Introducing Optimizely Opal
an all-new AI platform. See how it works

Warehouse-innfødte analyser

Test mot de viktigste forretningsmålene dine

Få ut det fulle potensialet i eksperimenteringsprogrammet ditt ved å teste og optimalisere mot de viktigste måltallene for virksomheten din.

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Utforsk mulighetene som ligger i våre lagertilpassede analyser

Det som skiller oss ut

Connect with your data warehouse

Seamlessly integrate with the most popular data warehouses including Snowflake, Databricks, Google BigQuery, and Amazon Redshift, for faster setup and data flow management. 

  • Connect instantly to your data warehouse with ready-to-use integrations.
  • Map essential data points and customize tables to make data readily available for experimentation.
  • Run health checks to confirm connection stability and data readiness before diving into experiments.
  • Ensure smooth data management across platforms for accurate, real-time analysis

 

Build custom metrics

Create and customize a range of metric types to track, measure, and analyze experiment outcomes in detail. 

  • Select from built-in metric templates: Conversion Metrics (track user actions), Numeric Aggregations (sum or average values), and Calculated Metrics (combine multiple metrics for more complex insights). 
  • Define metrics like revenue, page load time, or return rate, with the flexibility to sum, average, and aggregate values. 
  • Write formulas to create custom metrics, with basic arithmetic operations to calculate complex data, such as total realized revenue from various sources. 

Use the experiment scorecard

Analyze experiments with a comprehensive scorecard that gathers all essential data for quick and effective decision-making. 

  • Select an experiment, then add primary and secondary metrics in a dedicated view to tailor results to your needs
  • Automatically pull in experiment summary information like Days Run, Total Visitors, Page Targeted, and more for a holistic view of each experiment. 
  • See metric performance by variation, with details on metric value, percentage improvement, confidence interval, and statistical significance. 
  • Segment results by user attributes (e.g., platform or country) to uncover how different audiences respond to experiments. 

Unlock native analytics views

Unlock deeper insights into user behavior with built-in analytics views designed to support detailed journey and funnel analysis. 

  • Visualize user flows with funnel views to track how users progress through each step toward conversion. 
  • Use path views to analyze the sequences users take to and from conversion events, capturing both entry and exit points. 
  • Explore trends over time and segment data within dashboards, which can be shared across your team for strategic decision-making. 

Oppdag fordelene

On-the-fly-undersøkelser

Spar timer på manuell dataanalyse ved å generere kohortspesifikk innsikt raskt, uten å være avhengig av kostbare eller tilpassede dataspørringer.

Konsistente data

Sørg for fullstendig datapålitelighet og unngå avvik ved å bruke organisasjonens pålitelige datakilde på tvers av eksperimentering og analyse.

Trygge og sikre data

Oppretthold full kontroll over hvor dataene befinner seg ved å holde dem internt, noe som eliminerer juridiske bekymringer og muliggjør mer eksperimentering uten å flytte sensitiv informasjon.

Lagerintegrert statistikk

Bruk data fra alle digitale kanaler, inkludert e-post og CRM, til å gjennomføre dyptgående eksperimentanalyser med Optimizelys Stats Engine.

Pre-built connectors with the world’s leading data warehouse providers

Snowflake
Amazon redshift
databricks
BigQuery
This is super exciting, and I already see how we can use this on the fly. It will save us hours. My analysts will be over the moon for this.

Tapestry

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We define our own sessions, and we define our own metrics. Everything already sits in our warehouse as a single source of truth.

Chewy

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This is the holy grail of visualization that a lot of analytics tools don't have. This is awesome, especially if you can show the difference [in journeys] between different variants and your test vs. control groups. This is something we have never had. It's a very heavy lift and will save a lot of operation time from my analytics team.

Cox Automotive

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