Author Archives: Monica Quinn

by Monica Quinn

NRF: Unified commerce that puts shoppers first

 

 

In managing your retail business, your control is limited.

You can optimize schedules and inventory, but over the most volatile factor – your customers – you have far less control.

Yes, you can drive shoppers to buy more, soon.

Yes, technologists innovate to help you do it better.

But in all this, the challenge: to run retail profitably without forfeiting its heart – to meet people’s needs and bring joy.

Finally, together, we’re finding what it takes: unified commerce.

  • Unified visibility to help you improve performance.
  • Unified data to recognize customers and anticipate needs.
  • Unified operations to control inventory so shoppers won’t leave disappointed.
  • Unified technologies to give you insight into how to put shoppers first.

Visit us at NRF booth 4273 to start the conversation on how you can unify commerce with Retail Pro.

 

 

 

by Monica Quinn

The rise of subscription e-commerce

 

 

Shoppers want personalized experiences that are convenient and easy. Subscription commerce fulfills that need.

Of course, for retailers, that one, seemingly simple desire can be filled in a multitude of ways, which can sometimes be at odds with each other.

For example, a personal shopping experience may mean going to a neighborhood store, being greeted by name and engaging with an associate who knows your shopping history by heart.

It can also mean logging onto a favorite online store, also being greeted by name, but then interacting with a recommendation engine and having a package shipped directly to you.

When customers want certain items on a regular basis, subscription commerce is bridging the gap, letting customers feel a personal connection without having to expend the effort of a physical visit or performing endless online searches.

With subscription commerce, or “subcom,” retailers can delight customers while simultaneously benefitting from a source of recurring revenue.

Subscriptions have exploded in popularity, growing from $57 million in sales in 2010 to more than $2.6 billion by 2016.

McKinsey & Company reported that 15 percent of online consumers signed up for subscription services in 2017.

Retailers offering such services report having a much closer idea of warehouse staff and stock requirements, delivery destinations, shipping costs and likely future income.

Retailers generally have a greater sense of predictability, but in the most popular programs, what is delivered often includes a surprise—a carefully curated amalgam of products that the retailer has determined the customer will want.

The concept is popular because it’s fun and customers believe they are getting good value.

While shoppers can order specific items for delivery at specific times by subscription, (e.g., Harry’s Razors), samplings and curation are two common types of subscription services.

 

Samplings

Birchbox (cosmetics), Graze (snacks), and BarkBox (dog supplies) are among the most popular sampling services that consumers can sign up for by subscription.

Birchbox, which launched the curated sample subscription box trend in 2010, mails subscribers four to five new beauty samples and lifestyle items to try for a $10 monthly fee.

Curation is based on shopper profiles submitted by users on the Birchbox website.

Retailers earn recurring income on these subscriptions of sample products; they pay little or nothing for the products they ship on a regular schedule.

Customers join the service and understand it’s typically a sampling of trial-size products; those that aren’t desired are simply discarded rather than returned.

For example, Birchboxes can’t be returned. By offering trials of popular products, retailers hope to increase product interest that will carry over to their online stores.

The boxes offer retailers opportunities to delight customers, with curation, personalization, and pricing strategy being crucial factors.

 

Curation

Curated services are personalized with the shopper’s profile in mind.

For example, customers of the higher-end clothing subscription Stitch Fix, benefit from a personal stylist who selects several pieces of clothing based on the shopper’s style profile.

Upon receipt of the shipment, the customer has three days to decide what to buy and what to send back.

By sending the stylist feedback, shoppers can receive more personalized selections the next time.

Subscription services answer customers’ calls for more personalized offerings.

Shoppers are willing to pay for convenience and subscription services provide that as well as an element of surprise.

Successful retailers understand that subscriptions aren’t simply fulfilling a request — that can be accomplished with any sales transaction.

The surprise element of curation and sample subscriptions makes shoppers feel as though they’re getting gifts from close friends who understand the recipients’ taste — despite the recipients having placed the orders themselves.

The experience delights the customer, and therefore, the trend is likely to continue to be popular well into 2019.

 

 

 

by Monica Quinn

3 Benefits of AI for forecasting and operational efficiency

 

 

In retail, making the mark — or missing it — leads to the success — or failure — of the business.

Sales predictions, identifying new customers and developing relationships with trusted partners are all a part of that success, and retailers are gaining greater traction with technologies powered by artificial intelligence.

Here are three benefits of AI for forecasting and operational efficiency.

 

1: Reducing forecasting biases

In earlier times, company executives worked off of hunches that were based on their experience in the industry and with their companies.

One bad guess and the financial numbers for the quarter were sunk.

A second one and the company could be headed for ruin.

Today, artificial intelligence can be employed to augment the success of executives’ expert hunches, and warn against those ideas that are unlikely to work.

