Key figures for a better understanding of customers
E-commerce is currently facing a major challenge - or rather a huge mountain of data. While customers are increasingly discovering multiple, digital touchpoints, online retailers have to bring together the data and customer information distributed across various sources in order to make it useful for them. The aim is to create a clear picture of shoppers, their preferences and dislikes, behaviors and usage habits. BI tools such as Tableau support this by linking and visualizing all relevant data sources and enabling a holistic view through detailed data analysis.
Combining data in order to make strategically reliable decisions for the future is the formula for success in e-commerce. However, putting this theory into practice seems easier said than done: the systems, partner portals, internal and external platforms are often incompatible due to their different infrastructures and formats. Valuable insights from data analysis therefore remain hidden from many online retailers, as investments in extensive software solutions and specialist expertise are simply not worthwhile.
Es braucht Werkzeuge, die ohne Spezialwissen genutzt werden können sowie heterogene Datenquellen miteinander verbinden und das Ergebnis auch für technikferne Nutzende ansprechend visualisiert. <a href="https://www.taod.de/tech-beratung/tableau" data-webtrackingID="blog_content_link" >Tableau</a> ermöglicht genau dies. Daten können analysiert und kontrolliert, interpretiert und optimiert, Prozesse immer wieder nachjustiert werden. Kurzum: Tableau gewährleistet eine strukturierte Erhebung und Nutzung von relevanten Daten zur Aussteuerung von zum Beispiel Online-Shops.
Linking systems means creating a homogeneous database
Gründe, warum immer mehr Shop-Betreiber bei der Optimierung ihres Online-Geschäfts auf genaue Datenanalyse und damit auf die <a href="https://www.taod.de/services/bi-und-data-analytics-consulting" data-webtrackingID="blog_content_link" >BI-Lösung</a> von Tableau setzen, sind unter anderem moderate Anschaffungskosten und die einfache Integration unterschiedlicher Schnittstellen. Datensets aus bereits bestehenden Systemen, etwa DATEV, Salesforce und aus Webtracking-Tools wie Google Analytics, können einfach via Drag-and-Drop in einem Dashboard verknüpft, analysiert und in visuell ansprechender Form dargestellt werden. Auch externe Partner-Shops wie Amazon, About you und Ebay können problemlos eingebunden werden. Selbst, wenn eine benötigte Quelle nicht über die vorhandenen Konnektoren angesprochen wird, kann Tableau diese via ODBC-Konnektor mit allen anderen Datenformen verbinden.
Getting to know the customer better with key figures
A shopping basket analysis can be used to determine which products are frequently purchased together and which are top or flop items. A frequency analysis, on the other hand, determines data on first-time buyers or the average shopping basket. Ideally, it reveals how first-time buyers can be motivated to make repeat purchases in the store or how the value of the average basket can be increased through a voucher promotion, for example.
These key figures, combined with evaluations of returns, logistics and usability data, form an overall picture of customers that reflects their needs and online retail activities in their entirety. Tableau generates knowledge that cannot be found in static reports, but is always up to date. If individual factors change, these are incorporated into campaigns and services iteratively and tailored precisely to target groups.
Coca-Cola Bottling Co. Consolidated (CCBCC), the largest independent Coca-Cola bottling company in the USA, has even managed to track "shrinkage" with the introduction of Tableau and thus combat a problem worth millions. In line with last year's Coca-Cola slogan, this should have given the business analysts "moments full of joie de vivre".
