Richard Schenker is the Founder & Chief Customer Engagement Officer of Loyal Strategy Consulting and he is also a seasoned loyalty executive who talks with experience about Enterprise-wide data utilisation (DWDU).
We debate the difference between DWDU and customer-centric retailing and the interview reveals many aspects of DWDU in the retail environment.
Richard explains how data insight drives price and promotional optimisation and can be deeply used for category protection, focusing also on product development or product delisting decision making. Richard also advises how data insight can be used in guerrilla marketing rather than blindly following a marketing campaign.
Hosted by Amanda Cromhout
Paula: Welcome to Let’s Talk Loyalty, an Industry podcast for loyalty marketing professionals. I’m Paula Thomas, the founder of Let’s Talk Loyalty. Today’s show is hosted by my colleague Amanda Cromhout, The Founder of Truth an international loyalty consultancy firm based in Cape Town, South Africa. If you work in loyalty marketing, make sure to join Let’s Talk Loyalty every Tuesday, every Wednesday, and every Thursday to learn the latest ideas from loyalty experts around the world.
This episode is brought to you by Collinson, worldwide leaders in customer engagement and loyalty, creating an orchestrating customer engagement and loyalty initiatives and programs for some of the world’s biggest brands in travel, retail, and financial services. Doing it globally for over 30 years. Want to know more? Go to collinsongroup.com.
Amanda: Today I’m delighted to interview Richard Schenker. Richard is the Founder and Chief Customer Engagement Officer of Loyal Strategy Consulting. He has an incredible loyalty pedigree, having spent eight years at Bond Brand Loyalty. Previous time, also at Loyalty One and Shoppers Drug Mart and Hudson’s Bay Company.
So some incredible loyalty brands in the mix there. What Richard takes us through is how enterprise-wide data utilization really helps a business, achieve the results it can via to focus on the customer insight, we debate the difference between enterprise-wide data utilization and customer-centric retailing.
But then go on to really unpack how the enterprise-wide data utilization benefits retailers. There’s a focus on price and promotional optimization. We also discuss category protection and product development, or possibly product delisting, all led by customer insight. One of my favorite sound bites from the interview is how Richard talked about this data utilization in informed Gorilla marketing against one of the biggest brands in the world. I hope you enjoy the interview as much as I did chatting to Richard,
So it’s with absolute great pleasure today that our welcome, Richard Schenker from Loyal Strategy Consulting. Richard’s, the Founder and Chief Customer Engagement Officer. Richard, welcome to Let’s Talk Loyalty.
Richard: Thank you very much, Amanda. I’m delighted to be here. I’m a longtime listener of this podcast.
Amanda: Well, I know you’re gonna take us through your sort of loyalty career, but I think for anybody in the industry, um, knows well so many of the brands that you’ve worked with. So we can’t wait to hear about your loyalty career. But I think before we get into that, as you well know, there’s one very well known first question of Let’s Talk loyalty.
What’s your favorite loyalty program? .
Richard: Well, Amanda, as far as I’m concerned, a well-designed loyalty program is one that fosters both transactional and emotional loyalty to the brand proper. And in my view, Sephora’s Beauty Insider program in North America does exactly that. And let me tell you why. It has a well-balanced, easy to understand and attainable earn and burn structure that motivates and encourages transactional loyalty, their rewards bizarre.
With its reward specials, creates exclusivity, scarcity, and lots of excitement for their members. The Beauty Insider Program also employs three tiers as a mechanism to provide motivation for members to increase their financial commitment to Sephora. Now, I can tell you firsthand as someone who has three females in their household, that being a Rouge Tier member is a status symbol.
It’s also badge of honor, and it actually has bragging rights associated with it. Uh, Sephora has created various exclusive and coveted benefits that Ruggers cherish from special. Exclusive product invitations and much, much more. The Beauty Insider Program also has a renowned community, which is the envy of any loyalty community.
Now, this is where like-minded members are able to figuratively let their hair down and authentically share tips, tricks, and beauty regimens with member peers and sapora beauticians. It’s a place where members can immerse themselves in the Sephora brand experience. Now, I would say that the beauty inside of your community also helps to instill inner and outer confidence for members in how they best use Sephora products and achieve the beauty outcomes that they’re seeking.
