What is Web Analytica?

Web Analytica is a saas platform that is intended to be used by operations personnel and analysts at digital marketing agencies. Digital marketing agencies are expected to provide marketing analytics to their customers - by providing reports/dashboards of the data at the very least. The beginning step of any analytics project is getting your hands on the data and getting it into a useful structure. Since digital marketing agencies often manage dozens of customers, the simple act of grabbing the data from the different platforms they use (google analytics, google ads, facebook ads, hubspot, callrail, etc.) can be a huge undertaking especially when automations are not in place to make it replicatable for dozens of customers. Many digital marketing agencies have dreams of being more 'data driven' and providing their clients with deeper insights and intelligence, but they often have difficulty just clearing the hurdle of efficient data management. Web Analytica is a data management platform for digital marketing agencies. The end 'product' is a cleaned, well-organized, and structured database that the agency can then use with whichever business intelligence tool they see fit.

The user experience goes like this: a user from the agency logs in to Web Analytica for the first time and authenticates their connections to the plethora of platforms they use for their clients (google analytics, google ads, etc.). Web Analytica handles the ingestion of data from these various APIs. Raw data will go to Snowflake, where a full data pipeline will take that raw data and output schema that are optimized for use by operations personnel at agencies. The 'magic' of Web Analytica will focus on four main challenges to start.

1: Segmenting by customer

Digital marketing agencies do not typically assign a unique id to each of their customers, and it's not a certainty that the accounts for the agency customer all have the same name. For example, Agency X has a client, Mike's Surf Shop. The google analytics account name for Mike's Surf Shop is 'Mikes Surf Shop' and the account name for the google ads is 'Mike's Surf Shop Google Ads' and the account name for Facebook Ads is 'mikes surf'. This type of sloppy naming conventions makes it quite a challenge to write sql that ingests data for the whole agency and is able to segment by agency customer. Web Analytica will use fuzzy matching and potentially even LLM technology to intelligently group account names by agency customer and assign a unique id for each agency customer (agency_customer_id).

2: UTM tag management

UTM tags are the lifeblood of tracking user actions on the web - and in order to combine metrics like ad spend with leads generated, you often need to sql join data sources together on UTM tags. UTM tag management is a well known issue in web analytics and Web Analytica will do everything in its power to take "dirty" UTMs and make them usable by simple sql.

3: Segmenting multi-location customers

Digital marketing agencies often work with businesses that are multi-location. For example, senior living communities or fast food chains are multi location. This presents yet another level down the hierarchy with which data needs to be segmented. In the same way Web Analytica will intelligently group accounts by agency customer, it will also intelligently group accounts/sub accounts by location. A good example of this is senior living communities. Let's say Senior Living Community X has 40 communities nationwide and Agency Y does digital marketing for all of them. They need to be able to confidently segment the data by community. This includes segmenting all the Google Business Profile data, the hubspot data, the callrail data, and maybe even the ad campaign data (imagine Agency Y is including the community name in the Google Ads campaign name in order to attempt to segment the data). In many cases you are relying on perfect naming conventions for these profiles and accounts in order to segment by community/location and we humans are flawed so mistakes and sloppy naming are inevitable.

4: Organizing KPIs

Your customers often care about different KPIs. For example, a senior living community is interested in calls or web forms, while a b2b business is interested in pdf downloads of their annual state of the market. For the senior living community, calls might be tracked in callrail and web forms are tracked with some other software, and the b2b business might be tracking their pdf downloads in Google analytics. Multiply these complexities by the dozens of customers digial marketing agencies typically manage and you end up with a lot of work simply managing this data.