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ClickGeni

Digital click & cross-device attribution for efficiency & growth

People shop around when they buy online, so you need to ‘attribute’ (value) each click, to avoid wasting marketing money and missing opportunities. Imagine if you could correctly value every click and every marketing channel in the journey based on how it changes the probability of sale?   

Accurate attribution using custom machine learning is our speciality since Metageni founder Gabriel Hughes developed the original positional attribution modelling tool in Google Analytics, while he was at Google.

Since then ‘data-driven’ models have become possible but the challenge is making sure these are relevant to your business, and not just a one size fits all black box model. Your attribution model is unique.

Key benefits –

Paid and organic digital ROI

We use your first party analytics data to analyse all clicks to your site, so can include paid and organic search, social platforms, affiliates, email, video – indeed any visit to sale driver available. If you are not sure what we can measure, just ask: this is a custom build model.

Very high levels of objective accuracy

Do you know how accurate your attribution model is? Our models have high levels of objective accuracy measured on hold out samples, so you can see if yourself how good the model is, and reallocate your marketing spend with confidence.

Cross device attribution

Attribution by device leveraging your own tracking and our cross device inference model

Include your own customer and product signals

Most attribution models treat all customers and purchases the same, which makes no sense. Adding in your customer and product data makes your attribution models even more accurate and yields valuable insights, so you can better understand how to target your marketing to different groups of potential buyers.

Web to Call Centre sales

If you track call centre sales, we can attribute these just as well as ecommerce transactions, and have worked with most of the major call tracking providers.

Transparent decision rule models

This is machine learning but not ‘black box’ modelling. Metageni have developed an approach that translates your custom model into a set of rules so you can see not just how much value marketing drives, but how and why it does this, so you have deep insight into your marketing effectiveness.

A unique independent data-driven approach

We use custom machine learning trained on your data so you measure without relying on media tech platforms or agencies to ‘mark their own homework’. This gives you confindence to invest your marketing budgets wisely.  

Daily signals for tactical and strategic application

We can run the model reports through our in house reporting system or you can use ours. We can also make attributed visit level signals available for your marketing tools such automated bidding. We can produce dynamic reports for any of the major visualisation tools and will create custom reports your business as needed, so you have exactly the data you need to take action.

Is this for you?

  • ClickGeni is a custom designed and built attribution model and reporting pipeline built on your first-party analytics data.
  • Combine with MediaGeni econometrics to incorporate impressions and offline effects.
  • We work in close consultation with your marketing and analytics experts.
  • We offer free workshops around customer journey challenges, for brands who may be interested.
  • Interested to know more? Contact us:  hello@metageni.com

Learn how other clients have worked with us

MetaGeni MetaGeni Custom solutions for intelligent marketing decisions
MediaGeni MediaGeni Econometric marketing mix models
ClickGeni ClickGeni Data-driven marketing attribution
ConversionGeni ConversionGeni Predict and optimize who will buy
BidGeni BidGeni Paid search bidding
RetentionGeni RetentionGeni Retention and churn optimization

We’d love to hear from you

For more information on any of our products please get in touch

hello@metageni.com

Custom attribution : Insights

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Your attribution model is unique – what are the best practices?

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