One of the holy grail metrics for any business is Customer Lifetime Value (CLV, aka LTV). With sufficient data, you can place a dollar value for any of your customers, no matter what purchasing stage they are in, and consequently prioritize your actions based on it. The more data you have the more you can segment your customers which leads to a more accurate measure of CLV — for example, customers from different channels like Facebook ads or Google organic search; could have drastically different values.
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Shopify has a great ecommerce platform, but they are still a single point of failure for a possible catastrophic data loss that wipes out all record of what you’ve built in your store and business. (Or worse, arbitrary censorship or other obscure reasons for kicking you off their platform 🙈.) In the event the unthinkable happens, you will want to be able to reconstruct your business history and not lose all of the valuable data and learnings.
We partnered with DataBlade (DB) this year to build out a serverless data science solution for one of our clients’ main business needs, revenue forecasting. In this post I will discuss our architecture for the final product and how we overcame some implementation obstacles.
Scenario — You’re an up-and-coming ecommerce/SaaS startup. You’ve got your site up, you’ve A/B tested your message, and you’ve got your SEO and social ad buy. You’ve got your email drip campaign and reminders. You also have basic BI reporting telling you channel traffic and conversions. Traffic is decent, and revenue is growing. It’s likely that you are in the initial growth phase, you’re flying on skates and business is booming. You think you’ve found your voice; you’re expanding; you’re reinvesting for more growth; you’re going big on marketing… things seem to be going great for a while.