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Customer Retention. Automated

Front End vs. Back End Data: What's the Difference, and What are the Benefits of Combining the Two?

Posted by Kyle Marion on Sep 6, 2017 7:00:00 AM

In the world of predictive marketing, your messaging is only as powerful as the data that powers it. But where does that data come from?

e-Commerce retailers pull behavioral data about their customers from two different sources: front-end and back-end. Read on to learn what defines these two types of data, what the benefits are of using each, and why it is so beneficial to use them in tandem!

Front-End Data Defined

This is data that is gathered from the customer-facing, or “front” layer of the system -  the website code itself.

JavaScript code can track any number of interactions a customer can have with a website, such as viewing specific products, completing purchases, and entering email addresses into a form. This information is sent back to a marketing platform in the form of “events,” where it can be stored or used to trigger messaging.

 Back-End Data Defined

This is data that is transferred directly from the e-Commerce platform’s database. This database serves as the master record for order history and product information and is not typically accessible to customers; thus, it is referred to as a “server side” or “back-end” data layer.

Why Should I Use Front-End Data?

There are three primary benefits to using front-end data:

01. You get it in real time

Back-end database integrations are typically limited in how often they can grab new data. The reason for this is that they require a series of connections to the server itself; for each new chunk of data this integrations wants to obtain, it must send a request to the server, make a connection, and then receive the data queried from the database. If this were to happen constantly, it would weigh heavy on the server’s capacity, and likely cause the website to crash.

Front-end data, on the other hand, does not need to engage the store’s server; it simply transmits data directly from code on the website. No requests are needed, no connections must be established and closed - it is a simple process that can provide marketers with real-time data without hurting site performance.

02. It gives you more than just purchase data

Back-end purchase databases, by definition, only contain data that relate to orders that have been placed. But what if a marketer wants to, say, convert customers who create carts without completing their purchases, or repeatedly view specific products without pulling the trigger?

This is another area where front-end data shines. JavaScript code can be customized to track any number of on-site behaviors, regardless of whether or not these behaviors result in purchases, making this type of data collection a great mechanism for marketers to expand the type of data they have at their disposal.

03. It’s easier to set up

Lastly, there is a practical advantage to front-end data integrations - they can be relatively straightforward to set up.

Back-end database integrations generally require multiple,otherwise-incompatible platforms to play nicely with one another and automatically pass information back and forth in a manner each platform can recognize. As you might imagine, this is no small feat.

Setting up a front-end data integration, on the other hand, can often be as simple as copying and pasting pre-written code onto a website, with minimal customization required.

Why Should I Use Back-End Data?

Front-end data certainly has its value and place, but back-end data remains the backbone of the predictive marketing world. Why? Three primary reasons:

01. Reliability

Simply put: if you are pulling directly from the master purchase database, you can sleep soundly knowing that you’re working with accurate data.

Relying on only JavaScript to provide you with purchase data opens you up to a myriad of risks:

  • Scripts can’t catch everything. If a customer doesn’t allow cookies, has JavaScript blocked, or even just navigates away from a page before the scripts load, then you’ll miss out on their data. Aside from resulting in inaccurate metrics, this leaves open the possibility of embarrassing mix-ups like sending a “We Miss You!” email to a customer who purchased a week earlier using Incognito Mode.
  • Scripts can have errors. It’s also possible that interactions between your tracking code and other code on your site will produce errors, and prevent it from running, resulting in the same consequences as the above.
  • Scripts fire upon page loads. This means that every time someone reloads their order confirmation page, your scripts will tell you that they’ve placed a new order - when, in reality, they probably didn’t actually buy that same pair of shoes twelve times in a row yesterday.
  • Scripts offer no permanent storage. Scripts are useful for capturing live data, but this information still must be translated to another database for storage. Otherwise, your precious customer data will simply disappear into the ether!

02. It paints a comprehensive picture

Front-end data can tell you what is happening now, but it can’t tell you what has happened in the past - or use that past data to predict the future.

Historical data is a hugely valuable asset, and one that lets marketers not only build profiles around specific customers for one-to-one personalization, but analyze macro-level patterns to unearth actionable correlations and trends.

It’s important to note that even “real-time” campaigns that seemingly do not require back-end data, such as abandoned cart recovery, are limited without historical data to draw from. A cart recovery campaign that is powered only by front-end data can remind a customer that she left a facial serum in her cart, but it cannot scan years of purchase data, unearth significant correlations between purchases of that face serum and a particular eye cream, and dynamically recommend this eye cream as a cross-sell item, thus increasing the order’s potential value.

03. It lives in a more secure data layer

By definition, front-end information is more visible, as website code is publicly viewable. This can raise potential security concerns around accessibility of data. Back-end database integrations, on the other hand, exist within a private data layer, and are more secure.

However, when front-end integrations are done correctly, this risk is minimal. Two important boxes to check here are:

  • Front-end integrations should only send data from the browser, never to the browser. This prevents any possibility from Customer A’s data being exposed to Customer B.
  • Potentially damaging information, such as payment details, should not be tracked unless absolutely necessary (and should be encrypted if so).

Ensure that your front-end integration abides by these best practices in order to safely avoid any security risks to your customers.

The Benefits of Combining Front-End and Back-End Data

Both types of data integrations are extremely useful, in that each fills certain gaps that the other cannot. So, how do you decide which type of integration is best for you?

Good news: you don’t have to choose. The most effective and comprehensive predictive marketing programs rely on both front-end and back-end data to best serve their customers.

This combination provides the benefit of transient, real-time data (that doesn’t necessarily make its way into the purchase database), while also building a rock-solid repository of product and order data - including historical data - by drawing straight from the source.

By using front-end data as a supplement to a back-end database integration, rather than as a substitute for it, you receive the “flash” of real-time data and new, cutting-edge behavioral triggers without sacrificing the steadfast reliability and big-picture comprehensiveness that only comes from drawing directly from the master database itself.

At Windsor Circle, we specialize in helping retailers build these combined integrations quickly and accurately, and helping marketers use their newfound trove of diverse data to personalize their marketing communications and generate an average 20% lift in revenue. Contact sales@.com today to learn more about how we can help make your data work for you!

Topics: Customer Retention, Data Science

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