That is real emotional loyalty brand building at its best, and that’s why Sephora is my top. Okay,
Amanda: so I, I think what is super interesting for, uh, listeners to hear about Richard, is your career has been very varied, but also incredibly robust in a sense of loyalty, pedigree, , and, um, you’ve just launched your own business, loyal strategy consulting, and prior to that you were with a very well known.
Company that I think many people on this podcast have heard of before, bond brand loyalty, but obviously you’ve got retail experience, you’ve got financial services experience, and so much more. So rather than me talking through, can you give us a short background to your loyalty career?
Richard: Yeah, sure, Amanda.
Um, I’ll, I’ll try to keep it as short as possible. I’ve been in the industry for probably more years than I’d like to admit, . Um, but, but I actually began my career with a Hudsons Bay Company, which is Canada’s premier department store, and incidentally, the second oldest continuous company, uh, in the world.
It was actually incorporated in 1670 by Prince Rupert out of England. So it’s got a, a real legacy and, and history behind it. Uh, when I was there, I actually. Proprietary credit card marketing activities. I designed and launched their first formal loyalty program and built a coalition of automotive partners as, as part of that, uh, I actually then moved on to Shopper’s Drug Mart, which is Canada’s largest drug store chain, and built their renowned shopper’s optimum program and managed their financial services partnerships.
And then I transitioned actually to the loyalty agency side of the business after Shoppers Drug Mart, where I held, uh, several senior leadership roles with Loyalty One. Uh, for those who don’t know, uh, loyalty One is the owner-operator of the AOLs Reward Program in Canada. Yeah. And, um, yeah, I ma I managed, uh, about 15 different key partner relationships and we actually attempted to bring coalition into the us.
That’s a story for another podcast. Um, then as you mentioned, I moved to Bond brand loyalty and, uh, I was, most recently I was the managing director of the consulting practice, where I led a team of, uh, consulting professionals, uh, who designed and redesigned, uh, customer engagement and loyalty solutions for many iconic brands.
Across just about every business sector. And as you mentioned, last but not least, I’m excited, uh, to announce today that I’ve transitioned and, uh, started my own new company called Loyal Strategy Consulting. And, uh, my role at Loyal Strategy Consulting is, uh, chief Customer Engagement Officer. And, uh, the practice is really focused around, uh, helping brands, uh, develop customer engagement, uh, solutions, uh, loyalty program.
And customer experience management and several other types of services. So NetNet, I’ve spent my entire career in the business of customer loyalty.
Amanda: Yeah. Lovely. Thank you for sharing that. It’s super interesting and yeah, all the very, very best for Laurel Strategy Consulting. I can’t wait to watch it grow.
Thank you. So Richards, um, we had a lovely chat prior to this interview where we really connected on a subject. Became the obvious discussion point for today, and we both share a passion for data utilization. So, um, I’d like us to start off with your definition of enterprise-wide data utilization. .
Richard: Yeah, I think that’s a great question and, uh, something that our listeners are gonna be super interested in, in, in my experience, many brands typically only use loyalty for campaign marketing purposes, and my belief is there’s a real missed op opportunity here, Amanda.
Um, as your listeners know, launching, managing a loyalty program can be a very costly proposition, and any company that’s not using. Loyalty data across the entire business to make better informed decisions is not making the most out of their investment and loyalty. So, uh, to get to your que that answer to your question, enterprise-wide, uh, data utilization is really defined as the discipline of harnessing loyalty across every facet of the business to create efficien.
Foster better decision making and of course grow sales. So for instance, let’s, uh, let’s take a retailer with a loyalty program. Uh, their loyalty data should be harnessed beyond just marketing campaigns to grow basket size, your frequency of shop. It should be used strategically and collaboratively inside of the organization to make better decisions around things like merchandising.
Um, you know, optimizing their operations, uh, creating a better branded experience and even real estate decisions. And this is where I see a significant opportunity for organizations that are not harnessing and leveraging data across their. .
Amanda: Yeah, I couldn’t agree with you more. I mean, I feel so passionately about it.
That’s why I really enjoyed our, our pre-discussion. Yes. But we did actually have an interesting discussion cuz I tended to, I tended to obviously in retailing use the terminology customer-centric retailing or customer-centric utilization of data. So is it different? It’s customer, customer-centric, returning different, for example, from enterprise-wide data, utiliz.
