The future of retail is data driven
Why is data-driven retail important?
Businesses have to stay flexible. A company that never progresses—never improves upon its business model, marketing strategies, or brand image—will inevitably get left behind as customers, the market, and the world itself change around it.
Businesses need to adapt and evolve because the wants, needs, and preferences of customers are always evolving. But how do managers and executives know what to change and how to move forward?
In the past, this was largely a game of trial-and-error. Higher-ups would do their best to “listen” to the market and try to get inside the head of the customer. Sometimes it worked, but often it didn’t. Today, there is a much better way to steer the course of your business and stay current in dynamic markets. It’s called data-driven decision making.
Data is all around us, and more is created every time a customer interacts with your business. In recent years, technologies such as Customer Data Platforms (CDPs) have been developed to capture and analyze this data to figure out what it says about what shoppers like and want and need. Implanting data science in retail settings means that managers and owners can now really listen to customers and make targeted improvements that will increase customer satisfaction.
Data-driven retail is becoming the norm in many segments of the market as businesses realize the tangible improvements in sales and customer retention rates this data use is causing. Using this software and entering in the data-driven mindset is going to be crucial for all businesses as time goes on. Staying competitive, agile, and current is all going to depend on how well you can get this technology to work for you. The time to research and get familiar with this tech is now, and spending a little time and energy on learning could mean happier customers and bigger sales in the future.
Retail data strategy
The classic retail data strategy is composed of four parts: collection, compilation, analysis, and presentation. This is how customer actions are transformed into insights that managers and execs can use to improve their businesses.
Data is everywhere, but in order to do anything with it, you first have to find a way of capturing it. To do this, software applications are installed in your POS system, at employee workstations, on your website or e-store, and any other places customers make contact with your business. Data-driven solutions become more effective the more data you have, so these programs are built to gather as much as possible from each customer.
Once you’ve got all this information, you’re going to need to find a way to store and organize it. This is usually taken care of by a CDP or customer data platform. Here, every piece of data is assigned to a unique customer profile called a “single customer view.” This way, every new data point can be easily placed, and all the info about an individual customer can be accessed instantly.
After this, a wide variety of data-driven customer analytics programs will do their work. They’ll look at all the info you’ve gathered and start searching for patterns and trends. Some of these programs create snapshots of how customers are acting, feeling, and buying right now; others use statistical analysis and machine learning to generate predictions about what they’ll do in the future. The insight from this predictive analytics can help you recognize early signs of customer churn and step in ahead of time to improve customer retention.
Finally, all these numbers and models will be sorted through and given different priorities. The most important omnichannel metrics and KPIs will be collected together for review by management. Dashboard programs will keep these most vital pieces of data together in one place and present them to business leaders in simple and straightforward graphics and charts. These dashboards will be monitored constantly, and by looking at changes and trends in the datastream, those in charge can formulate strategies and plans of action to address problem areas and make improvements to the customer experience in retail stores and online.
Data-driven product placement
One of the most important applications of all these tools is data-driven product placement. With the right data-driven marketing strategy, you can pair customers with the items they want faster and more accurately than ever before.
Product placement is most effective online. There, the kinds of data available and the techniques used for advertising are especially good at driving purchases among shoppers.
When a customer visits your online store, data programs can track every item they look at and every search they make. They can tell how long people look at items, whether they put them in their carts or not, and whether or not they purchase them. Tracking these customer journeys gives a very good idea of what customers want—both specific items and related kinds of products likely to interest them.
With this info, advertising can then come into play to remind customers about items they almost purchased and suggest similar kinds of goods to them. Ads of this kind can be displayed on your site whenever customers return or shown to them elsewhere on the web in places like Facebook, Google, and Youtube. This kind of targeted paid social advertising is extremely effective and significantly more likely to drive sales than more randomized and best-guess-driven marketing.
