Enterprise Sales vs Digital Marketing: Calculating Customer-Level Incrementality in Online to Offline Measurement
Calculating Customer-Level Incrementality in Online to Offline Measurement
In today’s marketing world, having the capability to measure the offline sales impact of online media at the customer level is quickly becoming table stakes. However, having this capability is only the beginning of the online to offline journey.
There are two major implications that come with understanding the relationship between enterprise sales and digital media—historic key performance indicators (KPIs) may no longer make sense and the incremental impact of digital media tactics can now be measured at the customer level.
Organizations often miss the mark in one, or both, of these categories when they’re not fully prepared to base future digital marketing strategy on enterprise sales metrics.
Historic KPI Impacts
Digital marketing teams have become experts in optimizing their budget to drive ecommerce traffic and sales. But, for most retailers, online sales are only a small portion of total revenue.
As LiveRamp’s customer-level online to offline measurement capability is introduced to an organization, enterprise sales KPIs can become the focus of digital marketing optimizations. What happens when digital media budget optimizations are targeting enterprise sales?
First, seemingly unprofitable campaigns can now be seen in a new light and can be shown to effectively drive offline sales and new customer acquisition.
Secondly, adjusting tactics to optimize to 100% of enterprise sales revenue may mean the 10% of revenue that is ecomm-only may suffer. Intuitively, this makes sense—deploy marketing campaign strategies that may lead to a decrease in ecommerce revenue, as long as they lead to a larger increase in enterprise revenue.
It is crucial that ecommerce and digital marketing teams are aware of this phenomenon and prep their leadership accordingly. With solid data under your decisions, you’ll be able to confidently know how to adjust your mix and tactics to drive maximum marginal revenue.
Measuring Incremental Offline Impact of Digital Marketing
With a good handle on customer-level enterprise sales measurement capabilities and enterprise-focused KPIs, the next question can be addressed: what is the incremental enterprise impact of our digital marketing tactics?
Marketers today employ such tools as media-mix modeling and/or multitouch attribution but find it difficult to reconcile these approaches and confidently enact practical change. MMM’s focus is much higher level, delayed, and backwards-looking. Attribution models don’t accurately account for audience bias and can miss external macro-influences such as weather, promotions, and competitive moves.
We’ve found the secret to align, validate, and enhance the models is by utilizing targeted A/B tests. This can be an excellent way to determine incremental impact, especially for channels such as display. LiveRamp’s deterministic matching enables us to put tight controls around which customers receive display marketing, which allows precise messaging and greater statistical significance of results.
For example, we helped a large retailer understand the incremental impact of display targeting during a direct mail campaign by splitting the direct mail recipient list into two random groups. We delivered both groups direct mail and one also received targeted display through LiveRamp. We tracked the customers’ purchase behavior during the following weeks and saw a 5% lift in sales from the group that received messaging across both channels.
By executing this test through LiveRamp, we were certain that the customers’ exposure was controlled enough to infer that display was the tactic that drove the incremental sale.
Where Can We Go from Here?
Building an operational model that enables faster iterations of hypothesis testing through a closed-loop measurement system is the first step. As channel-level incrementality becomes clear for different combinations of media exposure, test into more granular hypotheses like customer segment-specific messaging.