When forecasting is treated as a science rather than a guessing game, companies will receive better results.

In general, people are either optimistic or pessimistic, and their forecasting skills are biased as such.

By using AI, a data-driven rationale is used to come to any conclusion.

Not only does this mean more accurate forecasts, but it also provides the “why” behind the numbers.

In times when the predictions are off, it is simple to go back to the data you feed it from POS like Retail Pro and see what went wrong and adjust for the next quarter.

That type of correction is much easier to adjust than a “gut feeling.”

 

2: Increase inventory accuracy

Understanding what products your shoppers are purchasing, and at what frequency, allows you to more accurately predict inventory needs.

AI will help reduce unwanted inventory, and, according to consulting firm McKinsey, overall reductions of 20% to 50% are possible.

For example, take AppCard’s “Pinky,” an AI loyalty and personalized marketing system comprising artificial neural networks and other machine learning approaches that are orchestrated and optimized via reinforced learning.

The network’s architecture ensures that Pinky takes into account correlations between transaction data in Retail Pro and neighboring days, weekly periodicity, holidays, weather effects and seasonality.

In addition, Pinky learns quickly and therefore doesn’t require huge amounts of data to be a trained rockstar.

Right now, Pinky can reliably predict revenue and target customers that are at-risk, but soon it will be able to predict a customer’s next visit and optimize target customer lists based on a merchant’s estimated lift.

 

3: Speed up customer acquisition

In addition, to strengthen existing shopper-retailer bonds, AI can also speed up the process of acquiring new customers.

Take a business that depends on cold calls to increase its customer base.

That’s not a time-efficient way of increasing your customer base, and it’s also expensive.

While LinkedIn and other professional networks are helpful to target potential customers, it’s not much use on its own.

Instead, by using an AI-powered software tool in a coordinated effort with social networking you can find prospects more quickly than a human.

You can automatically send them introductory messages, sync calendars and send meeting invitations.

Cold calling will soon be a thing of the past, replaced by a more targeted, efficient method.

 

AI learning curve

What happens if AI is employed and doesn’t do as well as the former CEO’s hunches?

Tighter inventories, understanding what customers want and broadening the customer base all benefit a retailer’s bottom line, but the effort is wasted if the company can’t deliver on its promises.

Customers who can’t find product on the shelf are unlikely to return. Loyal customers who find that styles have changed will be disappointed.

It takes a good bit of time for an AI solution to learn the business.

It can only learn as fast as data and experiences are being fed to it.

And remember, it does not forget.

Data and coaching increasingly improve the output.

By learning from mistakes, understanding what knowledge gaps there might be and encouraging continuous improvement, a company can employ AI solutions to seize the opportunity to do better for itself, as well as for its customers.

Holistic data fed into an AI system can help retailers gain actionable insights into how they can improve their business and put shoppers first.

Book your NRF 2019 meeting now to start the conversation on how you can unify data in Retail Pro and start making the most of your most important resource.

 

 

 

 

 

 

by Monica Quinn

Human engagement through Artificial Intelligence

 

 

It is a question that every retailer ponders: How are customers engaged with my brand and how can I keep them engaged?

In the past, surveys were a common tool for gauging customer interest, but today’s shoppers are inundated with requests for their opinions and most often those get lost in the shuffle.

Since humans are often too busy or forgetful to be reliable sources of feedback, some retailers are relying on machine learning — artificial intelligence (AI) — to learn if they are succeeding with customers.

Shoppers provide retailers with an abundance of data, simply by voting with their dollars at the POS: What styles or products are popular, what is not trending, what colors are in vogue, etc.

Size, gender and age are among many characteristics that are vital in creating personas, but also, shoppers’ actions are quantified.

The backbone of AI is machine learning; Programs take all that customer purchase history data in POS like Retail Pro, and unify it with other data sources like browsing behaviors, to analyze exactly how customers view a brand, a store, or a style.

Interestingly, shoppers tend to follow some repeated patterns.

For example, a person often buys the same things, behaves in a predictable way and follows similar intuitions.

By learning one buyer’s pattern, another’s might be revealed as well.

 

Human intuition

A recent survey by retail management firm BRP Consulting reported that 45 percent of retailers were planning to increase the use of AI to improve customer experience over the next three years.

For many years, retailers relied on sales associates’ perceptions to determine how engaged customers were.

But humans can be biased, and that information is therefore inaccurate.

Today, AI can combine the intuitiveness of human employees with a machine’s ability to analyze massive amounts of data in seconds.

AI helps retailers understand consumers, improve worker productivity, boost efficiency as well as raise sales.

 

AI-generated recommendations

AI is increasingly being integrated into commerce and retail experiences.

A great example of using AI to recognize customers’ preferences is Amazon’s Recommendation Engine.

