AI Prospect Research In Sales: How To Know Everything Relevant About Your Prospects Before Reaching Out
Table Of Contents
- Key Takeaways
- Content In Sales
- What Is AI Prospect Research In Sales?
- Should You Use AI For Prospect Research
- How to Get Started with AI Prospect Research
- Limitations of Prospect Research with AI
- FAQs: AI Prospect Research in Sales
Key Takeaways
- The Importance of Relevant Messaging and Volume: The article highlights the ongoing debate between prioritizing relevant, personalized messaging for higher conversions versus increasing outreach volume. In today’s B2B sales environment, striking the right balance between these two strategies is crucial for success. The use of AI in prospect research is presented as a solution to achieve both personalized outreach and maintain high volume.
- The Role of AI in Automating Prospect Research: AI tools are transforming the sales process by automating the research required to understand prospects deeply before outreach. This shift allows sales teams to access customized, relevant information quickly, enabling more personalized and effective sales strategies.
- Practical Applications of AI for Immediate Use: The blog post outlines how sales professionals can start incorporating AI into their workflow immediately, using tools like ChatGPT for basic research tasks. It provides examples and prompts for identifying prospect information, analyzing industry trends, crafting personalized pitches, and more, showcasing the versatility and utility of AI in sales.
- Advanced AI Research Tools and Customization: For more in-depth research needs, the article suggests exploring advanced AI options and custom GPT models. It discusses how sales teams can use these tools to tailor research processes to their specific needs, enhancing the efficiency and effectiveness of prospect research.
- The Future of AI in Sales and Prospect Research: By emphasizing the importance of AI in overcoming the challenges of traditional prospect research methods, the blog post argues that AI prospect research is not just a trend but a fundamental shift in how sales teams operate. It suggests that leveraging AI not only improves the quality of prospect interactions but also positions sales teams for success in a competitive digital landscape.
The Constant in Sales
Much in sales is debated, and gurus on social media and conferences always advocate for tactics and strategies to increase conversion, coach salespeople through burnout, better handle rejection, and encourage more prospect research before tackling the sale. Most sales leaders are tired of the generic advice, but the constant in a sales organization's life cycle has always been choosing amongst only two straightforward options: more relevant messaging for higher conversions, or higher volume to reach more people. More relevant messaging requires personalization andbetter timing, and higher volume can be achieved through all sorts of automation tools and a level of grit from the sales team.
The two seemingly opposite ideas of volume versus conversion has always been contended in terms of which one is more important in sales, but there's no way to understand which one is the higher return strategy, unless you clone a sales person and have one focus on generic sales messaging at great volume and the other on personalized messaging through a ton of research but at lower volume. In a perfect world we can achieve both, and most of the tools on the market has focused on increasing volume, just take a look at all the LinkedIn and email automation tools on the market and you'll know what I mean. All the tools crammed in this competitive map below is aimed to get information in front of your prospects as fast and as efficient as possible, but there aren't many tools that get information about yours prospect in front of you as fast as possible. I would argue that the latter is actually more important in today's B2B sales environment.
Why has it been traditionally difficult to automate anything except for generic firmographic and contact data? Perhaps the databases most sales teams pay for are aimed at volume, but more importantly the most relevant information for your company is the most custom, and there's no one tool that can provide all of those relevant information to you at scale. Another reason why automating prospect research is difficult is because there's usually not just one niche data and custom research you'd need, there's multiple that are all quite specific to your industry, or the product/service you're selling. Every sales person who's tried to comprehensively research a prospect to the best of their ability understands this, especially the frustration that comes with not finding anything useful after going down a rabbit hole for 20 mins, that is, until AI starts to be applied to prospect research.
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What Is AI Prospect Research In Sales?
AI prospect research is using AI enabled tools to systematically automate research and gather data, as well as interpreting the researched information at scale. A combination of automations, scraping, as well as AI that's hooked up to the internet that can perform research are patched together to achieve this. A huge part AI plays in data research is to distill the data down to it's most relevant parts and presenting that to the researcher, in this case the salesperson trying to look for specific information they can leverage to book a call, or close a deal. Before we dive deep into the specific of AI prospect research, let's look at the state of the world today when it comes to sales prospect research.
