and differentiated value.
In a world where Agencies and technologists drove execution of advertising and promotion, marketers moved to the back seat. These media vehicles were so complex and required dedicated specialist resources that were only needed occasionally by the brand.
An agency could afford to have a specialist expert allocated across multiple clients, so the model made sense. Unfortunately, those specialist resources pretty much used the same ideas with all clients, creating a herd mentality with little competitive advantage.
ML and AI tools reduce dependency on specialist resources, empowering the marketing team to drive and lead in a tangible and measurable way.
It’s not all good news, some jobs will go away, especially in areas of planning, operations and analytics. Because more is done in house by pure marketers aligned against revenue goals with tools that let them execute quickly and independently because of AI/they drive value from competitive advantage from proprietary models and customer insight.
Do You Think Marketers Will be Replaced by AI Robots?
Yes, but not in a bad way. Marketers suffer from a perception that marketing doesn’t deliver real value. Marketing has become too dependent on technology vendors and services vendors to deliver real value, leaving the marketer as the coordinator and budget manager.
AI is a force multiplier for the organization that has data and people who can use that data to create competitive advantage. If marketers adapt their skillset and learn to use tools and understand the concepts of AI, they can become and remain very valuable.
AI will reduce the need for people in certain functions, much like technology has reduced the need for book keepers, draftsmen and machinists. Like Tony Stark’s Iron Man suit, it increases the power of those who remain.
Marketing and sales operations roles are most vulnerable to replacement by AI tools followed by consumer insight and some strategic roles. However, a new discipline emerges, one that is expert in application of AI/ML to critical functions like planning and targeting, insight development.
Marketing departments become much more like performance marketing teams, monitoring trends and results, competitive response. The marketer tweaks tactics that are executed by tools that rely on AI and ML that can execute in a rapid and precise way.
Do You Have Any Other Thoughts on AI in Business and Marketing?
Marketing is at a crossroads. Years of promises and obsession with shiny new things have not brought results. CMO’s have incredibly short tenure. Agencies have been growing staff and retainer payments to support byzantine data management and advertising strategies.
Now the party is over, companies like P&G are pulling back spending and moving toward traditional media and in-house ownership of digital. Privacy concerns are limiting the potential ways to use technology to target customers.
AI won’t be the silver bullet that makes marketing great again. ML is not going to automate away all the marketing jobs. It will become more prevalent and accessible to marketers.
It will have a major positive impact on marketing as a discipline because it cuts out barriers between the people closest to the customer and the marketing tools that allow for effective interaction. AI and ML can put the power back in the hands of the marketers.
In this interview, Rob Franks provided great insights into how predictive lead scoring works, and how it can be used to prioritize which leads to target with marketing and sales efforts. I believe more and more companies will start to use predictive lead scoring in the next couple of years!
I am an author, speaker and consultant in marketing automation and artificial intelligence.
Do you need help with marketing automation or AI-based solutions? Contact me and let’s discuss how I can help you!