Customer Equity Accelerator Podcast

Ep. 21 | Arming the Front Lines with Bob McKinney, Head of Marketing at Batteries Plus Bulbs

Your customers are actively indicating how they wish to engage; you just have to find the signal. Bob McKinney, Head of Marketing at Batteries Plus Bulbs, has mastered finding it. Through leveraging a Google cloud-based platform of smartly interconnected systems, his team arms the customer-facing frontlines with the right information they need to build strong relationships. From providing ranked CLV in real-time to being able to save bad customer experiences, Bob shares how CLV has provided a singular focus for Batteries Plus Bulbs’ tactics, experiences, and relationships.


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Key Concepts:

Customer Lifetime Value, Marketing, Digital Marketing, Testing,

Who Should Listen:

CAOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, Ecommerce, Data Scientists, Analysts, CMO

 

Find the Voice of your Customers

 

Show Transcript

Allison Hartsoe - 00:01 - This is the Customer Equity Accelerator, a weekly show for marketing executives who need to accelerate customer-centric thinking and digital maturity. I'm your host, Allison Hartsoe of Ambition Data. This show features innovative guests who share quick wins on how to improve your bottom line while creating happier, more valuable customers. Ready to accelerate? Let's go!

Welcome, everyone. Today's show is about using customer lifetime value to arm the frontline of your organization with the information that they need to build strong customer relationships that have helped me discuss this topic is Bob McKinney. Bob is the head of marketing at Batteries Plus bulbs, and someone who I know has been pushing the edge of CLV use cases, particularly through the use of smart, interconnected digital technology. I think you're really going to enjoy his stories today. Bob, welcome to the show.

Bob McKinney - 01:05 - All right, Allison. Thanks for having me.

Allison Hartsoe - 01:07 - Hey, tell us a little bit more about your background and how you were drawn to CLV and digital tech. How did you get here?

Bob McKinney - 01:17 - Sure, sure. Yeah. I've actually. I've been in marketing, digital marketing in particular since the late 90s. What on local website in high school, just like everybody else started, I think in particular customer lifetime value, I think similar to a lot of your guests practicing a number of tactics around customer-centric marketing, but I didn't really have an umbrella or a way to encapsulate the general idea of course, until I went to a conference where Peter Fader was speaking. So

Allison Hartsoe - 01:44 - Was that, was that the first one where we were we met in New York. Was that the correct?

Bob McKinney - 01:50 - The first. Yep.

Allison Hartsoe - 01:50 - I had no idea. You've taken it so far in that period.

Bob McKinney - 01:55 - Yeah. That really changed everything because it's a lot easier when you have a general idea that you're going after versus I know all these tactics are the right way to go. It just, I don't know how to put them together. Right, and that's what that conference really changed for me.

Allison Hartsoe - 02:08 - That's what I love about it too. I've always felt like, you know, here's this bright light of focus. Let's just drive for that. So you started out in marketing. Did you start out in retail marketing? You'll give us a little more background on how you got to Batteries Plus Bulbs.

Bob McKinney - 02:23 - you know, started out in digital marketing from the beginning. So I'm probably one of the originals. But I actually have a background in computer science and master's degree in computer science, you know, a couple of marketing, kind of like the big data degree before there was such thing as big data and through the companies that I worked for in the past and most recently I worked for lands end and based out of Wisconsin, a clothing apparel manufacturer and retailer. The amount of data that was put in front of me was enormous, and it was like look at all this stuff we can do with this from a customer lifetime value standpoint. So with the computer science background and being the Director of Integrated Marketing then, you know, I could take everything I learned from Peter Fader and you know, all the other things that are out there and really apply it. And then taking that into batteries plus batteries plus has over 760 retail locations. And the amount of data that comes in and daily basis is amazing. And and what you can do with that to enrich the customer experience is pretty awesome. So I'm tying that all back to customer centricity was really easy with resources that are available now.

Allison Hartsoe - 03:26 - Wow, that's a lot of signal versus noise. You've got a lot of data in there. Would you say that you use all the data that you get or you used some percentage of it?

Bob McKinney - 03:34 - You know, we use it all to start with. I worked with a guy on my team here at batteries plus it's a quantitative analyst, and so we bounce ideas back and forth all day long, but here's the thing, the systems are so advanced now compared to where we were five years ago that you could throw every variable available into that model and you may only come out with one or two significant variables, but there's no reason why you can't put it all into the model if you have a robust data system. So

Allison Hartsoe - 03:59 - That makes sense. Tell us a little bit more about your team, how they're structured. Sometimes it matters who reports to whom and the organization regarding how you can get things done.

