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As this year's holiday sales season arrives, brick-and-mortar retailers are facing perhaps the most challenging market ever. Stiff competition from major online retailers like Amazon and eBay and deep price cutting from fellow store retailers are driving profit margins down to near-microscopic levels.
To fight back against these trends, brick-and-mortar merchants of all types and sizes must follow the lead of major financial and marketing firms and use sophisticated analytical tools. With the assistance of actionable data analytics, store operators can better understand shoppers' needs and preferences, increase their efficiency and productivity, and develop fresh sales opportunities.
Data collected from point-of-sale (POS) systems, customers' mobile devices, and various other resources can help store operators gain a better understanding of who their customers are and how they like to shop. They can then use these insights to improve the shopping experience, boost customer loyalty, set prices, and configure their inventory for maximum sales.
Here are 10 ways data analytics can help you prepare for the important holiday sales season:
Gaining a deeper, richer understanding of your customers is key to maximizing holiday sales. Scrutinizing customer data helps you uncover the best methods for contacting customers, such as how and when they prefer to receive emails, text alerts, and magazines, and whether or not they are open to receiving holiday-themed marketing materials and offers.
Under Armour takes this concept to the extreme. Using SAP’s HANA platform, the sportswear maker collects data in real time to analyze customer workout habits. Under Armour then interprets the data, gathered from wearable technology devices and previous purchases, to understand consumers and make sure it is developing the right products to fulfill customer needs.
Another important step is to accurately segment your customer base. This will give you a better grasp of purchase patterns, such as which products sold best to which types of shoppers during last year's holiday season, helping you boost the number of sales per visit. Customer base segmenting will also enable you to closely personalize marketing programs and other types of customer service, such as shopping advice and long-term product support. Research has shown that personalized marketing works far better than "blind" customer engagement efforts.
Virtually every holiday season arrives with hot products that everybody wants (yet are available only in limited quantities), as well as highly anticipated offerings that turn out to be total sales flops. Predictive analytics can help you peer into the future to project the likely demand for specific products.
By taking advantage of the fact that most new products really aren't entirely new but simply a variation of an already existing offering, it's possible to use historical sales data from earlier, similar products to predict demand for a new item. After all, when you think about it, just how new is a "new" phone, computer, game, toy, or fashion accessory?
Online retailers such as Amazon have introduced a new era of dynamic pricing. Also known as surge pricing, demand pricing, or time-based pricing, the approach enables retailers to set flexible, instantly changeable prices for products based on competitors’ pricing actions and current market demands.
Data analytics allows store retailers to accommodate and exploit this dynamic pricing, leveling the price playing field. With the assistance of big data insights, you'll be able to optimize your prices in real time depending on your current inventory, customer demand, and unfolding trends. By changing prices on the spot, you’ll be able to improve margins, boost sales, and—most urgently—compete successfully against Amazon and other large retailers.
For all its popularity, online shopping is essentially a cold and sterile experience. Stores, on the other hand, have the unique ability to make customers feel cared for and appreciated. Savvy store operators understand that simply offering the cheapest price or having the most liberal return policy is no longer enough to fight off online and brick-and-mortar competitors. It's now more important than ever for customers to feel satisfied at every stage of the shopping process, from browsing to product selection to the sales transaction to after-sale support to long-term engagement. Ensuring complete customer satisfaction requires crafting a smarter and more efficient shopping experience, designed from the outset to attract and retain new customers.
Among its other benefits, data analytics ensures that in-demand items are always in stock and enables merchants to send timely and relevant promotion and premium offers to past customers, helping to drive repeat store visits. Additionally, with predictive analytics, sales associates can help customers find exactly what they want without delay. Using a mobile device with a search function featuring multiple data points—including conversion rates, customer ratings, specifications, and product relationships—a product search can go beyond looking for what a shopper said and, instead, retrieve results based on what the customer actually means.
ULTA Beauty is one of many retailers embracing this approach. ULTA stocks more than 20,000 products, leaving many customers confused by the plethora of options. Addressing this concern, ULTA’s clienteling app allows associates to use comprehensive data collected from prior purchases to create custom-tailored cosmetic consultations and offer customers an informed, seamless shopping experience.
As every retailer knows, store traffic peaks during the holiday season. Yet, even during the busiest sales periods, there are traffic peaks and valleys related to specific days, times, and external conditions, such as bad weather and traffic congestion. By comparing past patterns to upcoming schedules, predictive analytics can help you know when to expect high traffic volumes and plan accordingly.
Keeping your store and team functioning smoothly and efficiently despite high traffic levels will go a long way toward preventing customer frustration caused by sales floor crowding, staff inattention, and long POS lines. In fact, high traffic volumes will actually encourage strong sales and repeat business, since customers will view your store's busy-ness as a sign of its immense popularity and success. Best of all, thanks to analytics-driven pre-planning, you'll never have to turn customers away at the door.
Lost revenue caused by understaffing and understocking is a common holiday season problem. On the other hand, thoughtless overstaffing and overstocking cuts into profits. Predictive analytics can help achieve optimal sales staff and inventory levels, ensuring that your store is prepared to make every sale it can—even on the busiest days and weeks of the year—and avoid the risk of having your loyal customers defect to other retailers.
Striving to beat online merchants at their own game, UK independent toy retailer The Entertainer uses analytics to continuously monitor inventory and offer its customers 30-minute click-and-collect and 90-minute delivery options. Thanks to its solid grasp on product data, The Entertainer is able to update its online catalog and offer products that are available and will be delivered on time.
Adding festive decorations isn't the only way to get shoppers to buy more things during the holiday season. Data generated by POS systems and customers' mobile devices lets you track shopping patterns and dwell times, helping you understand how stock placement impacts the sales of specific items.
Want to call attention to your store's new holiday-themed products? Place them along the most regular paths taken by shoppers. Do you have some items you need to move quickly? Place them in the areas where customers spend most of their time. Want to keep customers in the store after they've picked up this year's hot product? Make sure they have to walk past other gift-themed items before they can exit.
How important is layout to inspiring spur-of-the-moment sales? Well, think about the last time you visited a casino hotel and had to trek across the gaming floor before reaching the elevator bank to reach your room. That's not a coincidence.
The holiday season stretches store resources to the limit and beyond. By generating continuous reports on key business operations, data analytics can tell you which business units require more resources, such as extra mobile devices for sales associates, information kiosks, digital signage, delivery vehicles, and so on. Analytics helps you understand exactly how each business unit is doing and gives you informed insights on how to support them.
A team of happy, unharried sales associates engages with customers more effectively while enhancing your store's image. If your sales, backroom, and front-office team members feel unsupported and overwhelmed during the hectic holiday season, their negative attitude will spill over to customers and business partners, poisoning key relationships. You may also risk losing some top team members to competitors that can promise a more pleasant working environment.
Analytics can help you create and maintain a smooth and steady workflow throughout the year. Routine tasks can be automatically allocated to slow periods, enabling your staff to focus on intense—but not overwhelming—workloads on the busiest days of the year. The result is a strong, resilient team that can handle a huge volume of business with grace and ease.
Despite scary headlines like "The Death of Stores" and "The Great Brick-and-Mortar Meltdown," there's every reason to expect that your store(s) will still be in business during the 2018 holiday season. On the other hand, it's also likely that competition will be even keener next year and that store operators will have to work even more intelligently and innovatively to maintain and expand their retail foothold. This is all the more reason for you to step up your current data collection efforts.
After all, the data you collect this holiday season will prove invaluable when the next holiday season rolls around.
This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.