Being a retailer In the „Age of Customer“, how do you manage to stick out of the mass and outshine even the big players in e-commerce? The key to success for your online shop is no longer just a matter of excellent usability, but primarily customer centricity: you can truly enthuse your customer by putting him in the spotlight consequently. This also includes individual, situational approach through customization as well as playing highly relevant (emotionalizing) content. Supported by inspiring content marketing as well as excellent customer service you can create the ultimative shopping experience (customer experience, CX). Yet, even though this winning formula is well known theoretically among many online-retailers, its practical implementation is all too often inadequate. This way any provider squanders not only valuable potential but also a promising opportunity for their online shop to obtain an exclusive market position
In our four part series we show you valuable practical tips on how to avoid precisely that and how to impress in the above mentioned online areas. Here you can read Part 4: Me-Commerce: For Each User An Own Shop.
Me-Commerce: A Personal Shop for Every User
We have dedicated the last part of this series of articles to a key factor that is essential for success in implementing all of the previously mentioned online disciplines (feel-good atmosphere, customer-centricity, emotionalization): shop personalization. This represents the critical success factor for the ultimate shopping experience of the future, because every online shopper wants to feel they are receiving a made-to-measure approach and individual advice. However, numerous hurdles mean that online retailers still have great difficulty in individualizing their shops. We will show you how you can win when competing with your rivals and how you can tailor your shop for each user:
Personalization 2.0: Offer Every User a Shop with Individual Relevance!
The common methods for shop personalization such as machine learning or BI tools are a long way from offering really individual relevance — primarily for the following three reasons:
Pigeonholing and Simplification:
Since classic personalization tools are not real-time capable, they need to save all the analytical findings and retrieve them all over again when a user visits your shop — a process that takes increasing amounts of time as the data volume increases. For this reason, certain characteristics are put together (clustering) to form fixed groups (personas) of supposedly similar users — a clear shortcoming of this is that even though the personas are actually made up of very diverse personalities, they are treated as if they are all the same.
"Personalization" at Amazon often means the recommendation of one and the same product, which is merely a feature such as, for example, Color or size. We believe: an inspiring, emotionalizing shopping experience looks different!
Person-Aware Individualization:
The characteristics that are typically used for clustering—age, gender, background or interests—do not do justice to the diversity of your users either. This is because what is relevant for them does not just depend on their personality, but also on the situation in which they find themselves at that moment: An online shopper looking for inspiration and distraction during a train journey must be addressed in a totally different way to if that same shopper is looking for a product under time pressure in the office.
Personalization through Recommendations:
Unfortunately, most online retailers restrict themselves to recommending or offering the purchase of suitable or similar products at specific points in the buying process (e.g. retargeting or recommendations like Amazon). However, an individual shopping experience is more than recommendations using the device of "customers who bought this also bought this." By using these or similar methods, you could even annoy your users or discourage them from making a purchase in some circumstances.
With personalization 2.0, online shoppers will only see what is relevant to them: In the example, you will see the result lists of the same search term that has been entered by different users. They were resorted exactly to the respective user profile.
The answer to the above problems is personalization 2.0: This enables you to adapt each separate dynamic page element individually to the current user of your shop — for example the product lists, search result and category lists, content, media, key visuals or header. This incorporates not only person-specific but also situation-specific characteristics, without grouping them into narrow clusters. This means that you can offer every online shopper a tailor-made subjectively relevant shop — spanning across devices and channels. The result is a truly custom-fit approach and individual advice for each user throughout the entire shopping experience. In order to successfully make the transition from e-commerce to me-commerce, you need to be totally driven by data. This is how to turn your company into a data-driven business:
Data-Driven Corporate Culture: Radically Re-Think Your Corporate Culture!
To be able to fully understand online shoppers, you need to ensure that all of your data is usable and compile it into a 360° panorama of your users. This requires your company to become a connected business: Do away with the silo mentality and consolidate all data in one platform. For this to work, you need a tailor-made change management strategy that systematically plans out the implementation of the following points:
Agile, interdisciplinary organizational structure:
Rather than simply throwing together all of your company's departments, you should follow Forrester's collaboration strategy as a guide: Appoint one central transformation team (IT specialists with management skills) to act as a "command center". Interdisciplinary teams can act as the backbone, and should be characterized by agility, openness and flat hierarchies.
