Retail predictive analytics is the practice of collecting data on retail operations and using it to improve a business’s insight into customers, make decisions about offerings and marketing and, ultimately, learn how to improve the business. Good analytics have become indispensable to modern retailers. This article explores the many techniques and application areas of retail analytics.
What Is Retail Predictive Analytics?
Retail predictive analytics is the practice of using historical data to make predictions that answer those big questions and many other small ones that also impact the success of retail operations. In practice, this can involve forecasting explicit numeric outcomes or identifying the factors that impact those outcomes and modeling what happens when those factors change.
- Retail predictive analytics is the valuable practice of understanding and anticipating customer behavior.
- It can provide actionable foresight into future sales, including the likely success or failure of ideas for promotions and marketing.
- Retail analytics output can only be as good as the data on which it’s built, so thoughtful and thorough data collection pays off in the form of better insights and decisions.
- But: As data science capabilities increase, it can be easy to forget that old methods of understanding customers still work, too — and can even be superior. Sometimes the best way to learn something about a customer is to ask them.
Retail Predictive Analytics Explained
Running a retail store requires a lot of planning. When a customer walks in, you must already have what they want to buy or you won’t win their business that day. If that happens often enough, you may lose their business forever. The item on your shelf today must be ordered in advance, sometimes hours, days, weeks or even months earlier. But “playing it safe” to make sure you can always meet that demand — that is, always stocking more than enough of everything that any of your customers might possibly want at any given time — is logistically and financially impossible.
So, businesses need a way of predicting, or outright influencing, what customers are going to want to buy and when. The more a business knows about its customers, the better it can cater to their needs. Retail predictive analytics is one of the essential practices that helps businesses forecast what’s coming and offer guidance on how to improve performance and offerings, sometimes down to the level of individual people who might patronize the business.
How Predictive Analytics Works
First and foremost, predictive analytics run off data. But in a business environment, it’s not always a given that you have data you can usefully analyze. Therefore, the first step in retail predictive analytics is to think about the questions you want to answer and what data you’ll need to answer them. Think of this data as coming in two forms: data you’ll likely collect anyway and data you need to make a special effort to collect.
Data you’ll collect anyway might be information on sales, transactions at locations, online orders or myriad other reflections of your retail operations. This is often termed “digital exhaust,” because your business generates it anyway. Maybe you’re already collecting it perfectly with your current sales, customer and inventory management systems, or maybe you need to make small tweaks to make sure the data being collected is useful to your analytics efforts.
But there will also be data you wish you had, data that requires extra effort to collect, through customer satisfaction surveys, for example. Such surveys must be created and fielded before you can get the data to analyze. The specific data you collect should be a function of what you need to know, a relationship that’s explored later in this article.
Once the data is in hand, there are generally three types of predictive analytics tasks to perform on it: description, extrapolation and inference.
Description. The goal here is to summarize and present the data in ways that help business decision-makers improve their understanding of the company’s operations. A giant pile of data is hard for humans to make heads or tails of, but data analysts can very quickly generate useful summaries and graphs that tell a story. The quantitative methods at this stage tend to be simple, as the tasks call for presenting breakdowns, applying filters and calculating simple summary statistics like averages, standard deviations and growth rates. Sophisticated operations will have entire dashboards full of summary tables, statistics and visualizations that get updated regularly and automatically; KPIs and benchmarks are often part of this. Output from this task might provide answers to questions like: What’s our No. 1 selling product? What’s the busiest month and the slowest month? Which stores, states and regions have been experiencing the fastest growth? Description isn’t necessarily a prerequisite for the other activities, but it helps analysts understand what’s in the data, which, in turn, can help them ask better questions and choose better statistical models for the other steps.
Extrapolation. In retail analytics, extrapolation is about forecasting what your data will look like in the future. That forecast can then be translated into a prediction of the future conditions your business will face, which the business can use to prepare for those future conditions. In extrapolation, you start with current and historical data because you know with certainty what today’s data looks like and what past days’ data looks like. By applying statistical techniques to that data (regressions are popular here) and making adjustments from your knowledge of the subject matter, you can often generate useful forecasts that present patterns and give a sense of the direction in which your key metrics are moving. Demand forecasting and inventory forecasting are common and useful practices that involve extrapolating insights from existing data.
