Establishing a Data Foundation for Omnichannel Digital Engagement
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If you are a retailer, the question to ask yourself is can you put your customer in a segment that includes their purchase paths, purchase journey and cycles, channel engagement patterns and frequency? If you can do this, then you are maximizing your data sources and should be able to get predictive with your customers and truly cater to them on a personal level. The term "omnichannel" has been around a long time, but the definition of it has become broader as communication and commerce channels just keep increasing and, in some cases, completely merge.
The customer journey of today transcends traditional channels as consumers have become more channel agnostic. Convenience, availability, and proximity are equally, if not more important than price and brand. As a result, retailers are finding that they need more customer visibility across all their channels. Whether in-store, online, in-app, or social media, customers expect the ability to have a seamless, continuous experience across the brand or device. Research shows that businesses that adopt omnichannel strategies achieve 91 percent greater year-over-year customer retention rates compared to business that do not, according to a survey conducted by Aspect Software.
Achieving a unified, omnichannel view of the customer can be inherently complex. This requires integration of multiple data sources from household-level data, transaction logs, attribution sources and insights from multiple digital properties and channels. But most retailers have an immature capability to capture and monitor data across all their channels and lack the ability to intersect with their customers in real time, or more importantly, in a timely manner.
By integrating all these data sources, it is possible to identify buying patterns that help develop customer segments and customer profiles. Based on the customer profiles of each segment, retailers can tailor communication to their customer to foster relationships by personalizing messaging in relevant, meaningful ways to deliver content, promotions, offers and communication at exactly the right place and time. This segmentation allows for the streamlining of advertising and marketing efforts and spend, leading to higher ROI and LTV per customer.
Let's take a look at a few steps important steps when considering building a foundation to create a true omnichannel digital strategy.
The first step is understanding the difference in the story you can tell about your customer today and what you would like to tell. It's someone's responsibility to own the governance process around this type of data for any organization. If you are a marketer, this person will be your new best friend. This process starts by looking at what data is available today by taking inventory of all data sources, systems and repositories and understanding how they connect and any hurdles in unifying this data. The big questions are:
- Where is this data coming from?
- Where is it stored?
- What needs to happen to create a loop?
The next step is determining what is missing from your customer data. Think about those stories that you want to tell about your customers and what you learned in step 1. It's time to take that information and layer it over your customer profiles and channels to understand where the gaps are. In most cases your company will have central a repository (warehouse or lake) that everything is being sent to where the data can be queried to tell your customer stories. And separate groups within your company are going to have their own stories that they are looking to tell.
One you understand where the gaps are in your stories you can start to develop a plan for what is needed to improve or fill out your data profiles. A few examples of these could be:
- Visibility into mobile app or web behaviors
- Attribution intel
- Transaction details
- Marketing campaign engagement
At this stage it is important to understand if there are any data gaps along with gaps in the systems themselves. From here you can start planning what an ideal data model looks like and what tools might be necessary to fill the system gaps. These system tools range from a customer data platform, mobile analytics platforms, customer delivery platforms, attribution tracking and more. Work with your data governance lead to build out your ideal model and tracking plan.
Once your data model and tracking plan are complete its time to transition into a more robust data dictionary that holds the definitions for the characters in the stories you want to tell. At this stage your team is either implementing with existing systems or you are beginning the process to identify third-party solutions for the tools mentioned above. All of which, we can help with!
Once assembled, with data flowing through your stack you should have well documented sources and destinations that create a visibility loop. More complex setups may include both a data warehouse (where you can do structured analysis on your customers and create cohorts and triggers to kick off engagement workflows across any of your channels) and a data lake where you can perform unstructured analysis on your customer data that can also send additional attributes downstream to your warehouse.
Companies with extremely strong omnichannel customer engagement retain on average 89% of their customers, compared to 33 percent for companies with weak omnichannel customer engagement. (Aberdeen Group) It's not difficult to derive actionable insights from customer data and analytics, but it does require careful planning and a small arsenal of 3rd party tools. But this type of granularity is required to understand the intersection between marketing communications, online channels, and physical stores. With these insights, so many aspects of your customer engagement strategy can be optimized and automated for a full 360 degree view of your customer.