Four systems, four truths
The ERP knows the orders, the newsletter tool the opens, the chat the complaints. Anyone wanting all three exports to a spreadsheet and is out of date again the next day.
Data · Live
scroll Customer Intelligence Hub
All customer data in one place. And every morning a list of what to do today.
Customer data sits side by side in the ERP, the newsletter tool, the chat and the campaign system. The hub brings it together, enriches it, scores it against key figures and derives concrete recommendations. Each with a reason, none without approval.
Where it breaks down
Everyone knows part of the customer. Nobody knows the whole.
The data is there, it is just scattered. And because nobody has the complete picture, decisions come from the gut and from whatever somebody happens to remember.
Scoring by feel
Which customer matters, who is about to leave, where a call is worth it. Without shared key figures every person decides differently, and when someone leaves it starts over.
Findings without consequences
Analyses get produced, presented and then fade away. Between the figure in the report and the action in daily work, the connecting step is missing.
The solution
A data anchor that does not end things, it starts them.
The hub is deliberately not another rigid CRM. It is the layer beneath it that collects data, enriches it, scores it and turns that into proposals somebody can accept or reject.
Sources come in on a schedule and are mapped onto one shared customer picture, for business customers as much as for consumers. Enrichment happens automatically: company data, VAT identification checks, industry classification. Every enrichment is logged, including provider and finding.
On that picture the hub calculates the key figures sales and marketing actually need: how recently, how often and for how much someone bought, how high the churn risk is, and what the relationship is worth over time.
From that come recommendations. Each names its trigger and its rule. Rejecting one requires a reason, and recurring reasons become a rule that suppresses or tightens comparable cases in future. The system learns from the rejections, not from agreement alone.
Customer recordEnrichment with an audit trailRule-based segmentsRecommendationsLearning loopConsent
In practice
Three scenes from an ordinary week.
Monday, 8:40
Instead of hunting for a report, a list is waiting: twelve recommendations, prioritised, each with its trigger. Four get accepted, two rejected, and the rejections sharpen the rule for next week.
A customer calls
The complete profile is there at a glance: orders, recent conversations, notes, open matters, consent. Without anyone opening three systems.
A campaign is prepared
The target group is built as a rule, not as a spreadsheet export. It keeps itself current, and consent is checked separately per brand before anything goes out.
How it works
Connect, enrich, score, recommend.
Connect
Existing systems are connected and then run on a schedule. The first full sync immediately shows how clean the data really is.
Enrich
Missing company data, industry classification and checks are added. Every run is logged so it stays traceable where a value came from.
Score
Buying behaviour, churn risk and customer value are calculated by the same rules for everyone. That makes scoring comparable instead of personal.
Recommend
Scoring and events produce proposals. Accept, complete or reject, and the rejection becomes the rule for next time.
Let us show you on your real data which recommendations would come out of it.
Arrange a conversation Core capabilities
What the module brings.
Customer record for B2B and B2C
One picture per customer, with contacts, orders, conversations, notes and free additional fields.
Automatic enrichment
Company data, VAT identification checks, industry classification. Every run logged with provider, finding and trigger.
Key figures instead of gut feel
Buying behaviour, churn risk and customer value by uniform rules, recalculated continuously.
Rule-based segments
Target groups emerge from conditions and keep themselves current. Analysis segments stay out of any send.
Recommendations with approval
Every recommendation names its trigger and its rule. Nothing executes automatically, the status stays traceable.
Learning loop from rejections
A rejection needs a reason. Recurring reasons become rules that suppress or tighten comparable cases.
Campaigns and consent
Send decisions per customer, consent tracked separately per brand, throttled delivery and approval before anything goes out.
Event timeline
What happened when, triggered by whom. Across every connected system in one place.
Interface for agents
Other tools and assistants reach profiles and recommendations through a defined interface.
Two-factor sign-in
Access only with a second factor, roles and permissions per area, a complete change history on the records.
Tasks and tickets
An accepted recommendation becomes a task with an owner, instead of a note nobody finds again.
Automations
Trigger, condition, action. For the recurring routines where case-by-case approval adds no insight.
Where it works
Three roles, three measurable outcomes.
Sales management
Churn becomes visible before the revenue is missing. The follow-up list comes from key figures instead of memory.
Customer service
The context is there before the first sentence. Fewer internal queries, shorter calls, fewer transfers.
Marketing
Target groups without a data export, consent cleanly separated per brand, and a solid basis for the next campaign.
A look inside
Two views people actually work in.
On the left the morning work list, on the right the customer record during the call. Both are where a decision gets made, not a report somebody reads later.
Now Churn risk has risen AcceptReject
Week Quote without response for 14 days AcceptReject
Week Customer value up, no contact AcceptReject
Info Consent about to expire AcceptReject
A rejection needs a reason. Recurring reasons become a rule that suppresses comparable cases in future.
The list the day starts with
Prioritised, every recommendation with its trigger. Accepting creates a task with an owner, rejecting requires a reason and sharpens the rule.
