Welcome to the next installment of the mythbusting series brought to you by INCRMNTAL. In this edition, Arun Srinivasan, CEO/co-founder of Clarisights, dives into the topic: With scales comes structure.
In this article, we want to debunk the prevalent myths and misconceptions around whether the most scaled up enterprises actually have the best structure - for data, for teams, etc. And, if not, why is that?
Given Clarisights specializes in providing better structure around data and associated marketing reporting for large enterprises (ranging from Uber to Universal Music Group), we felt it was natural to invite their CEO Arun to write on this topic.
In our mythbusting series, our mission is to unravel these types of misconceptions, one by one, and shed light on the truth. If you would like to check out the first piece on incrementality and user-level data you can check it out here.
The assumption is that, as businesses scale, they are able to build what they need internally and create better structure for these needs. We tend to assume that mature companies have better processes, better data structures, robust workflows, and advanced tools to support their business and teams.
This is true in many cases. Amazon first built out AWS as an internal system; Spotify first built Backstage as an internal-only developer tool, ahead of open sourcing it and now making it commercially available; Meta is well-known for building their own data centers.
There are many other examples from some of the most disruptive companies. Even so, with scale comes chaos, mess and unnecessary silos. A good example is what happens to data availability and reporting as organizations scale.
In the current environment, growth marketing teams are under increased pressure to show work and drive impact. Growth marketers have access to a virtually endless supply of data across marketing performance in various channels to support this. This data can be sliced and diced in many ways to generate insights to inform actions that lead to more traffic or conversions at lower costs. At scale is when the true challenges begin.
Neither the tools marketers use nor the team structures are built to take best advantage of this data. As a result, many opportunities go unnoticed. Teams suffer from inefficient investment and use of resources, especially since getting your job done often becomes more important than data democracy and cross-org info sharing. Without tooling positioned to overcome this, marketing teams and their investments suffer.
In this blog post, we’ll talk through why this occurs, the common challenges, and the impact this has on team’s and their strategies. We’ll then cover how some teams have approached addressing this.
Businesses become more complex as they evolve to scale up and enterprise stages. In addition to growing headcount, they add more business lines, sell more products, expand to more markets, etc.
For Growth Marketers, this all means more opportunities to drive positive results. More marketing channels, campaigns and creatives, etc. This often means billions, or even trillions, of rows of data owned and managed by various teams across the org.
In an environment in which budgets are under increased pressure from internal stakeholders, growth teams are expected to prove the incremental impact of marketing initiatives. This means, within all this data, marketers need to find pockets of efficiency to either reduce acquisition costs or increase sales - ideally both. They need to constantly test and find arbitrage opportunities.
Marketers empowered with data are at the core of accomplishing this.
As businesses grow, they inevitably adopt some form of the Modern Data Stack (MDS), relying on a general purpose BI tool (such as Tableau or Looker) for their source of truth reporting. Organizations invest vast resources, both in tooling and people to stitch together tools and to build and maintain this “reporting stack”. The hope is that this will answer all marketers’ reporting and analytics needs and take advantage of the opportunities in the data.
The reality is that a standard BI tool is excellent for KPI snapshots and executive-level overviews; however, the stack of tools stitched together has a number of gaps that prevent capturing this opportunity. These gaps include:
Dashboards are considered the key end-product for many data teams. They're great until marketers ask for a filter that is missing or a cut of data that they need. This then leads to what we often see referred to as ‘the spreadsheet problem’ in order to fill this gap in the modern data stack.
Spreadsheets are flexible and also familiar to every marketer. As a result, nearly every enterprise growth marketing team leverages spreadsheets extensively. While this can be an effective stopgap measure, it has some challenges, including:
This often leads to data sanity and accuracy issues. Spreadsheets are every data team’s governance nightmare.
Challenges from scale are not limited to the tools. The data silos inevitably drive more siloed team structures, limiting collaboration. This makes it difficult to evaluate different tactics holistically, such as:
The failure to connect these dots means teams will operate with blinders, solely focused on hitting their own goals. They won’t consciously think about the impact of their day-to-day work to the broader business. Marketing teams will over-invest in certain areas and overlook others.
Imagine you are a paid acquisition manager for a food delivery business. You’re continuously scaling up Paid Acq campaigns because you see CAC reducing, while overlooking that these customers are not engaging with CRM campaigns 1 month later. The data infrastructure needs to facilitate monitoring this seamlessly, including layering other datasets (cohorted revenue, CLTV, etc.) for analyzing campaign performance holistically. Otherwise, you are likely to miss this insight and misuse marketing budget.
This can cost businesses millions, or even billions, in sales, and be the difference between winning and losing.
Some of the best growth stories of the past decade have addressed some of these challenges with, what is known as, ‘Pod structures’. This includes Lyft, Pinterest, Uber, and Airbnb, and has helped teams drive cross-functional collaboration on key initiatives.
Sigal Bareket, Lyft’s former Head of Growth Marketing, shared the following with me:
“It's essential to have teams that are experts in different disciplines. However, problems start when these experts try to solve business challenges using only the tools their discipline offers and are not looking at the same data.”
While we see progress in team structures, this is largely not being addressed in the data and reporting space. Businesses are actively assessing how to make their marketing teams and investment more efficient in this tougher growth environment. In order to do this with even greater success, they’ll need to lean into finding solutions outside the traditional tech stack.
Arun Srinivasan is the CEO/co-founder of Clarisights, a self-service marketing reporting platform filling in a gap in the traditional marketing reporting tech stack. Clarisights empowers enterprise marketing teams to run their day-to-day reporting and ad hoc analytics without dependencies on data teammates, SQL or spreadsheets. This enables teams to improve marketing efficiency and steer budgets more effectively.
Clarisights works with some of the most sophisticated marketing teams, including those at Uber, Delivery Hero, Universal Music Group, On, and HelloFresh, to help them understand and visualize their data. To work at such scale and solve the needs for modern enterprise marketing teams, Clarisights built much of their foundational engineering from the ground-up.
Prior to co-founding Clarisights, Arun spent his career working in marketing at a number of companies, including bwin Sports and Hostelworld. Clarisights’ other co-founders, Ankur Gupta and Ashu Pachauri, previously worked as engineers at Google and Meta. In 2018, Arun, Ankur and Ashu founded Clarisights after seeing that enterprise marketing teams were poorly served by BI-dependent dashboards and to build a flexible reporting solution focused specifically on marketing use cases.