Richard: That’s a, that’s a really good question. And I, I would argue that customer-centric retail is in the same family or the same discipline as enterprise, uh, data utilization. Uh, I would say with enterprise, why data utilization? It’s inherently assumed that the customer is placed at the center of all decisions.
Based on the data that’s gonna be used, which is very similar to customer-centric, uh, retailing. But let’s also keep in mind that enterprise-wide data utilization is not just confined to a retailer as, uh, as you shared customer-centric retailing. Um, I would advocate that, uh, , every business should be using data across their organization.
So let’s go beyond retelling. Let’s take a financial institution. Uh, having a 360 degree view of customers is of paramount importance. And actually what we’re seeing in the FI sector today is. Many loyalty programs are expanding right across, uh, the entire lines of businesses of, of, of the banking community.
So typically loyalty programs in the banking sector started with credit cards. They migrated to debit cards. Now there’s issuance or redemption on deposit accounts, lines of credit, uh, wealth management, mortgages, insurance. Uh, so we’re seeing. That these banking institutions are placing a value on garnering data across all lines of business to make better informed decisions, and most importantly, to consolidate their customer’s banking needs with their brand.
Amanda: Absolutely. Absolutely. It goes beyond retailing and it’s super interesting to hear about it in, in non-retail retailing organization. Cause I think it is so it’s better known in the retailing environment. But back to retailing. Cause I do think, um, there’s a lot here for us to just unpack for now.
Um, how do you feel this all fits in with some of the more common use terms like promotional and price optimiz?
Richard: So I’m happy you’re going back to retelling cuz I’ve spent many years in retelling and that’s, that’s really where my passion is. Um, let me take an example of a high frequency retailer, let’s say a grocer or a pharmac, a pharmacy chain.
Just to better illustrate this for our audience. Um, in terms of promotions, um, understanding how each customer segment responds to a product promotion, uh, can provide category managers with tremendous insight. Um, traditionally, some of the metrics that are used by category management are metrics such as unit movement, uh, sales margin, really to assess the success of a, a category or product promotion.
Now, With customer loyalty data, we can actually now start to dissect results to really understand who the promotion did or did not appeal to, and even why. Um, it, it really does provide a deep and rich understanding into the impact of, uh, promotion beyond just the aggregate product. Uh, Category results, we can actually now drill down right down to the customer level to start to unearth responses specific to a promotion.
So the insights gleaned from that type of analysis can actually be very vital and they can actually facilitate, uh, excellent campaign learning so that can permit retailers to create more relevant, more targeted promotions and offers and, uh, greater content in future promot. So that’s, that’s what I would say about the whole notion of promotion.
Um, you asked around, uh, what is, what are the implications around price optimization? So, um, I would advocate that offering a one price fits all strategy can. Only lead to forgoing margin dollars. So, yep. Um, as we know, different customer segments place different values on price discounts. So let’s take a grocer as an example.
A traditional grocer might have different customer segments. They may have a convenience segment, they might have a price sensitive, uh, customer segment, a price insensitive customer segment, maybe a quality seeking segment. Or a scratch cooker segment. I, I think you get the idea. There’s various different, uh, segments within a grocer’s, uh, customer base.
But understanding the key drivers of the relationship will allow the grocer to invest differential against each of the segments. Um, so for instance, uh, if we were to take something like. , uh, case soda, which is on the front page of every grocery flyer. Circular . Yeah. Uh, as we know, uh, because it’s a traffic driver, it’s typically a lost leader and, uh, it typically does garner some cross sell, but it can actually have a detrimental financial impact that a gross wouldn’t necessarily see by examining aggregate sales.
So as an, as a for instance, the price and sensitive segment will always purchase soda regardless of the price and the portion of the price sensitive customer segment will cherry pick and stock up on soda at a deep discount. usually below cost and not purchase anything else in the store. Um, I often say that we’d be better off paying that customer a couple dollars just to stay out of the store because they’re just eroding margin in the store.
Um, you know, having customer loyalty data at your fingertips would allow the grocer to make a different pricing decision and preserve margin erosion. So, um, they could precisely market discounted soda to only those customers who purchase soda at full price. And have a propensity to cross shop or increase frequency of shop during that type of promotion.