Customer since2019
Revenue 12 mo.€84,200
Open matters2
Buying strong
Churn elevated
Value high
Order 4711 · 9 days ago
Complaint resolved · 3 weeks ago
Industry added · source verified
One picture instead of four systems
Key figures, history and the origin of every enriched value in one place. The context is there before the conversation starts.
Abstracted representation with invented values. Which key figures and columns appear in your installation is something we define together.
Where it sits
Not one of the four impact areas. The foundation beneath them.
The platform has four impact areas and beneath them a foundation of data basis and operation. The Customer Intelligence Hub is part of that data basis: it does not answer a process itself, it supplies the customer picture the modules in the impact areas work on.
Culture
Enable people, do not replace them.
Agents
Automate processes, create room to work.
Knowledge
Secure experience and make it usable.
Content
Scale communication, on brand and on quality.
Foundation · Data basis
Structured, linked data as context for every module. This is where the hub sits.
Foundation · Operation and sovereignty
Cloud, hybrid or on-premise, on the Local AI Server if you wish.
Modules that build on the customer picture
Inbound ManagementChannels in one place, answers with context
Reduziertes CRMQuotes straight out of the conversation
Agent-as-a-ServiceTakes over recurring routines
Marketing Performance MonitorThe channel view beside it
Content CreatorTarget groups for outreach
Corporate MemorySourced knowledge as context
The most common follow-up question: the hub is not a CRM and does not replace one. Reduziertes CRM is a separate module for sales work on the individual matter, meaning quotes and closings. The hub sits underneath and supplies the customer picture that this and the other modules draw on. A grown ERP likewise stays where it is.
Operation and data sovereignty
Customer data is the most sensitive material you hold.
Which is why you decide where it lives. Compliant in every variant, with roles, permissions and a complete change history.
Cloud
Operated in European data centres, with monitoring and updates during live operation. The fastest route into regular use.
Hybrid
Sensible when individual sources may not leave the building while the rest should still benefit from a shared evaluation.
On-premise
Entirely in your own network, on the Local AI Server if you wish. Enrichment can then be limited to local methods as well.
One quote says more than many promises
“Everyone used to keep their own list. Today we all look at the same one, and we no longer argue about the numbers, only about the order. That is a considerably better argument.”
Head of Sales, Retail group with two brands
Pricing
One setup, one predictable operation.
Tiered by connected data sources, size of the holdings and number of users. Setup covers connection, reconciliation, scoring rules and onboarding the team.
| Package | For whom | Scope * | Setup * | Operation * |
|---|---|---|---|---|
| Starter | First consolidation | 2 data sources · up to 25,000 customer records · up to 20,000 updates per day · 5 users | from €7,500 | €199 / mo. |
| Business | Sales and marketing in regular use | 5 data sources · up to 250,000 records · up to 200,000 updates per day · 20 users · recommendations and learning loop | from €16,500 | €449 / mo. |
| Pro | Several brands, high volumes | unlimited sources · throughput by agreement · agent interface · your own enrichment providers | from €27,500 | €890 / mo. |
CorporateGroup structures with several tenants, a dedicated operating environment or particular requirements for evidence: on request.
Holdings and throughput
Two separate figures. The holdings are the stored customer records, the throughput is updates, events and webhook calls per day. Small holdings with high throughput cost more to operate than large ones that rarely change.
Enrichment per call
Company data, checks and industry classification run through external providers and are passed through at cost. Included are 5,000 enrichments per month (Business), beyond that by effort. Repeated checks on the same record count once per interval.
Sending and campaigns
The send itself runs through your existing provider and is billed there. The hub controls segments, consent and approvals. Two connected sending accounts are included, each further one €39 per month.
Operation includes maintenance, updates and a monthly quota for AI usage. Server and hosting costs are billed separately.
* Guide values. Scope, volumes and prices are adapted to your actual needs in the quotation, because the number of sources, the throughput and the depth of enrichment drive the effort.
All prices are net, plus statutory VAT.
Frequent questions
What decision makers ask first.
What happens automatically, and what does not?
Automatic are the data reconciliation, the enrichment and the calculation of the key figures. Everything with an outward effect, meaning outreach, sending and quoting, requires approval. That separation is set deliberately and can be relaxed selectively for individual recurring routines.
Does this replace our CRM?
Usually not. The hub sits over the existing systems and brings together what is scattered across them. Where a CRM is only used as an address book anyway, it can take over. That is a decision within the project, not a precondition.
Where does the data live?
Wherever you decide. Cloud in Europe, hybrid or entirely in your own network. With local operation, enrichment can also be limited to methods that do not leave the building.
How long until the first recommendation?
The first full reconciliation usually runs in the first week. Solid recommendations emerge once enough history has come in, typically after four to six weeks. Until then the hub is already usable as a shared customer picture.
The first step
Two sources. One reconciliation. An honest inventory.
We connect two of your systems and run the first reconciliation. After that you know how clean your customer data really is and which recommendations could already be derived from it today.