Tips to leverage predictive AI for improved lead conversion

Meta Title: Leveraging AI platforms for cost-efficient lead conversions. 

Meta Description: Read how industries across the world are leveraging AI tools to improve their lead conversions and reduce costs

Leveraging AI-based predictive analytics for cost-effective lead conversions

The most effective way to close a sale is by creating an emotional connect with the client. But when sales reps have to deal with tens of thousands of calls for just a few converts, it’s impossible to empathize with prospects and drive a meaningful conversation. Over 84 percent of lead generation campaign calls made by insurance companies are either dropped or declined. Roughly 7% of them turn out to be potential prospects.

In order to save time, money, and efforts, it is imperative to focus only on high-value targets and deliver an optimum conversational experience. The key is to let artificial intelligence based predictive analytics take over the data generation and lead qualification process, and deliver high-priority leads which are easy to convert. This way, sales reps can give their best to every lead and create a long-lasting emotional impact.

Leveraging AI-based predictive analytics to improve lead conversions:

The goal here is to find leads that are worthy of a sales rep’s time and efforts. So, let’s see how AI-based predictive analytics works its magic in everything from data generation to lead scoring, eliminating all the repetitive lead generation tasks, and delivering only the most valuable leads.

Stage-1 Lead Generation:

To generate leads, you need verified customer data. AI tools can dig through terabytes of local and online information, to find customers as per the ideal target audience profile. 

Let’s see how it acquires data from various disparate sources;

• Internal data/offline: AI lead generation tools can mine through existing customer databases in CRMs, to identify associated leads and potential clients. With advanced natural language processing(NLP), it can analyze customer conversations (via emails or chats) to predict positive inclinations towards buying a product, by understanding various words/phrases in the context of the conversation.

• Online data: AI-based data mining solutions can scour the entire internet (social media activity, content platforms, etc.) to identify behavioral similarities between new prospects and existing customers and predict their likeliness of being potential customers. 

Along with advanced data mining, AI solutions can also factor-in unstructured data, like reviews or testimonials, to predict the likeliness of being potential prospects. Based on this prediction, it certifies each customer record as a verified lead, ready to progress further. 

Stage-2 Lead Qualification:

Now comes the tricky part; deeming leads worthy of chasing! Let’s say you’ve received 20,000 verified profiles from the lead generation stage. Are all of them worth pursuing? Absolutely not! This is where predictive analytics plays a very critical role. 

AI platforms have proven to be highly effective in establishing a strong ‘buying intent’ in leads.  It uses a model known as predictive lead scoring. In a manual lead scoring process, the agents need a formula for analyzing customer profiles, scoring leads and qualifying them.  

In AI-based predictive analytics, customer profiles are analyzed using machine learning algorithms, which lookup various customer data-points and automate various formulas to qualify leads. It operates based on 2 types of data;

Historical data:

In this, the AI tools look through historical customer records to identify key similarities between new leads and existing customers. These similarities can be parameters like end-use, geography, or company positioning.  These parametric similarities are used to predict the quality of a lead and its probability of getting converted.

Factual data:

This where AI tools evaluate leads based on factual data procured from external data sources. It includes data like;

• Disclosed revenue: Some leads have higher sales revenues processed than others. (published online)

• IP address: If an organization only does business in a specific geographical area, then predictive lead scoring solutions can filter it out based on the IP address of the lead. 

• Firmographics: Understanding a business’s characteristics (positioning, employee strength, turnovers, markets served, etc.) to predict the likeliness of a purchase.

• Existing interactions: If a lead as already opened an email, or clicked on an email link from your company, then this is a crucial indicator of the buying intent. 

• Web analytics: AI monitoring tools monitor internet content specific to a topic, like automation or Insurance, and track the IPs consuming that resource. After that, they match it with the leads known for using the IP to predict which leads showed buying signals, and scores them by their likelihood of making a purchase.

Using the above types of factual and historical data, AI effectively narrows down leads from a few thousand to a few hundred. AI solution providers like Suyati, offer holistic predictive analytics solutions for all types of businesses. Suyati’s flagship Buyer Rhythms solution enables call centers to automate entire lead funnels, by integrating AI tools without upgrading existing infrastructure.  

Stage-3 Lead Conversion:

Let’s say you’ve filtered out 200 prospects from 20,000 fresh leads. Are you going to engage with all 200 of them? Of course not! The AI plays an important role here as well. Remember, the goal here is to create emotional connect, rather than just drive a typical salesy conversation.

With the help of the predictive lead scoring, you can now categorize the final 200 leads into 3 segments; High priority, medium priority, and low priority.  Let’s say there are about 40 leads in the high priority segment… what can be done to make those 40 calls absolutely productive and meaningful? The answer is, accurate customer insights!

Based on the data procured in the lead qualification stage, the AI tools can perform further predictive analysis to procure important insights, that can empower sales reps to close leads more efficiently. Important insights like customer preferences, the ideal product to pitch, and a suitable time of contact, can be procured via predictive analytics.

Such information arms sales reps to drive meaningful conversations, empathize better with clients, and create a genuine emotional connect. As per a Harvard Business Review done for lead generation, Insurance companies using AI were able to increase their leads by almost 50 percent.

In Conclusion

If every lead is generated, qualified, and nurtured through predictive analytics, then the conversion probability surges up dramatically. This is because the final lead is a culmination of 3 important processes;

• Customer data verification

• Establishing buying intent

• Deriving customer insights/touch-points

AI-based predictive analytics makes it simple for businesses to find high-value leads based on the above 3 aspects, which form the fundamental basis of any effective lead conversion process. 

Artificial Intelligence in sales and marketing is on the verge of revolutionizing lead funnels for all industries. According to Accenture, as many as 79% of insurance executives believe that AI will transform the way their companies engage with customers. AI eliminates the wasteful efforts in large scale call campaigns. Making fewer calls that are productive can have significant impacts on the costs of calling (Computers, Call tariff, Staff time) and can greatly increase a call center’s capacity. 

If you're looking for an expert automation solutions provider, you can contact Suyati. Suyati is a pioneer in implementing cutting-edge industrial automation solutions, offering both standalone and integrated solution packages.