Customer 360 with ArcadeDB

86% of Companies Don't Have a True Customer 360

Only 14% of organizations have achieved a genuine 360-degree customer view. The rest operate with fragmented data scattered across dozens of systems — CRMs, support desks, e-commerce platforms, marketing tools, mobile apps, point-of-sale terminals, and call centers. The average enterprise uses over 90 different software applications, and the cost of this disconnection is staggering: an estimated $1.4 trillion annually in lost productivity and missed opportunities.

The business impact is direct:

Duplicate records: The same customer appears as different entries across systems, leading to multi-send marketing and inconsistent service
Missing context: Support agents can't see purchase history. Sales teams can't see support tickets. Marketing can't see either
Stale data: Most CDPs and CRMs update in batch cycles (hourly or daily), meaning interactions are stale before they reach the teams that need them
Invisible relationships: Tabular systems store attributes about customers, not relationships between them. Household connections, referral chains, and social influence go untracked
Churn blind spots: Without relationship context, you can't see that a churned customer's network connections are also at risk

Traditional CDPs and CRM platforms were designed for a simpler world. Customer data today is relational, temporal, unstructured, and high-dimensional — all at once. A single-model database cannot handle that.

The Cost of Not Knowing Your Customer

5-25x more expensive to acquire a new customer than to retain an existing one
+222% increase in customer acquisition costs since 2013
25-95% profit increase from just a 5% improvement in retention
60-70% success rate selling to existing customers vs. 5-20% for new prospects
$1.4 trillion annual cost of disconnected enterprise data

A Customer Is a Network

Customer Data Is a Graph

Every customer exists within a web of relationships: products they've purchased, devices they use, addresses they've lived at, people they've referred, support tickets they've opened, campaigns they've clicked, events they've attended. This is not tabular data — it's a graph.

Relational databases model this with foreign keys and JOINs. A simple question like "what products did members of this customer's household purchase last month?" requires multiple self-JOINs across tables. As the number of hops increases, performance degrades exponentially.

In a graph database, relationships are first-class citizens. Each hop takes constant time regardless of data size. A query that traverses from a customer through their household to all household members' purchases to product categories is milliseconds, not minutes.

But a graph alone isn't enough for a complete Customer 360. You also need:

Documents for flexible, schema-variable customer profiles
Time series for behavioral patterns and lifecycle tracking
Vectors for similarity-based segmentation and lookalike audiences
Full-text search for searching support tickets, chat transcripts, and agent notes

ArcadeDB provides all five in a single database.

Identity Resolution: The Foundation of Customer 360

A single customer might appear as dozens of separate records across your systems: different email addresses for work and personal, multiple devices, anonymous web sessions, a loyalty card at the store, a phone number on file with support, and a social media handle. Identity resolution is the process of linking all of these into one unified profile.

This is fundamentally a connected components problem. Each identifier (email, phone, device ID, cookie, loyalty number) is a node. Each co-occurrence — same login session, same transaction, same support call — creates an edge. Running a connected components algorithm on this graph produces resolved identity clusters automatically.

Crucially, graphs handle transitivity naturally: if Device A is linked to Email X, and Email X is linked to Phone Y, the graph automatically knows that Device A and Phone Y belong to the same person. Traditional tabular systems require multiple merge-purge passes to discover this. The graph sees it in a single traversal.

ArcadeDB adds full-text fuzzy matching to handle the messier cases — matching "Jon Smith" with "Jonathan Smithe" or "123 Main St" with "123 Main Street, Apt 4B" — all within the same database, no external entity resolution service required.

Graph-Based Identity Resolution

Link customer identifiers from multiple systems into unified profiles using graph traversal and fuzzy matching:

-- Find all identifiers belonging
-- to the same person via shared
-- touchpoints
MATCH (id:Identifier {
    value: $knownEmail})
  -[:OBSERVED_IN]->(session:Session)
  <-[:OBSERVED_IN]-(other:Identifier)
WITH DISTINCT other

-- Expand: find identifiers linked
-- transitively through sessions
MATCH (other)
  -[:OBSERVED_IN*1..3]-
  (transitive:Identifier)
RETURN DISTINCT
  transitive.type,
  transitive.value

Fuzzy Name Matching

-- Find probable duplicates using
-- full-text similarity scoring
SELECT a.id, a.name, b.id, b.name,
  a.name.similarity(b.name)
    AS name_score,
  a.address.similarity(b.address)
    AS addr_score
FROM Customer a, Customer b
WHERE a.id < b.id
  AND a.name.similarity(b.name) > 0.75
  AND a.phone = b.phone

Combine deterministic matching (same phone) with probabilistic matching (similar name) for high-confidence identity resolution.

