How predictive analytics is reinventing the art of lead generation

Smart business owners recognize that predictive analytics is the key to increasing conversion rates and generating more revenue from quality leads


One of the biggest challenges all businesses struggle with is attracting high-quality leads. A poor lead conversion rate will cost a company a lot of money in fruitless marketing efforts and missed opportunities to generate sales from actual paying customers. Russell Ruffino is a renowned small business coach that founded Clients on Demand. Ruffino says that failing to attract new leads and not converting the existing ones are two of the biggest mistakes businesses make.

“If you don’t know how to convert leads into clients consistently, you sell from your heels,” says Russell Ruffino. “You discount your services which kills your margins.”

Smart business owners recognize that predictive analytics is the key to increasing conversion rates and generating more revenue from quality leads.

A number of data scientists have developed tools to assist companies with their lead generation strategies.

Can predictive analytics really improve the average value of your leads?

According to WordStream, The average website conversion rate across all industries 2.35%. Conversion rate data from offline marketing strategies doesn’t seem to be available, but is presumably lower than online, inbound marketing strategies. However, savvy marketers can easily generate conversion rates that are five times this figure.

There are two factors that affect the conversion rate of any marketing funnel:

The quality of the leads that you generate

The effectiveness of your marketing message and ease of completing the funnel

Both of these elements can be optimized by using increasingly sophisticated predictive analytics tools. The following insights will help you understand the relevance of predictive analytics in lead generation.

Predictive analytics improve the accuracy of lead scoring models

The biggest mistake that new businesses make is viewing every lead equally the same. Growing businesses quickly discover that some leads are much easier to convert than others. In order to maximize revenue without wasting resources on undesirable leads, you will need to create a lead scoring model.

A report by McKinsey found that predictive analytics is incredibly valuable for lead scoring. They highlighted a case study of an IT company that used predictive analytics to increase their lead conversion rate by 30% by changing a single variable in their lead targeting funnel. They achieved these results by developing an analytics platform that monitored conversion rates of the different companies they targeted with their marketing. They were able to identify the size of the companies that were most likely to make purchases.

The financial industry is also finding ways to monetize their customer base. They have started using machine learning to predict customer behavior and identify the customers that are most likely to add value to the company.

Endor is an example of a company that has used predictive analytics to help banks find the best customers. They pride themselves on offering a 360-view of customers to help financial institutions improve their marketing, operations and risk management strategies, harnessing the power of their proprietary “social physics” technology and easy-to-use predictions engine.

Predictive analytics assists with automation of the initial lead engagement process

Identifying the types of leads that are most likely to convert is one of the most important processes. However, the value of predictive analytics goes well beyond lead targeting. It also helps minimize conversion decay during the early stages of the lead generation process.

Automation can help improve engagement with customers and increase the probability of generating a sale. Unfortunately, depending on an automated lead engagement system is not going to work unless it is properly constructed. If you are using chatbots to engage with your customers during their first interaction with your brand, it is important to make sure they are properly programmed to handle a wide range of likely inquiries.

McKinsey states that this is another area where predictive analytics comes into play. You can use predictive analytics to identify the most likely questions that customers will ask when they speak with your chatbot. This will help improve the user experience and increase the likelihood of a conversion.

Predicting the most probable responses to different aspects of your lead generation funnel

For years, marketers have used Google Analytics conversion tracking and tools like Prosper202 and BeMob to monitor conversions of their online funnels. These tools have been invaluable in helping them identify the traffic sources and creatives that yield the best conversions. They have used these tracking platforms alongside split testing tools to identify the best performing elements of their campaigns with a high level of statistical significance.

More advanced tracking tools have made it possible for them to track different elements of the lead conversion process. This can give them very granular insights into the behavior of certain customers that are exposed to specific types of marketing messages. Hypothetically, marketers can see how 25-35-year-old women that they attracted through Facebook ads respond to certain types of landing page copy or conversion buttons, relative to users in the same demographic that are acquired through Google AdWords.

Predictive analytics algorithms behind these tools are ideal for creating more holistic marketing messages for your target demographic. Once you have determined the best converting demographic and your lead scoring model, you can use this technology to seamlessly optimize your entire marketing funnel to match their needs and expectations.

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