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Streamlining Revenue with Semantic Search

Aryan Trip
Aryan Trip,RevOps Mechanic Clientell
5 mins read
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Author’s Note: “Great leaders create more leaders, not followers.” This quote captures the essence of how an LLM must be viewed by the contemporary business operators. As a leader your vision must be to impart your ideology and create systems that outlive you. An LLM is fundamentally a really smart College grad who understands your business but needs nurturing and then it will in itself be sufficient to lead the business to new economic milestones.

Semantic search and large language models (LLMs) are two emerging technologies that have the potential to revolutionize revenue operations. Semantic search can be used to improve the accuracy and relevance of search results, while LLMs can be used to generate personalized content, automate tasks, and generate insights. When combined with workflow automation, semantic search, and LLMs can help revenue teams improve their efficiency and effectiveness at all levels of the hierarchy.

What is Semantic Search?

Semantic search is a type of search that goes beyond simply matching keywords to results. Instead, semantic search algorithms try to understand the meaning of the search query and return results that are relevant to the user's intent. For example, a traditional search engine might return a list of websites that contain the keywords "sales CRM software." A semantic search engine, on the other hand, might return a list of websites that compare different sales CRM software products, or that provide reviews of sales CRM software.

What are LLMs?

LLMs are a type of artificial intelligence (AI) that can generate and understand human language. LLMs are trained on massive datasets of text and code, and they can be used to perform a variety of tasks, such as:

  • Generating text, such as news articles, blog posts, and creative content for marketing et.e al.
  • Translating languages allowing to open products to different markets
  • Answering business queries in a comprehensive and informative way
  • Writing different kinds of creative text formats of text content, like advertisements, code, scripts, speech, email, letters, etc.

How can Semantic Search and LLMs be used to improve revenue efficiency?

Semantic search and LLMs can be used to improve revenue efficiency in a number of ways. For example:

  • Personalized sales pitches and emails: LLMs can be used to generate personalized sales pitches and emails for each customer, based on their interests, demographics, and purchase history. This can help to increase the open and click-through rates of sales emails, as well as the likelihood of converting leads into customers.
  • Improved customer service: Semantic search can be used to improve customer service by helping customer support representatives quickly and easily find the information they need to answer customer questions.
  • Increased sales productivity: LLMs can be used to automate tasks such as data entry, lead qualification, and customer follow-up. This can free up sales reps to focus on selling and building relationships with customers.
  • Better decision-making: LLMs can be used to generate insights from sales data, such as customer churn rates, win rates, and sales performance trends. This information can help revenue teams make better decisions about how to allocate resources and improve their sales process.

How can workflow automation be used to enhance the revenue efficiency of the org at multiple levels of hierarchy?

Workflow automation can be used to enhance the revenue efficiency of the organization at multiple levels of hierarchy by automating repetitive tasks, streamlining processes, and improving communication and collaboration.

Chief Revenue Officer

  • Automate dashboarding and reporting: Workflow Automation allows decision makers to establish consistency on their reporting and dashboarding needs. This can help reduce the Turn Around Time significantly as the data mining, model training and updating benchmarks to get to the relevant answers mellows dramatically. Also the operational cost of maintaining heavy data teams and infrastructure management reduces, leaving cashflow to be reinvested.
  • Improve communication and collaboration: Workflow automation can be used to create and automate communication and collaboration workflows between sales reps, sales managers, and other members of the revenue team. This can help to improve communication and collaboration, and make it easier for everyone to stay on the same page, aligned on the same KPIs and metrics that actually lead to revenue. \n

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Sales Manager

  • Create a workflow that automatically routes a lead to the appropriate sales rep based on their location and industry. The workflow could use semantic search to identify the customer's location and industry from their contact information
  • Automate lead qualification: Workflow automation can be used to automatically qualify leads based on their contact information, website activity, and social media engagement. This can free up sales reps to focus on selling and building relationships with qualified leads. Untitled (4).png

Account Executive

  • Automate data entry: Workflow automation can be used to automatically enter data from sales calls, emails, and other sources into CRM systems. This can save sales reps time and help to ensure that data is accurate and up-to-date.
  • Automate customer follow-up: Workflow automation can be used to automatically send follow-up emails and tasks to sales reps based on the status of their deals. This can help to ensure that all deals are followed up on promptly and efficiently.
  • Create a workflow that automatically sends a personalized sales email to a customer after they attend a webinar. The email could be generated using an LLM and personalized based on the customer's interests and demographics. Untitled (5).png

LLMs are great, but what’s the catch?

Major problem with emerging technologies is the unpredictability and LLMs and semantic search are no different. Here are a few broad problems that needs investment and initiative:

  • Bias - LLMs are trained on huge data sets thus might generate content that might be biased towards certain individuals.
  • Accuracy - Current models need to be hyper-trained to minimise hallucinations.
  • Cost - Deployment, training and retraining is a bit heavy on the purse if not done right.

How Clientell Approaches this:

Untitled (6).png Clientell extensively uses Workflow Automation tools and techniques that bring predictability and confidence into each link of the operation. With introduction of fundamental LLMs trained by Clientell’s Auto-ML technology on company’s proprietary data across CRMs, Mails, Calendars, Meeting, Call recorders, CDPs etc. Organisations can now be sure of the accuracy of the output generated by the LLMs for Revenue forecasting, Follow up automation, risk hedging and other use cases as mentioned above. This niche is ripe for exploration and only a few of us might be able to realise the true potential of this technology in our lifetimes.

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