Will marketing clouds really solve your customer data challenges?
Before handing over your data woes, you still have to unify data and integrate the MC to your larger martech stack.
The problem of siloed customer data has stymied brands for decades. Despite major investments, marketers regularly lack access to the depth of data the brand itself has collected — at least not with the speed and agility required to deliver on the promise of 1:1 marking that brands have worked so hard to achieve.
Many marketing cloud providers claim to solve the problem by creating end-to-end marketing solutions — all your customer data in one place, connected to a full panoply of execution channels. But will an MC really solve your customer data woes?
For some reasons, an MC may still require you to stitch together data and connect it to execution systems — even after you thought you had handed off the problem once and for all.
- Internal silos. To build out their marketing clouds, providers are acquiring solutions at a breakneck pace. However, product acquisitions happen a lot faster than product integrations. You may find that an MC’s data and execution systems are still siloed, even years after purchase.
- First-party data. So far, MCs don’t have the sophisticated data layer required to connect easily or quickly to first-party customer data sources. Although every MC claims otherwise, in reality, this often needs extensive IT or consulting support — up to a year or more in complex environments. Even after all that work, marketers’ ability to access and interact with all that data remains very limited.
- Walled-garden approach. Most MCs are engineered to be a closed ecosystem. This means that enterprises cannot deploy best-of-breed solutions to stay competitive without risking data and channel fragmentation. Of course, this gives MCs tremendous pricing power over their customers.
Before handing your data woes over to an MC, you should understand the extent to which you will still be on the hook to unify data and integrate the MC to your larger martech stack. Here are three critical questions to ask providers:
1. Does your MC currently extend to all your critical customer channels? Are channels within the MC fully integrated, i.e., do they support omni-channel orchestration driven by a standard, complete set of data?
The elimination of data and execution siloes is critical to achieving 1:1 marketing. Silos inevitably slow campaign execution or hamper extreme personalization — or both. Be aware that many MCs don’t extend to important customer channels such as direct mail, customer service, or best-of-breed web and mobile channels. Even when all these channels are contained within a single MC, they often remain a series of disconnected data-plus-execution siloes. This may leave you with the burden of unifying and orchestrating to customer channels — even within the MC itself.
2. How much time and work will it take to integrate a new customer data source or marketing execution system not native to the MC?
If you are committed to both a best-of-breed strategy as well as 1:1 personalization, the adoption of an MC will require stitching together the MC with solutions that lie outside it. Even if you decide to put all your eggs in one MC’s basket, you may still end up with the same problem, if the MC doesn’t extend to channels critical for your brand, like direct mail, customer service, etc. It is essential to understand exactly how much time and effort data and systems integration will entail. You don’t want to end up with yet more silos because you lack the time or resources to build the proper integrations.
3. What percentage of your total customer data will marketers be able to leverage when building campaigns? Will they need technical resources to do so?
You want to be able to use all your customer data, every time. For example, a customer’s real-time browsing behavior is great information in determining their interests, but what if you could:
1. Combine that information with that customer’s purchase data from the last six months ago.
2. Use AI to intelligently identify where in the customer journey each customer is and where you need to take them next?
Even if you do the work to break down data silos, be aware that most MCs don’t have the data layer necessary to handle the depth and breadth of data — and certainly not at the speed — required for 1:1 personalization.
An intelligent hub across all your data sources and channels
If you think an MC can solve all your customer data problem once and for all, you may need to think again. Fortunately, an enterprise-grade customer data platform (CDP) provides a simple and elegant answer to all three of the questions above. It does so by providing an intelligent hub that takes in raw data from any source and pushes actionable data out to any customer interaction channel.
However, a true enterprise-grade CDP is not just a series of data pipes to connectors. It also enables marketers to interact with all the data, quickly and intuitively test ideas, build and validate segments, and orchestrate them across any and all channels—without waiting on technical resources.
Not surprisingly, many brands investing in MCs are complementing them with CDPs to:
- Integrate de facto channel silos within the MC.
- Connect MC to best-of-breed systems that lie outside the MC itself.
- Empower marketers with the speed, agility and depth extreme personalization requires.
- Control costs by accelerating first-party data into the MC and gaining flexibility to configure their martech stack to fit their needs.
Whether or not you commit to an MC, best-of-breed, a combination of the two, or any other configuration, the right enterprise CDP is engineered to support the three key capabilities that 1:1 personalization demands:
- Speed — the ability to detect and act on customer behaviors in near-real time—and for marketers to build and execute intelligence-driven, cross-channel campaigns in days, not months.
- Agility — the ability for marketing to access and act on all that data, respond nimbly to individual customer behaviors (e.g. browsing or purchasing) as well as market opportunities and iterate quickly per results from testing and measurement.
- Depth — the ability to work with unified, full-spectrum data—both historical and real-time data, enriched by actionable AI and other advanced analytics.