So again, these are critical insights which your category manager can use to actually grow sales profitably. Yeah,
Amanda: I mean, I, I absolutely love listening to you because, especially because of this protection of margin. I mean, I always used to use this, a very simple example in my retail days of. Like there’s a lot of customers who, you know, everybody, regardless of income is price sensitive to toilet bleach, for example.
Yes, but nobody really is gonna be that price sensitive to chili infused olive oil. You know, if I’m in the, if I’m in the caliber of cuisine that I’m, I want to find that particular product for the dinner party tonight, then it doesn’t actually really matter. How much it’s gonna cost me within reason. So, you know, and, and it’s a big, um, it’s a difficult story to often persuade particularly.
um, everyday low price retailers cuz they, you know, everyday low price means everything’s gotta be the lowest price. But actually no, not necessarily. So, um, I love this debate and I find it very fascinating. So it’s good to share stories with fellow retail passionate individuals, , um, while we’re still in the story of retail, um, , let’s move completely really outta the marketing mix in a way, and talk about more practical operational things like how does data utilization help, you know, with things like a retail layout, the store layout, and so on.
Richard: Yeah, so I, I, as I mentioned at the outset, um, It’s really important to leverage the data across the business, and we’ve seen examples in the marketplace or organizations such as Dun Hume and others who’ve worked with the likes of Tesco and Kroger to leverage data right across the organization. And these decisions are being made based on customer specific data.
So as it pertains to store layouts, um, loyalty data can be used by retail merchandising teams to really better understand how customer. Their stores. You know, in fact, uh, several high frequency retailers use best customer transactional basket data to actually inform the fiscal department locations, the category or product adjacencies and even planogram decisions, you know, where that product sits within sort of the grouping of products.
So having, as we all know, having the right product in the right location can be absolutely vital to driving sales. So, For instance, um, if a Grocery’s best customer segment happens to be, let’s call it a time star customer, typically time star customers are price and sensitive and understanding how that customer shops the store and what products.
And categories he or she shops for is highly beneficial if that customer expects to, you know, quickly pick up a product that they need. Um, building merchandising layouts to better suit that high value customer segment will absolutely ensure that the grocery retains the high value customer. And additionally, building sort of natural, physically located category and product level adjacencies will all lead to enhanced cross-sell.
And several more sort of natural and convenient promotion, uh, promotional opportunities. So really important to start to leverage data based on how your high value customers shop your stores. Definitely.
Amanda: Absolutely. Like, uh, I mean this, we’ve had, um, in a, in a less strategic viewpoint actually, I’ve got an experience of.
How the operational team needed to rush out merchandise cuz it was a very busy sale period, or what the particular pressure was at the time. And just using the enabling the store manager say, wait a minute, this store in broad brush terms is more skewed towards the kids wear shopper versus the men’s wear shopper to replenish.
Yeah, kids were, or put, kids were in a more prominent position and so forth, just, and it was so obvious for those of us with the data and so refreshing for the guys who are having to make those decisions otherwise. So, um, I enjoy your view on that enormously. I think that’s, yeah, it’s
Richard: very important. It’s very important.
Sorry to be very agile in terms of being able to move. Things around based on what the business needs are. So, um, those organizations that are successful in terms of using the data also have to be, as I said, very flexible and willing to actually, uh, make investments in the data, but also make operational changes at store level.
Amanda: Yeah. Yeah, yeah, yeah. And that, that takes quite a lot of change management to get the store Absolutely. Work or leadership to be able to do that. So it doesn’t happen overnight, as we all know. I mean, if we think about the use of, um, staying in the retail environment, you know, how does this data drive?
We’ve talked about product decisions in terms of promotions and price, but actual longer term decisions like, um, product development or removal of products from the store.
Richard: Yeah, I think, um, for retailers, the biggest opportunity, uh, around harnessing. Uh, loyalty data for, let’s say new product development and delisting products as this follows.
So if, let’s say for instance, a, uh, a grouping of customers are buying a lot of national brands and let’s say the health and beauty area of a pharmacy, and, uh, they tend to be price sensitive customers, we would want to ensure that the organization. Complimentary private label products in those particular, uh, groupings or categories.
