A loyalty program not only identifies shoppers but also incentivizes them to be identified. In exchange for points, punches, or cash back, each shopper will raise their hand and self-identify during each transaction.
Second, through AppCard’s integration with Retail Pro, we are able to track each transaction on a SKU level in real time for each purchase. We then marry these two bodies of data, creating a shopper profile with a running historical database of transactions.
This allows us to segment shoppers based on behavior and transaction history to target them with relevant marketing content.
Choosing a loyalty program
It’s not hard to argue the benefits of a loyalty program. But sometimes, the hardest part of this process is navigating the marketplace – there are tons of loyalty platforms out there.
We’ll break it down into three “generations.” We like to look at the types of technology as generations since each generation is an improvement to the previous.
First-generation loyalty solutions: Punch cards
The first generation of loyalty and rewards technology launched about 11 years ago.
These pioneers of the digital loyalty industry evolved the infamous paper punch card into a plastic card that could be scanned at the register.
These tablet-based systems rest on the store owner’s counter and give customers points for entering the store.
Customers earn punches no matter what they purchase, which allows $2 customer to earn the same as a $20 customer.
These devices also give customers the opportunity to sign up with their email address, allowing the business to market to them via email.
Third generation loyalty programs are those that integrate with the point of sale, allowing the business to track specific customer purchases, down to the SKU.
With AppCard for Retail Pro, retailers can gain a consolidated view of their transaction data alongside customer information across multiple locations.
This level of customer insight enables you to automate points and rewards based off purchases and amount spent, and you can start to learn customer buying behaviors and market to them individually based on their own spending habits.
AppCard uses machine learning tools, which allows for a much more targeted, and automated marketing process.
Using holiday shopper data to shape your marketing
So now we are going to apply the marketing concepts we just reviewed to the annual opportunity that is the Holiday season.
To do this we will start by asking the questions we need answers to in order to develop and execute on our targeted marketing strategy.
What is the holiday shopper opportunity?
So our first question is “What is the holiday shopper opportunity? To answer this let’s take a look at some of the statistics from the recent holiday season.
Our first stat from NRF shows that businesses see an uptick of up 20% during the holiday season. Third Door Media shows that the average business makes about 20% of their annual revenue during the holidays.
Finally, and a little more dramatically, NRF reports that over $625billion dollars are spent during the winter holidays.
What this amounts to is a major increase not only in revenue but a major uptick in foot traffic in your store.
The question is, how do we take advantage of this?
Identify & segment shoppers
To fully take advantage of this spike in foot traffic and shopper revenue we need to identify these shoppers and segment them in groups that tell us who they are and how we should communicate with them.
On a high level we will be able to segment shoppers into two groups, our regular shoppers and first-time shoppers. This seems obvious, but something as simple as knowing whether they are regulars or new shoppers allows us to curate our marketing to communicate more directly.
Our next segmentation effort will be on the transaction level. This is more granular segmentation that will allow us to create more personalized marketing communication.
For example, if a shopper buys a new pair of athletic shoes, we have two specific opportunities:
Make a recommendation to them on an additional and complementary product like a pair of athletic shorts
Create marketing based on the average life cycle of the product. A pair of athletic shoes typically should be replaced once every 6 months, so you may use this type of segmentation to send what we refer to as life cycle marketing messages whereby you target holiday shoppers that bought athletic shoes 6 months after the purchase to come back for a new pair.
We can also identify shoppers on a tiered level based on how much they spend.
How does segmentation work?
How are we able to segment these customers? On the back end, we're building a profile based on what they've purchased, how often they shop, and what their preferences are.
For example, one shopper, Jessica Smith, shops every 30 days, has spent nearly $3,000 in the past year, and is in the top 12% of shoppers based on her spend amount.
Using this information, we can tell she is a loyal customer and we should encourage her to bring friends in, and reward her for doing so! Shoppers like Jessica can be some of your strongest brand advocates.
According to a recent study by Motista, consumers with an emotional connection to a brand have a 306 percent higher lifetime value and stick with a brand for an average of 5.1 years vs 3.4 years. Further, they tend to recommend brands at a much higher rate: 71 percent vs. 45 percent.
