Most of my work these days revolves around helping clients put their data together into a full 360° multi-channel view in Adobe Insight, and then use that data to drive real business results.
I thought it would be interesting to showcase some examples of how marketers are using Insight, in Customer-centric datasets, to make real marketing decisions.
Why would we do this? Think about how you interact with brands that exist online and offline.
You might use a search engine to research a new computer — using both paid & organic search results as you do your research, then end up at an electronic retailer’s website and not purchase a computer. You look like a loss — you consumed paid search investment and made certain keywords appear to “under perform.” You even explore some of the educational material that a manufacturer sponsors on the retailer’s website, but you still don’t buy the computer.
A few days later, however, after some research, you walk into the retailer’s store, and buy not just the computer, but a cart full of accessories so that you go home ready to roll.
The purchase path didn’t end on the website. The purchase didn’t even happen on the website! But there was a pretty big conversion that happened.
How does that look in typical web analysis reporting? It looks like ineffective or under-performing paid search and natural search keywords, poorly performing site content, bad website conversion… and great in-store sales!
That’s the value of a 360° multi-channel view of your customer:
You gain visibility into every interaction, from the digital initiation to the brick-and-mortar conclusion. And your optimization decisions can be informed by every aspect of the customer’s experience.
How does this look where the data actually has to come together? Let’s explore…
Dataset Schemas in Insight
You might be very familiar with how Web data is typically organized. A Visitor might have one ore many Visits, each of which might encompass one ore many Page Views or other types of Hits.
As you move to a Customer-centric type of dataset in Insight, your dataset schema also evolves. Rather than just Web Visitors, there’s a higher-level concept of Customers. Customers might be Web Visitors… or store purchasers… or people who have had both types of experiences.
A Customer might have one or many Experiences with you, and each of those Experiences might have one or many Events.
We sometimes name these levels differently, due to varying nomenclatures from organization to organization. But the high-level concept is the same.
If it helps you understand dataset schemas a bit more — and the opportunities for their diversity in Insight — check out my post from a couple of years ago, A Tale of Three Dataset Schemas.
Putting Together the Customer View
Now that we understand how the schema can be customized and how we’re empowered to organize our data however will best suit our analysis, we can start to consider what data sources we want to include.
Some of the most common data sources in this type of implementation include Web Data (usually SiteCatalyst-collected data from DataWarehouse), Customer data from your internal data warehouse, cost & impression data from your display ad management platform, paid search cost & impression data from SearchCenter or another paid search management platform, send/open/click data from your email service provider, and more.
We’ve developed the Unified Customer Process in Adobe Consulting to deliver this reality. It includes the process of discovering your available data and mapping it into a unified form, and the technical end of putting it all together in Adobe Insight.
Once we’ve combined all of those datasources, using the keys from each that tie the Customer’s experiences together, we’re ready to dive in and get some Insight, Results & Action!
Insight! Results! Action!
Before we organized all of our Customer data in Insight… back when we were just looking at Web data, we may have answered questions like:
“Which sites on which I’m running display ads delivered the most Revenue & Revenue per Visitor last week?”
Imagine the new metrics we might have available to us with our various data points about the Customer integrated…
- Ad Spend
- Ad Impressions
- In-Store Revenue
- In-Store Revenue per Customer
- Total Revenue (Web Site + In-Store)
- True Return on Ad Spend (ROAS)
Our analysis can suddenly look much, much different.
Suddenly, in one tool, we can see how our spend within a given marketing channel impacts our store revenue for customers who interacted in that channel, along with our Web revenue, the combined revenue, the return on our investment.
Often, this illuminates certain efforts — sites, placements, creatives — that looked great in the online-only view, but aren’t great in a whole-customer view. It also helps us see efforts that looked bad in the online-only view, but are driving real conversion or high lifetime value customer in the offline world.
And we can select, segment, & filter on any attribute we have in the dataset:
- Time (when the events happened)
- Attributes about the customer from my datawarehouse (age, gender, etc.)
- Browse behavior (products viewed, areas of the site explored)
- Other marketing tactics the customer interacted with
- And more…
As a result, here are some real life optimization decisions that some of my clients are making in optimizing their online marketing
- Re-adding previously cancelled sites & placements that looked “bad” from the Web-only perspective, but are actually high performers considering the offline channel
- Cutting sites & placements that looked “great” from the Web-only perspective, but show lower ROAS than others considering the offline channel
- Optimizing under-performing placements by testing new creative that’s working better at offline conversion in other similar placements
- Finding ways to reduce spend while increasing ROAS due to the multi-channel view
- And, of course, more…
Those are real, high-impact, marketing decisions & optimizations happening every day within client organizations that are employing a 360° multi-channel view of the Customer.
That’s Not All
I’ve shown some examples of visualizations with display ad data here, but don’t forget about all of the other marketing channel data you could include. We have clients actively using SearchCenter (paid search) cost & impression data in Insight, as well as many other marketing tactics… emails sent & opened, for example.
Still, perhaps you’re not quite ready to tie all of your Customer together in one place and gain these benefits.
If so, there’s another step you could aim for first, and it entails bringing just the data related to what your online placements are costing you into Insight and combining it with your website traffic data. I’ll explore that option next week.