So using loyalty data to understand what type of customer you have, whether they’re buying on price or off price. Uh, are they price sensitive? Uh, are they, uh, buying a lot of national brands? Do I have pockets where I don’t have. Complimentary private label brands that can be sort of the germination of understanding what products I should be bringing to market from a private label perspective.
And then similarly, in terms of product you listing, uh, wouldn’t it be helpful to understand which customers would be impacted by the discontinuation of a product? Often, what tends to happen. Products are discontinued because of either poor sales or there’s a new, better sort of model that’s coming out in the marketplace.
Yeah, so a product gets retired. . Now, the problem with aggregate sales data is, it doesn’t tell the full story, but customer level data will allow the retailer to better understand the implications of such a decision to retire a product skew. So, um, you know, I, if you have a high value customer who indexes high across virtually every category in your store, and you’re removing their favorite product because you’re discontinuing it.
you’re potentially at risk of seeing that customer a trite and leave to the competition. So, um, you have to really understand the basis of who the de-listing is gonna actually impact. If it’s a, a low value customer that typically buys the product, then probably less of an issue. But if it’s a high value customer, you may decide not to retire at that.
or what you may wanna do as part of your loyalty program, you may want to give them the first right of opportunity to stock up on that product if you actually have to retire that product.
Amanda: That’s a great idea. Like, I mean, there’s gonna be times when operationally a product has to be retired, I guess.
Especially if there’s a forecast of, um, Just of change as you, as you’ve said, new, new versions coming out and so on. But it can be so frustrating for the user if that’s their favorite product. And yeah, if your most loyal customers are gonna get super upset by that, what an opportunity to use the loyalty program rather than just kind of not, you can’t not delist it cuz you actually have to.
So I like that. I like the combination of sort of the customer service approach and the marketing approach combined with the data of which customers should. Should be sort of treated with kit gloves to, to make sure they’re, they’re well looked after. Definitely. Um, what about taking this to the next level, like almost.
Rather than just, so thinking about it from a product point of view, but the whole, uh, category environment. How have you seen enterprise-wide data utilization play out for, from a category protection point of view?
Richard: So, Amanda, this is the one that I’m actually most passionate about, and, uh, I’m passionate about this because I’ve actually lived through this with two organizations.
So I’m gonna, I’m gonna share with your listeners as much as I can to sort of protect the innocent . Um, . But, uh, let’s talk about two large, high frequency retailers and, uh, this is a pretty compelling story. So let me start with the drugstore chain example. So many years ago, while I was working for this chain, we actually built a highly advanced analytics team that worked in tandem with the category management team.
And their role was to essentially examine all customer loyalty transactional data. What we unearthed was that there was. There were two key product drivers that were germane to their relationship of the most valued female loyalty member cohort. And it came down to these two SKUs and these two SKUs, which I can’t reveal.
Uh, were actually in the health and beauty category. So when those two SKUs were actually present in the customer’s basket, those members actually over-indexed substantially in their spending across all of the key categories in the store. So as you can imagine, this was a really crucial insight that was used to make some very, very big merchandising.
Decisions and changes. So consequently, um, action was taken to ensure that the company had the greatest breadth and depth of product selection and at the best possible price in all the categories where which these two SKUs actually belonged to. Additionally, uh, the location of this category was altered and changed for the customer shopping convenience.
all the stores across the chain and complimentary adjacent categories were now re-situated, um, based on this vital category. So, , you can see this could have never been, uh, found through aggregate sales data. Um, because we were looking at a slice of customers, the highest value customers, and understanding the basket composition of those customers.
So having that sort of personally identifiable, uh, information was really the golden, uh, needle in the haystack for this organization. So, That was example number one. Let me move into example number two. So, uh, again, many years ago I worked with a grocer in Texas to understand the key drivers of the relationship between, um, the brand and some very high value customers, and again, through advanced analytics.
Uh, that we, uh, that we operated for this organization. We discovered that there was one SKU that was present in this cohort’s basket. And, uh, when that SKU was purchased, the customers actually over-indexed, uh, across a multitude of everyday grocery. Shopping categories and stock up categories.
Unfortunately, at that time, Walmart actually started to move into the trade area of, uh, this particular grocer, and we started to discover, uh, a fair amount of attrition of the proportion of these loyalty members from the grocer. . What we found out through customer research was that these customers were actually buying the same product, albeit inferior and quality at Walmart.