The current state of prospect research involves pulling in different sources such as Crunchbase funding data, LinkedIn, job boards, Google news, and earnings reports to find any ounce of relevant information that your sales team can use, and I would argue the above are still very generic data. Even the generic research sources above are hard to automate, especially the ones that require human interpretation. Take a quarterly earnings reports for example, to research on a specific topic, let's just say you're interested to know if the company have any priorities for data security investments, requires the sales person to first find the earnings report, which is buried in different parts of a company's website depending on the design of the website. An experienced researcher would open up the webpage and head straight for the investor relations page, find the latest earnings report, sometimes earnings transcript, open it up in a format that differs per company (sometimes PDF download, sometimes webpage, sometimes a powerpoint deck), then from the often hundreds of pages or decks, you'd try to look for a snippet that talks about data security. A tech savvy sales person would use the quick command Ctrl F (Cmd F on Mac) to search for the keyword "data", or "security" and click through the matches one by one to find if any of those matches are actually talking about data security, or is it unrelated and came up because they are referring to "financial data" or "security of our inventory". Imagine the frustration for the salesperson when you go through this process and finds nothing, and now multiply this process and the frustration by a few hundred a week, which is the volume necessary in most sales driven organizations. Would you still research earnings reports knowing this is the process? I know that I dreaded this, even though I still did it for a while, in the end I decided that it was more effective if I ignored the research and achieved greater volume from a wide blanket approach, spraying and praying as they often say.
Now in the noisy world of B2B sales, AI prospect research is like finding a map in a treasure hunt. It's all about using smart tech to dig up gold—info on potential clients that can turn a cold call into a warm handshake. Let's now dive deeper into how this game-changer works and why it's becoming the secret weapon for sales teams everywhere.
Should You Use AI For Prospect Research
Prospects that are a perfect fit for what you're selling is the dream for every salesperson and every time we pick up the phone we hope the other side fits that description. This is where AI comes into play, acting like your high-tech magnifying glass. It sifts through mountains of data to find clues about businesses that might need what you have to offer. Here's the thing: not all companies are the same, and neither are their needs which is why AI prospect research becomes powerful. It doesn't just throw a list of names at you. It goes deep, analyzing everything from industry trends to specific company challenges. This means you're not just reaching out to anyone; you're reaching out to the right someone with the right information.
But how does it really work? Well, it starts with list building. But we're not talking about any list. We're talking about a list that's been tailored just for you, filled with companies that are more likely to say "tell me more" instead of "not interested." This isn't about shooting in the dark; it's about making every shot count.
Then comes the magic word: personalization. Remember, you're not the only one trying to grab the attention of these companies. They get bombarded with messages all day. AI helps you cut through the noise by giving you insights that let you tailor your message. Maybe it's highlighting a challenge you know they're facing, or showing off a bit of knowledge about their industry. It's like saying, "Hey, I get you," which can make all the difference.
How to Get Started with AI Prospect Research
Using ChatGPT For Basic Research
Before we jump into specific tools you can use, let's talk about how you can start incorporating AI into your workflow right now. If you're not already using ChatGPT, Perplexity, or some other type of LLM to speed up your tasks, this is the perfect chance to get started. Lot's of start-ups are purchasing ChatGPT for their employees to assist with various tasks like data parsing, content generation, and many other mundane work that could be sped up. As sales professionals, it's time to start using AI chatbots to help with your prospect research process. If you already do this, feel free to skip to the next section.
If you don't already do so, I would suggest opening up another window to ChatGPT, this works especially well if you can put this window on a side monitor. Now just remind yourself to use ChatGPT as much as possible when you're researching on your prospects, from summarization to researching on what they care about, the use cases are quite versatile. Refer to the table below for a few examples of how you can incorporate ChatGPT into your daily workflow, I've also included the prompt and some tips!
