0.1.3 Why we arrived at the sprint pricing model

0.1.3 Why we arrived at the sprint pricing model

At CIFL, we've gone through a 3-phase progression of pricing models:

  1. Project-based (circa 2017-18): I'd build a bunch of custom Sheets + BigQuery analysis templates, then spend long stretches of time traveling, camping and hiking.  If you're looking for long stretches of time off, tough to beat project-based work - but it's no way to build a team.

  2. Retainer-based (2018-2019): We offered the 'Agency Data Pipeline' as our only service - a 3-6 month commitment, where the CIFL team built + maintained our clients' data pipelines, for a single flat retainer fee.  Great business model, but we left a lot of money on the table by requiring the long-term commitment.

  3. Sprint-based (2020): We offer 3-week development sprints for $5k flat (at current rates), then offer maintenance + support for a discounted monthly retainer (~$2k / month).  We do not require clients to commit to any size of contract up front - this allows them to get up and running, and build confidence in our partnership over time.  So far, it's been great for business, and retention has been strong.


If you're starting your data consulting business today, I highly recommend offering sprint-based pricing (with maintenance retainers) up front. 

Building a Data Agency

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Getting Started + FAQs

  • ***ALL THE TEMPLATE LINKS***
  • **GETTING HELP**
  • The business of data, end-to-end
  • Why'd we republish this course?
  • Who is this for, and what will you learn?
  • What is a data pipeline?
  • [THROWBACK ALERT] WTF is ADP?

Additional (FREE) CIFL Courses

  • Getting Started with BigQuery SQL
  • Data Studio the Lazy Way

0.1 The Sales Flow

  • 0.1.1 Finding your niche
  • 0.1.2 Building an inbound content strategy
  • 0.1.3 Why we arrived at the sprint pricing model
  • 0.1.4 Why we publish pricing
  • 0.1.5 The initial sales call1
  • 0.1.6 The roadmapping process1
  • 0.1.7 Deal closing + contracts

0.2 Staffing & Resourcing

  • 0.2.1 The sprint flow + roles
  • 0.2.2 Hiring reporting + data modeling analysts
  • COMING SOON - Making a staffing plan & budget

1.1 Planning Development Sprints

  • 1.1.0 Meet the Tracking Plan
  • 1.1.1 Breaking the roadmap into a Tracking Plan
  • 1.1.2 Mapping our raw data source requirements
  • 1.1.3 What you'll do with data source schemas
  • 1.1.4 Mapping out data source schemas
  • 1.1.5 Populating key starter questions for reporting
  • 1.1.6 Scoping out each Site or Client

2.1 Data Feeds - Getting Started

  • 2.1.1 Intro to data feeds
  • 2.1.2 BigQuery initial setup
  • 2.1.3 Setting up your BigQuery tables
  • 2.1.4 Pushing data from Sheets to BigQuery
  • 2.1.5 Supermetrics quickstart for beginners
  • 2.1.6 Pulling data from unsupported APIs into the Tracking Plan

2.2 Data Feeds - Stitch

  • 2.2.1 Stitch initial setup
  • 2.2.2 Pulling GA data using Stitch
  • 2.2.3 Pulling Adwords data using Stitch
  • 2.2.4 Pulling FB Ads data using Stitch

3.1 Intro to dbt

  • 3.1.1 Intro to dbt for SQL data modeling
  • 3.1.2 Planning your Data Models
  • 3.1.3 Creating your dbt project
  • 3.1.4 Connect your BigQuery database to dbt
  • 3.1.5 Managing your dbt project via Github

3.2 Data modeling with dbt

  • 3.2.1 Writing your 'processing' level SQL queries
  • 3.2.2 Writing your 'join' level SQL models
  • 3.2.3 Sidenote: on debugging dbt models
  • 3.2.4 Pro tip: standardizing URL structure
  • 3.2.5 Pro tip: using dbt macros
  • 3.2.6 Writing your 'admin' level SQL models
  • 3.2.7 Writing your 'math' and 'visualization' level SQL models
  • COMING SOON - Data documentation in dbt
  • COMING SOON - Data + schema testing in dbt

3.3 Productionalizing your dbt project

  • 3.3.1 Intro to productionalizing your pipeline
  • 3.3.2 Using dbt cloud to run your SQL models on a schedule
  • 3.3.3 Scheduling your data pipeline orchestrations
  • 3.3.4 Testing changes to your data pipeline
  • 3.3.5 QCing data using Supermetrics as a check

4.1 Visualizations in Data Studio

  • 4.1.1 The "PDA" reporting design framework
  • 4.1.2 Designing reports in the Tracking Plan
  • 4.1.3 Executing the reporting build
  • 4.1.4 Reviewing reporting
  • 4.1.5 Pulling data from BigQuery into Sheets

5.1 Sprint wrapup + review

  • 5.1.1 Conditions for closing out a sprint
  • 5.1.2 Wiring up reporting with live data
  • 5.1.3 Sharing draft models + visualizations with clients
  • 5.1.4 Transitioning to support mode

Congrats!

  • Wapow! You made it.
  • Interested in working with CIFL?