When it comes to customer data, we’re undergoing a period of massive change. eMarketer reports there are 2x the number of Internet connected devices today as there are people; by 2020, that number will be almost 5x. Meanwhile, according to the Winterberry Group, organizations use on average more than a dozen distinct SaaS tools, with some using as many as 30 tools. That’s a lot of data sources and outlets.
The classic 3 V’s of big data, volume, velocity, and variety, still apply today, but in different ways, shapes, and forms than in the CRM and web eras. With mobile, there is much more data being created passively via the radio, and thus generating and transmitting all the time. The variety is much greater than even on web because of all of the device telemetry data, the geospatial element, as well as the native data points. The velocity is also unlike anything we’ve ever seen since data is being generated with every single movement and swipe.
But what makes the current moment so challenging isn’t just the data itself. The software deployment model, how data gets onto these remote devices, and, most important, the end use cases are all different, too.
The dominant interaction types on most of these new devices is apps, and those apps themselves are fundamentally different from browsers. They are compiled code and shipped software that live locally on your phone, TV, Kindle, etc. They are decentralized from the browser, with no agile development. On the other hand, because native apps can access and leverage device functionality, they can more readily access features like new cameras, accelerometers, barometers, glass to support 3D touch. All of this innovation in hardware has the potential to create a much richer experiences for end users.
New data types such as push tokens and exceptions never existed in web environments yet are now paramount for success. Conversely, all this incremental data collection, when not managed properly, can add significant overhead, risk, and complexity into an app experience.
Taken together, these changes have serious implications for marketers. For example, in a world where the mobile device is now the hub for every step in a customer’s journey, marketers need converged, multi-purpose data platforms, not just ad platforms masquerading as customer data platforms, to take advantage of “through-the-line” opportunities that exist on mobile. At the same time, people are multi-tasking, engaging with customer support reps, and, yes, still buying in retail stores, and all of that needs to be taken into account, too.
Data convergence in the multi-screen era doesn’t mean necessarily that we’ll have an “all in one” monolith for ad serving, email, social, web, and so on, but it does mean that these efforts will be joined at a business and data level like never before.
In the face of these challenges, companies need to change how they handle data at every step along the way, yet they can’t just hit the pause button on business as usual and overhaul everything in order to do that.
Here are four steps companies need to take in order to not just survive, but thrive, in this new era:
Defining a strategy is typically the first step in any endeavor, and data management is no exception. To define your data strategy, you must:
● Identify your goals: Do you want to improve growth, retention, audience insight? Whatever it is, be clear.
● Map your data: Map your data to your goals by considering factors like KPIs, how you’ll think about segmentation, and what your engagement triggers will be.
● Create naming conventions: Your naming conventions should be clear and simple to understand. Far too often, organizations use inconsistent and/or difficult to understand naming conventions, and that can wreak havoc down the line.
● Build a hierarchy of user IDs: As we move away from anonymous web tracking, take advantage of the data that’s available — including identities — to develop an omnichannel understanding of customers.
● Outline your use cases: Determine how you will use the available data to achieve your goals by clearly outlining your use cases.
● Align use cases with technology: Ensure a clear alignment between your use cases and your technology stack and consider what your stack needs to look like in the future to help solve for your core business challenges.
● Remember privacy: Privacy is a trade-off of personalization, and you must have the right privacy controls in place to respect your customers’ requirements.
Your data collection process can impact both your users’ experiences and your ability to take action. With that in mind, your data collection process needs to:
● Do no harm: Collect data once and do so in the right way in order to avoid bloating your app with unnecessary code that can degrade the user experience.
● Be consistent: Accept no compromises in capturing data consistently across all screens, but remember to account for the native data types on each. Failing to account for these native data types can lead to an 80/20 scenario where you have 80% of the data but are missing the 20% that is native to each screen, and it’s that 20% that typically drives the overwhelming majority of value.
● Make data capture use case agnostic: Step back and think about the bigger picture. While execution should be highly use case specific, having a single source of truth that can support diverse use cases will stand the test of time.
The value of a data platform should go beyond the sum of its parts. The best way to inject additional value into the stack is to enable greater control of data through filters, enrichment, and segmentation. To do so:
● Be diligent about converting signal to noise: Don’t pollute downstream systems with an abundance of data. Limiting what you send makes your analysis easier and your costs lower.
● Merge identities around a single customer view: Augment direct matches with data science, but only after maximizing user identity matching.
● Enrich data to get a more complete view: Bring data feeds in from all of your SaaS tools as well as from relevant third party tools.
Finally, you need to simplify the process of operationalizing data to all the different endpoints across the entire lifecycle of your business. This simplification requires you to:
● Empower end users to move quickly
● Sync continuously to avoid wastage
● Bring data back “in” from executional tools to learn and optimize
When thinking about data connections, you also need to keep in mind the fast pace of change in today’s environment. The vendors leading the pack can change at any time. One of the great benefits of having a data platform detached from execution is the ability to add execution and analytics tools as needed onto a central data hub, as well as remove those wants that are no longer needed without losing historic data.
In the multi-screen era, a reactive, tool-centric approach is simply not an option. It adds significant cost, complexity, and risk to your business. Ultimately, you end up with a tangled web of client and server side integrations that leads to unnecessary overhead and creates user experience, privacy, and security challenges.
That’s why you need to spend time thinking about all of your data use cases across marketing, analytics, data science, attribution, CRM, help desk, you name it, and build a data strategy to support them holistically. Following the above-mentioned steps will enable a truly 360-degree view of the customer that’s not only insightful but also meaningful to the business.
Michael Katz and David Spitz are, respectively, CEO/Co-Founder and CMO of mParticle. This blog post was adapted from their October 2016 webinar of the same title.