Building your marketing strategy
Customer segmentation allows us to identify specific data points about our shoppers. These data points are useful year-round and can help us to create a data driven strategy that boosts revenue year-round as a opposed to just a flash in the pan effort.
Our strategy will consist of 3 stages, short-term, mid-term and long-term, allowing us to target shoppers year-round.
Our short-term effort will be based on high level segmentation and a little creativity.
This segment is designed to generate immediate revenue post-holiday season, build relationships with regular customers, turn new customers into regulars, and turn top shoppers to brand promoters.
Our mid-term effort will target customers based on a product purchase and will be designed to stabilize a shoppers buying cycle and generate complementary purchases.
We will do basket analysis on how we can move shoppers upward in these segments.
To generate immediate revenue, we will send two marketing messages, one to the regulars and one to the first-time shoppers. This will be very easy as we will be able to create one template for both groups.
Your regular shoppers will need less motivation (10% off) and less incentive to make another purchase.
The new shoppers will need something slightly more enticing (20% off.)
The goal is to provide your business with immediate post-holiday revenue. The by-product of this is to show existing shoppers your appreciation, further cementing your relationship, and to jump start a return visit from new shoppers.
Two campaign strategies include:
Gift Card Campaign
Target shoppers who purchased gift cards with an incentive to “treat yourself” or forward a discount to the gift card recipient to get the most out of their gift!
It’s true that lots of shoppers through the holidays might not be your target audience for repeat purchases, however, encourage them to leave a review on your social media pages to share how well the gift was received!
Our mid-term strategy allows us to get more personal with our shoppers and begin communications based on specific product purchases. There are two methods we can call on to utilize this data and incentivize additional store visits and purchases.
Life cycle marketing
The first is what we at AppCard refer to as life cycle marketing. In this strategy we look at the items that shoppers have purchased and target them with a reminder message to make another purchase based on the likelihood they will need to replace the item.
An example could be: “Time to re-up on your new fragrance! Shop our fragrances and earn 25 bonus points!” This works well with items like cosmetics, fragrances, and skincare.
The second method is recommendation based. In this manner we can look at what shoppers have purchased and send recommendations for complementary products. We often refer to this capability as digital styling, as you can take a peek into your shopper’s closets, see what’s on the hangers, and then make recommendations to them on additional wardrobe purchases.
An example of this could be “How did you like your handbag? Shop our latest wallets or accessories to find the perfect match and receive $10 off!” This works well for apparel purchases.
This type of marketing can be throughout the year, but the biggest opportunity lies in your holiday data, as there is a sizable increase in purchases during this period so you will have a larger shopper base to target.
Our long-term opportunity lies in preemptive communication leading up to the next holiday season.
By segmenting your shoppers by who made holiday purchases the previous year, you can begin the holiday marketing season early with a more personalized approach.
We recommend targeting these customers in early November with a message to get their holiday shopping started early with a larger incentive. You will also want to give a reason for this to help build on your relationship.
The message should look something like this:
“It’s that time of year again and we wanted to reach out to our best customers! Get started on your holiday shopping and save a little money this year, too.”
You can also target customers who shopped at your store on Black Friday or Small Biz Saturday, providing them a secret incentive for next year – target these shoppers with a special gift.
You can also target customers using their favorite brand with new inventory and updated versions of items purchased last year. Examples of re-purchase strategies could be: “Treat your special someone with this year’s hot new watch styles! Shop with us this holiday season and receive $10 off!”
Winter apparel and children’s apparel is also a great opportunity for repeat visits. An example of this could be “Patagonia’s latest children styles have arrived at our shop! They grow up so fast, enjoy $10% off a purchase of $100 or more.”
A few tips
Before we get into tracking ROI, I want to leave you with a few tips and hints to running a successful system.
1. Train your employees
I am going to start this section with a statement: The success or failure of any customer facing initiative will rise or fall on the shoulders of the customer interfacing piece of your business. In the brick and mortar world, this is your cashiers.
In order for your customers to participate in the loyalty program your cashiers must interact with them and acknowledge the program during every transaction. This means that part of the checkout process must now include a simple question asked by all of your employees. They must simply ask each shopper the following: Are you a rewards member? What is your phone number?
