How can retailers satisfy today’s customers? By applying analytics to every step of the customer journey

Keeping your customers happy with assortment optimization

12

Customer loyalty blossoms with analytics

6

The connected consumer

17

What is omnichannel analytics?

3

Groundbreaking personalization

15

Demand forecasting and the 7 habits of effective retailers

9

How do you satisfy customers who can shop anywhere, anytime? Mobile de-vices, social media, not to mention Amazon … all have led to major changes in customer behavior and big headaches for retailers. In seconds, customers can research products and compare prices through multiple channels. Well, what if you could just as quickly respond with relevant offers, competitive prices and the right merchandise?

And what if you could take it one step further? What if you could take all the data you’re collecting on those multiple channels, combine it with product information, and apply analytics to improve retail operations and the custom-er experience? You can with omnichannel analytics – and the articles in this e-book will show you how.

table of contents

What is omnichannel analytics?Smart retailers know it’s not just about marketing anymore

Picture this: A shopper sees some shoes she likes

in a magazine or on a fashion blog and starts

researching reviews, prices and availability online.

Then she finds a store where she can try them on.

And while in the store, she uses her phone to see if

she can find a last-minute better deal.

It’s a dizzying process for the retailers trying to keep up while shoppers like this one jump from one channel, device, website and location to the next.

For years, that’s what omnichannel analytics has meant in retail. It’s been about bridging all those digital and physical channels to recognize customers wherever they are, collecting data and understanding the retail customer’s purchasing journey – all to market the right product at the right time on the right channel.

But what if retailers could do more with all the data you’re col-lecting on all those different channels?

Omnichannel analytics for retailYour omnichannel analytics strategy can be so much more. You can take all that customer data, combine it with product data and apply analytics to:

• Laser focus your marketing.

• Optimize merchandising.

• Adjust your supply chain.

• Enhance store operations.

• And even improve cybersecurity.

Here’s how SAS’ Lori Schafer explains omnichannel analytics:

“It’s allowing the retailer to apply analytics to every step of the customer journey. To be able to give that customer a much better experience, not just in terms of how we market to her, but also in terms of the ideal product assortment in each location, whether it be in-store or online. And also, how much and where the optimal inventory should go so we can satisfy that customer’s needs wherever she wants to shop.”

That’s true omnichannel analytics: using data from mul-tiple channels to improve all retail operations while also improving the customer experience. It’s accomplished through a strong data strategy, analytical merchandising, intelligent marketing and an open ecosystem for analytics.

Omnichannel analytics supports unified commerceIn a recent Retail TouchPoints article, “All CIOs Want for 2017 … Is Unified Commerce,” the authors describe the true omnichannel shopping experience as unified commerce, which “goes beyond omnichannel by en-abling a single, holistic platform, combining in-store, mobile, e-commerce and every other function within the enterprise.”

And that’s what omnichannel analytics provides – it supports a true unified commerce environment for retail. And 85 percent of retailers state that creating a unified commerce environment is their top priority, according to a recent Boston Retail Partners survey.

Breaking it down by area, here’s some of what retailers can accomplish with omnichannel analytics:

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• Laser focus your marketing by understanding customer uniqueness, knowing how digital and physical channels affect one another, and building effective targeted marketing to cover millions of interactions and consumers each day.

• Optimize merchandizing by planning the business for higher profitability, knowing what stores have similar product preferences, creating customer- centric assortments and knowing the right pricing strategy for each product life cycle.

• Adjust your supply chain by knowing if promotions are effective across channels to predict and plan demand, ensure the right levels of inventory and know where to fulfill e-commerce orders to reduce shipping costs.

• Enhance store operations by quickly analyzing your business and knowing what drives the customers’ path to purchase, communicating with customers in real time and determining optimal staffing and product placement to optimize productivity and increase sales.

• Improve cybersecurity by knowing if your network is secure and quickly uncovering suspicious activities to protect customers, minimize fraudulent activities and reduce losses.

Take the shopper in the opening example. Smart retailers with omnichannel analytics would have identified her as a multichannel super shopper – their ideal customer. They’d know her social media habits and populate her favorite fashion blogs with personalized ads showing her the latest shoe styles for the season. If she clicked through their ad to order the shoes but abandoned her cart, they’d send her an email saying that they’d noticed she’d left something in her cart – and reminding her of their free shipping and returns.

If that still didn’t result in a sale, they’d follow up with a text message, inviting her to come in to the store because they have the shoes she wants in stock (thanks to their localized assortment). And when she went in to the store, she’d get a welcome text message as soon as she walked in, providing more information

Omnichannel analytics allows the retailer to apply analytics to every step of the customer journey.

