You can quote your “Return on Ad Spend” to two decimal places. But when you spend $1 on your analytics team, how much revenue do you get back? When you take a hard look at your data warehouses, your dashboards, and your budget for analytics people and tools, what would your “Return on Analytics Spend” look like last quarter?

Most COOs and CFOs at retail brands can’t answer this question. They see analytics as a cost center. And maybe for them, analytics is a cost center. But elite retail brands somehow use analytics in a way that produces revenue, that saves money, that uncovers new opportunities they never saw before. When you’re interviewing a new ad agency, you ask them “what am I going to get out of my ad investment?” Why isn’t that question asked of analytics teams? The answer for elite retail brands, is they see a 2x, 5x, or 10x “Return on Analytics Spend.”

Fast Stats

  • 1%: Identifying the solution to fixing 1% of churn often pays for 100% of marketing budgets.
  • 80/20: Getting the 20% of users who buy more than once to pay for the 80% of users who churn after 1 purchase.
  • 3: The typical number of purchases a customer makes before they become a loyal, repeat purchaser.

How do I use Analytics to make more money?

At Latticework Insights, we’ve spent the last 8 years working with over 80 brands transforming their analytics team from a cost center into a profit center.

In order for analytics teams to generate money, they need to become elite analytics teams.

How do they do it?

Domain Expertise: Elite analytics groups deeply understand the domains of Marketing, Finance, and Operations. They can speak the language of customer acquisition, financial margins, and operational efficiency.

Novel Point-of-View: They have a deft ability to look at problems in a new way. To flip the question on its side. To find the question behind the question. To create a new lens on the business.

Technical Proficiency: They are at the top of their game and can easily shift between disciplines. They are excellent at building ETL data pipelines, creating SQL models, doing DBT transformations, creating dashboards, creating statistical models, discovering insights, and presenting to stakeholders.

Opportunity Analysis: They don’t just calculate LTV. They forecast it. They run it through scenarios. They look at novel user segments. They find “what if” scenarios that can help unlock new revenue growth.

What do they produce?

1) New Revenue Opportunities: Elite analytics teams discover revenue that was hidden in the dashboards. The customer tier worth 10x the average. The lapsed segment that responds to one Klaviyo flow. The 1% churn fix that pays for the entire marketing budget.

2) Eliminated Waste: Elite analytics teams automate manual data tasks. They find the discounts eroding margins. The ad channel producing one-and-done customers. The email campaigns that don’t move the needle.

3) Faster Decisions: Elite analytics teams produce trust. They empower other teams to make definitive conclusions. They run meetings that inspire confidence and drive action. Speed compounds: the brand that adjusts spend weekly beats the brand that adjusts quarterly.

Return on Analytics Spend is the other ROAS. Elite retail brands hold the analytics dollar to the same standard as the ad dollar, and they expect it to return a multiple.

References

FAQ

How do I get budget for an analytics project?

Present it like ad spend: a baseline, a target, and a payback window. A good analytics project should return 2-5x its cost within 6 months, in found revenue, avoided waste, or faster decision making. Develop metrics specifically focused on revenue growth.

Is a data consultant cheaper than hiring an in-house analyst?

A senior fulltime analytics hire costs $150K+ annually, plus tools and management. A VP-level analytics hire is closer to $250K. Full time hires often only cover one skill area. Full time hires may be unable to articulate a “return” on your investment in them. Whether you hire a consultant or build a team internally, you should demand: a wide range of skills; domain expertise in marketing, finance, or operations; and an ability to clearly articulate a “Return on Analytics Spend.”

What should a proof-of-concept engagement deliver in 30 days?

One trustworthy number live in a dashboard: a working pipeline from your core platforms into a data warehouse, plus a payback estimate for the full build. If a POC can’t do that in 30 days, walk away.

When does a fractional Chief Data Officer beat a full-time hire?

When you need data leadership more than headcount: strategy, vendor selection, and team direction. A fractional CDO gives $50-100M brands executive-level judgment without a $300K+ salary.

About Tim Shea & Latticework Insights

Tim Shea is the Founder & CEO of Latticework Insights working at the intersection of Data Science & Retail for 25 years. Tim is a thought leader in technologies such as Snowflake, Fivetran, and LangChain as well as LTV:CAC, Retention, Financial Forecasting, and Growth Analytics. Tim’s clients include Sweetgreen, NBA, UFC, Converse, Princess Cruises, Reddit, Quora, and Salesforce.

Latticework Insights is an LA based Data Science Agency that provides Data Leadership to help Retail Brands become Elite Retail Brands. Latticework focuses on reconnecting brands’ fragmented SaaS systems, bringing data together from Meta, Google, TikTok, Shopify, Amazon, and Salesforce to help brands see their business more clearly and graduate to their next stage of growth.

Latticework is an elite consulting firm with decades of technology expertise in ETL, Data Warehousing, Data Visualization and Agentic AI as well as retail expertise in LTV:CAC Modeling, Retention, Unit Economics, and Financial Forecasting.

If your analytics budget can’t yet name its own return, get in touch. Putting a number on it is exactly where Latticework starts.