Industry analysts and thought leaders are stating we live in “the age of data.” Today, businesses can collect and store trillions of data points at marginal cost. And the amount of data collected, electronic devices, and sensors is growing at an incredible rate. If you’re in Retail, just think about all the data you may collect from loyalty campaigns, in-store and online transactions, electronic shelf labels, BYOD and hand-held self-scanners, in-store RFID and so on.
Data ≠ AI (at least, not always)
Of course, when speaking about large amounts of retail data, almost by default people start thinking about Artificial Intelligence (AI). And why not? We do see AI increasingly being applied in retail stores, e.g., by using
automatic age verification technology in which face characteristics are analyzed and matched with a reference database to determine a shopper’s age. And along the same lines, also automatic item recognition to expedite the self-checkout (SCO) process, for example by helping a customer find the right
fresh produce item on the checkout screen, is AI-based. These kinds of data-driven solutions make grocery shopping more convenient for customers, reduce the number of staff interventions in the self-checkout process, and help reduce costs.
But there is more to retail data than just AI. Also, when managing and optimizing day-to-day store operations, timely access to proper retail data is crucial. Just think about operational data like sales per square foot, customer retention rate, item turnover, average transaction value, sales per employee and sales per store, foot traffic per day-of-week, and average dwell time… There are many KPIs retailers need to track to keep their business profitable and competitive. Here, data is a crucial success factor.
Data gives decision makers a headache
The title of this article is “Data is the ultimate success factor.” But another option could have been: “The interpretation of data is the ultimate success factor.” According to Forrester (2022), 82% of decision makers have difficulty forecasting and controlling costs because of poor data. Eighty percent mention data quality as an issue, and 3 out of 4 decision makers (76%) say they have difficulties in understanding the data presented to them. What these numbers tell us is that without proper data analysis, data correlation and data visualization, it will be hard to distill meaning out of the terabytes of data we have at our disposal. However, it is not that easy to interpret data in such a way that it contributes to your retail success without having a clear data strategy and smart data analysis tooling in place first.
Challenge 1: Data silos
There are a couple of challenges that need to be tackled. To start off, data is often stored in so-called silos. Historically, different steps in the customer journey collected different types of data that were stored in separate databases, and this situation continues until today. For example, customer and loyalty campaign related data are maintained by the marketing department and stored in a CRM system. Transaction data is kept in a POS data repository, until now mainly for legal and tax purposes. Inventory data is kept in the back office in an item database and maintained by the purchasing or operations team. E-commerce data is kept in yet another (online) repository. And probably additional data silos exist in (your) retail organization.
Challenge 2: Data quality
Besides being siloed, data often is also stored in different formats, thereby using different unique keys to identify the same person or item. This makes it hard to combine and compare data. Moreover, data is not always up-to-date, consistent, and complete across different data sources: for example, loyalty campaigns keep track of customer details, but do not always have the latest address on file - while the e-commerce systems are up to date since the (same) customer wants items to be shipped to her or his current address. So next to data silos, data quality represents issues for retailers.
Challenge 3: Data analysis
The availability of time is yet another challenge: to be able to make data ‘actionable,’ one needs enough resources to analyze and interpret the data first. Retailers struggle with complex and time-consuming processes based on outdated data, hindering effective decision-making – let alone near real-time decision making – also, the visualization and reporting of data takes lots of time and energy.
Solution: Vynamic® Advanced Analytics
Diebold Nixdorf recognizes these challenges with data interpretation and reporting and has developed a new retail analysis platform. Vynamic Advanced Analytics offers retailers a state-of-the-art data analytics and visualization platform that empowers you to make data-driven decisions efficiently and effectively. Running in the cloud and designed with ease-of-use in mind, it basically tears down the data silos by seamlessly combining and integrating data across multiple sources. It offers sophisticated data aggregation and drill-down capabilities, so that data can be viewed and analyzed instantly. For this, it offers an interactive dashboard so you can play around with various data fields and KPIs. Moreover, it lets you create role-specific views and reports, to save time on your daily, weekly, and monthly reporting cycles.
Part of the cloud-native Vynamic Retail Platform, Vynamic Advanced Analytics is offered as-a-service to retailers and is aligned with local data privacy laws by storing (customer) data in the appropriate region where you’re doing business. Together with Diebold Nixdorf, you can transform your retail data into a tangible business value!
If you want to learn more, please get in touch with your local Diebold Nixdorf representative.
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