Inference. This is the hardest task to do, and it is where an organization might need to develop or recruit hard-core statistical expertise. Inference is where you start asking causal questions, like “How much does factor X contribute to outcome Y?” This kind of finding is easiest to get from experimental data (e.g., data from A/B testing). Causal inference is what lets you make predictions about what will happen when you change things in order to affect particular outcomes of interest.
For example, if you held a promotional sale on your most popular items, how could you determine the impact of the discounts on your sales and profit? Just because sales went up during the promotional period doesn’t necessarily mean the promotion drove the increase. Maybe sales would have increased anyway, or maybe there was another factor causing an increase in demand (perhaps a competitor closed some stores and your business was going to get an influx of customers regardless). Causal inference involves looking for ways to statistically isolate the key factor you want to focus on, in this case the sales, and account for other sources of variation.
Types of Retail Analytics
There are many ways to break down retail analytics into different types or categories, but, when thinking about how to set up data collection and analysis, it’s useful to start with the kinds of questions you want to answer. Consider how far you need to zoom in or out on your operations to get insightful answers by finishing the sentence, “To answer this question, I need to collect data about…”
Are you fundamentally asking questions about customers? Store operations? The whole company? Individual transactions? Answering these questions helps segment data collection and analytics efforts into useful levels. Let’s take a look at some of the most common examples, knowing that not every one will apply to every company (for instance, if you exclusively sell through physical stores, multichannel analytics won’t be useful).
Shopper-level analytics asks and answers questions about how individuals interact with the brand and its products in venues where you can track data. How do customers move through the store? Where do they go on the website, and how often are they leaving items in or removing items from their cart? Are their comments and questions positive, negative or neutral in tone? These analytics aren’t particularly useful at forecasting near-term sales, but they are excellent inputs for refining and improving customer experiences, which can improve sales over the long run by strengthening loyalty.
Have you ever seen on a website “Customers who bought this item also bought…,” with suggestions for what other products you might like? That’s shopper-level analytics powering a recommendation engine that is trying to increase the retailer’s sales, while also making it easier for customers to discover things they might want.
Transaction-level analytics zoom in beyond the shopper to the individual transactions. Data fields for these analyses may include what was purchased, when the purchase was made, through what channel the purchase was made and what form of payment was used. Transaction-level analysis could help a retailer measure the success of a marketing campaign or sale by tracking the timing of related purchases. When combined with shopper-level data, it might even tell a business which customers purchased as the result of receiving an email or a coupon.
While the previous two types of analysis look at the purchaser and the purchaser’s behavior, on-shelf analytics deal with the items themselves. How fast do the items sell? What identical or similar items are competitors selling and for how much money? What about substitutes? In a physical store, retailers might base their estimation of an item’s contribution to their offerings on how much money it brings in, compared to the opportunity cost of stocking it (because shelf space is finite, and you could always stock something else instead). You might think of a product’s “productivity” in terms of dollars of profit per day/per week or month or year/per feet of shelf space they occupy. A physically tiny item doesn’t need to sell as fast or for as big a profit to be worth keeping in stock, compared with larger items that represent a more significant commitment of limited shelf space.
Remember, though, it’s called on-shelf, not item, analytics. While it’s easy to deep dive into individual items, that approach may lose the strategic perspective of how items fit together. Maybe batteries don’t sell very well but, without them, a few customers who come in to buy products that require batteries will leave disappointed instead of happy. Is that happiness worth more in the long run than whatever would replace the batteries on the shelf? Think of it like “burgers and fries”; on a per-item basis, fast food restaurants would have much better margins selling only french fries, but it is the lower-margin sandwiches that drive the sales of those fries.
In some cases, businesses deliberately sell products for scant profits, or even losses, to gain customer happiness. If a customer has come to expect something, or if an item is inextricably linked to your brand, the individual-unit economics may capture only a tiny fraction of its value to the business.