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about the shoes. They’d also notify a sales associate that she was there and what she was looking for so that they could help her try on the shoes and complete her purchase on their secure network.

That’s what omnichannel analytics looks like – it’s a seamless experience for the customer. And that’s what retailers will have to provide if they’re planning to stick around.

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Customer loyalty blossoms with analytics1-800-FLOWERS.COM uses SAS® Analytics to deliver a one-stop shop for customers’ gifting needs

Convenience is big in the retail world. But when it comes to

e-commerce, consumers have an array of options. Certainly,

a retailer must initially entice shoppers with attractive offers.

But to build a relationship – not just close a sale – a retailer must

also engage with customers and understand their preferences

and buying habits.

1-800-FLOWERS.COM is doing just that. The online floral and gourmet gift provider doesn’t want shoppers to waste time searching for gift ideas. Instead, the retailer wants to be the ultimate destination for gift giving, no matter the occasion or recipient.

To effectively market across its growing family of brands, 1-800-FLOWERS.COM turned to SAS Analytics, which allows the company to better understand its customers, suggest the right gift ideas and build the foundation for long-term relationships.

“We want to become a one-stop shop for all your gifting needs, no matter who you’re shopping for,” says Swetha Valluri, Director of Analytics of the Gourmet Food and Gift Brands (GFGB) at 1-800-FLOWERS.COM. “While we’re known for beautiful floral gifts, we’re also a leader in gourmet food gifts.”

Building on a history of convenienceFrom fresh-cut lilies to sugar cookies to fruit baskets to chocolate truffles, 1-800-FLOWERS.COM has a vast assortment of gifts. With brands such as Harry & David, Cheryl’s cookies, Fannie May chocolates and The Popcorn Factory, the company’s goal is to make sure its customers never have to – or want to – go anywhere else. And that’s where analytics comes in.

“We started creating a multibrand experience, and our brands complement each other nicely,” Valluri says. “If roses aren’t want you’re looking for, how about chocolates? Chocolates not appropriate? How about cookies? Or pears? We have

a whole spectrum of options. Analytics helps our customers find the right gifts for that special person they’re shopping for. And making that connection helps ensure a great customer experience and repeat business.”

Speaking to customers with one voice1-800-FLOWERS.COM initiated its online presence in the 1990s and expanded over the years. However, its brands had different back-end systems, so customer data flowed into a common environment in different formats. The retailer wanted to move away from silos to seamlessly market, cross-sell and up-sell among its brands. To do so, it needed comprehensive data about each customer’s journey across all channels.

Today, 1-800-FLOWERS.COM relies on SAS Analytics to mine data in its enterprisewide database. “SAS gives us access to all of the data, and we’re able to coordinate and share code, centralize the process and collaborate more effectively,” Valluri explains.

Analytics drives every part of the business, and the insights we gain from SAS help us increase customer engagement and satisfaction. We don’t just want a sale – we want a lasting relationship.

Swetha ValluriDirector of Analytics

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Additionally, SAS Analytics enables 1-800-FLOWERS.COM to segment customers, send targeted emails to those segments, and see how marketing campaigns are performing. So when the company runs reports, it knows whether to extend a promotion or try something new.

“SAS really helps us understand customer behavior,” Valluri says. “We can analyze the data and say, ‘OK, this segment reacted very well to this promotion, so we should run something similar again.’ And it drives decisions, like helping us determine what’s coming next season.”

Now 1-800-FLOWERS.COM can answer questions such as, if people buy flowers, what are they likely to buy next? SAS also helps the company differentiate customers by likes and dislikes, products, occasions and other categories. 1-800-FLOWERS.COM can discover more opportunities, such as promoting items that are traditionally out of season or suggesting cookies to someone who just bought flowers.

“We generate reports every morning, and they’re crucial to our business,” Valluri says. “We can make data-informed decisions that answer questions like, ‘Should we leave this picture of chocolate-covered strawberries on the website or replace it with something else?’ Or, ‘Should we send this email to this customer segment?’”

By targeting individuals and their preferences, the company can build longer, more profitable customer connections.

“If someone orders cookies from Cheryl’s for the first time, we can suggest other products they might be interested in,” Valluri says. “Or if someone is a longtime customer and a huge brand advocate, that person will receive an offer based on their preferences and buying habits.

“Analytics drives every part of the business, and the insights we gain from SAS help us increase customer engagement and satisfaction. We don’t just want a sale – we want a lasting relationship.”