Bob McKinney - 04:09 - Absolutely. So my team is, we have a great group of marketers, right? They're constantly trying to push the boundaries of customer experience and trying to have the best experience for the customers in general. So, my team, we support 760 locations for the website. We support traditional marketing from television to newspaper too, you know, we even have a catalog for our commercial customers. So it's all across the gamut and um, you know, there's the whole idea in the past year or year and a half were to bring all of that and bring it all together as an integrated marketing team. And that's what we've done. And you know, be able to spread the data across. That entire team has been an eye-opener of where should we spend dollars and what channel should we use to go after these types of customers. As I said, I have a great group of marketers that have been working and doing great work independently, the other departments, but now with our integrated marketing.

Bob McKinney - 05:06 - And then the systems that we have available, the feedback is instantaneous, so we could put something out there around whether or around any kind of event that would affect our business and get instant feedback from our call centers or from the, our store owners were franchised system. So we actually have a couple of customers. We have the, you know, your traditional retail customer B2B and we have our store owners. So which is a whole other type of marketing?

Allison Hartsoe - 05:31 - It just makes sense. Yeah. Yeah. So I want to go into the instant feedback section in just a second because I think that's fascinating and I don't think everybody does that. That's actually quite cutting edge. Let's talk for just a second about your team. What kind of roles do you have in your integrated marketing team?

Bob McKinney - 05:47 - Sure. So we do have two departments, but they work closely together. So we have our traditional marketing team led by a vice president of marketing and she's great. She has 20 plus years of in traditional marketing. And then out on the other side, we have our digital or e-commerce team led by a director that side and she's always pushing the boundaries of UX design and they're always doing like coding competitions with Google and things like that for amp pages and all the cool stuff that's going on right now. So like I said, I have a great team, and you know, both those positions report up to me and then I report to the CEO, so we want to try something. What I don't have a lot of layers would be, yeah, we don't have a lot of layers of bureaucracy to go through, and we say, hey, you know, we think this is going to have an impact on customer lifetime value and customer experience. Let's try it. Right. And away we go.

Allison Hartsoe - 06:35 - That's fantastic. We are fortunate to be structured that way and not have too many cross-functional purposes. So let's go back to that instant feedback idea. So we kicked off the show with the ability to use customer lifetime value to build customer relationships. And I always feel like this is one of those hidden elements under CLV. Like we talked CLV, we talked customer centricity, we talk customer equity, but really it's about building customer relationships. Why should I care about using CLV to build customer relationships? You know, maybe I'm already nice to my customers.

Bob McKinney - 07:09 - Sure, absolutely. So one of my favorite speakers early on was a guy named Avinash. He said, Google, not sure if you heard of him, but a great guy. He always uses the dating analogy. So I always think a CLV is that way as well, you know, the CLV, it's just a way for our customers to tell us how they want to be engaged with, right? If they're responding, they're not responding to marketing. And, and a response, in this case, is a conversion, right? So to build that relationship up, if they're ignoring us by not converting, then we should dial back, or it's an indicator of how we should be interacting and building that relationship with that customer. That customer is actively telling us how they want that relationship to go. Right? So you just have to be looking for the signals that are available to you, especially with the systems that we have available today. You know that couple, but heterogeneity, that was Kinda the foundation of all this is saying, hey, all those customers are different, so don't treat them the same and treat them on a one by one basis and you can do that with the computing systems that are available today.

Allison Hartsoe - 08:08 - Well let's talk more about that. The heterogeneity and the wonder one. Tell us a little bit more about how you've applied the concept of CLV heterogeneity and the inter-relatedness of all the technology.

Bob McKinney - 08:20 - Sure. So we spent a lot of time and just a lot of brain power trying to figure out how to build the best systems to let them have the best experience. So some of the systems were big guy, Google cloud platform user and just because of the ease of use where you know we have a lot of google analytics data, a lot of the analytics, 360 and big query data. And so we take all that, and we're saying, hey, the systems that are available to us on a one by one basis, we take that data into that system and then build off of that as Fader or anybody would tell you in value is, do you want to group on behaviors, right? That's you don't want to group on a demographic or those kinds of things. They may be indicators later on, but realistically it's behaviors. But what if we can help shape those behaviors and that's what we really want to do with that on that one on one kind of relationship with our customers. So

Allison Hartsoe - 09:12 - it's like Don Peppers, one to one marketing, but now in the 21st century,

Bob McKinney - 09:17 - Absolutely, absolutely. And, and you have some people would say especially with things are going on with privacy laws, things like that. And some people would say, hey, should we really, should we be doing this? My answer to that is absolutely because we're not using any API data or anything like that. We don't know who you are but based on the behavior that way we think you want to be treated this way. And you respond to it, and in real time we have that data feedback and say, hey, if you don't respond to our marketing or our experiences within our stores or experiences on our website, we're not going to keep pestering you with the same message. Right? That's the beauty of this is marketing does a great job of not wasting a whole bunch of marketing dollars, but at the same time, and the bigger value here is we're not upsetting our customers by sending them messages that they don't want to see. Right?