With location analytics, we’re zooming out to cover a whole store, or multiple stores, or a region, or the whole world. How does one store compare with another? Do customers who shop at your store near their home shop at other locations when they travel? Are some items popular in the Southwest but not in the Northeast?
Within location analytics, there are often countless ways to divide and summarize your data. Exploratory analysis is important here, and with modern computing power, tracking trends and hypotheses is often very inexpensive if you’re collecting the data anyway. Experience will help you learn which perspectives are useful and which are just meaningless summary statistics. For example, knowing you get almost twice as many orders from Cleveland, Ohio, as from Pittsburgh, Pennsylvania — even though they’re geographically close cities of about the same size — could be useful information, as you wouldn’t expect that much difference. Knowing you get almost twice as many orders from Columbus, Ohio, as from Pittsburgh is less interesting, however, as Columbus has almost twice as many people.
It’s also possible, and sometimes very helpful, to combine levels within location analysis. For example, instead of just looking at the store level, the city level, the region level or the country level, what if you looked at correlations between them? Which stores and cities are the most representative of the regions and countries in which they operate? That kind of analysis can provide improvements in how you test and roll out new products by enabling you to pick test locations that provide better evidence regarding how a larger rollout would perform. Many companies do this by city demographics, which is one reason why Rochester, New York, is a popular destination for corporate brands and testing. But with good analytics, demographic matching can be a starting point, instead of a final decision; you’ll learn from years of collected data which cities have reactions to your product that are most predictive of how those products will sell nationwide.
In multichannel analytics, the idea is to look at shopping behavior across all the different ways you sell a product. That might include the company’s website, mobile app, ecommerce marketplaces, delivery through third parties such as Instacart, physical company stores, specialty stores and more. If your business only has one channel, you can skip this section. But businesses with multiple channels should know how differently customers interact with them and which products are better fits for which channels. For example, a drugstore may find that customers buy certain items online because they find them embarrassing to purchase in person, even though they come into the store for everything else. A grocery store might find that although customers fell in love with home delivery during the pandemic, which items get delivered is uneven. Perhaps analytics indicates that only 10% of fresh fish sales are happening through delivery, possibly indicating that customers are having issues with delivery lags and are finding that cold items aren’t arriving very cold, so they’ve stopped adding perishable items to their orders.
This is another area where businesses can run experiments, but it’s important to remember the human experience when planning that data collection effort and not let the pursuit of information interfere with running a good, customer-friendly business. Businesses often try to improve their understanding of what motivates customers to shop in one channel versus another, for example, by running in-store only promotions or online specials. But stores trying to incentivize in-person purchases during the pandemic faced loss of reputation among customers who saw those promotions as antithetical to public health efforts, and customers with disabilities never like it when businesses make it harder for them to get deals that are easily accessible to others.
The previous five types of retail analytics are all about what goes into producing outcomes that businesses desire, be they increased sales, profits, loyalty or something else. But it’s also useful to analyze those metrics directly to learn what direction your key outcomes are heading in and what the seasonal patterns are. It’s not wise, however, to do only outcome-level analysis; that risks failing to learn the “why” behind the business’s successes and failures.
For example, it’s good to know during which time periods a business makes most of its money. For bars, it might be Friday and Saturday night. For a lot of retailers, it’s the end-of-year holiday season, while new gym memberships skyrocket immediately after. At some vacation destinations, it’s a particular season. Outcome-level analytics will put specifics around these trends so decision-makers can be prepared and also notice aberrations earlier, hopefully in time to respond.
Benefits of Retail Predictive Analytics
There are many benefits that retail predictive analytics can bring to a business. Here are five common benefits that make practicing analytics valuable to many, if not all, medium- and large-size retailers.
- Increased sales. This is the No. 1 goal most retailers have in mind when they set off on an analytics journey. Good retail analytics supports retailers’ decisions to double down on winning strategies and tactics, fix or replace losers, figure out the right time to send certain marketing messages and promotions and to which customers, and more. Almost all good uses of retail analytics directly or indirectly support sales growth.
