Making decisions based on sampled data can have a detrimental effect on your marketing performance. Improvado helps you build 100% accurate reports from Google Analytics by pulling data on a daily basis. This allows you to prevent sampling errors and deliver accurate reports to your clients.
Email is one of the most efficient revenue channels. Granular analytics can help you achieve better results and speed up your experiments. Automate your email reporting with Improvado.
Optimise your marketing campaigns for the growth of leads, minimising CPA or CPI, and not fail to reach revenue growth. Not all leads are equal in terms of revenue. Attribution models help to overcome that constraint by attributing every touch point to revenue.
Growth marketing is all about experimentation. Running as many experiments as possible and making decisions fast is crucial if you want to achieve sustainable growth. All of that is barely possible if your team has to build CPA, CPI or CPL reports manually. Improvado helps companies to automate marketing reporting and speed up their decision-making processes.
Deep dive into eCommerce analytics with Improvado. Reduce 90% of the manual work and get more insights from your data. You can also automate product performance, ads, SEO and email efficacy reports.
Improvado's data preparation and Marketing Common Data Model accelerate the speed at which your data is made available to your business and save on the costly engineering resources that are traditionally required for complex data transformations.
Marketing teams and data analysts spend too much time cleaning data instead of analyzing it. Improvado's data preparation provides a self-service, point-and-click tool to quickly identify errors and apply rules that you can easily reuse and share, even across massive data sets.
Execute reusable recipes to unify and transform data from almost any data source. Then, move it to your data warehouse or business intelligence tool, where it can fuel the dashboards that power your data insights.
300+ point-and-click functions allow you to perform the simple and complex transformations, discovery or data science preparation that your business needs.
Marketing Common Data Model is the data normalization layer that simplifies marketing analytics. Don’t waste time renaming and matching different dimensions and metrics across all your marketing platforms. With MCDM, all your metrics are unified and ready to explore.Marketing Common Data Model
Improvado does a great job of allowing for the flexibility of variable
Improvado does a great job at allowing for the flexibility of variable output demands.
The entire team from start to finish has been great to work with and they address problems quickly and effectively.
Professional, responsive and willing to go the extra mile to help. They are a solution to connect to most advertising platforms and provide a multitude of metrics from these platforms
They are willing to go the extra mile to attend to your query and help to the best they can.
Helping to connect to various advertising platform such as Google Ads, Facebook, DV360, Trade Desk.
They are a solution to connect to most advertising platform and provide a multitude of metrics from these platforms
Industry standard recipes accelerates the speed at which the data is made available to your business, as well as saves costly engineering resources, which traditionally required for complex types of data transformations.
Improvado offer approach with data consolidation on the fly. Combine, map and normalise data to produce a comprehensive data set for your marketing activities.
Easily and quickly build your own data pipelines within an intuitive spreadsheet-like user interface. Zero engineering required.
300+ point-and-click functions to perform anywhere from simple to complex transformations, discovery or data science preparation your business need.
Discovering exactly what is in your data and how it might be useful for different analytic explorations is key to quickly identifying the value or potential use of a dataset.
This exploration process allows you to gain an understanding for the unique elements of the data such as value distributions and outliers to inform the transformation and analysis process.
Structuring is needed because data comes in all shapes and sizes. Data lacking human-readable structure is difficult to work with using traditional applications. Even well-structured datasets often lack the proper formatting or appropriate level of aggregation required for the analysis at-hand.
Cleaning involves taking out data that might distort the analysis. A null value, for example, might bring an analytic package to a screeching halt; it may need to be replaced with a zero or an empty string.
Particular fields may need to be standardized by replacing the many different ways that a state for example might be written out -- such as CA, Cal and Calif -- with a single standard format.
Enriching allows you to augment the scope of your analysis by incorporating disparate internal or 3rd-party data into your analysis. This step includes executing common preparation tasks such as joins, unions or complex derivations.
Purchase transaction data, for example, might benefit from data associated with each customer's profile or historical purchase patterns.
Validating is the activity that surfaces data quality and consistency issues, or verifies that they have been properly addressed by applied transformations. Validations should be conducted along multiple dimensions.
At a minimum, assessing whether the values of an attribute/field adhere to syntactic constraints as well as distributional constraints.
Specify whether you want to receive raw or mapped data based on your business needs.
Once aggregated, side-by-side comparisons of important cross-channel metrics can be used to address some of your biggest business decisions.
Set your rules and define how credit for sales and conversions is assigned to touchpoints in conversion paths.
Google Analytics provides APIs to collect, configure, and report on user-interactions with your online content.