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Demand forecasting and the 7 habits of effective retailersDan Mitchell, SAS Business Director for Retail and CPG

Demand forecasting gives businesses the ability to use historical

data on markets to help plan for future trends. As more data on

consumers and products becomes available, the need to use this

data to anticipate demand is critical for establishing a long-term

model for growth.

In a sense, demand forecasting is attempting to replicate human knowledge of consumers once found in a local store. Long ago, retailers could rely on the instinct and intuition of shopkeepers. They knew their customers by name, but, more importantly, they also knew buying preferences, seasonal trends, product affinities and likely future purchases.

Demand forecasting attempts to replicate that sophistication through analytics-based evaluation of available data. By examining buying behavior and other bits of data left behind by the consumer, a retailer can mimic that knowledge on a broader scale.

Anticipating demand for the modern buyerToday’s consumer often journeys from digital space to physical space and back again, moving among devices, apps and displays. The buying process might start with researching a product online, continue with comparing prices from a mobile device, and finish with an in-store purchase. Or consumers may see merchandise in a store, then search on their phones to score a last-minute deal.

In a world where you can have practically any item shipped to your door, it’s important for retailers to make a connection with the buyer. A variety of buying options is a delight to consumers – and a rich source of intelligence for retail-ers, if you know how to capitalize on it.

This omnichannel retail environment intensifies the need for better answers to the perennial questions of retail planning. What merchandise should be stocked, in what sizes/colors, at what quantities, in which locations? How, where and when should products be displayed, priced, promoted, ordered

or shipped? How can we maximize profit without eroding the quality of the shopping experience and customer satisfaction?

Demand forecasting gives you the ability to answer these questions. But the sheer number of variables involved in the omnichannel world makes demand forecasting and merchandise planning on a global scale highly complex.

In its 2017 benchmarking study, Retail Systems Research found, naturally, that some retailers do this better than others. And the ones that consistently outperformed others shared a differentiating set of thought processes, strategies and tactics.

A variety of buying options is a delight to consumers – and a rich source of intelligence for retailers, if you know how to capitalize on it

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They make smarter allocation decisions. Winners know they need to put products where they are most wanted or needed, and they trust demand forecasting to help them make the best localized store assortments and fulfillment decisions for direct-to-consumer orders.

They blend art and science. While informa-tion is seen as a critical asset – along with tangible assets such as stores, distribution centers and inventories – 62 percent of the winners also credit their success to “a healthy blend of art and science.” They value decision makers’ years of experience and understand the importance of well-tuned internal operational processes.

The Retail Systems Research report closes with a checklist of do’s and don’ts related to demand forecasting, customer analytics and localized assortments for retailers who want to be (or remain) winners. “If retailers can follow these simple steps, they’ll go a long way towards optimizing their merchandising life cycle and creating a more compelling buying experience for customers,” the report states. “If they don’t, they risk being consigned to the dustbin of history.”

They are ahead of the game in imple-mentation. Winners were twice as likely to have technologies already in place for attribute-based merchandise plan-ning and demand forecasting to help with price, promotional or assortment planning. And they are twice as likely to have integrated their planning, allocation and replenishment systems.

They are optimistic about information. Higher-performing retailers are twice as likely to expect big things from demand forecasting. They are also nearly twice as likely to “highly value” attribute-based merchandise planning systems.

They bring analytics into the process earli-er. They know that basing future plans on prior year or season sales will create self-ful-filling prophecies. The lost opportunities of the past will be repeated in the future. Instead, they bring insights from custom-er analytics into the demand forecasting process upfront, not just as a sanity check at the end of the planning process.

They value more rigorous forecasting. Nearly two-thirds of winning retailers (74 percent) rated demand forecasting technologies as “very important” to their success, compared to 58 percent for the others.

They put a high value on analytics. Fully 77 percent of winning retailers rated analytics as “very important” to their retail success, compared to 48 percent of average or lagging retailers. They know they can’t get by without integrating more predictive capabilities into their decision-making processes, and they understand why this is so.

one

five six seven

two three four

Specifically, the winners were the ones who engaged in seven productive habits:

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Keeping your customers happy with assortment optimization5 benefits of merchandise intelligence

Dan Mitchell, SAS Business Director for Retail and CPG

Hopefully there aren’t any retailers out there who still don’t

understand that their customers are in control of their business.

As a retailer, you must constantly monitor your customers’ interests and preferences along with your business processes to make sure they’re optimally aligned. Consumers are too savvy to shop in any way other than on their terms.

And the more customer touch points you have, the more effort you’ll need to expend to make sure your assortments are the best possible in every channel and location. If you do that, the payoffs are significant.

