A guide to retail data systems
Retail Data Collector
What is retail data collection?
Over the past few years, data software has come to play an increasingly important role in retail. Companies of all sorts have begun to use retail data collector programs to adapt to and predict market trends and better understand and cater to the wants and needs of their consumer bases.
As online and brick and mortar shoppers increase, this special software was able to hone its data collector skills, finding out what customers liked, wanted, and needed. With a better understanding of the customer, companies have been able to advertise better and more accurately target specific groups and individuals. This has made them shop and buy even more, creating the cycle which had led to such unprecedented numbers within U.S. retail sales data.
The more widespread deployment of retail data collection software has quickly changed many things about the market. Above all, it has become a practically indispensable tool for driving revenue and staying competitive. Surveys, focus groups, and even data software programs that are only a few years old are rapidly becoming antiquated in the face of new tech. Shoppers, too, are now beginning to expect companies to have better intuition about what they want and need, making companies with older systems run the risk of causing dissatisfaction.
Fortunately, these new tools aren’t prohibitively expensive, aren’t typically hard to use, and can produce tangible results. The retail landscape is changing rapidly. Fortunately, companies across many industries and backgrounds could investigate how to make better use of their data.
Training of data collectors
Retail data has played a part in business for decades, but the way retailers approach these data has been evolving in recent years.
When data first became a concern of major companies, a data collector person or team was appointed to write handout questionnaires, research business competitors, and sift through mountains of sales information. Computers naturally made this an easier task, eventually outmoding the field data collector entirely, but the process still consisted of endless spreadsheet inputting, nebulous statistics, and inefficient data capture.
Now, data collector jobs are being performed by a new generation of software which is both quicker and more impactful to the success of companies generally. These data collectors can access more diverse kinds of data from more sources and can do it at a much faster rate than earlier programs. The most important advantage of these new collectors is that they aren’t just collectors. These programs are attached to complex systems of which collection is only the first step. All the gathered information no longer just sits in a spreadsheet to be sorted through by management—data organization and sorting programs combine with analytics software to generate insights which are both easily accessible and incredibly useful in day-to-day operations. Best of all, the AI tech included in many of these new systems means companies no longer have to spend any time or resources on the training of data collectors—they teach themselves!
Retail analytics tools
Retail data collection today is really part of a larger system which companies use to gather data on customers and then leverage that data to drive profits and customer satisfaction.
The first range of tools comprises just the basic information collecting programs. These generate packages of information by examining points in the retail structure where customers directly interact with the business itself. These touchpoints include POS checkout systems and transactions, online marketplaces and websites, digital surveys and questionnaires, and even other places on the web where actual or potential customers engage with your company’s ads.
Collector programs generate an enormous amount of data which must then be compiled and organized in order to be of use to companies. This is achieved through the use of a customer data platform or CDP. A CDP is a database of individual customer profiles which each hold unique info about that person’s wants, needs, and relationships with the company. Each new piece of data fed into the CDP from the various collection programs is sorted by a special algorithm and placed in the file of the person who generated it. This helps in creating demographic models and monitoring consumer trends; it also allows all the info on any given customer to be called up instantly by management.
Once the data is cleaned and sorted, it can be tackled by retail analytics tools which generate key insights and tell business leaders what all the assembled information means. Analytics programs can perform numerous kinds of analyses and functions to discover patterns and connections within the data. They can track customer tastes, differentiate and assess vital consumer segments, and even build models to predict the behavior of data in the future.
Finally, dashboard programs, which condense all these insights and statistics into simple graphs and charts enable upper management to understand all of these data points and their significance. From there, it is up to them to decide how to use this information to make customers happier, inspire loyalty, and drive revenue.
Retail data analytics
Retail data analytics is the true core of the retail data process, and it is these programs which make information collection meaningful.
Analytics can mean many things. Retail data analytics companies produce software for practically any application, and companies often pick and choose their software to fit the unique characteristics and challenges of their own business models.
The main function of these programs is to perform a large part of the data analysis which makes sense out of the raw collected data. They run complex tests to discover patterns and trends which equate to real-time changes in customer bases and the market.
Analytics tools can determine which demographic or segment is the most loyal, uncover the causes of churn, figure out what parts of the buyer experience are most and least popular, list the products people like the most, track seasonal changes in purchase size and composition, and produce just about any other kind of information management might need.
Among the most powerful and indispensable types of analytics tools are forecasting programs. These take in all the past data on a certain bit of information and generate predictions about how the data will behave in the future. This may sound like simple statistical calculations and extrapolation, but modern forecasters do much more than this. These applications utilize machine learning and artificial intelligence to generate models of much higher accuracy and precision and are constantly rewriting their own parameters and assumptions to create better predictions. Forecasting is perhaps the most important part of data analytics because it allows businesses to plan ahead of time to meet challenges and take advantage of opportunities. Knowing what customers are going to do and to want tomorrow allows companies to constantly adapt, enabling them to remain competitive in shifting markets and continually provide a high level of customer satisfaction.
Though figuring out how to make effective use of all this information is still not easy for upper management, analytics programs provide indispensable support and guidance in taking the right course of action for success.
Types of retail analytics
Types of retail analytics are as varied as the kinds of data there are in the retail sphere. Nonetheless, we can broadly outline two varieties of data analysis that take place in a fully constructed system.
Data analysis in brick-and-mortar stores is what most who know about retail data will be familiar with. Most of the information here comes from the POS, where info can be captured about customer identities, items purchased, purchase sizes, and visit frequency. This info can be leveraged to determine things about consumer loyalty, buying trends, seasonal sales fluctuations, and more.
Online data analysis deals with many of the same kinds of info, since customers still input their personal info and purchase items. However, additional data is also available. The search function on online stores allows the software to learn which items customers are looking for, even if your business doesn’t stock them. Programs can also keep track of all the items a customer has viewed on the site, tracking changes in taste, correlating them with temporal factors, and seeing how they relate to recent changes in inventory, ad campaigns, or prices.
A more detailed and comprehensive list of retail analytics examples and scenarios could go on forever, but it is important to understand the main categories of on- and offline collection and analytics and their similarities and differences.
Retail data systems
After learning all this, the next question is likely, “how do I set up a working retail data system?”
What the right system is will depend on the nature of the business you do. What you sell, who your customers are, how big your operation is, where you’re located, and many other factors will help decide the kinds of tools you need.
There are hundreds of companies currently producing software for retail data systems. Often, these applications are highly specialized, dealing with a specific kind of data and performing some unique kind of analysis on it. Lexer, however, takes a different approach. We produce a single comprehensive system covering everything from the initial collection process and CDP organization through analytics and dashboard visualization.
Collecting various specialized tools and making them all work together as a system takes a great deal of time and resources. Having a single, ready-made platform which covers all the essential bases can be much simpler and easier—the ideal solution for anyone just starting out in the world of data collection and analytics or experienced companies looking to streamline their extant process.
Whatever you do and whoever your customers are, Lexer can craft an effective solution to help you do better business and serve your clientele more effectively.