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AI in B2B sales: from messy data to meaningful sales encounters

The Data Handbook

How to use data to improve your customer journey and get better business outcomes in digital sales. Interviews, use cases, and deep-dives.

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Aki Pöntinen

Business development



This blog is part of our book: Data-driven digital commerce – turn data into revenue. Download a copy of the book in the green box to the left or at the end of this blog.

Not only can artificial intelligence take over the most boring parts of a salesperson’s work, it can also augment it. Need to figure out a price range to catch your prospect’s attention or fancy some intel on the competition? AI can lend a hand – allowing salespeople to focus on more interesting tasks.

In B2B sales, there are three types of data sets that can be used: internal sales and customer data, external data from sales technology providers and a combination of the two. Various companies offer software that combines publicly available data about clients, prospects or competitors with internal data when integrated into a CRM (customer relationship management) system. With enough relevant and structured data, AI could be equally useful to salespeople at various stages of the sales funnel.

Sales data is often “soft” in the sense that, for example, appointment notes on CRM systems are messy and unstructured and thus difficult to utilise. However, AI and natural language processing capabilities are developing rapidly and are already able to understand and analyse unstructured textual data. While these solutions are primarily available in English, the capabilities for other languages are being built. AI can also be used to help capture data, making the process easier and faster while enabling more useful AI use cases.

Throughout the sales funnel, there are several stages at which AI can be useful. This begins with identifying relevant audiences and ranges all the way to enhancing customer loyalty at the end of the funnel:

1. Customer selection – identifying ideal customer markets and generating leads

  • Marketing automation can be boosted by identifying prospects based on what they have been doing online. AI enables marketers to scale their marketing activities by creating more personalised and automated campaigns for individual customer segments based on data.
  • Conversational AI, such as chatbots, can utilise information from CRM systems to give better and more relevant answers, generate leads and identify prospects. By automating parts of the background work and enriching CRM data it can provide valuable information to salespeople before they contact the client.
  • Using existing data on a CRM system, AI can generate predictive lead scoring, meaning it can evaluate data points from previous leads to assess how good a fit a new lead might be.
  • AI is capable of differentiating tones of voice and word choices, which can be used in the analysis of responses. In a client’s answer to an introductory email for example, does the reaction seem positive or neutral, or is a refusal the most likely outcome? Analysing the text, AI can recognise which leads are more likely to lead to sales.

2. Opportunity identification – turning leads into sales cases

  • Based on internal sales history data, AI can conduct predictive opportunity scoring. How likely is this lead to turn into an appointment? How probable is it that a deal will be closed?
  • Each sales opportunity contains certain data points that can help AI recommend ways to advance sales opportunities. Have all possible steps been taken with a particular prospect? Would they need additional information, or another phone call?
  • In-meeting assistance from AI gives salespeople additional information during a sales call. Software can be used to create a transcription of an ongoing call, analyse the content and give the salesperson recommendations as to what to do next, for example to ask more questions, remember to mention pricing or let the customer keep talking.

3. Solution development – utilising data to create sales proposals

  • Do you know what kind of pricing your prospect is expecting? If not, AI-powered pricing and pricing model recommendations can be useful. AI can, for example, go through past transaction data and analyse previous wins and losses to create a price-range recommendation based on multiple factors.

4. Preference building – competitor and market analysis

  • In B2B sales, it’s impossible to avoid having competitors, but finding them all manually is a lot of work. There are services that can gather all publicly available competitive intelligence using AI, scraping it from blog posts and websites and curating it to create valuable information about the existing competition. By combining this with internal data from CRM, it can identify and point out the most likely competitors and their selling points, which helps in making counterarguments.

5. Agreement – closing deals

  • Similarly to stage 2 above, here AI-powered in-meeting assistance can help predict a client’s possible objections and suggest ways to counter them. Certain patterns occur frequently in conversation; by analysing them, AI learns what the next step might be and enables salespeople to prepare for objections and overcome potential obstacles.
  • Based on information about previous deals, AI can analyse data to assess, evaluate and forecast the likelihood of a deal being successfully closed and therefore suggest how much effort should be put into it.

6. Customer success

  • Congratulations, you’ve got a deal! Now it’s important to remember customer retention and loyalty. Relationship analytics keep a close eye on CRM data: is the relationship changing in any way? Should the client be introduced to a new range of products? What else could be offered on top of existing services? If there are signs of the relationship fading, AI can nudge you to act early.

AI isn’t taking over from salespeople – it’s supporting them by augmenting sales

In addition to the previously mentioned examples, many non-selling administrative tasks, information gathering and data extraction can be outsourced to AI. This means automating the most boring chores, like feeding data points, making CRM updates and compiling unstructured soft data. AI also brings predictive elements to traditional analytics capabilities, helping to predict the future and support sales efficiently.

It’s obvious that sales organisations that make use of technology and data have a significant competitive advantage over those that don’t. What’s important to note is that AI is only useful when there is sufficient relevant data available in a form that makes it efficient to handle; and if there’s none available, or not much, then AI won’t be of any use. Hence, each sales organisation should be gathering data regarding what has been done and how things are going in order to understand where the organisation should be heading.

However, no salesperson should be concerned about losing their job to AI. Working together, real people and AI can be more efficient than either could be on their own. If anything, AI handling the boring and repetitive tasks automatically, more accurately and faster than a person ever could frees up time for salespeople to focus on the interesting parts of the job – making it more enjoyable and more productive.

This blog post is part of the handbook Data-driven digital commerce – turn data into revenue. The aim of this handbook is to help a sales, marketing, IT or ecommerce leader to formulate an overall understanding of the critical themes for turning data into concrete business impact.The book includes interviews with global thought leaders on the topic as well as industry experts from companies including Supermetrics, Singular Society and Kesko.

Download the Data Handbook

The Data Handbook

How to use data to improve your customer journey and get better business outcomes in digital sales. Interviews, use cases, and deep-dives.

Get the book