And because they were going to Walmart, they were moving their entire grocery spend and their stock up spend. So all of a sudden this grocer was not just losing Yeah. The one product spend, they were losing the entire, you know, uh, grocery spend. So it, as you can imagine, it was a, a substantial thing. So knowing what we did know about the inferior quality of the product, uh, at Walmart and the loss of the full grocery shop, we actually developed a targeted campaign to educate the customer about the merits and the benefits of the quality of that particular product.
at the grocer versus the inferior quality at Walmart. So it was a, it was a pretty hard-hitting, uh, campaign to really take a shot at Walmart. Um, this was coupled with a significant continuity promotion offer, uh, using bonus points and price discounting that really provided the impetus for those who tried to customers to come back.
And I’m pleased to report we didn’t win all the customers back, but we won. I would say a, a good majority of those customers back, not just to buy that particular product, but as they came back for that product, they came back and started to spend in all the everyday grocery categories and the stock up categories.
So again, this is another great example of sort of that, that golden needle in the haystack of loyalty data. So, you know, these two case studies really underscore the importance. Merchandising and category management using not just a agri aggregate data, but using loyalty data to help propel their business.
Amanda: Also that, that example you gave versus Walmart, it’s just like, it’s almost like gorilla marketing, but. Informed gorilla marketing rather than just Exactly, exactly aggressive throwing or throwing around the, the advertising dollar. So, um, that’s really beautiful to hear, like how. You know how you are. I’m not saying smaller necessarily, but the more niche retailer was able to take on a giant using informed insight, which is great.
Yeah, that’s pretty cool. That’s the way it absolutely should be. Um, something that I know is a great passion is your of yours as well. Um, and we shared the same passion and we’ve touched on it a little bit still in the retail environment around how you, how the data can really be used for parts of the business that you typically wouldn’t think it would be, uh, used for.
So things like, um, staffing, um, or real estate decisions. So can you share with us your experiences of how data util data utilization has been used on much more operational level rather than the sort of strategic or marketing level? .
Richard: Sure. And, um, you know, as we know, staffing is such a critical part of the customer experience.
Uh, staff are on the front lines and they’re brand ambassadors, so it’s, it’s really important. Let’s just say again if you’re, if you’re a retailer, but of course this can apply to hospitality, to airlines. To, uh, banking, um, wouldn’t it be valuable to understand when your best and most valued customers shop your store?
Uh, you know, savvy retailers have the ability to leverage loyalty data to start to throttle staffing quality and levels to ensure that they’re actually delivering an exceptional customer experience for their customers. So, for instance, if you. That on Saturday between nine to three o’clock, uh, in the day, you can expect to see a much higher propensity of your most valued, uh, shoppers or high value shoppers to, uh, uh, shopping your store.
Then you might want to think about maybe having a more. Full-time component of cust of, uh, sorry, employees in your store, maybe your most experienced sales associates who have strong relationship skills and product knowledge. Um, so leveraging the loyalty data to understand when your high value customers shop.
And we always want to give a great customer experience, but it’s most important to make sure that you’re not disenfranchising your very best customers. So loyalty data can be used to actually. Predict when your best customers are gonna be there and then you can make some operational decisions. And that’s sort of on the staffing side.
Uh, you mentioned real estate, um, especially in retail building stores is a major capital investments. Yeah. And brands really need to get this right. Um, we’re hearing in the news today about Dead Bath and beyond. Uh, you know, Downsizing shutting stores in Canada. Um, these are major capital investments and you wanna make sure you get them right.
Historically, real estate decisions are made on things like. Uh, population base, new housing starts. Um, you know, the competitive mix cannibalization of other stores, how accessible that store location will be. Um, zoning, laws, taxation, human resource pools, you know, a whole, uh, litany of considerations. These are all very important considerations, but loyalty data can actually be used to help refine some of these decisions.
So some customer-centric loyalty data considerations could include the composition of customer segments in the vicinity, understanding, you know, how many high value customers you’ll have coming to the store. Uh, the current potential value of the customers, uh, to what extent are they brand ad, um, uh, overlaying some attitudinal information.