By asking this simple question, your participation rates will increase by 500%. Statistically, if you rely on your shoppers to remember that there is a program, instead of reminding them, you can expect a 10%-20% participation rate. If they ask this question, you can expect upwards of 70%. (AppCard Stats)
Uniquely, AppCard tracks your employees and the percentage of transactions that each employee does using the loyalty program. This will allow you to identify who is engaging shoppers and who needs a little TLC to get with the program.
2. Give shoppers incentives to join
The second tip is to provide shoppers with an incentive to join the program. The incentive can be $5.00 off your next purchase. This is particularly powerful during the holiday season as shoppers look to save a little money where they can.
You should look at this not as a discount you are giving, but rather a purchase you are making. You are paying your shopper $5.00 to give you their identity so that you may track their purchases and curate relative content to them to increase your conversion rates.
3. Give cashiers incentives to enroll loyalty members
Lastly, it’s fun to run cashier competitions, incentivizing cashiers to sign up the most members. Perhaps you can provide them with a gift card at the end of the month or allow them to pick their shifts for a week.
Now we are at the fun part. Suppose you choose to implement a system and strategy like AppCard for Retail Pro, you identify your shoppers, track their transactions, segment them into groups, and send them highly relevant marketing content.
How do you know that it worked?
This is the question that all marketers hate. Traditionally it has been very difficult to prove ROI, but not anymore. With a system like AppCard for Retail Pro, you have all the ingredients and tools that you need to answer this question with confidence.
You have the shopper’s identity, you have the list of shoppers that received the marketing content and you have the ability to not only track which ones came back to the store, but also what they purchased and how much they spent as a result.
For instance, you can target 3,000 shoppers that bought tennis shoes, send them 10% off their next pair and track that 378 of them came back in and made a purchase. You can then see that the 378 people spent $4,719 as a result and now you can quantify, to the penny, exactly how much revenue your customers have spent as a result. Pretty cool right?
Now there will always be someone that asks, “Well how do I know they wouldn’t have come in anyway?”
Use a control group
One of the most powerful ways to prove marketing ROI is to identify a control group, along with your target audience. A control group is a set of people who fit the criteria you’ve assigned to the campaign, but to whom you do NOT send the offer.
At the end of the marketing campaign, you then compare how the members of the campaign performed from a visits and revenue perspective against the control group, who did not get the offer.
This is how you can truly determine whether your marketing offer and messaging had an impact on the target audience.
This is the same concept we can now use to prove ROI in our marketing. If we send a message to 3,000 shoppers that previously purchased tennis shoes, and told them that because they bought tennis shoes, they get 10% off a pair of inserts, it would be hard to say that any of them returned just because of the message.
If we created a 10% control group, and 300 of them received no incentive, we could measure the purchasing pattern between the two identical groups and determine the true effect of the marketing.
We could prove that the 2,700 people that received the message spent an average of 11% more than the those in the control group and prove true ROI.
Meet Pinky, AppCard’s AI brain
Let me introduce you to your marketing secret weapon: Pinky. Pinky is AppCard’s AI solution, which analyzes your transaction data in real-time daily. Using your company's transaction history, Pinky forecasts your likely revenue by market up to 30 days in the future.
The forecast helps you to make staffing arrangements by location, predict Inventory needs and budgetary requirements.
Pinky now averages a 2.5% margin of error, making his revenue forecasts more accurate than Facebook's neural network tool.
AI analysis for campaign results
Just to take this one step further Pinky analyzes campaign results, including lift from control groups, to optimize campaign targeting. This basically means that AppCard’s AI learns your shoppers’ habits in conjunction with their responsiveness to campaigns to deliver the right message to the right shoppers at the right time.
In this real-life example from one of our retailers, Pinky targeted shoppers who missed a predicted visit with an offer for $1 off their next visit.
Within 1 week of the text message campaign, 68% of shoppers visited the store to redeem their discount.
How it works
AppCard will upload your current customers to the platform, allowing you to consolidate customer lists from your POS, current loyalty platforms, email marketing systems (Mail Chimp, Constant Contact, etc.) and texting services you may currently use.
Consolidating these CRMs will allow management the ability to manage their databases under one umbrella. Appending transaction history will allow you to automatically send personalized offers and incentives based on past transactions. We do not limit the number of subscribers, which our customers love. You receive unlimited data driven email marketing via AppCard.