- Improved profitability. In the long run, most companies are trying to optimize for profit, not only sales. By getting better at collecting and analyzing sales data and looking at it in the context of margin data, companies can find ways to evolve the composition of what they sell to move toward higher-profit transactions. For example, consider a very popular but low-margin, entry-level product. An overly simplistic analysis might conclude that the product isn’t important because it contributes little to the bottom line. But a good analytics operation might discover that selling more of that low-margin item drives higher profitability because it gets customers excited about the company’s entire product line, driving loyalty and future upsells.
- More refined targeting for marketing campaigns. Good data collection and analysis can increase the chances that marketing campaigns will meet with success. You might learn that certain campaigns work better for some customers than others, do better in warm regions than cold, fare better in spring than in fall. You might even be able to determine how these contributing variables interact. For example, a retailer selling items to help people get organized could find that elder customers respond best to promotional offers in the spring, when they’re starting new projects, while middle-aged customers respond best near the end of summer, when they’re planning back-to-school shopping for their kids.
- Enhanced customer experience and loyalty. Modern technology affords companies many options for measuring and improving the customer experience, from simulation-based modeling of customer flows through a store’s space to collecting data from cameras showing where customers actually linger to A/B testing of different versions of an experience, be it online (easiest to test) or in person. Each business will have different metrics it is trying to optimize (for example, some bars might consider it a success when customers want to linger, while a coffee shop serving the morning rush knows their customers want to get in and out as fast as possible). In all cases, giving customers superior interactions leads to increased loyalty, repeat business, better reviews and glowing referrals.
- Better staff scheduling. One of the biggest challenges for physical retail locations is predicting the appropriate level of staffing needed for a given day and time. You don’t want salespeople milling about with no customers to help, but you don’t want a small team to be overwhelmed by a flood of customers, either. Relatedly, one of the biggest challenges faced by retail employees is lack of predictability in their schedules — to the point where government intervention has been required at times to protect workers’ rights. Retail predictive analytics can help businesses better forecast when stores will be busy and when they won’t. This means better matching between staffing levels and customer demand, and also more predictability for employees. This is a win-win-win for businesses: fewer times when you lose business or loyalty due to understaffed stores that result in a poor customer experience; fewer times when you lose money and productivity paying people to wait for customers who never arrive; and happier employees and lower staff turnover.
17 Ways to Improve Retail Businesses With Predictive Analytics
There are many ways to improve a retail business driven by analytics running on good data. While this list isn’t exhaustive, every business should find something relevant to refine the way they collect and use data to boost their performance.
Revenue projections are one of the most common applications of retail predictive analytics, and with good reason. The information is useful for planning purposes, whether you’re deciding what kind of financial services are needed from a banking relationship or whether you’ll have enough cash flow to open a new store next year. The simplest prediction is a projection derived from a review of past and current data, which can be enhanced by breaking it down by product type, individual SKU, region, store and more. Top-line revenue forecasts are usually more accurate if they’re rolled up from such granular predictions.
Forecast Product Demand
Demand forecasting is similar to revenue projection, in that you’re trying to figure out how much the business is going to sell. Often, revenue predictions are part of (or are easily calculated from) demand forecasts. Demand forecasting methods range from finding patterns and running regression analyses to seeking expert opinions and running surveys about customer intentions and how their needs are changing or staying the same. Good demand forecasts give you a better idea of what will be needed for your operations, which, in turn, leads not only to better supply chain planning and analytics but also to more useful inventory management through analytics.
A lot of predictive analytics involves spotting trends and extrapolating from them, based on the assumption that they will continue. But no trend continues forever, especially in business. Tastes, technology, customers, fashions, regulations, raw material availability and more are always in flux. Sometimes predictive analytics can give businesses an early warning that change is occurring. For example, an analysis might identify aberrant behavior/departure from expectations long before it makes a difference to the business’s bottom line — giving you the opportunity to adjust your predictions.