Shoppers see items similar to ones they have viewed, as well as others that are commonly purchased displayed prominently alongside the item currently being viewed.

Different recommendation “entry points” are integrated into Amazon’s tool as well, which maximizes cart value.

Recommendation engines help retailers forge a relationship with their customers.

Users can click on the “Your Recommendation” link to display a page that contains categorized products that might be of interest, or they can refer to the section containing similar items with previously viewed products.

McKinsey has reported that up-sell technique is responsible for some 35 percent of Amazon’s revenue.

Small wonder why Amazon has decided to make the underlying technology available through AWS with the “Personalize” tool.

Another instance of a successful personalized user experience is Netflix’s extensive “smart” list of movie and TV show suggestions.

Roughly 70 percent of all content watched by subscribers is a personalized recommendation, according to Netflix.

Shoppers enjoy personalization because it makes transactions feel customized, which leads to a feeling of “specialness.”

And predictive technology lets retailers promote products in a targeted way, which provides customers with a curated experience.
 

Commit to understanding their preferences

By continuously revising the ad content across various marketing mediums, the likelihood of a purchase increases, although retailers must guard against ad fatigue that can easily evolve into ad blindness, ultimately leading to ad blocking.

Customer engagement has always been a top concern for retailers; the cost of cultivating new customers is steep.

Enticing customers to return by demonstrating a commitment to understanding their preferences pays off not just at the cash register, but also in the word-of-mouth advertising that is gained by being intuitive and responsive, which AI solutions are expert at providing.

 

 

 

 

 

 

 

by Monica Quinn

A little data-driven loyalty goes a long way

 

 

Retaining existing customers by keeping them satisfied and engaged is far less costly than identifying and appealing to new customers, yet too often retailers let their regulars drift away.

Why do retailers allow that to happen, especially when the costs of customer acquisition are so high?

Many retailers are primarily focused on expanding their customer bases: More customers equal more revenue.

However, if current customers aren’t nurtured, they begin to feel taken for granted.

Once a customer feels unappreciated, the separation from the retailer begins.

For example, stores will offer introductory rates, special financing, free shipping or percent-off savings to new customers — while leaving loyal customers to feel left out in the cold.

Savvy retailers understand that to win against all the competition out there — online as well as brick and mortar — new customers shouldn’t be favored over the current ones.

Loyalty programs can help show “the regulars” just how appreciated they are.

 

Reward all paying customers.

Cash-paying customers can’t take advantage of certain loyalty programs; some merchants require customers to download an app or link to a credit card in order to get loyalty points.

Retailers are recognizing the problem and are starting to look for solutions.

For example, the AppCard loyalty and personalized marketing platform is a multi-tender solution, which means customers can pay how they want, and still reap member benefits.

In addition, retailers such as Target, which offer rewards through its credit card offerings, are launching a more neutral system in order to reward more customers through its loyalty program.

Being flexible — or payment agnostic —is a smart business strategy, because cash payments make up over a third of all transactions.

 

Understand how customers want to be rewarded.

Customers enjoy earning — and spending — rewards.

A recent survey of approximately 1,000 online shoppers conducted by Bizrate Insights for Internet Retailer found that 70% of respondents wanted free shipping in exchange for their loyalty.

An impressive 61% also enjoyed receiving reward points they could redeem for discounts.

Unpopular perks: early notification of sales (11%) and exclusive access to products or store events (9%).

 

Develop personalized loyalty programs.

Successful loyalty programs “speak to” your customers’ tastes.

Shopper identities can be tied with SKU-level purchase information from POS like Retail Pro, and this data is used to automatically deliver personalized offers that see increased conversion rates and provide a better shopper experience, which in turn gains shopper loyalty.

Deep learning uses that historical transaction data in your POS to provide loyalty programs with customer preferences and anticipates a shopper’s next move, delivering recommendations to increase engagement and prevent churn.

AI built into solutions such as AppCard, help you “learn” your customers’ unique buying cycles and anticipate customers’ desires, delivering the right message, to the right customer, at the right time.

Every retailer should understand what its best customers want and design a loyalty program for those shoppers, based on data analysis.

One loyalty program does not fit all customers, so understanding the differences and how to best reach them is imperative to preventing shopper “drift.”

Using insights generated from unified data, you can build a loyalty program and overall retail experience that helps you put shoppers first.

Book your NRF 2019 meeting with us to start the conversation on how you can unify commerce with Retail Pro.

 

 

 

 

by Monica Quinn

6 Essential Elements of a Winning Independent Retail Strategy

 

 

Looking to improve inventory productivity and control?

Watch this final part of our 3-part Retailer Success webinar series to see principles and tactics that will help you manage inventory better and compete profitably.