Retailers who can pull this off while focusing on the freshness and quality of an optimized assortment of products that account for local market demographics and preferences will be top performers. Fortunately, there are solutions for merchandise planning and assortment optimization that make this work easier and less risky.

In a recent RSR report, Merchandising & The Omnichannel Impact, experts identified assortment optimization and demand forecasting as merchandise planning must-haves.

Using retail analytics with your predefined business processes along with assortment optimization enables you to choose the most profitable mix for a specific touch point. The best solutions work by projecting inventory profitability based on several objectives such as consumer loyalty, product relationships and category role.

The results offer insights on the effect of business rules on your assortments interactively while you are planning, so you don’t have to rely on hindsight to make future adjustments. These insights allow category managers to run what-if scenarios to determine the proper mix of business rules that will balance profitability and customer preferences. It creates the ideal product selection, facings, and inventory recommendations for every item and every store – rapidly localizing all your assortments.

Technologies that make a di�erence for retail winners: Predictive at the coreMerchandising technologies rated as “high value”

IN-SEASON DEMAND FORECASTING FOR PRICE, PROMOTIONAL, OR ASSORTMENT Retail Winners 69%

Others 45%

ASSORTMENT OPTIMIZATION Retail Winners 67%

Others 52%

INTEGRATED PLANNING, ALLOCATION, AND REPLENISHMENT SYSTEMS Retail Winners 56%

Others 28%

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Fine-tune your customers’ experienceAssortment optimization offers five advantages:

1. Tune your assortment using customer insightsA loyalty index or basket analysis results can be used as part of the financial objective to make it easy to use shopper behavior to adjust the assortment selection. You can expand your inventory for items that are driving shopper trips, and not endanger sales of items often purchased together.

2. Balance of breadth and depthBy using decision-tree grouping, the optimization software can effectively balance shelf breadth and depth in your store clusters or stores based on how people shop. Customer analytics will show you where and when to shift products from locations that have too much duplication to locations that don’t have enough variety.

3. Emphasis on quality in varietyThe ability to control variety gives you the power to determine the scope of variety you can offer, whether sales increase, and how much overstock invento-ry can be trimmed from low-selling items by improving the usage of each shelf. Using a concept called critical mass percent (the minimum units per group) helps ensure the quality of the selected groups, creating an assortment that is a more precise representation of your customers' shopping behaviors.

4. Unique view of the merchandisable spaceThe best customer analytics optimization solutions consider the entire physical usable space on the shelf by simultaneously filling the item deep and stacking high to fill a pre-calculated quantity (i.e., minimum units). If the shelf depth and available height has been maximized, the same solution might suggest adding another product facing to satisfy the minimum units required in order to use supplemental space only when needed.

5. Easy-to-interpret recommendationsOptimization engines perform two passes for each location to measure the opportunity cost. The first run considers a theoretical optimal assortment

without user constraints, while the second run shows the results of any applicable constraints. With this information, you can explain in simple terms why any item was dropped or selected and why any customer display group was dropped or selected. Additionally, optimization logic automatically solves for the best configuration to fill the shelf space with appropriate facings wide, high and deep.

Well-tuned data provides more meaningful customer insightsJust like an orchestra that takes its time to tune before performing, using the data you already have and assortment optimization, you will be able to be better prepared with the right merchandise in the right place at the right time to meet your customers’ expectations. Retailers that stay focused on their customers’ needs will be able to stay ahead of their competition by incorporating retail analytics.

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Groundbreaking personalizationShop Direct customers will now be able to benefit from even more personalized product displays – highly targeted to improve the overall shopping experience.

Shop Direct, the UK’s second-biggest online pure-play

retailer, has 4 million customers who shop on the Very.co.uk,

Littlewoods.com and VeryExclusive.co.uk websites.

SAS® Customer Intelligence solutions coupled with SAS advanced analytics, SAS Visual Analytics and SAS solutions for Hadoop will help Shop Direct’s website learn from individual consumer behavior then adjust what products are displayed in real time.

SAS will help the retailer convert browsers to buyers more often by balancing customer needs with customer value and risk.

Shop Direct is at the forefront of digital and mobile retailing. Now, 100 percent of its transactions take place online, with over 63 percent of those completed on mobile devices. During the last 12 months, SAS has helped Shop Direct successfully build a system capable of analyzing two years of historical customer, sales and inventory data in real time.

SAS provides Shop Direct with a flexible, hosted suite of data management, an-alytics, data visualization and real-time decision capabilities. This combination allows multiple users to explore massive volumes of data, then create descrip-tive, predictive models. Shop Direct now can organize, integrate and analyze its data, then present it in a format that’s easy to act upon and friendly to casual business users.

