Customer relationships are as important in ecommerce as they’ve ever been in traditional retail — perhaps more so, because online merchants are competing in a global battlefield, meaning that providing good store experiences really matters to companies operating in this space.
Having a brick-and-mortar store in the right location with the right aesthetic can be enough to pick up some easy sales, but the closest online equivalent to having a good location is ranking well for prime keywords, and that’s much harder to achieve.
Data analytics, meanwhile, fuel the engine for large-scale growth. The accessibility of rich data on every trackable element of online activity (and that’s a large pool) is what allows businesses to comprehensively optimize their operations. Using it to full effect, today’s big brands are persistently finding new and smarter ways to improve efficiency.
So what’s the connection between these two mainstays of the modern merchant? They seem disparate — one emotive, nuanced, seemingly analog, and the other objective, exact, firmly digital — but they inform and contextualize each other. Follow along as I provide a more in-depth look at what I mean by this:
When machine learning first started getting some traction in mainstream business, it met with some resistance from old-fashioned types who didn’t see its value for complex processes like customer relationship management. After all, they reasoned, people are difficult enough for other people to understand — they’re capricious, awkward, often ignorant, and unpredictable.
On the other end of the spectrum, those who were firmly on the digital bandwagon might have considered the investigation of specific customer cases to be unnecessary. When you have pulped and parsed data from hundreds or even thousands of customers, what’s the point in getting hung up on specific events? That’s how you miss the wood for the trees.
What smart retailers eventually realized, though, is that the inferred distinction is false. It isn’t a matter of analog versus digital — it’s a matter of micro versus macro, and if you want to make the most of your online sales opportunities, you need to consider both. Here’s why:
Let’s say (as is entirely plausible) that you either run or work for an ecommerce business. Your system is configured to gather information through Google Analytics or whichever analytics platform you prefer, so your database continues to grow. You thus have access to countless metrics of varying significance: everything from dwell time to average conversion value.
You can spend hours poring over that data, but that time won’t necessarily produce any notable conclusions. The problem? A lack of context. When you look at performance metrics, you see only fragmented reflections of complicated situations. You see how many pages the average visitor looks at, but you don’t know why, so you can’t know what (if anything) it means.
The value of an involved customer relationship management process, then, is in giving you the framing you need to make sense of your data. The more feedback you gather from your customers, the more you can read into your metrics — whether you’re able to understand why a certain page is performing so well, or left with a curious disparity between what your customers say and what your data says (this can be remarkably illuminating, because people are often mistaken when trying to detail their own behavior).
On the other side of the coin, of course, you have the prospect of dedicated customer relationship managers trying to succeed using little more than direct interaction, buyer personas, and nebulous concepts such as gut feeling. It’s extremely valuable to know how to interact with people, yes, but it simply isn’t enough if you’re trying to achieve optimal results.
As noted, we’re not that great at explaining (or even remembering) our own actions. Think about your favorite websites, ecommerce or otherwise: why do you like them so much? Are you absolutely confident that the reasons you can cite fully explain your attachment? Do you recall precisely why you clicked on the link to this piece? However certain you are, I’m inclined to suggest a sliver of doubt. Our subconscious minds heavily shape our decisions.
But the issues with self-assessment don’t stop there. Consider that the human race is excellent at self-deceptions, be they harmful or innocuous, and a great example is that of musical taste. In principle, we should all just accept that we like whatever music we like, and feel no shame or guilt about it — but that’s not how culture works.
Here’s an specific scenario: let’s say that you regularly browse and buy from a music store (whether involving physical media or digital downloads), and one day someone from the store reaches out to you for some feedback about your store experiences. Because you’re embarrassed about your musical taste and you feel confronted, you offer a warped account of the features you like and dislike, resulting in feedback that won’t be too useful.
Our retail habits can be deeply personal and emotional, so our recollections are invariably sullied by ambiguity, redaction, and creative reimagining. But analytics data doesn’t degrade, and while it can be unclear, it can’t be mistaken. Only by using it to underpin your CRM can you establish the foundation you need to achieve consistent results.
In some ways, you can draw a parallel between this link and the one that binds all marketing channels together in modern multi-channel marketing. Retailers that would once maintain distinct departments for distinct tactics — a social media team, an email marketing team, a paid advertising team — now understand that they’re better understood as parts of much broader customer journeys.
Similarly, it isn’t ideal to have a team concentrating on CRM and another handling data analytics. Data analytics should be the connecting tissue between all departments as opposed to a pursuit in itself, because context-free data holds little value, but contextual data is key to turning a small online store into an international retail powerhouse.
Aiming to get more from your ecommerce store, then? Start concentrating more strongly on weaving analytics through everything you do. It’s become highly accessible in recent years, particularly for those using high-end solutions such as Magento’s Commerce Cloud edition or Shopify’s multi-channel centric Shopify Plus — especially since each one (along with various other retail platforms) has straightforward API connectivity with Adverity DataTap. So what are you waiting for? Stop treating data as a gimmick, and start getting results.
Overall, then, the link between ecommerce customer relationships and data analytics is simple: it’s codependency. You can’t have valuable ecommerce data without understanding the customers that produce it, and you can’t manage customer relationships effectively with no way to reliably track the consequences.