Data-driven commerce: How to successfully use data for your business

What are the application scenarios for data-driven commerce?

The analysis and evaluation of user data has long been established in e-commerce. Here you can see where data-driven commerce is frequently used and what it offers:

• Optimized supply chain: Items are linked to relevant KPIs (key performance indicators). If demand for a product falls below a certain level, it is automatically removed from the range. If the opposite is true, it is reordered in good time.
• Programmatic advertising: Algorithms use available user data to display banner ads or commercials on websites that are specifically tailored to the user.
• Next Best Action: Automated suggestions for further actions, special prices, complementary offers, the type of customer contact (e.g., email or phone), or the intelligent management of online campaigns are generated.
• Segmentation of customer groups: Which customers look at which products and for how long? This provides an opportunity to tailor future collections to the tastes of specific customer groups.
• Dynamic pricing: Companies can use data analysis to adjust their prices to the current market and competitive situation, as well as to availability and delivery conditions. This even works in real time.
• Churn management: AI-supported data models warn—for example, in telecommunications companies—of the possible loss of customers. Sales and marketing can then implement targeted customer loyalty measures to retain these customers, for example, with individual discounts.
• Personalized customer journey: AI-supported 1:1 personalization enables targeted communication with users through individual offers across all channels and touchpoints—in short: the right product at the right price at the right time.

What role do customer journey and AI play in data-driven marketing?

Until now, data has tended to be used to optimize specific sections of the customer journey. But every online marketer's dream is a consistently personalized customer journey. This is where the full potential of data-driven commerce lies.

Meet customer expectations with a personalized customer journey

The reason: the ability to personalize the entire customer journey opens up a huge opportunity for customer loyalty. If you have enough data from the customer's previous visits to your store, you can eventually accompany them throughout their entire journey through the store with personalized communication and tailored offers – from the first click to checkout and after-sales. In short: pure personalization!

This perfectly matches today's customer expectations: your shop visitors want to be noticed and receive personalized advice. They want their digital tour of your product range to give them the same pleasant feeling they get in a brick-and-mortar store, where a salesperson values them as a regular customer and provides them with detailed advice. Customers who feel this way will visit your shop again and again. Thus, the personalized customer journey brings the vision of the perfect digital customer experience with a feel-good atmosphere within reach.

Personalization throughout the entire customer journey creates a comprehensive experience and improves customer satisfaction.

Gaining smart data thanks to machine learning and AI

But in order to really take care of potential buyers in this way, you naturally need a lot of data from them. And even that data does not initially provide any truly useful information. The trick is to analyze these data sets— also known as big data — in such a way that patterns and correlations can be identified and smart measures can be derived from them.

This is where artificial intelligence (AI) and machine learning come into play. AI tools and algorithms are used to analyze the available data and, based on this, carry out individualized actions in the online store. In addition to individual product recommendations, these can include the selection of relevant payment methods. Discount logic can also be developed and applied—again, fully automatically and in real time.

The advantages of AI tools and personalized customer journeys are obvious: user communication becomes more individualized and offers can be tailored and presented in such an attractive way that the result is both strong customer loyalty and excellent conversion rates. It cannot be said often enough: the future of e-commerce lies in a customer experience that puts a big smile on customers' faces as they browse an online store – and save the URL to their favorites.

Is data-driven marketing also suitable for my business?

We have good news for you: even smaller online shops can significantly improve their performance with data-driven marketing. It is no longer a large budget that is decisive, but rather the intelligent use of data with the help of the right tools. Even if you only have a small marketing budget, data-driven commerce can help you reduce wastage, increase your ROI (return on investment), and ultimately save costs.

It doesn't have to be a sophisticated data-driven marketing strategy spanning several quarters. Even small steps can bring about amazing improvements. One useful tool, for example, is the so-called ecommerce search engine function. This collects and analyzes search entries and draws conclusions about the preferences and needs of the user.

At the same time, future search results are automatically optimized. Even this small feature significantly improves the customer experience and has an impact on the conversion rate. So, just get started!

With the help of an ecommerce search engine, search results can be customized according to personal preferences.

What are the first steps?

Below you will find the most important steps for getting started with data-driven commerce. Artificial intelligence (AI) forms the basis, as it offers a wide range of possibilities for better understanding your customers' needs, making predictions about their behavior, and providing personalized content across all channels and devices and throughout the entire customer journey.

1. Assessing the current situation, goals, and tools

What is the current situation? What data can I already access, and which key figures should be analyzed? At which points in the customer journey can I obtain reliable data? Which tools are right for me? What are my goals?

These are all questions you should ask yourself first. Then implement the necessary tools for data collection and select the metrics and data sets that are relevant to your goals.

2. Start with a small application scenario

Where could personalized content be displayed, and where do all shop visitors currently see the same thing? If there is a range of possible content, AI can optimize the selection. An A/B test ensures that the optimization is based on the status quo. This is because A/B tests allow you to test content or strategies against each other. The resulting conversion rates then provide information about the relevance of the content or offer for the corresponding user segment.

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The most important basis is, of course, your data collection. It's like a garden: you have to be patient and keep weeding until it bears fruit. Applied to the online shop, this means that you adopt the more successful version of the A/B test and monitor it over the next few weeks. Collect data over a longer period of time and observe the figures and measurements that the tools display. It is best to define reporting periods in which the metrics are viewed and exported.

3. Develop a strategy and stick to it

After a certain amount of time, you will be able to recognize patterns, valleys, and peaks (such as seasonal fluctuations, trends, and currents) in the data analysis. With each cycle, each data analysis, and each optimization, a clearer strategy emerges.

At this stage, it is essential to continuously adjust goals and KPIs in order to identify weaknesses in the strategy and improve them. Ultimately, the strategy can then be implemented across multiple channels, such as email marketing, personalized newsletters, and banner ads.

Personalization can be used, for example, in the form of relevant product recommendations in emails.
(Source: Screenshot email from gepps.de)

What are some useful tools to get started?

There are many tools that can help you with data-driven commerce. Here is a small selection:

• Data analysis: Google Analytics, Google Search Console, or Searchmetrics Suite
• Social media monitoring: Buzzsumo, Oktopost, or Buffer
• Marketing Automation: HubSpot, Silverpop
• Reporting: Google Data Studio, Domo, Tableau, Qlik

Data collection and analysis in data-driven commerce never stops. Analytical methods and models are constantly optimized and evaluated to improve data quality and update the strategy accordingly. Staying on top of things is everything!