“ We’re all about making it easier for our customers to shop. That’s why we’re passionate about personalization. We want to tailor everything for our customer; the shop she visits and how we engage with her before, during and after she’s shopped. Our partnership with SAS will allow us to get even better at using data analytics to show our customers the right products at the right time to capture their attention.” Alex Baldock, Group CEO at Shop Direct

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The connected consumer IoT’s impact on the future of retail

Daniel Newman, Principal Analyst of Futuurm Research and CEO of Broadsuite Media Group

Have you walked into your favorite store and gotten a person-

alized notification on your phone with a coupon for something

you buy every week? Or pulled up an app on your phone that

tells you the exact location of a product you want to buy – and

shows how many are on the shelf? If you haven’t experienced

this yet, no doubt you’re expecting it in the near future. More

retailers are using the IoT to connect with consumers just like

this. It’s something that could revolutionize how consumers

shop and what they expect from their favorite retailers.

Indeed, I’d say customer experience – or expectation – might be the driving force behind retailers using the IoT to create a smoother omnichannel experience for the connected consumer. Today’s consumers don’t want to find a different store online, on their phone and in their neighborhood. They want a consistent pres-ence, with consistent service, every time. (With incentives to boot, of course.)

Data rules the retail roostSo, how are retailers using the IoT to create a better omnichannel experience for the connected consumer? I’d say there are two big ways. And they both come down to data.

Armed with the data-gathering prowess of the IoT, retailers are using technology products from vendors like SAS to pull together omnichannel analytics that shed light on their customers’ actions across a variety of platforms – mobile, social, etc. This approach can yield valuable insights on the macro and micro levels.

What kinds of insights? Imagine:

• Knowing when a connected consumer is searching for a competing product and being able to instantly text them an incentive to buy yours instead.

• Knowing when the customer is driving past your brick-and-mortar location and sending a coupon for an item they’ve recently looked at online.

• Knowing what true trade area demand is by blending online and offline sales with web and in-store traffic data for any given store.

• Knowing that the connected consumer in Export, PA, is – on average – only willing to spend a certain amount for a certain product so that you can adjust your price before it ever hits the stores.

Uniting data from the connected consumer The kind of data that bridges platforms isn’t just nice to have. It’s data that can help close a sale and keep businesses running – or, more likely, ahead of the pack. In fact, 78 percent of retailers1 say it is either important or “business critical” to integrate their online and in-store experiences to create a solid omnichannel experience for their connected customers. Analytics performed on this data can help them better adjust inventory – keeping waste low and the connected consumer satisfied. It can determine which promotions were successful, right down to the moment the customer engaged and/or acted on the offer. Basically, it’s taking the “gut and guesswork” out of retail and using every piece of multichannel data instead.

Technologies that make an impactOK, so now companies have access to the IoT to learn about their customers and figure out smoother, cooler, better ways to serve them across channels. That’s awesome. But how do they go about putting those smart incentives and automated communications and personalization plans in place? There are a few technologies that can help.

• Wi-Fi, beacon and RFID technologies. Some say that by 2021, almost 80 per-cent of retailers will be able to customize the store visit for customers.2 Why? Because RFID and beacon technologies will be able to tell them exactly where connected consumers and product inventory are in the store! Imagine having a treasure trove of data – what someone eats, what size they wear, their favor-

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ite brand – and being able to entice them with alerts of new products and sales right when they walk in the door. All without having to increase your sales staff.

• Near-real-time analytics. All retailers dream of getting a better handle on their market. But few are able to do it until the sales reports are in for the quarter. Using things like smart price tags3 can change that. Now, businesses can see in near-real time if a certain price point is working for its customer base and change it – automatically – if they need to lower or increase it to maximize sales. That’s a total game changer! No more failed launches or promotions – there’s always an opportunity to fix it and improve the chance of sale.

• Inventory transparency technology. One of the most annoying things in terms of customer experience is failed inventory – when a customer believes something is in stock, makes a trip to buy it, and finds out it isn’t there. New inventory technology like smart shelves4 can help by alerting personnel in real time when inventory is low. It’s just one more way to make sure the connected consumer is happy.

There are a lot of new technologies and ways to keep the connected consumer satisfied across various platforms. Businesses hope this will drive sales, and maybe even keep their brick-and-mortar shops in business. Indeed, the data and insights provided by the IoT can help retailers more firmly establish their brand and extract even greater customer loyalty if used well. After all, as humans we tend to be loyal to the people – and companies – that know us best.

I’d say customer experience – or expectation – might be the driving force behind retailers using the IoT to create a smoother omnichannel experience for the connected consumer.

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