Develop a Culture of Analytics within the Company:
Define specific sub-tasks for each individual department in your company with the aim of data consolidation. The level of achievement and the benefits of these tasks should always be measurable using tailored KPIs — firstly to monitor success, and secondly to convince each team in the long term that the data analytics represents a high degree of added value for their own work.
Empower All Employees:
To exploit the full potential of your data, it is necessary to democratize it: Make the data available to all employees and enable them to understand it and use technical tools when working with it. To do this, you need to integrate them systematically into the processes and offer them training. Alongside this, you also need a suitable tool that interactively visualizes your data and is intuitive to operate.
Actionable and Smart Data: Ensure That Your Data Creates Value!
Forrester has found that only 29% of all companies have thus far succeeded in gaining actionable insights from their vast mountains of data, even though they collate their data as described above. As is so often the case, the key here lies not in the quantity (big data), but rather much more in the quality and the right selection of data (smart data).
Which strategy is the right one for optimizing your data quality also depends on the type of data that is being optimized. It is therefore essential that you can differentiate between two information types: master data and transactional data. "Master data" refers to the mostly static key data that contains state-oriented information about your business objects (products, customers, suppliers, staff, etc.) — for example the addresses of your customers or the price of your products. As master data is essentially required for all of your ongoing processes, the quality of this is crucial for success. Luckily there are helpful tools for master data management (MDM) that take care of this with the greatest possible degree of automation. By contrast, the management of your transactional data should be manual: This data is transaction-oriented information that is recorded during your business processes (e.g. tracking the user behavior). During the recording of such data, typical errors can occur (for example, often not all page impressions, conversions or clicks are recorded correctly), which are still best detected by the human eye.
Intelligent Analysis and Situation-Aware Individualization: It All Depends on the Right Technology!
What personalization method is the best for you is dependent on many different factors. Weigh it carefully here!
Once you have compiled and cleansed all of your data, you need advanced technology that intelligently analyzes this and automatically incorporates the results into the structure and design of your shop. You should choose the right personalization method very carefully as this should be an exact match for your company culture, strategy and online shop. However, to implement personalization 2.0, you must always select an advanced analysis method (advanced analytics or Analytics 3.0) that goes beyond conventional BI tools and meets the following criteria:
Consolidation of All Data in One Platform:
As described above, you need to consolidate and jointly analyze all of your data from every available source and department in order to create a complete picture of your users. In addition to a culture of analytics in your company, you also of course need technology that enables this.
Real-Time Analytics of Big Data:
Choose a tool that offers top performance and real-time capability with in-memory computing and data-parallel analyses: This analyzes your raw data and forms user groups (statistical siblings) that resemble the current user (real-time clustering), starting from the moment of the page impression (live or on the fly) and afresh for each dynamic page element. In this way, the data does not need to be grouped into fixed personas, and you truly do justice to the diversity of the online shoppers. A further advantage is that for this method, the identity of the online shopper is totally irrelevant, meaning that you tailor your shop even for anonymous users and first-timers even before the first click and at the same time can protect the privacy of your users.
The ultimate shopping experience: every single dynamic element on the homepage of this fictional shop is tailor-made for the current online shopper. Maximum relevance for each individual visitor!
Automated Optimization of Your Shop:
To then obtain actionable insights from the results of your analytics, you should also run prescriptive analytics. This independently determines what should be done to achieve the greatest probability of attaining a goal defined by you and, if necessary, automatically initiates this action. For example, this enables you to show each online shopper the media content that emotionalizes them, or to offer specific buying incentives that discourage them from leaving. In addition, you can re-sort search result and category lists according to subjective relevance in order to offer your users only those products that they are highly likely to appeal to them, are in stock and have low rates of returns.
If you follow all of our tips from this blog series and enhance this with advanced technology such as operational intelligence that meets the above criteria, you will operate with complete customer-centricity and offer each user their own shop. For online shoppers, this means unfailing relevance, a custom-fit approach and the best advice — in other words, the ultimate shopping experience that excites and inspires. Your users will thank you by staying, buying, returning and telling those around them about your perfect shopping experience.