Complete Customer View in One Query

Retrieve a customer's full context — profile, household, purchases, support history, and engagement trends — in a single query:

MATCH (c:Customer {id: $custId})

-- Household members
OPTIONAL MATCH
  (c)-[:MEMBER_OF]->(h:Household)
  <-[:MEMBER_OF]-(member:Customer)

-- Recent purchases
OPTIONAL MATCH
  (c)-[p:PURCHASED]->(prod:Product)
WHERE p.date > date() - duration('P90D')

-- Open support tickets
OPTIONAL MATCH
  (c)-[:OPENED]->(t:Ticket)
WHERE t.status <> 'closed'

RETURN c,
  collect(DISTINCT member) AS household,
  collect(DISTINCT prod) AS purchases,
  collect(DISTINCT t) AS open_tickets,
  -- Lifetime value from time series
  ts.last(c, 'CustomerMetrics',
    'lifetime_value') AS ltv,
  -- Engagement trend
  ts.rate(c, 'Interactions',
    'event_count',
    now() - duration('P30D'),
    now()) AS engagement_rate

The Complete 360° View

Once identities are resolved, ArcadeDB provides a true 360-degree view by combining data from all five models in a single query:

Graph — Relationships: Household members, referral chains, product affinities, social connections, account hierarchies
Documents — Profile: Flexible, schema-variable customer attributes. One customer has social media data; another has enterprise contract metadata; a third has IoT device telemetry. No rigid schema required
Time series — Behavior: Purchase frequency trends, engagement decay, session patterns, lifecycle stage progression. Track changes over time, not just the latest snapshot
Vectors — Similarity: Customer behavior embeddings for segmentation, lookalike audience creation, and similarity-based recommendations
Full-text — Context: Search across support tickets, chat transcripts, agent notes, and email correspondence for sentiment patterns, recurring complaints, and feature requests

The query on the left retrieves all of this in one round-trip: the customer's profile, household members, recent purchases, open support tickets, lifetime value from time-series, and current engagement rate. A support agent sees everything they need before the customer finishes saying hello.

Churn Prediction: The Relationship Signal

Traditional churn models use individual customer attributes: recency, frequency, monetary value, support ticket count. These features catch some cases but miss a critical dimension — social influence.

Research consistently shows that customers whose network neighbors have already churned are significantly more likely to churn themselves. If three of a customer's referrals have cancelled, that customer is at risk — even if their own engagement metrics look fine. This "network effect on churn" is invisible to any system that doesn't model relationships.

ArcadeDB combines graph-based features with time-series behavioral signals for a much richer picture:

Network churn ratio: What percentage of this customer's connections have already churned? (graph traversal)
Engagement trend: Is their interaction frequency declining over the last 30/60/90 days? (time-series rate analysis)
Behavioral shift: How far has their recent behavior drifted from their historical baseline? (vector distance)
Support sentiment: Are their recent tickets increasingly negative? (full-text search + document analysis)
Household risk: Have other household members shown signs of disengagement? (graph traversal + time series)

Multi-Signal Churn Risk Scoring

Combine graph, time-series, and vector signals to produce a composite churn risk score:

-- Churn risk: network + behavior
MATCH (c:Customer)
  -[:REFERRED|CONNECTED_TO*1..2]-
  (neighbor:Customer)
WITH c,
  count(neighbor) AS total_neighbors,
  sum(CASE WHEN neighbor.status
    = 'churned' THEN 1 ELSE 0 END)
    AS churned_neighbors

WHERE c.status = 'active'

RETURN c.id, c.name,

  -- Graph: network churn ratio
  churned_neighbors * 1.0
    / total_neighbors AS network_risk,

  -- Time series: engagement decay
  ts.rate(c, 'Interactions',
    'event_count') AS engagement,

  -- Vector: behavioral drift
  vectorDistance(c.recent_behavior,
    c.baseline_behavior)
    AS behavior_drift

ORDER BY
  (0.35 * network_risk
   + 0.35 * behavior_drift
   + 0.30 * (1 - engagement))
  DESC
LIMIT 100

Returns the 100 highest-risk active customers, ranked by a weighted blend of network churn, engagement decay, and behavioral drift.