Offer Personalized Shopper Recommendations
One great way to drive sales is by making it easier for customers to find things they need and want. In the age of online shopping, one of the most powerful tools for doing that is personalized recommendations. With enough data on customers, state-of-the-art predictive analytics technology can be very good — even scarily good. There’s a now-famous story about how retail analytics helped Target find out a customer was pregnant(opens in a new tab) before her family did, despite her not buying a single baby or pregnancy item. The key detail was that she switched to unscented lotion, which, according to Target’s analytics, usually indicates pregnancy. So the company started sending her promotions for baby-related items. Mining vast data troves of detailed customer transaction histories can yield all kinds of insights, and recommendation engines can find specific transaction patterns that repeat (like the lotion-switching pattern). Another personalized recommendation approach is to find a group of people whose tastes are similar to those of a particular customer and suggest to that customer things that the group collectively likes. Netflix recommendations use this general principle. Some artificial intelligence methods are now producing high-value recommendations that experts can’t even explain.
Market Basket Analysis
Some items just “go together,” like a battery-powered device and the batteries that power it. Market basket analysis is a technique that predicts what customers might like, based on what kinds of things tend to be bought together. It can be used to predict a common pattern among all customers or just for a specific customer. Consider the example of customers who buy peanut butter and also buy bread. A retailer might use this information in a few different ways: (1) to suggest bread to customers when they buy peanut butter; (2) to offer coupons to customers who don’t buy this combination in an attempt to convert them to buying both from you regularly; (3) to stock more bread when having sales on peanut butter, in case demand for one drives demand for the other; (4) to spark further data exploration — e.g., finding out which brands of bread and peanut butter most often go hand in hand. Market basket analysis can be a way to generate personalized recommendations, imagine new promotions, develop hypotheses for further analysis and more.
Good retail analytics can help you detect a trend sooner than you might otherwise. Market basket analysis might help you spot a nascent trend, for example, if two items that were rarely purchased together are suddenly bought together quite frequently. Investigating why could lead to an insight about changing consumer preferences that might have otherwise taken months to notice. Retailers can also spot trends by catching aberrations from what sales “should” be, according to historical data. Wide adoption of changing food preferences can be seen in consumer sales data, for example, and retailers identifying that trend sooner can adjust their future ordering and start catering directly to new customer preferences ahead of the competition.
Understand Customer Behavior
Insights into the “why” behind customer behavior have tremendous business value, but you have to build up to them. When businesses collect data on consumer behavior, the first thing they usually can do with it is to describe what customers do. How many envelopes do they buy? How long do they browse for books? At which stores do they spend more? The next step is predicting behavior. How many envelopes will they buy this month? How many customers (and maybe even which ones) will browse the book selection for a long time versus how many will come in, get a specific title if it’s in stock and then leave? Which stores are going to do best next quarter?
But the holy grail of retail predictive analytics is understanding customer behavior — knowing why they do things. You want to know that this customer browses for books because she loves the experience, and the better you can make it, the longer she’ll stay and the more she’ll buy, while this other customer is running errands and shopping from a list for a family member and will go to whatever store has the best selection and the fastest service. And it may be useful to know why certain customers are spending more money than expected — for example, the difference between a higher-revenue store that’s simply in a high-income neighborhood versus a store in a lower-income area whose revenue is high thanks to an incredible team of employees and managers whose methods deserve expansion to other locations. As this example may make clear, quantitative analysis can’t always get you all the way to an insight this deep. One common mistake people make when using retail analytics to understand the “why” of customer behavior is to stick only to their data, forgetting about the qualitative tools that are available. Data analytics will often point to the insights you can get about customer behavior by simply asking them questions, whether individually or in surveys.
Improve Customer Experience
Improving customer experiences is one of the main advantages that retail analytics can produce, as discussed in the “Benefits” section above. With online experiences, retailers can do testing and track behavior to make sure websites are attractive and easy to use; can A/B test different messages, pictures, layouts or other design choices; and can track important metrics, like how many items are left in abandoned carts or how useful product suggestions are to customers (i.e., how often do they buy suggested items?). In person, it’s a little more difficult to collect granular data, but new technology is providing more options, from trackers on shopping carts to cameras that monitor crowd levels. Returning to the idea of qualitative analysis, you also always have the tried-and-true method of asking customers questions about their experiences and how likely they are to recommend your store to a friend.