Preference information and the cannibalization of these segments of customers. Should you open a store that’s within, let’s say, you know, two or three kilometers away from another store? Are you gonna start to move, uh, certain customers into a, uh, specific store? So again, loyalty data is more of a supplement.
In this particular case of some of the traditional metrics that are. . Yeah. And
Amanda: it’s great to hear how you’ve spoken about the use of internal data, and obviously I think many retailers use external data sources, you know, geolocation and mapping and so forth. There’s so many sophisticated modeling approaches to this, but at least if you, you, you have this enterprise-wide capability to be able to marry that with the external data, how much more powerful the decision is going to.
So Richard, I actually ha only have one more question, official question to ask you, and it’s one of my absolute favorites that I do bring up in most of the Let’s Talk loyalty interviews. But if we talk about KPIs, and we’ve spent today’s interview talking about data utilization across the broader enterprise rather than just in marketing, how would this translate into measuring customer profitability versus traditional growth in sales, for example?
Richard: So Amanda, of course, measurement is of paramount importance. So what I would advocate is that traditional metrics are still very, very important. Um, you know, having said that, organizations need to begin to integrate customer metrics into the business evaluation process. At all levels of the organization.
So whether you’re a pharmacy chain, a hotel, house of brands, uh, a bank or an airline or customer-centric, uh, scorecards need to be embedded in all of the lines of business performance. So in retail, uh, traditionally it’s sales unit, movement and management. These are sort of the hallmarks of measurement.
Um, merchants or category managers need to be driven and rewarded based on new metrics. So things like, uh, growth and high value customer segments, frequency of spend of those cohorts. Cross category penetration, margin per member cohort. Um, this requires a lot of transformation within an organization, but we need to start to bring some of the new metrics in place with some of the traditional metrics.
Amanda: Absolutely. Yeah, totally. And to be able to share those across the organization so everyone understands how the change is happening and how much more powerful it is. Um, yeah, very much, very much back up. Everything you’ve said there. I love it. So that’s the end of my questions. Thanks Richard. But, um, I don’t want to finish this too, too abruptly.
So is there anything else you’d like to. .
Richard: You know what I would say, Amanda is, uh, an enterprise loyalty, uh, data utilization strategy is not for the faint of heart. Uh, it’s really a long-term strategic commitment, and it’s a shift that requires top down and bottom up commitment across. And, um, I would just advocate there’s some things that organizations need to think about because it will impact your business.
Uh, there’ll be changes to people’s roles, uh, changes to the accountability, and even the compensation. Uhha of existing processes, um, new complexities that are brought in, new success metrics as we talked about through this, uh, podcast, uh, a new culture in terms of how you view your business and, um, really new investment that’s gonna be required.
So, What I would like to leave your listeners with as as follows, if you’re only using your customer loyalty data for marketing purposes, you’re really under utilizing the significant investment that you’ve made in your loyalty program and you’re probably not getting the most outta your loyalty program.
I would. Absolutely encourage your listeners to really think about, uh, starting to map out how you can, um, strategically and financially embed an enterprise-wide, uh, loyalty, uh, data utilization, uh, protocol within your organization. I think we’ve seen countless examples from, you know, the Tescos of the world, the Krogers of the world, uh, here in Canada, shoppers Drug Mart, Loblaws, and so many other.
Organizations who have really reaped the benefits of
Amanda: this. I was listening to you now, I always compare this journey to, we were talking earlier about running, so I always compare it to being like an ultramarathon, not a hundred meter sprint. So .
Richard: Exactly. It’s a, it’s a long-term initiative and uh, you just have to put time and effort against it and get organizational buy-in because it is very transformation.
Amanda: Yeah, absolutely. I could listen to you all day, Richard, cuz it’s a subject I love. So thank you for sharing your, your experience and your insights. And once again, from all the listeners of Let’s Talk Loyalty and from the team at Let’s Talk Loyalty. Good luck with your new venture with Loyal Strategy Consulting.
Thank you for joining us.
Richard: Thank you very much, Amanda. It’s been a delight to be on your show.
Paula: This show is brought to you by the Australian Loyalty Association, the leading organization for loyalty professionals in Asia Pacific. Visit their news and content hub for the latest loyalty insights from around the world or, why not submit your own article for publication? For more information on their loyalty services and networking opportunities, visit australianloyaltyassociation.com.
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