In the B2B world, where sales cycles are long and decisions complex, capturing buying signals before competitors do is a real strategic advantage. Today, companies that make intelligent use of intent data can transform these signals into concrete opportunities and optimise their customer acquisition.
Why should B2B buyers be interested in intent data?
According to a study by Forrester, 68% of B2B buyers carry out online research before even contacting a supplier. What’s more, only 5% of companies are actively intending to buy at any given time, which means that 95% of your potential customers (prospects) are consuming content and comparing their options before raising their hand.
Intent data enables you to identify these weak signals and position your company in the right place, at the right time, with the right message.
How can you use intent data to accelerate your B2B sales?
Integrating intent data into a B2B strategy makes it possible to identify the potential customers most likely to buy. By detecting signals of interest, your sales team can personalise their interactions and prioritise high-potential opportunities.
What tools can be used to identify buying signals in B2B?
Several strategic account marketing solutions (Account-Based Marketing) and behavioural analysis tools can be used to capture buying signals. These technologies analyse online behaviour, searches and interactions with content to identify companies in the buying phase.
The three types of intention data
- First-party data: collected directly via your website, CRM, email interactions, webinars, etc.
- Second-party data: from partner platforms (e.g. G2, TrustRadius) that share insights into buyer behaviour.
- Third-party data: aggregated by tools such as 6sense, Bombora or LinkedIn Sales Insights, it analyses the behaviour of companies on several external sources.
Why is third-party data strategic?
Third-party data enables us to detect growing interest in a solution long before a potential customer (prospect) makes contact. A Gartner study reveals that companies that use this data see a 63% improvement in their conversion rate by aligning their marketing campaigns with the signals captured.
What’s the difference between lead scoring and intent signal analysis?
Lead scoring is based on internal criteria (potential customer profile, interactions with the company), whereas intent signal analysis is based on external data (online behaviour, research into specific solutions). If you combine these two approaches, you can improve the qualification of potential customers.
Intent data and purchase prediction: how to capture the right signals?
1. Understanding the B2B buying journey
Unlike B2C sales, the B2B purchasing decision involves several stakeholders (6 to 10 decision-makers on average, according to Gartner). The buying cycle is divided into several stages:
- Exploration: the company consumes content without interacting directly with a supplier.
- Evaluation: comparing several solutions and consulting opinions.
- Active intent: requesting information, a demo or a quote.
- Decision: purchase and final negotiation.
Why does AI improve the prediction of B2B purchases?
Artificial intelligence plays a key role in analysing intention signals. Using advanced algorithms, it detects buying trends and anticipates the needs of potential customers with greater precision. So you can adapt your strategy in real time and optimise your sales pipeline.
2. Identify high-value intent signals
- Increase in content consumption: a potential customer consults several blog articles, downloads white papers or views webinars.
- Increased activity on LinkedIn: a decision-maker comments on and shares publications related to your sector.
- Recruitment of a CMO or Digital Director: a sign that a strategic project is underway.
- New funding or acquisition: often indicates an imminent need for innovative solutions.
- Participation in specific events and webinars: a sign that a company is looking to deepen its knowledge of a specific subject.
- Increased research on comparators and evaluation platforms: increased activity on G2, TrustRadius or Capterra is often an indicator of interest.
What are the challenges for companies using intent data?
While intent data offers a strategic advantage, there are a number of challenges to exploiting it:
- Reliability of sources: not all data is relevant. For you, it is essential to filter out the signals that can really be exploited.
- Integration with existing tools: connecting this data to CRM and automation platforms can be complex.
- Respect for confidentiality: the use of data must comply with current regulations (RGPD, CCPA).
Case study: an industrial company using Bombora and 6sense identified an increase in interest in its products before its sales staff were contacted. By adjusting its advertising campaigns on Google and LinkedIn, it increased its conversion rate by 32% in six months, while reducing its customer acquisition cost by 27%.
Strategies for making full use of intent data
1. Move from static lead scoring to advanced predictive analysis
It’s better for you to adopt an approach based on AI and machine learning. Rather than relying on a fixed score, algorithms analyse changes in intent signals in real time. Also, cross-reference several data sources. The signals should be enriched by third-party data (6sense, Bombora) and market insights. Also take context and timing into account. A potential customer with a high score doesn’t mean he’s ready to buy. Analysis of the market and external signals is essential.
Finally, integrate intent signals into dynamic scoring models to prioritise sales leads according to their real maturity.
2. Automate and integrate signals into your CRM
Set up dynamic workflows: trigger ultra-personalised campaigns based on the buying signals captured.
Marketing and sales alignment: seamless collaboration to transform signals into concrete actions.
Automatically notify your sales staff when a sales lead reaches a critical score to ensure maximum responsiveness.
3. Leverage social networks and targeted advertising
LinkedIn Sales Navigator: identify companies in the active search phase.
Google Ads and LinkedIn Ads: adjust ads according to the signals captured.
ABM (Account-Based Marketing): personalise marketing actions by strategic account.
Set up intelligent retargeting campaigns based on intent data to capture potential customers who are still hesitating.
Conclusion
Intent data is revolutionising B2B strategies. Not only can it be used to anticipate purchases, but also to optimise the allocation of marketing and sales resources. The future of lead scoring lies in a more intelligent approach, using AI and real-time intent signals.
How can you integrate intent signals into your marketing strategy?
Do you want to detect your potential customers’ intent signals before your competitors do? Contact us for a customised strategy!