Segmenting customers is one of the most valuable techniques marketers have available. Customer segmentation places customers in groups based on things they have in common that are different from other groups. Usual ways of segmenting customers include by gender, approximate age, income, location or region and more. For example, you might observe that retirees, leveraging their flexibility to avoid crowds, shop at different times from working people. Not all segmentations are useful, though; breaking down customers by hair color might yield some insights for a salon, but not many other businesses are going to find that variable worth tracking. Good retail analytics will give you lots of options for segmentation while also helping you quickly figure out which breakdowns are useful and which are not.
Thanks to email, it’s never been easier to give customers personalized coupons. Retailers know what customers buy when their email address is attached to their orders and can send a coupon to an individual customer because they have that email address. Personalizing coupons takes a lot of work (and is all but impossible at scale without technological assistance) but can be worth it. The perfect coupon, from a profit-maximizing perspective, is one that gets someone to buy something they wouldn’t have bought otherwise but doesn’t give away money by offering a discount on something the customer was going to buy anyway. With personalized coupons, retailers can offer discounts on items rarely or never purchased but which you have strong reason to suspect the customer might enjoy if they tried them. You can also use personalized coupons to conduct enlightening experiments by offering different kinds of customers different deals and observing their responses (or lack thereof). Not all coupons must be about maximizing profit; they also can be about learning about customers and understanding them better.
Enhance Inventory Management
Any business that needs to keep items stocked on shelves or in warehouses has had challenges with retail inventory management. It’s impossible to do it perfectly. You don’t want to hold too much and risk not being able to sell it quickly or at all (that ties up space and capital, and some of that capital may not be recoverable), but you also don’t want customers to be looking for something and not find it because you didn’t stock enough. By improving the accuracy of demand forecasts — i.e., predictions regarding how many of which items your customers will want — you can reduce inventory management risk. Knowing you need 10,000 boxes of diapers in stock plus or minus 500 is a much better position to be in than knowing you need 10,000 plus or minus 5,000. Having the right things in the right places and knowing what kinds of orders you’re likely to receive can also decrease lead time for a business and the order processing time experienced by the customer.
Collect Data for Expansion
When thinking about expanding operations, a business faces the challenge of guessing what the new market will be like but without having any direct experience. This is true whether the business is considering venturing into a new country or launching a new product line. Retailers can start with existing data to generate hypotheses, and even forecasts, by defining assumptions about how the new market will be different from or similar to existing markets. But there’s often no replacement for collecting new data or purchasing external datasets that speak directly to your expansion plans. For example, an expensive luxury brand considering expansion into a new geographic territory might start by analyzing whether its products would fit into the budgets of that area’s target consumers. If the new customers can’t afford or can’t justify the expense of such high-end offerings, it won’t matter if the rest of the analysis is correct.
Improve Trade Promotions
Trade promotions usually involve a manufacturer partnering with a retailer to promote a product the manufacturer produces and the retailer sells. Good data collection can be tremendously helpful here because general sales figures by location and time period won’t tell much about how a particular product is moving at different price points. The same techniques used for other kinds of promotions would work here, just applied to the subset of transactions that include products made by the manufacturer partner. Understanding what motivates customers can also be very helpful. For price-sensitive customers, discounts are going to drive sales better than anything else, but for customers looking for something experiential or high end, a manufacturing partnership opens doors you couldn’t on your own. For example, a manufacturer might be able to provide limited-edition versions of items, or less expensive merchandise not usually available, to go along with the product(s) being promoted (e.g., a company T-shirt with the purchase of a technology product).
Customize Loyalty Programs
Loyalty programs started in the travel and hospitality markets but, in recent years, have been adopted throughout the retail sector. With a loyalty program, a company is essentially buying information about, and access to, the customer in exchange for a rebate or discount. Whether customers are earning points they can spend, getting access to better prices or receiving custom offers and a free cupcake on their birthday, loyalty programs allow businesses to reward repeat customers while also encouraging them to attach a unique identifier (their loyalty program ID) to every interaction they have with the company. That is a tremendously powerful way to gather and bring together information across channels and locations. As businesses get to know their customers, they can customize specific offers and rewards, further increasing customer engagement and improving the value of the loyalty program for both the customer and the retailer.
