6sense alternatives for account intent signals

6sense scores accounts for you. Its platform takes in a customer's CRM and marketing history, its own intent data and its partners' data, and returns an intent score and a buying stage for each account, with advertising, email and sales tools built on the result. This guide is for the revenue operations or data lead who wants the signals themselves, dated and sourced, and means to do the account scoring in a warehouse or a model the team controls. It covers what 6sense documents about its signals and scores, what the platform is built for, which other sources to weigh, and how to build stages of your own on a signal feed. Statements about other companies were read on their own pages on 4 October 2026 and are linked where they are made.

The two products are built for different jobs. 6sense trains predictive models on a customer's own CRM and marketing data, observes research across publisher content, identifies the accounts behind anonymous website visits and offers contact data, with applications on top. Fokals is a data feed: evidence-backed intent scores and the dated company signals behind them, on one company index with technographic, hiring and announcement data, delivered by REST API and as bulk files. Every buyer reads the same published record, so a model built on it can be explained, audited and reproduced.

What 6sense documents about its signals and scores

• The signals. 6sense calls its data the Signalverse. The page lists keyword intent taken from what buyers research on B2B publishers' content, topic intent from partnerships with research sites (it names G2, TrustRadius, Bombora, PeerSpot and Gartner), firmographic and technographic data, IP addresses and cookies, and contact data.
• The models. The predictive modelling overview lists models for account and contact profile fit, intent, account and contact reach, and persona importance. Their inputs are first-party website activity, keyword intent from the Signalverse and third parties, and historical data from the customer's marketing automation platform and CRM. The same page says a customer works with 6sense consultants to set up a model specific to its organisation.
• The intent score. The intent model article says the score predicts how likely an account is to be interested in buying the product being sold, runs from 0 to 100 and is updated daily.
• The stages. The article on predictive buying stages names five and ties each to a score range: Target from 0 to 19, Awareness from 20 to 49, Consideration from 50 to 69, Decision from 70 to 85 and Purchase from 86 to 100.
• The applications. The home page groups them as Revenue Marketing, with advertising, email agents, workflows and audience building, and Sales Intelligence, with account prioritisation, list building, company and people search and alerts.
• Access to the data. The API documentation describes APIs that match an IP address to an account, return firmographics and segments, score leads and enrich people, and states what access each one needs. The pricing page names three Sales Intelligence packages and shows no price figures.

What 6sense is built for

• A prediction fitted to your own pipeline. Models built on your CRM and marketing history reflect what your past deals looked like.
• Research behaviour. Keyword and topic intent reflect reading and searching, which can begin before a company does anything in public.
• Identifying website visitors. 6sense documents an API that matches an IP address to an account.
• Contacts and activation in one place. People search, enrichment, advertising and email sit in the platform.

Two scores from 0 to 100 that measure different things

Both vendors publish an intent score from 0 to 100, and the two numbers answer different questions. The table sets 6sense's documentation beside the Fokals methodology.

A score of 70 from each is a different statement. In 6sense it places an account in the Decision stage of a model set up for that customer. In Fokals it says that one company's own actions on one topic have reached that level, and the score carries its five strongest signals as evidence, each with its date, kind and source. A rep can open the posting or the announcement behind the number, and a model reviewer can trace a feature to the facts that produced it.

6sense and Fokals as sources

The left column summarises the 6sense pages linked above.

Why a team looks for an alternative

The reasons concern fit, and they are no criticism of the platform.

• Scope. The team wants signals, not applications: it has its own tools for advertising, email and selling.
• Delivery. It wants rows in a warehouse, with keys and dates, on a schedule it sets. Fokals delivers every dataset on one stable company ID, with the observation time on each record.
• Licence fit. It builds a product or a model for others, and that use has to be written into the licence, whoever the vendor is. A Fokals agreement is written for internal use, embedding in a product or redistribution.
• A model the team owns. It wants inputs it can list, weights it can read and versions that stay fixed under it. Fokals delivers every signal with its kind, topics and weight, freezes each scoring version by name and gives at least 90 days' notice of a breaking change.

Other sources of intent to weigh

The descriptions come from each vendor's own site, read on 4 October 2026.

