Two customers walk into your store. They both spend $100. One of them will spend $300 with you over the next 3 months. The other one is never coming back. The problem is: your dashboards have no idea which is which. And the difference between knowing and not knowing is existential to your company.

Fortunately, telling your customers apart isn’t some advanced data science project. Elite retail brands invest in a process called the SMART Framework, a methodology using five ways of looking at your customers.

  • Speed: How fast does this customer buy again?
  • Margin: How much did we spend to produce this product and acquire this customer?
  • Attribution: How did this customer find us?
  • Retention: What activities can we do to bring this customer back?
  • Tiers: Which type of customer is this: Small, Medium, or Large?

Averaging your customers together erases these answers. The SMART Framework, the proprietary diagnostic that Latticework Insights runs with every client, enables retail brands to graduate to their next stage of growth and become elite retail brands.

Fast Stats

  • 80%: In most Retail brands, ~80% of customers never make a second purchase.
  • 200: The typical number of days before a customer makes a second purchase.
  • 3: The typical number of purchases a customer makes before they become a loyal, repeat purchaser.

How does the SMART Framework help me?

At Latticework Insights, we’ve spent the last 8 years working with over 80 brands ushering them through this SMART Framework.

Retail brands that invest in understanding their customers through the lens of the SMART Framework experience a massive transformation:

Speed: Once brands understand how quickly a customer is likely to buy again, they can figure out what their marketing budget is for acquisition and what their playbook is for retention.

Margin: Once brands understand their Gross-to-Net Walk (what they pay for COGS, CAC, Shipping, Returns, Duty, Freight, and overhead), they can quantify the incremental value of every future purchase, also known as their Contribution Margin.

Attribution: Once brands understand which advertising channels produce which customer types, they can reduce their marketing spend and lean into the channels that produce the highest quality customers.

Retention: Once brands understand how often customers buy and what activities they can do to accelerate customer purchases, they can build a retention playbook (email, SMS, loyalty) that reliably brings each customer back for the next purchase.

Tiers: Once brands understand that they don’t have one type of customer, they have many customer types, each of whom exhibit wildly different behavior patterns, they can custom tailor their marketing and growth strategies towards each customer tier.

FAQ

How long does it typically take to get a customer to buy a second time?

About 200 days on average for retail brands. Elite brands work to compress this: customers who return within 30 days are far more likely to become loyal repeat purchasers.

How do I calculate Contribution Margin using SQL?

In your data warehouse, join orders to costs and subtract COGS, CAC, shipping, returns, and fees from net revenue per order. The result is per-customer Contribution Margin, the number averages hide.

What is the average CAC on Meta vs Google vs TikTok?

For retail DTC brands, Meta CAC typically runs $50-80, Google $40-70 (branded search far lower), and TikTok $30-60 with wider variance. What matters more is which channel produces your best customer tier.

What is the most effective re-marketing channel Meta vs Klaviyo?

Klaviyo wins on cost for customers you already own; Meta retargeting wins on reach. Elite brands use Klaviyo email and SMS as the retention workhorse and Meta to reactivate lapsed customers.

How effective are discounts to get customers to buy more?

Discounts reliably pull the second purchase forward, but they train your best customers to wait for sales. Margin-aware brands target discounts by customer tier instead of blasting everyone.

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 dashboards can’t tell your $100 customers apart, get in touch. The SMART diagnostic is exactly where Latticework starts.