Sales is competitive, there is a constant demand from the market to be better than your peers, especially in a B2B setup where businesses find new tactics & methods to outsmart the competition.
There was a time when door-to-door sales was the only way of selling which eventually was outsmarted by an inside sales job reaching to your phone or computer via digital outreach. Even with this, people found ways to automate the outreach by using smart tools or implementing some kind of automation like web scraping, etc.
As AI gets commoditized, writing a beautiful pitch to a customer or generating a call script doesn’t really take much effort. In fact, this is probably causing the end customers to receive similar and generic pitches that don’t really translate into appointments or deals.
Generating a pitch via AI is really easy but how about the efficacy of the pitch which is often subjective based on a lot of factors. Moreover generic pitches or calls don’t get you the expected results. We often hear about personalizing our outreach but the crucial question is how do you do it with scale as manual prospecting is both cumbersome & error prone.
There are a couple of third-party data enrichment software available that give you additional information about a particular lead or a lead list based on either maybe the Linkedin profile or a website URL. This may work in some cases but when you try doing this on scale, the pricing/subscription of these platforms is complicated and may put thresholds on enrichment after a duration.
One more thing that can cause hurdles in enrichment is the customization of the sales process & utilization of data procured from multiple sources. Customization of the sales process according to a specific business need is quintessential to the effectiveness of the outreach which should be designed to close more deals.
The earlier way of automation was just to procure the data from some third-party APIs or get it from scraping. The pertinent question after that was how you would use this data as oftentimes the data is unstructured and context-driven.
Unless the data is in a structured format or can be transformed into a structured format, the data doesn’t really generate much value to personalize the outreach.
There may be very few people who do not know about tools like ChatGPT or Perplexity. The LLMs have penetrated deep into the daily search/query usage, however, this can be also leveraged to design & customize a sales outreach that can prospect very efficiently and generate a more personalized outreach.
B2B outreach & prospecting if manually done requires the salespeople to go through the company data in tools like Zoominfo, Apollo, Tracxn, etc. Though these platforms may give you relevant information based on your pricing plan & the quality of the platform, implementing them on scale & customizing it as per the sales process is often tricky.
What if an AI-based system can –
or training an LLM model to rank or score a particular lead for better prioritization.
At the end of this implementation, the salespeople may get a list of leads with a summary of the lead along with a few potential approaches based on the customized criteria.
AI opens up opportunities to crank up your sales efficacy not just from the ability to automate & improve efficiency. The ability of an AI-powered system to collect raw data, turn it into valuable leads, insights, and information, aid lead scoring that makes prioritizing straightforward, and add deeper data to enrich leads make it invaluable to your efforts.
Sales is competitive, there is a constant demand from the market to be better than your peers, especially in a B2B setup where businesses find new tactics & methods to outsmart the competition.
In this blog, we’ll explore some of the leading LLM models available in 2025 and the most effective Gen-AI use cases. We’ll also highlight the industries where Gen AI is having the greatest impact.
Subscription model selection can be a daunting task for a product owner. Subscription models usually need to encompass the right pricing strategy to cover the costs on a large level at the same time consider margins.