• Demandbase. Demandbase presents an ABM platform with advertising and sales tools and, on its data page, data delivered through APIs. Its intent page says its own intent draws on direct access to the bidstream. See the guide to Demandbase alternatives.
• Bombora. Bombora says its Company Surge data sets the latest three weeks of a company's research on a topic against a 12-week baseline, across a co-operative of publishers, B2B brands and data providers. See Bombora alternatives.
• G2. G2 Buyer Intent reports buyers researching software on G2, Capterra, Software Advice and GetApp: product and category page views, competitor comparisons and pricing page engagement.
• Informa TechTarget. Informa TechTarget offers intent data that it describes as first-party, observed from engagement with buying-cycle content across its media properties.

Each of the four reports buyers' research in some form. Fokals intent is built from what a company itself does in public, with the evidence attached, so it complements any of them.

Scoring accounts yourself on a signal feed

The intent dataset gives you two datasets to build on. Company Signals is the feed: one row for each dated signal, with its observation time, source, kind, topics, weight and detail. Intent Scores is the weekly score built from it. Stages of your own use both, joined to your CRM.

1. Choose topics. List the topics with their labels and groups, and keep the few that describe a customer about to buy from you.
2. Build two features for each account and week. From Intent Scores take the highest score among your topics and whether any carries a surge. From Company Signals take the number of distinct sources in the trailing 28 days, which says whether the website, the careers page and the announcements agree.
3. Write stages you can explain. The rules in the query are an illustrative design of your own: quiet with no row, stirring below 50, active at 50 or more, confirmed when an active account also has a surge or two sources.
4. Fit them to outcomes. Label each account and week with whether an opportunity opened in the next 60 days, compare the rate by stage, and move a threshold only between test periods.

-- PostgreSQL, with company_signals.topics loaded as jsonb
-- accounts(account_id, company_id): CRM accounts matched once on website domain
-- my_topics(topic): the topic ids you chose
with weekly as (
  select w.company_id,
         max(w.score)                             as top_score,
         max(case when w.surge then 1 else 0 end) as any_surge
  from company_intent_weekly w
  join my_topics t on t.topic = w.topic
  where w.week_start = :week
  group by w.company_id
),
feed as (
  select s.company_id,
         count(distinct s.source) as sources_28d
  from company_signals s
  where s.observed_at >= :week_end - interval '28 days'
    and s.observed_at <  :week_end
    and exists (
      select 1
      from jsonb_array_elements_text(s.topics) as x(topic)
      join my_topics t on t.topic = x.topic
    )
  group by s.company_id
)
select
  a.account_id,
  w.top_score,
  case
    when w.top_score is null then 'quiet'
    when w.top_score < 50    then 'stirring'
    when w.any_surge = 1 or f.sources_28d >= 2 then 'confirmed'
    else 'active'
  end as stage
from accounts a
left join weekly w on w.company_id = a.company_id
left join feed f   on f.company_id = a.company_id
where a.company_id is not null;

Four properties of the data shape the result.

• Go-to-market events arrive beside the topic signals. A first sales posting in a new country, a first enterprise-segment posting, a founding sales hire and a leadership change by role are delivered in Company Signals as dated events of their own. The filter above keeps to your topics, and a second feature built on these events adds expansion and leadership change to the model.
• Every signal carries its weight. Company Signals delivers each signal with its kind, its topics and its weight, and the methodology documents how the weekly score is built from them, weighted by recency. Between weekly scores, a recency-weighted sum of your own ranks accounts day by day, with the published score as the weekly reference.
• The version is part of the row. Keep the scoring version with every row you score on. Versions are named and frozen, and a breaking change ships as a new version with at least 90 days' notice, which is the time to refit your stages.
• A matched account with no row was quiet. A score records what a company did in public over the trailing 90 days, so a quiet week is information too. Count the accounts that match a company ID first, and read the stages on the matched set.

Weekly rows are written once after the week closes and are never revised, so the test is point-in-time by construction: the score you join to an outcome is the score that was known that week. The article on how the scores are built sets out the signals and the scoring, and prioritising accounts with intent scores shows the weekly ranking and its join to fit.

Frequently asked questions

The queries and code on this page are examples to adapt. Test them in your own environment before you rely on them.

What this page says about the products it names was checked against their public documentation on 4 October 2026. Product and company names are trademarks of their owners. Fokals is not affiliated with them or endorsed by them.