The interconnectedness of tools and data in eCommerce
When you look at the world, what do you see?
At Froomle, we see systems and programs, each following overlapping rules as they interact with their environment. This is especially true within companies, where it is vital that we take our view of these interconnections a step further. Instead of following their interactions in a linear, consequential way, we need to start thinking of them in a circular, endlessly connected fashion. But what does interconnectedness mean for your company?
The need for transparency
Transparency is crucial to understanding the performance of your systems. Supporting this is data, with data pools feeding into data lakes to deliver a holistic view of your company’s performance and highlight the best way to utilize captured data to make better, informed decisions.
System interconnectedness automatically ensures feedback loops, allowing you to observe patterns and emerging trends, both positive and negative, and take appropriate action.
In addition to the benefits of the internal holistic view for your company, there are endless benefits for your customers. Truly interconnected software and systems ensure a smooth customer experience, which in turn boosts your brand and increases your revenue as your customers purchase more and more often.
Interconnectedness and 1:1 personalization
When we look at interconnectedness for 1:1 personalization, we see that every single customer interaction can be personalized. The technology is so advanced that we can personalize everything from content and products to entire websites, similar to the technology Amazon deploys. We can even implement voice personalization, with AI-driven robots carrying out personalized spoken interactions.
Nevertheless, eCommerce companies frequently choose to do one of two things that affects the overall customer experience and performance of personalization. Either they choose to personalize just one position on one of their channels and then ignore the resulting data insights, or they choose different providers for personalizing a variety of positions but don’t connect data from a variety of sources. Unfortunately, this results in losing the benefits of shared knowledge that comes from interconnectedness.
Choosing multiple suppliers can sometimes be the best choice. However, it is vital that all data feeds into the same company-wide data lake, so your chosen suppliers transfer the knowledge they learn about your customers to you, enabling you to make smarter choices.
Dashboards and reporting
Dashboards and reporting are as important as the data itself. Without sufficient, good quality data, the output of dashboards and reporting will not be helpful. But without strong, appropriate dashboards and reporting, it’s challenging to generate insights from good quality data.
Instead of a complicated, closed system, it is more productive to have all suppliers and departments feed their data into one large data lake. This can then feed multiple suppliers and generate insights that you use to create business rules that are swiftly acted upon by AI. Deep machine learning techniques can also improve processes, offer insights, and highlight emerging patterns that are unnoticeable to the human eye.
Interconnectedness in practice
In theory, the interconnectedness of systems, the importance of data, and the strength of dashboards are often clear. However, putting these ideas into practice can be challenging. To give an example that every retailer experiences: a potential customer visits your site and adds items to their basket but does not make a purchase. During this visit, you capture data on your potential customer and retarget them with a follow-up email reminding them about the chosen products. Unfortunately, this doesn’t often deliver outstanding results.
A more successful follow-up email strategy is to retarget each specific user with items that are liked or purchased by other users that display similar behavior to your target user. This can be easily done through a collaborative filtering technique that analyses data pairs. The team making the marketing email campaigns must then ensure that their suppliers have AI and deep machine learning capabilities to be able to create personalized recommendations based on user similarity to other existing users in your data set.
The best possible example of this in practice is Amazon. Their website looks different for every user, populated with different product recommendations, promotional banners, and welcome messages. Plus, they personalize retargeting emails, as well as carrying out a lot of other personalized actions. They can only achieve such a high level of 1:1 personalization because their departments share knowledge and information, using captured data to better understand their customers to provide an ever-improving experience.
Interconnectedness and personalization in your company
But Amazon isn’t the only company to be able to achieve such a high level of personalization. AI is now readily accessible, with a variety of solutions ranging from standardized to customized, all focused on process automation and optimization, to help eCommerce companies of all sizes achieve the highest possible conversion on each of their funnels. Interested in hearing more? Contact Froomle and discover how your company can benefit.
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