Read on to learn how direct-to-consumer companies can leverage a modern data stack
Whether you like the food or not, McDonalds is a leader in the food industry.. To achieve “Over 99 Billion Served,” the business has undoubtedly built an impressive data stack. Consider that when a customer purchases a single BigMac, that order touches over 25 different backend systems.
While most Direct-to-Consumer and e-Commerce companies don’t operate on the same scale as McDonalds, they still have a staggering amount of data systems to manage. The average startup in 2020 has its mission-critical data stored in 10-30 different data platforms. This leads to an enormous problem.
In order to view the complete picture of an individual customer or order, companies need to reconnect the dots across dozens of systems. At best, this is a matter of re-assembling a large sprawling digital puzzle, usually in SQL, python or Tableau. But many data systems simply don’t play nice with each other. Many systems contain overlapping, conflicting or outdated pieces of the customer. Other systems store aggregated and anonymized pieces of the customer that can’t be rejoined with a single record. Companies quickly discover that there’s no “single version of truth” internally and that re-assembled puzzle starts to look like a Frankenstein version of real life.
Fortunately, there exist businesses that tackle this problem. A pipeline solution like Fivetran makes the retrieval of disparate, rapidly-evolving data extremely simple. Companies like Snowflake have made the storage of enormous, loosely-structured data a cinch. And companies like ours, Latticework Insights, are bringing a new guard of skills and domain-expertise to help direct-to-consumer, e-commerce, SaaS, and marketing companies finally move onto a modern data stack.
In 2020, it’s more critical than ever that companies can speak data science and translate that rigorous work into marketing insights to move the business forward. Without the ability to bridge that gap, many companies are left struggling to interpret a tsunami of proliferating data sources to understand things like:
Attribution: Many media channels like Facebook and Google fight to “take credit” for conversions. Allocating credit appropriately takes a deep understanding of both the channel nuances as well as the ability to stitch it all back together in SQL.
Lifetime Value vs Acquisition Cost: The LTV:CAC ratio, a golden metric scrutinized by VC’s and Growth Marketers alike, requires not just the integration of dozens of data sources, but also the know-how to build reports that make sense to industry pros.
Getting to “Ah Ha!”: One of the most common (and frustrating) questions we often hear from clients is some version of “can we just see our Sales and Marketing data in one dashboard?” It’s possible, but it’s often done by large teams manually copying and pasting data into spreadsheets, or by expensive system integration teams, or not at all.
These things don’t have to be difficult. At Latticework Insights we help companies invest in modern data stacks and modern skillsets to help propel their companies into the future.