Enhance Pricing Options
The more you know about customers and their price sensitivity, the better able you are to offer them not just better products but better ways to buy those products. Customers who balk at a large up-front payment might appreciate being able to pay over time and/or an offer of a money-back guarantee. Some customers who love an individual item but find it too pricey might buy it in bulk or as part of a bundle. Sometimes businesses can offer discounts by age (e.g., senior discounts), time of sale (e.g., early bird specials) or other segmentation factors.
Improve Marketing Targeting
In the same way that retail predictive analytics helps retailers decide which promotions to offer to different people, the practice can also help test and determine which marketing messages work best on which people. Low-hanging fruit in using analytics for targeted marketing might be messaging them about products they already buy often, but knowing customers’ priorities can be hugely helpful. For example, customers with a pattern of buying mostly eco-friendly products are more likely to respond to a sustainability message than customers whose behavior indicates that they’re always trying to find the lowest price.
Personalize In-Store Experiences
Personalizing a customer’s in-store experience may be the hardest item to accomplish on this list because physical spaces aren’t as easily changed as online sites. But with retailer apps installed on smartphones, it’s possible to send relevant information, promotions, deals, survey questions, fun facts and more to loyal customers as they walk through the aisles of your store. Even lacking that technology, employees can still provide a customized human touch with the right information at their disposal, but not all businesses lend themselves to that level of human personalization. For example, a jewelry retailer might have no need for an app but live or die on the personalized customer service provided by staff, while a big-box store couldn’t possibly offer white-glove service to all customers but would find an app-based approach valuable, even if only a small percentage of customers used it.
A Single Commerce Platform for Omnichannel Retail
By this point it should be clear that retail predictive analytics is impossible to do by hand or with spreadsheets. Only an integrated enterprise resource planning (ERP) system such as NetSuite’s ERP is capable of collecting the data, storing it usefully and running retail predictive analytics for a midsize or larger retailer. Businesses need software capable of drilling down to individual locations and even transactions, while rolling up data segmented in as many ways as they can imagine using the available data fields.
NetSuite offers a scalable ERP solution for retailers that can handle many of the tasks discussed in this article, from segmentation to multichannel monitoring to forecasting, and that can grow with a business as it progresses from, say, a dozen stores to dozens of countries. And, with NetSuite’s SuiteCommerce, retailers can integrate their data across in-store and online channels to seamlessly integrate customer touchpoints.
Retail predictive analytics is a valuable practice for businesses looking to improve sales, prepare for the future and develop greater understanding of — and relationships with — their customers. As companies get larger, retail analytics moves from valuable to indispensable, and the difference between good and bad analytics operations might spell the difference between having a competitive edge and not surviving the next recession.
Retail Predictive Analytics FAQs
How is predictive analytics used in retail?
Predictive analytics is used extensively in retail settings to forecast customer demand and company performance, run experiments to improve customer satisfaction and experiences, design promotions that are targeted and useful to shoppers and offer suggestions and information that customers find valuable.
What are examples of analytics used in retail sales?
The output from retail analytics is easy to find. Targeted email advertising and promotions, suggested items on a shelf placed underneath a product or offered before you check out on a website, customized incentives and rewards in loyalty programs — all are examples of retail analytics being used to influence the customer experience. Stores will also change their layouts, policies, promotions and even aesthetics in response to data analysis and research.
What are examples of predictive analytics?
Predictive analytics involves using data to make educated guesses about the future. Demand forecasting is one of the primary sets of techniques retailers use; examples might include trying to guess what their total sales will be for the next fiscal quarter or figuring out how many hot toys to stock at each local outlet this holiday season.
What are the 4 steps in predictive analytics?
There are many ways you can divide up a predictive analytics process, but one useful way might be:
- Collect data (on the questions that matter to the business).
- Describe the data (descriptive statistics, summaries, rollups).
- Understand the causal relationships in the data (understand why things happen).
- Make predictions with the data (forecasting outcomes of interest).