| Use Case | Example Prompt | Tips |
|---|---|---|
| Identifying Prospect Information | "Generate a brief overview of [Company Name]'s business model, key products, and target market." | Use specific company names and be clear about the information you're seeking to gather precise and relevant details. |
| Analyzing Industry Trends | "What are the latest trends in the [specific industry] as of [current year]?" | Include the specific industry and current year to get the most up-to-date information. |
| Crafting Personalized Pitches | "Create a customized pitch for [Prospect's Name] based on their interest in [specific interest]." | Tailor the prompt to reflect the prospect's known interests or pain points for a more personalized approach. |
| Competitor Analysis | "List the main competitors of [Company Name] in the [specific market] and their market share." | Provide as much context as possible to get a comprehensive analysis. |
| Email Drafting | "Draft an introductory email to [Prospect's Name] that mentions our solutions to [their problem]." | Be clear about the prospect's problem and how your solution addresses it for a targeted draft. |
| Follow-up Strategy | "Suggest a follow-up strategy after an initial meeting with [Prospect's Name]." | Mention any specific details from the initial meeting to tailor the follow-up strategy. |
| Preparing for Meetings | "Generate a list of talking points for a meeting with [Company Name] about [topic]." | Include the company name and the specific topic of discussion to prepare relevant talking points. |
| Social Media Outreach | "Create a LinkedIn message to [Prospect's Name] that highlights our interest in collaborating." | Personalize the message by mentioning why you're interested in collaborating and how it benefits them. |
The above are quite generic but make sure you're adapting these use cases to your industry and provide the relevant context to ChatGPT. Here's a few tips when working with ChatGPT:
- Be Specific: The more detailed your prompt, the more accurate and useful the response will be.
- Stay Updated: Regularly update your prompts to reflect current events and trends to ensure relevance.
- Follow Up: Use ChatGPT to help draft follow-up messages, but always personalize them before sending.
- Compliance Check: Ensure any generated content complies with your company's communication guidelines and policies.
- Continuous Learning: Regularly review and adjust your prompts based on the responses and results you're getting.
Conduct Web-Surfing with GPT4, Gemini, and Plug-Ins
If you need to do more advance research or need updated data about a company or industry, you might want to consider opting for a paid version of these AI chatbots such as GPT4. With a paid version you can also create your own private GPT which you can reuse regularly without giving it too much instructions. We use several GPTs internally, for example when targeting public companies, you can create a specific GPT to summarize a company's latest initiatives as releases in their latest earnings report.
You can also apply this to recent news search, or prompt the GPT to conduct a series of steps to mimic how you would do research in a logical and multi-step way. Keep in mind that although providing lots of detail and step by step instructions will provide clarity, there's a point where too much information in the instructions will cause hallucination and confuse the AI. It is crucial to spend time to test the prompt and iterate it through several versions.
AI Prospect Research Tools
The application layer of AI is quickly evolving to become saturated with all sorts of tools you can leverage to achieve an array of tasks at work and in your personal lives. Many has referred to the current AI environment as the beginning of the early days of the internet. This present many opportunities for entrepreneurs to innovate but even more opportunity for an early adopter to stand out from the crowd, especially in B2B sales where differentiation and uniqueness wins the day when it comes to capturing attention and harvesting eyeballs.
Most tools today that incorporate AI into their offering consists of mainly personalization, which is essentially content generation. Sales teams understand the value of personalization as we mentioned in the beginning of this article where tailoring the message moves the conversion metric, and with AI personalizing at scale, these tools hope to achieve both personalization and volume, bringing us to the ideal of volume and conversion.
We see the world a little differently at LavaReach. Our thesis has always been that personalization without the right context and data is just noise. We believe that the companies that are personalizing on generic information such as LinkedIn bios and and where a prospect has worked in the last 3 years is simply not good enough, and contribute to the garbage in garbage out style of AI personalization. We are not only saying this through pure conjecture, we actually have the scar tissue to back this up. Prior to pivoting to LavaReach, we worked on a business called Sellwisely which was a purely LinkedIn personalization and automations tool that sent "personalized" LinkedIn connection requests based on generic LinkedIn data visible on a prospect's profile. Although the business showed early signs of traction, we hit a wall when we started scaling up and selling to more sophisticated sales leaders. The feedback was centered around the generic sounding lines personalized by AI, and the lack of real relevancy.
For example Apollo being the well funded company they are, offers a AI assistance feature that only takes generic data about the company and your value proposition to craft emails, without any indication that the personalization is relevant to the prospect. Many other tools suffer the same symptom of garbage in garbage out, but a few tools are working in the right direction, such as Clay and Regie.
Where things become interesting is pairing AI personalization, with custom research data. With custom data, we also hit another stone in sales prospecting which is lead prioritization and identifying warmer and higher intent leads. We'll touch on how we achieve this in the next section.