In today’s real-time global economy, retailers are hunting for strategic optimizations to improve experience and spark long-lasting brand engagement.
About 85% of companies think AI will offer the competitive advantage they’re after, but only one in 20 (5%) is taking advantage of its capabilities today, according to a report by MIT’s Sloan Management Review and the Boston Consulting Group.
Retailers seek out AI-driven technologies to profitably engage customers and reduce time, cost and error in decision-making.
Research Gate reports, “AI allows retail to gain sharper predicting tools that ensure the making of sharper business decisions. Algorithms intensify the ability to view business implications and translating results like higher sales and lower costs through customer service, product inventory, and staffing.”
But for many retailers the data needed for actionable insight is fragmented and scattered across the organization, perpetuating skewed learnings and inefficiencies in customer experience.
Clean, connected data is indispensable for AI.
With Retail Pro, you have the critical, advantage-gaining resource for AI: years of structured POS data. AI learns from your data for you, giving insight for helpful customer engagement that strong operations alone cannot deliver.
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
Note to retailers: Be willing to disrupt the ways you sell products to customers.
That was one of the key messages at the National Retail Federation Big Show 2019 in New York City earlier this month.
How can disruption work to a retailer's advantage, when common sense says customers appreciate stability and a sense of continuity when shopping at their favorite stores?
Sometimes, familiarity breeds contempt, as the old saying goes.
Don't be afraid to try something new, particularly if research backs up your instinct for change is correct.
Here are three ways retailers can use disruption to their advantage to delight the customer.
1: Be human
Many retailers have adopted technology that helps them respond more efficiently to business needs, but they should also be meeting customer needs effectively.
Break away from a technology for technology's sake mindset.
Every store's competitive advantage is its staff.
From founder to sales associate, those are the people who set the tone and the sale environment.
The customer wants to be uniquely known in a way that is meaningful to him or her, said Lindsey Roy, chief marketing officer of Hallmark Greetings, during her closing keynote for the NRF's Student Program.
Customers are sensitive to a brand’s authenticity and they notice its in-person interactions as well as those through social media.
While technology can fulfill some vital company tasks such as inventory requests, point-of-sale needs and logistical information, providing a meaningful human contact is crucial to nurturing a customer connection.
Customers should feel as though a retailer values their business enough to provide an associate to assist when necessary.
2: Get physical
Brick and mortar stores are becoming important as a way for retailers to combine online and in-store experiences to engage meaningfully with today's consumers.
Digital brands are now opening physical locations; offering an in-store experience is a key retail differentiator.
Despite some very convincing chatbots in the e-commerce world, shoppers enjoy the rush of adrenaline they feel when they find the just-right product in a physical store.
Even Amazon, the creator of the ultimate product recommendation engine, is acknowledging the importance of having a physical presence with its launch of Amazon 4-star retail stores.
So far, the Amazon 4-Star locations offer top-rated products, curated for each specific location.
They are designed with the "discovery shopper" in mind.
Encouraging that sense of wonder in shoppers strengthens the bond between retailer and customer and fundamentally promotes loyalty.
3: Use your data
Today's retail needs technology, but it should largely be implemented to improve how associates interact with customers.
By collecting and aggregating customer information, stores can provide richer experiences for shoppers.
Retailers that don't correctly identify customer pain points run the risk of rolling out expensive technology that doesn't enhance the shopping experience.
Technology is not a substitute for the human touch.
A recent survey by PWC found the payoffs for valued, great experiences are significant: up to a 16% price premium on products and services, in addition to increased loyalty.
Artificial intelligence can gather data using chatbots, for instance, and then use that information to assist employees who are busy working to satisfy customers' needs.
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.
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.
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.
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.
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.
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 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.
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.
Retail Pro International (RPI) is a global leader in retail management software that is recognized world-wide for rich functionality, multi-national capabilities, and unparalleled flexibility. For over 25 years, RPI has innovated retail software solutions to help retailers optimize business operations and have more time to focus on what really matters - cultivating customer engagement and capitalizing on retail's trends. Retail Pro is the chosen software platform for omni-channel strategy by retailers in 130+ countries.