Personalized Cross-Sell

Recommend products based on what similar customers and household members have purchased:

-- Cross-sell: household + similar
-- customers' purchases
MATCH (c:Customer {id: $custId})

-- Household purchases
OPTIONAL MATCH
  (c)-[:MEMBER_OF]->(:Household)
  <-[:MEMBER_OF]-(hm:Customer)
  -[:PURCHASED]->(hp:Product)
WHERE NOT (c)-[:PURCHASED]->(hp)

-- Collaborative filtering
OPTIONAL MATCH
  (c)-[:PURCHASED]->(:Product)
  <-[:PURCHASED]-(sim:Customer)
  -[:PURCHASED]->(sp:Product)
WHERE NOT (c)-[:PURCHASED]->(sp)

WITH c, collect(DISTINCT hp) +
  collect(DISTINCT sp) AS candidates
UNWIND candidates AS rec

RETURN DISTINCT rec.name,
  vectorDistance(rec.embedding,
    c.pref_vector) AS relevance
ORDER BY relevance ASC
LIMIT 10

Real-Time Personalization & Cross-Sell

Companies excelling at real-time personalization see 40% more revenue than their competitors. 70% of companies using advanced personalization earn at least 200% ROI. Yet most CDPs are limited to batch-processed segments that are hours or days old.

ArcadeDB enables true real-time personalization by combining three signal types in a single query:

Household-level awareness: What have other household members purchased? A spouse's recent laptop purchase might signal an accessories cross-sell opportunity
Collaborative filtering: What did customers with similar purchase patterns buy next? Graph traversal finds this without pre-computed matrices
Content relevance: Vector similarity between product embeddings and the customer's preference vector ranks candidates by personal fit
Timing awareness: Time-series engagement data ensures you reach the customer at the right moment, not when they've already disengaged

The result: personalized recommendations that consider household context, social signals, content relevance, and timing — computed in real-time, not from a stale batch job. Organizations report 10-20% increases in customer lifetime value from graph-powered cross-selling.

Customer Journey: Every Touchpoint, in Sequence

Customer journeys are not lists of events — they are paths through a network of touchpoints, channels, and decisions. Graph databases model these journeys as connected chains, making it possible to:

Trace conversion paths: Which sequence of touchpoints (ad click → website visit → product view → cart → purchase) leads to the highest conversion rate?
Identify drop-off points: Where do customers leave the funnel? Which step loses the most prospects?
Analyze multi-touch attribution: Which channels contribute to a conversion, and in what order?
Detect behavioral anomalies: A customer who always follows a certain pattern suddenly deviates — that's an early signal worth investigating

ArcadeDB's time-series engine adds temporal intelligence to journey analysis. You can track not just the sequence of events, but the time between them — how long did the customer spend between browsing and purchasing? Is that faster or slower than their segment average? Are conversion windows shortening or lengthening over the quarter?

Journey Analysis

Find the most common paths that lead to conversion, ordered by frequency:

-- Top conversion paths
MATCH
  (c:Customer)-[:INTERACTED]->
  (e1:Event {type: 'ad_click'})
  -[:FOLLOWED_BY]->
  (e2:Event {type: 'page_view'})
  -[:FOLLOWED_BY]->
  (e3:Event {type: 'purchase'})
WITH
  e1.channel AS entry_channel,
  e2.page AS landing_page,
  count(*) AS conversions,
  avg(duration.between(
    e1.timestamp,
    e3.timestamp)) AS avg_time

RETURN entry_channel,
  landing_page,
  conversions, avg_time
ORDER BY conversions DESC
LIMIT 20

