The AI use case that really drives revenue

4 min read
03.07.2024 15:09:35
The AI use case that really drives revenue

From data chaos to AI-controlled matchmaking

Imagine this: Your sales team is wasting 75% of its time on the wrong contacts and administrative tasks.

Yet, the promise is that artificial intelligence (AI) will solve all these problems in sales and marketing, and new tools are coming onto the market every week. Pure chaos, right?

Just three years ago, AI was the “bleeding edge” in marketing and sales. Now the headlines are screaming that AI is taking over our jobs.

The reality check

We share the initial findings of the Sales Efficiency Report and analysed the responses from 130 B2B companies to find out which use cases of AI and automation are really driving sales.


Listen to the podcast (German):

Spotify Apple Podcasts Amazon Music | Midlife Entrepreneur Podcast ListenOnYouTube

Spoiler: The top use case is to validate, enrich and qualify CRM contacts

And we show you in three simple steps how you can actually use this game changer. In this episode, Marc and Valentin talk about the path from AI hype to practical application.

Which AI use case brings the most value in terms of sales growth?


Marc, who has been building marketing and sales teams, implementing appropriate software since the age of 16 - and Valentin, who leads development teams and plans and implements software products like no other - take us through the steps. Let's find out how to filter, validate and qualify valuable customer contacts from a flood of data. Get ready for tangible tips on how to close more deals and increase sales funnel efficiency.

Let's get started in three steps

Step 1: Data input

Where does CRM data come from?

Your CRM system such as HubSpot or Salesforce only requires first name, last name, company name or business email (optional). Sounds simple, but where does this data come from?

The sources of CRM data

Historically, CRM data comes from various previous activities such as old newsletter lists, previous sales staff and much more. Examples are:

  • Contact forms
  • Calendar entries
  • Newsletter subscriptions
  • Webinar registrations
  • Website chats
  • Customer service interactions
  • Whitepaper downloads
  • Phone calls
  • Business cards
  • LinkedIn contacts

In addition, there are tools for identifying visitors to websites such as Albacross, Leadinfo, Salesviewer and RB2B (USA only) that identify visitors to the website. Or Teamfluence, which tracks interactions such as likes, comments, followers on LinkedIn.

Challenges of CRM data

This data is often outdated, people have changed jobs, and you don't know how to approach it. Plus, the timing isn't right - unless you take everyone out to lunch.

Step 2: Enrich and validate data

This is where providers that deliver signal-based sales data and intent data come into play. But beware, signal or intent data does not equal purchase intent - it's not the holy grail.

Develop relevance

Triggers can be job positions that are being sought or an increasing number of employees. You can create relevance through consistent timing and relevant topics.

Data enrichment

Use different data providers and public data to validate and enrich contact details. Scrape LinkedIn profiles and company pages for signals such as employee growth, industry, job postings, language, last post, comment and more.

In-depth lead research with Claygent

You probably already know Clay, they recently raised $62 million at a $500 million valuation and are now taking off. Clay is integrating Claygent to do even deeper lead research.


Go deeper: search for blog articles, white papers, board members, projects, and customer lists. All this data helps to get a more complete picture of your leads.

Step 3: Qualify leads

Qualify leads

With a match score from OpenAI, you can see whether a lead is a good fit. Use criteria such as number of employees, growth, and job ads. Score your contacts with 1–3 stars based on their fit to your Ideal Customer Profile (ICP) and your buyer persona.

Set priorities

Divide your leads into low, medium and high priorities. Update your CRM to know where the lead is in the buyer journey and who the account owner is. Set SQL, MQL and assign account owners.

Conclusion & summary

The most popular AI use case from the feedback of 130 B2B companies is CRM enrichment. By using your database correctly, validating and enriching it and finally qualifying the leads, you create a basis for efficient matchmaking. This is how you turn the data chaos into a targeted, revenue-boosting sales strategy.

B2B marketing and sales automation can help to build efficient revenue teams. AI can improve the work process in marketing and sales, especially in terms of data quality and enrichment. The Sales Efficiency Report shows the current trends and use cases of AI in sales. The engagement strategy and segmentation along the buyer journey are crucial to generating relevant and qualified leads.

The most popular use case is: CRM enrichment

  1. Input: Analyse existing data and define who you want to reach.
  2. Validate & Enrich: Validate and enrich data with relevant information.
  3. Qualify: Qualify and prioritize leads according to Ideal Customer Profile (ICP), Buyer Persona (BP) and Buyer Journey.

And then?

The harsh reality: General newsletters are often sent to all contacts.

The dream outcome: making targeted contact with the right, qualified leads.

Learn more about these use cases in this webinar, we presented the initial results of the Sales Efficiency Report.