How Does LavaReach Cut Through the Noise of Prospect Research for Sales Teams
LavaReach was born out of the frustration that our previous business showed little value in terms of personalization to our customers in the sea of AI personalization tools. Again we dwelled on how to solve the garbage in garbage out problem, and ultimately the logical conclusion was that if we as a company want to be masterful at personalization, we have to first become stellar at data enrichment and prospect research. We also landed on the fact that we have to give our customers control over how they want to manipulate the data into the AI to ensure any output sound genuine and relevant as if you're writing slike a tenured industry sales person to another seasoned executive who knows the ins and outs of the problem space.
Our mantra is simple: identify, enrich, personalize. Identify and enrich both requires AI as well as a combination of resourceful data scraping and automations, and personalization requires a depth in understanding of our customer's business, industry and collaborative process to perfect the final personalized output of every communication.
Together we are able to provide data to our customers that would have otherwise took individual BDRs hours on hours out of their workday.
In the below example we show how we're leveraging AI to quickly return relevant news and extract insights.
In this example our starting point to identify leads is relevant news, where we use our Search Engine workflow to identify accounts. The Search Query is the topic you want to extract, this could be business expansion for a particular industry or regulatory changes, or anything you can think of. In the example we are looking for Cybersecurity breaches in the US. We can then create unlimited amount of extraction prompts that crawls these articles and return relevant info such as who are the impacted companies or key info we can use in our outreach such as date or people impacted.
Limitations of Prospect Research with AI
There are still a lot of limitations in prospect research, mainly bound by the ability of AI to browse the web freely and interpret the results as relevant or not. There are many AI agents that does very specific things, but not many that can do very general things. And a lot of sales research is general and up to interpretation.
When you are thinking about leveraging AI for prospect research, think first about how you would do it without. Better yet, think about how a 6 year old would do it. How much judgement when you do prospect research, and how much variance is there to each situation? The more judgement you have to apply, the more variance there is in the results, the higher the likelihood that AI will misjudge and return bad data. In those cases, it's still often easier if you just did it yourself, or choose a higher volume, more generic approach.
So, what's the bottom line? AI prospect research is reshaping the landscape of sales outbound, especially in B2B sales. It's not about working harder; it's about working smarter. By leveraging AI, sales teams can focus their energy on prospects that are more likely to convert, using personalized insights to make connections that count.
In a world where time is money, AI prospect research is your time machine if applied properly. It's about making every moment and every message count. Whether you're building lists, crafting personalized pitches, or aiming for that conversion, AI should at least assit in you day to day!
FAQs: AI Prospect Research in Sales
1. What is AI prospect research?
AI prospect research involves using artificial intelligence tools to systematically gather and analyze data about potential clients, helping sales teams to personalize their outreach efforts more effectively.
2. How does AI improve prospect research?
AI enhances prospect research by automating data collection, analysis, and interpretation, allowing sales professionals to access relevant information quickly and focus on personalization and engagement.
3. Can AI replace human salespeople?
No, AI is designed to assist salespeople by providing them with valuable insights and automating repetitive tasks, not replacing the human touch that is critical in building relationships and closing deals.
4. What are some common uses of AI in sales?
Common uses include identifying prospect information, analyzing industry trends, crafting personalized pitches, competitor analysis, and drafting email content.
5. Is AI prospect research expensive?
The cost can vary based on the tools and level of customization required. However, many AI tools offer scalable pricing models suitable for different budget sizes.
6. How accurate is AI in prospect research?
While AI can significantly improve the accuracy of prospect research by analyzing vast amounts of data, the quality of insights also depends on the input data and the AI model's sophistication.
7. Do I need technical skills to use AI for prospect research?
Basic technical skills can be helpful, but many AI prospect research tools are designed with user-friendly interfaces that require minimal technical expertise.
8. How can I start using AI for prospect research?
You can start by exploring and experimenting with AI tools like ChatGPT for basic tasks, then gradually move to more advanced tools and custom solutions as needed.
9. What are the limitations of AI in prospect research?
Limitations include the potential for inaccuracies due to data quality, the need for human oversight, and the AI's ability to interpret data contextually.
10. How does AI personalize sales outreach?
AI personalizes sales outreach by analyzing detailed information about prospects, including their industry, challenges, and interests, enabling sales teams to tailor their messages and solutions accordingly.
About Daniel Zhao
Daniel Zhao is a multiple time founder with years of first-hand experience in B2B sales and revenue leadership. He has a consistent track record of helping companies experiment and implement outbound in SaaS and other industries. Throughout his career, Daniel has set up numerous outbound motions for the first time for companies that previously had not found success with sales led customer acquisition.