CDP vs. ArcadeDB

Capability	Typical CDP	ArcadeDB
Identity resolution	Predefined identifiers	Any identifier + fuzzy
Relationship traversal	Not supported	Native graph
Update latency	Hourly / daily batch	Real-time
Behavioral patterns	Basic segments	Time-series analytics
Similarity search	Rule-based segments	Vector embeddings
Unstructured data	Limited	Full-text search
Schema flexibility	Rigid objects	Schema-flexible docs
Data retention	Capped (e.g. 3 years)	Unlimited + auto-downsample
Vendor lock-in	Proprietary platform	Apache 2.0 open source

Beyond CDPs: What Traditional Platforms Can't Do

Customer Data Platforms like Segment, Salesforce Data Cloud, and others were designed for a simpler era. Their limitations become apparent at scale:

No relationship awareness: CDPs store attributes about customers but not relationships between them. You can't traverse household connections, referral chains, or social networks
Rigid identity stitching: Identity resolution is limited to predefined identifier types. You can't follow arbitrary relationship chains or use fuzzy matching on names and addresses
Batch latency: Profile updates happen hourly or daily. By the time marketing acts on a signal, the customer has moved on
Ecosystem lock-in: Identity resolution is often locked into the vendor's proprietary ecosystem, matching only identifiers within their products
No temporal intelligence: CDPs store the latest state, not the trajectory. They can tell you a customer's last purchase, but not whether their purchase frequency is declining
Unstructured data gaps: Support tickets, chat transcripts, and agent notes — some of the richest customer context — are poorly handled

ArcadeDB is not a CDP replacement — it's the intelligence layer beneath it. The graph, time-series, vector, and full-text models provide the analytical depth that CDPs lack, while the document model stores the flexible profiles that rigid schemas can't handle.

Why ArcadeDB for Customer 360

Building a true Customer 360 typically requires assembling multiple specialized systems: a graph database for relationships, a CDP for profiles, a time-series store for behavioral tracking, a search engine for unstructured data, and sync pipelines to keep it all consistent.

ArcadeDB unifies all of this in a single engine:

Graph: Customer relationships, household structures, referral networks, product affinities
Documents: Flexible customer profiles that accommodate any schema
Time series: Engagement trends, purchase patterns, lifecycle progression
Vectors: Customer similarity, lookalike audiences, behavioral segmentation
Full-text search: Support tickets, chat logs, agent notes, email correspondence
Three query languages: SQL, Cypher, Gremlin — use whichever fits your team

Apache 2.0 — Forever

Customer data is among the most sensitive and strategically important data in any organization. You need absolute certainty that the database holding it won't change its license, restrict your deployment, or demand surprise commercial fees. ArcadeDB is Apache 2.0 forever. No vendor lock-in, no bait-and-switch, no per-seat pricing. Deploy it on-premise behind your firewall, in your cloud, or embedded in a commercial product — the license will never be used against you.

Platform Comparison

Capability	Typical Stack	ArcadeDB
Graph traversal	Neo4j	Built-in
Customer profiles	MongoDB / CDP	Built-in
Behavioral analytics	TimescaleDB / Mixpanel	Built-in
Similarity search	Pinecone / Weaviate	Built-in
Ticket / log search	Elasticsearch	Built-in
Entity resolution	AWS ER / custom	Built-in
Data sync	Kafka / CDC	Not needed
License	Mixed / proprietary	Apache 2.0

Industries

Retail & E-commerce: Unified shopper profiles across online, in-store, and mobile with household-level cross-sell
Financial Services: KYC, regulatory compliance, multi-product cross-sell, relationship pricing
Telecommunications: Network influence on churn, household bundling, social-aware retention
Healthcare: Patient journey tracking, care coordination across providers and facilities
Insurance: Policyholder households, multi-policy cross-sell, claims context
Media & Entertainment: Subscriber engagement, content affinity, social sharing patterns

Client Success Story

"We had customer data siloed across 12 different systems with no single source of truth. Implementing Customer 360 on ArcadeDB gave us a unified view in real-time. Our support team can now see a customer's complete history — purchases, support tickets, preferences — instantly. NPS scores increased by 23 points, and our agents resolve issues 40% faster."

— Head of Customer Experience, Multinational Retail Corporation
(Specific company details withheld per confidentiality agreement)

Business Impact:

23-point increase in Net Promoter Score
40% faster issue resolution
31% increase in cross-sell conversion
Complete 360° view in <50ms queries
12 data silos consolidated into 1 database

Customer 360