To date, the world of eCommerce marketing has largely been confined to all things digital. But as the bar for retail experiences keeps rising, the segregation between online and offline needs to come to an end.
It’s Time to Change How We Think About Online vs. Offline Retail
Let’s be clear: There are major differences between online retail and offline retail. The mindset of shoppers differs in the digital and physical realms, as do their behaviors and the overall shopping experiences.
But it’s exactly those differences that help create a very symbiotic relationship between eCommerce and brick and mortar retail. For example, Kohl’s discovered that closing stores impacted eCommerce as well as brick and mortar revenue in those markets.
Meanwhile, studies on consumer behavior reveal interesting relationships between researching and purchasing products across channels. In some cases, particularly for highly considered purchases, shoppers want to “see, touch and feel” products before buying. This desire leads more than 55% of consumers to visit stores before buying online. In other cases, consumers are already in stores and turn to online channels for validation, with Bazaarvoice reporting that 82% of smartphone users consult their phones on purchases they’re about to make in store.
Now, it’s time to continue moving this relationship forward by using offline retail data to elevate eCommerce marketing efforts.
How Offline Data Can Elevate Your eCommerce Marketing Efforts
If your eCommerce marketing team isn’t already ingesting offline purchase data, it’s time to get those feeds in place. Because once you do have that data handy, there are several steps you can take to elevate your eCommerce marketing efforts.
Some of the top use cases for offline data in eCommerce marketing include:
1) More Holistic Reporting
In today’s cross-channel world, consumers move fluidly from one channel to the next. They might move from Facebook to your website before opening an email and then finally making a purchase in store. How do you tie that in store purchase to online marketing activities?
If you ingest offline data, answering that question becomes much easier. In general, combining offline purchase data with online engagement data can provide a more complete and accurate picture of how various marketing activities across channels influence customer behaviors and purchase decisions.
2) Deeper Customer Insights
When you marry online and offline data, you suddenly have deeper insights into your customer base. For example, you can see a breakdown of customers by channel preference to identify groups of online-only, offline-only and multi-channel shoppers. From there, you can understand how these groups of shoppers behave differently and adjust your marketing strategies accordingly. For one beauty retailer, this type of analysis revealed that online and offline shoppers require different types of post purchase recommendations down to the products featured and the timing for those messages.
Additionally, given previous findings that multi-channel shoppers spend nearly 500% more over their lifetime, you can develop a strategy to get online shoppers into stores and vice versa. To do so, you might offer offline shoppers free shipping or encourage your online shoppers to buy online and pick up in store to get them in the door.
3) Increased Replenishment Opportunities
Intelligent replenishment models that track customers’ individual buying cadences and automatically trigger restock reminders have made running replenishment campaigns for online shoppers easy and effective. But what about all of your customers who shop offline?
Bringing in offline purchase data eliminates this blindspot by making it possible to run a variety of post purchase campaigns, including intelligently timed replenishment reminders, that target customers who buy in stores. As a result, this data allows your eCommerce marketing team to engage with more customers and do so with more timely and relevant messages.
4) Improved Location-Based Marketing
In most cases, your offline purchase data will also include information about customers’ locations, which can create valuable opportunities to engage them with more relevant content and offers. For instance, you can easily promote local store events over email and evaluate product recommendations based on seasonality in each region. Further, if you sell any type of local goods — be they products from local artisans, team apparel or anything else — you can use location information to send more targeted promotions (and do so in a timely manner, such as sending an email with your Eagles gear to Philadelphia shoppers after a big win).
At a higher level, you can also use the location data to identify regional trends in shopping behavior. You can then use these insights to better personalize your online marketing across channels.
5) More Intelligent Predictive Models
Across the board, enriching your online data with offline data can make all of the metrics you track more complete and, therefore, more powerful. This is especially the case when dealing with predictive models, which become more intelligent as more data becomes available.
Consider customer lifetime value. Going forward, it will be critical to measure customer lifetime value based on predicted future spend rather than past spend alone. Predictive models assign customer lifetime value by tracking signals like frequency of site visits and browsing history. Adding offline purchase data to the mix of signals can strengthen this model by adding even more insights on which to base its predictions.
6) Better Insight Into Loyalty
Loyalty programs have dominated retail conversations recently, and the shift toward smarter loyalty programs shows no signs of slowing down. If you’re thinking about building a loyalty program or adjusting what you already have in place, combining online and offline data can help in a big way.
Specifically, the combination of online and offline data paints a complete picture of customer behavior and preferences, which is critical for constructing what loyalty means for your brand. Armed with that information, you can build a loyalty program that best suits customer preferences and encourages behavior that aligns with those of your most loyal, highest value customers.
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