Customer Data Management for Retail & E-Commerce

Data quality helps with cost cutting and consolidation, for more resilience.

Retail and e-commerce have experienced a rollercoaster of emotions in recent years. Most recently, online retail was the main beneficiary. Now, inflation, disrupted supply chains and geopolitical tensions are causing hangovers. Optimized customer data can cushion risks somewhat.

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Especially now that both online and brick-and-mortar retail are under economic pressure, cost discipline is becoming increasingly important. It is important to think about existing resources and to use them in such a way that, ideally, more can be achieved. This also leads to a generally presentable or higher return on investment.

Data quality is one of the important enablers here. Since the pandemic years at the latest, this has not only affected e-commerce, whose business is per se based on data and processes. Stationary retail is also much more affected than it was five to ten years ago. In times of lockdowns and contact blocks, it has conquered a piece of the digital world with initiatives such as Call & Collect or Click & Collect, among others. And with the combo 'ordered online and delivered to your home', it has penetrated further into online retailing, increasingly appropriating the digital business model of e-commerce, so to speak.

Challenges of the industry

In addition, as the example of Amazon shows, e-commerce also operates brick-and-mortar stores, while brick-and-mortar retailers are increasingly operating online. The boundaries are becoming blurred, although pioneers like Amazon are already closing their brick-and-mortar stores in some cases. Ultimately, the decisive factor, whether online or stationary, is to retain control over the data, to be aware of the value of the data, and to always maintain it in the sense of classifying it as a corporate asset.

"Data can do much more than simply be up-to-date, complete, correct and unambiguous. Quality-checked data is the basis for efficient processes in retail."

Here's how data quality supports:

Checkout optimization:
Quality-checked data makes it easier for customers to check out in e-commerce, for example, because data can already be transferred correctly for repeat purchases both on mobile and desktop. The abandonment rate decreases, sales increase.
Artificial intelligence:
Quality-optimized data can support the use of artificial intelligence methods. Because if the training data is optimized, the AI can work better.
Embargo check:
Depending on the area you are active in as a trader, business with certain people or countries is prohibited. Quality-checked data helps to check against sanctions and embargo lists.
Household formation:
If you know who your customers are based on data, you can start to cluster, summarize, form households. Gaining information, but also working with that information intelligently. Who belongs together? Who do I write to on what topic?
Personalization:
Both online and brick-and-mortar retailers can tap into knowledge from high-quality data to approach their customers in a personalized way, for example, in multi-channel marketing with the next-best-offer embedded in channel-sensitive messages.

"The effort required to check and correct data is small compared to the costs incurred due to incorrect order processing and lost revenue."

Konstantinos Konstantinidis, Senior Account Manager at Uniserv

"The effort required to check and correct data is small compared to the costs incurred due to incorrect order processing and lost revenue."

Konstantinos Konstantinidis, Senior Account Manager at Uniserv

Key challenges in retail & e-commerce

Quality-optimized data is an enabler in many respects and a success-critical building block in all customer-centric processes in retail and e-commerce. Customer data of appropriate quality ensures efficient and reliable processes and increases customer satisfaction, as these three possible use cases show:

More sales and lower costs through better checkout

The checkout is the phase in online retailing that determines whether a purchase is ultimately made or not. The abandonment rate is up to 70 percent. While hardly anyone in a retail store would think of simply leaving the filled shopping cart behind and leaving the store empty-handed, this happens in seven out of ten cases in digital retailing. This is unfortunate, because a lot of revenue is wasted.

There are many reasons for this, most of which are home-made. They range from a complicated, non-transparent checkout process to forms that are far too complicated and ask for data that is in some cases unnecessary and not legally permissible, to an internationally incompatible form structure. In addition, there is often a lack of tools to facilitate quality-oriented data entry. In many cases, merchants also force customers to create an account, even though data protection legislation expressly requires guest orders.

Retailers who follow a few rules and make changes to their fully or partially digital business will see a significant reversal in the trend of abandonment rates. And along the way, they will gain high-quality data that will then again support the efficiency of all processes.

Starting with guest orders, simple forms that mirror the country-specific address structure in terms of field layout are particularly helpful. German addresses, for example, are entered field by field, French addresses line by line. With targeted tool support such as auto-completion, a low-threshold, smart checkout process with a high level of acceptance can be realized through single-line entry, especially when mobile.

Improved value creation in headless commerce architectures

In IT, 'headless' describes an architectural concept in which the front end and back end are decoupled from one another. Headless e-commerce architectures are an example of this. The great advantage lies in their flexibility. Updates are possible in short cycles. Geographical or content variants of a front end can be rolled out quickly for a better customer experience.

Compared to classic ones, headless architectures are flexible and less complex. This makes them particularly attractive for e-commerce, for example in multi-shop systems. The focus there is on the customer experience. Online shoppers should have the best possible visitor experience. This can go so far that different versions of the user interface are displayed several times a day.

This is made possible by the decoupling of front-end and back-end. What the customer sees on the smartphone, on the screen or on his smartwatch is physically separated from the technical substructure. The presentation layer (frontend) and application layer (backend) are connected via REST APIs (application programming interfaces). Functions, for example for data quality such as address validation and auto-completion, can be integrated as independent services from the cloud. This means, for example, that the checkout can be smartly optimized in the direction of added value: fewer abandoned purchases at the virtual checkout, but more completed purchases with high-quality data.

Last mile - online a neuralgic point

Optimize the delivery of ordered goods on the last mile, i.e. on the way to the customer, with data quality functions. This is where criteria such as adherence to delivery dates, punctuality, and sustainability determine the overall impression of the purchasing experience.

Younger target groups in particular, such as Generation Z, are increasingly paying attention to sustainability and climate protection. Well-maintained addresses help to organize delivery more sustainably. Supplementary information, for example, such as parking permits, representatives, and local conditions help to avoid returns and multiple trips.

If route planning is also improved and neighboring destinations are served by one driver instead of two, or if packing stations are also included as a delivery option, a large sustainable whole is created from many small aspects. A lot happens on a small scale, triggered by data quality, and then it works.

The decisive factors for an intelligent last mile are complete and well-maintained customer data and efficient route planning in delivery. Quality data, in turn, adds value to this. However, this also requires central, holistic data management that brings together all essential customer information. Different delivery options should represent complementary information and make customers' preferences more visible.

This is exactly where our Customer Data Hub comes in: It breaks down data silos and consolidates existing data into a unified, centralized dataset with a 360-degree view of each customer as a complete data truth. This means, for example, that packing stations can also be recorded, as well as all circumstances at the delivery location, such as parking permits or specific delivery times.