Founding his company to dramatically improve the analytics solutions available to Direct-to-Consumer and other e-commerce companies, CEO Brian Eberman and his team provide companies with the forecasts, insights, and operating data they need to run their growing business across multiple sales and advertising channels.
With 13 patents in Speech Technology, Internet Search, and Robotics along with a Ph.D., M.S., and Bachelor’s Degrees from MIT, Brian has a deep understanding of AI, Robotics and how data analytic focused companies can improve their processes.
M.R. Rangaswami: The Direct To Consumer market has hit a few headwinds lately. What are some of the more recent challenges you are seeing in the category and how are companies adjusting?
Brian Eberman: These are definitely interesting times for direct to consumer (DTC) brands and e-commerce brands overall. In speaking to our customers the following seems to be the most common challenges they are trying to resolve:
Supply chain is still a problem
The pandemic caused major disruptions to the supply chain. And as brands started to get products back in stock the growth of e-commerce slowed so many now have an inventory overhang. Inflation and gas prices also skyrocketed adding to delivery costs and holding costs. Even the price of CO2 for beer is up 20%.
E-commerce growth is still up, but slowing.
Consumers spent almost 2 years sequestered in their homes buying primarily groceries and durable goods from their couch. Now people are returning to retail stores, eating out, traveling, etc. So that bucket of discretionary dollars DTCs were able to tap into is suddenly a lot smaller.
Ad costs are up.
Platforms like Meta and Google were great channels for brands to acquire customers relatively cheaply. But the secret is out and everyone is using the same playbook causing a sharp increase in price – nearly 40% based on some reports.
As if to pour more salt in the Meta and Google ad wound, Apple decided to eliminate tracking cookies and MAIDs with their iOS 14 release, citing privacy concerns, and essentially wiping out DTCs ability to track sales of their products back to their ad campaigns.
M.R.: What role does data analytics play in helping these companies and other e-commerce companies navigate these new market dynamics.
Brian: Data analytics plays a key role in helping DTC address these short to longer term challenges. Let’s look at the iOS 14 tracking issue first because the gap is pretty straightforward and this problem has been getting a good amount of attention lately.
To address deprecation of cookies and MAIDs, DTCs are using first party server-side tracking, which is still respected by the browser, to collect first party data that can then be paired with analytics to build attribution models that can track sales back to their ad channels and campaigns.
These models are by no means perfect. They are click-based because they can’t see the customer advertising views, however, early indications suggest they provide longer term attribution and better cross-channel attribution than what’s currently being provided by ad channels themselves.In light of the other challenges, and from investor pressure, we are seeing DTCs shift their strategic focus away from hyper growth and more towards sustainable profitability.
So in addition to measuring media metrics, like ROAS, companies are interested in using data analytics to accurately measure the contribution profit of their business and evaluate the performance of each of their brands and products based on this metric.
If supply chain issues and slower e-commerce growth continues, DTCs want to know which brands and products they should focus their efforts on. In addition to this, there is definitely a renewed focus on customer lifetime value (CLV) among DTCs.
They are looking to leverage analytics and data science to more accurately compute their customers’ CLV based on behavior and other data attributes. Their objective is to identify customers with the highest projected CLV and leverage this data in their marketing, product choice, and financial forecasting. For example: they can customize retention strategies towards customers with high CLV projections, they can use customers to also develop “look-a-like” models for ad targeting, they can use CLV as a signal to train machine-drive optimization in ad channels.
M.R.: What are some of the gaps you see in these current data analytics tech stacks and services that these companies rely on and how do you see these solutions evolving?
Brian: I think most of the e-commerce analytics stacks that we see DTCs investing in were initially designed to measure and optimize digital advertising. So while many of them have robust ad reporting systems, they are pretty light on actual data analytics and even more so on data science. Even the dashboards are prescribed to provide aggregate ad reports. So the application is limited to marketing, even though there are several parts of the business that contribute to profits.Here’s how we see data analytics evolving:
Optimize the performance of the customer versus the media channel.
Instead of looking at the performance of broad based cohorts in ad campaigns, these systems will look at the performance of individual customers as measured by CLV versus the cost to acquire them.
Develop accurate forecasting and prediction models.
These systems will include causal modeling technology that takes a set of customer behaviors and hundreds of other data attributes to accurately predict future behavior, e.g. projected CLV, Churn probability, etc.
Cross-departmental integration. Data analytics systems will be able to ingest data from all parts of the business, create integrated analytics, and publish reports that give operators much deeper insight to help them optimize the business. Example, finance can share inventory data with marketing to optimize merchandising and promotions. Marketing can project forward customer sales as a function of advertising spend which provides finance with gross cash projections.
M.R. Rangaswami is the Co-Founder of Sandhill.com