While ads can’t be personalized as well in brick-and-mortar retail locations, in-store product placement is still possible and can be optimized with data software. POS data about the products people are buying can be combined with online purchase data to identify the things people buy the most and when they are most likely to buy them. Then, by advertising your most popular products during the times people are most likely to buy them, you can drive customer lifetime value even higher. Knowing what and when customers want things also ensures that these items can be kept in stock during peak buying times, so you’ll never lose a sale because you ran out of product.
The new data-driven approach to retail differs in key ways from how businesses were run and what their goals were in the past. Before the widespread use of data in retail store settings, businesses worked as hard as they could towards increasing sales. Marketing strategies were rudimentary and often ineffective, and revenue was valued above all.
Sales and revenue are still vitally important and indispensable to success. Now, however, businesses are realizing that, with data, sales can be increased by listening to customers and crafting better shopping experiences for them. The happier the customer, the more they buy and the more often they shop with you. Business leaders are starting to ask not “what can we do to bring up sales?” but “what can we do to make customers want to shop with us?”
An especially notable result of this new data-driven culture is that customers are being rewarded for frequent shopping and brand loyalty. Coupons, special discounts, and membership perks make dedicated shoppers feel appreciated while also encouraging them to buy more.
The new world of data-driven retail is marked by this kind of win-win mentality, and it’s making everyone want to shop more. As a result, the more data enters into the world of retail, the more the entire market will thrive. Win-win.
Data analytics retail
So data is changing things, and it’s happening fast. Setting up a whole data analytics software might seem like a lot of work, but before long, data analysis in retail is going to be ubiquitous and a basic key to success.
Luckily, implementing data analytics in retail industry settings is much easier than it seems, and no business is expected to set it up all by itself. In response to the rising demand for and importance of this software, scores of companies have emerged, each offering their own special retail data solutions. One of the most popular and impactful of these solutions is Customer Data Platforms.
Because of the abundance of providers, individual software companies have diversified and specialized, each tending only to offer tools for just one or two aspects of the greater data process—but all in all, there are about 4 main types of customer data platforms. Some companies focus on the collection aspect, offering add-ons to physical and digital POS systems. Others build CDPs and maintain the large servers which hold customer data. Many companies create unique analytics programs, designed to look at and crunch the data in new and innovative ways. Finally, others develop dashboards which can be customized and specially tailored to the specific needs of individual companies.
Being able to properly leverage customer data depends on choosing the right CDP vendor and partner—a company which understands both your goals and the kind of business you run. Lexer is unique among software providers for offering an entire suite of programs covering everything from collection to visualization and providing tools for every team, including marketing, service, and retail. If you’re just getting into data and want a system which is both intuitive and effective, or if you’re looking to replace an aging and/or fragmented network of separate programs, Lexer is the best choice.
Once you’ve decided on a CDP vendor, you need to build a compelling CDP business case that resonates with stakeholders.
Examples of retail analytics
By now, it should be obvious the many benefits of collecting customer data. To drive the point home, it might be best to conclude with some real world data-driven decision making examples.
A fashion and apparel brand sets up a program which keeps track of all the things people click on and search for on their online site. The company does not have a winter clothing line, but realizes that terms like “jackets,” “coats,” and “mittens” rise exponentially around the holidays. The design team begins work on a small winter line, and marketing works to create in-store and online advertisements to announce it to the public. When the cold months roll around that year, the business sees the best sales figures in years.
A outdoor and sporting goods store wants to inspire greater customer loyalty and encourage people to shop with them more often. They set up a program which tallies how much and how often customers are buying. This is analyzed to locate the top five percent of shoppers by quantity of money spent and goods bought. These individuals are then given special memberships which entitle them to a discount on future purchases based on how much they buy each time. Each discount, however, expires after two weeks. The result is that these customers are driven to buy more each time they visit in order to get a bigger discount next time, and come in more regularly than before.
📄 Customer Data Platform (CDP)
📄 Customer Loyalty and Retention Software
📄 Retail Data Analytics Solutions
📄 Customer Experience in Retail