Part 3: Six Essential Elements of a Winning Independent Retail Strategy

  • See how high-achieving independent retailers differentiate themselves to compete successfully in the changing world of Amazon retail
  • Learn the major factors you can implement immediately to differentiate your business in this 6-point plan from Management One

 

by Monica Quinn

Join us at NRF 2019 to see unified commerce that puts shoppers first

 

 
You already have the data you need to unlock your business potential.

But when your data is fragmented and scattered across the organization, it perpetuates inefficiencies in customer experience.

Book your NRF 2019 meeting to see how you can connect data in Retail Pro for unified commerce that puts shoppers first.

  • Single point of truth about your inventory, operations, and customers
  • Data-driven operations that improve customer experience
  • Streamlined retail management across digital and physical channels
Book NRF meeting >

 

Not going to NRF this year? Request your consultation now

 
 
 

by Monica Quinn

Customer-sourced innovation: How retailers leverage direct customer insight to drive innovation ROI

 

 

86% of shoppers will pay more for a better customer experience.

Your team can brainstorm and implement ways to innovate CX… but when you implement changes, how do you know which factors help or hinder improvement?

And how do you know what really is a “better” customer experience?

Watch this webinar to see how customer-sourced innovation can help you close the gap between what retailers think shoppers want, and what shoppers actually want – because innovation really shouldn’t hurt the bottom line.

You’ll see:

  • Real stories of customer-sourced innovation
  • How to set and improve on your Customer Experience Baseline
  • Tactics to reduce guessing in your innovation initiatives
by Monica Quinn

Streamline retail operations with integrated POS and ERP data

 

 

Retail is alive with changing trends like omnichannel that give your customers more ways to shop your stores.

With more ways to get your goods into shoppers’ hands, smart inventory and operations management across channels is becoming a bigger focus for serious retailers.

Integrated Retail Pro® POS and SAP Business One® ERP software enables a smooth exchange of data, streamlining in-store and head office operations for greater efficiency.

By capturing retail data from the POS at all your store locations into a single centralized platform, you can access critical real-time information to make fast, informed decisions.

You can even take proactive control through automatic alerts and automated merchandise planning, forecasting, and replenishment – so you always have the right amount of trending inventory in stock to meet demand and sell more.

Want to learn how you can increase efficiency with automated operations for your stores? Request your consultation today.

 

by Monica Quinn

5 types of data to shape your customer experience

 

In today’s real-time global economy, retailers are hunting for strategic optimizations to improve experience and spark long-lasting brand engagement.

Data is key to building those one-to-one connections with customers that give them a loyalty-inspiring, memorable experience. And data is key to keeping your brand relevant in the context of shoppers’ lifestyle needs and interests.

Here are 5 examples of data retailers use to shape shoppers’ experience with their brand.

 

1: Product story

 

 

At their inspiring new London flagship store, United Colors of Benetton shares content on selected products, on the Benetton brand initiatives and, thanks to the use of an integrated RFID antenna, they release technical information about the products that are placed on any of their three digital interactive tables.

Product storytelling draws customers in, inspiring both a purchase and a longer-lasting relationship with your brand.

 

2: Shopper details

 

 

Aesop uses user-defined fields (UDF) in Retail Pro to capture the specific type of data they need for their merchandising, marketing, or clienteling strategies.

The retailer tracks data like skin and hair types for each customer, so their retail associates can make relevant recommendations to returning customers.

Acting on shopper details like these lets customers know you’ve noticed them, and you care that they shop from you.

 

3: Preferences

 

 

Rimowa uses Retail Pro customer management and analytics features to actively manage their customer profiles and mine their demographics data. With Retail Pro, they access and act on their shoppers’ preferences for special editions and colors.

Keeping track of shoppers’ preferences allows you to offer a higher level of contextualization in customer engagement, and ensures you’re giving customers what they want.

 

 

4: Geolocation

 

 

Worldwide Golf Shops use shoppers’ home address to segment their email marketing for store events like sidewalk sales and demo days in shoppers’ area. They also use other customer information collected at the POS to personalize their emails.

Using geolocation data for store events helps you keep your brand a part of shoppers’ everyday life, in a memorable way.

 

5: Purchase history

 

 

Minor league baseball team, the New Orleans Baby Cakes, uses deep reporting in Retail Pro Prism to target and engage with VIP shoppers with fun merchandise unveiling events for those loyal customers.

Today’s fans have many choices of where to buy so stores must constantly evolve to meet customers’ tastes and create a customer experience that provides something for that fan who has “everything.”

 

These are just a few examples of how your retail data can shape customer experience. Here at Retail Pro, we care about helping you build a strong foundation for powering a unified experience of ease and inspiration across stores and digital retail.

Visit us at NRF to see how you can unify your digital and store experiences for unified commerce that puts shoppers first.

 

 

Photo credits: United Colors of Benetton, Aesop, Worldwide Golf Shops, Ryan Micklin