Allison Hartsoe - 10:04 - No, they don't want to see it. I mean, other than the fact that they don't respond, how do you keep going? Are you going to give him 100 messages or 50 messages or is it a factor of time? How do you know when someone is not necessarily isn't interested in a relationship? Where is that statistical cutoff point?

Bob McKinney - 10:20 - I think everybody that interacts with our brand obviously wants some type of relationship. Right? So if you come to our website or go to our stores, you're initiating that contact, right? You're initiating that experience with us from that point on. Some of the, I don't want to give too much right here. Okay. Some of our key indicators are sure, as our time on site is a good one, but any kind of behavior analysis within fight traffic. Right. So there's some great work out there from some are packages and things like that for like random forests and statistical packages that'll help you determine, hey, the behaviors on the website, this type of behavior leads to this type of conversion rate and first order value. So like their initial purchase is a big indicator for us of how interesting with our brand are they are. And then some other indicators within that saying, hey, if they're uncertain pages of our website, they're more likely to be a commercial customer, and we start to treat them that way. We have surveys on the website; we have even different ab testing going on.

Bob McKinney - 11:20 - We have all kinds of behavior analysis going on as the person's going through our website and going through our store as well and over the phone. So one of the big topics that we're really focusing on is obviously our phone call conversion. Really? Yeah. That's a real.

Allison Hartsoe - 11:36 - I think that's a great example. We talked about this before, and I was really. I was stunned at how fast you could make ends meet between what was happening with the customer in real time. Yeah. Tell us a little bit more about that.

Bob McKinney - 11:48 - So to start this out saying, hey, why? Why would you want to look into the phone systems? Right. In the contact centers and how we're interacting is a big deal for us is that we have over 760 stores. Our main job from a marketing department is to send customers to those stores, whether it be bought online and pickup in store, order off the website or a phone call or driving directions to that store. So what happened was we started really ramping up our marketing to drive those phone calls and to drive those driving directions problems. Once that customer hit the store, it was kind of a black box to marketing, which we don't like black boxes, so said, hey, how can we figure out once they hit that store, what's happening and can we make that experience better? And that kind of led into the phone call realm of data science. Right now we're working with a couple of companies, so we do some in our corporate stores. We have some corporate services. We do phone call recording just like a lot of companies do, and we get instant feedback from that because we're running those calls through.

Bob McKinney - 12:44 - Instead of having just coaches, listen to them, we have an internal, call it a Bot that we built to listen to those calls in real time and figure out, hey, what is the sentiment analysis on this and what is the entity analysis on this? What does the conversation about and is it something that we could get better at? A great example of this, there's a lot of customers call for pricing availability and hey, is this an interaction that should take you no more than 30 seconds or a minute and that's what we want to determine. And that's kind of what it was. Behavior things are, hey, will they be. If we give them pricing availability and it takes 10 minutes to answer those questions, are they actually going to come into the store? I would say probably not. And so if we can streamline that process and make it a great experience on the phone, the chances of them actually come to the store or a lot higher and that's really what we focused on saying, hey, you know, this is great that we're getting all these phone calls every month or stores, but are we converting them over the phone and are they getting into the store?

Bob McKinney - 13:42 - And that was our, you know, our next step is saying, hey, how do we know they got into the store or how do we get them into the store? Some of the newer technologies with re-marketing, things like that, we can kind of push them along that path depending on their customer lifetime value so we can determine that from the call. So like technology that's been around forever, caller id, we're running that through Dun & Bradstreet and other data providers to try to build out that model for customer lifetime value instantly. So that store. And then ultimately the commercial salesperson that's assigned to that store has as much knowledge as they can have on that customer to provide a great experience. Right? So if I call and say I'm a customer and I come into the store and uh, I, you know, I make a purchase and at the end of that purchase, the associate asked me, hey, is this for business or personal use? And you say business use. We want to let you know about all the great things we do for business customers by way of having data available for that commercial salesperson to reach out to you and say, Hey, these are all the packages that we have available and products available for commercial accounts and based on your behavior, this is what we'd recommend for you. Right? So it's a great experience for both salesperson and the customer. So we're not standing in front of the customer trying to offer them things that they just will never be interested in.

Allison Hartsoe - 14:54 - So let me recap this and make sure I got it. Because it sounds like you're connecting multiple systems from the website to the call center, but you're really pushing the call center and the ability to understand through sentiment or text analysis and then perhaps the behavior that they might have illustrated on the website and reverse call look-ups, who your commercial team should be reaching out to. So somebody comes in, and they illustrate a signature that is a general homeowner, then that might not be the first call that is made by your commercial sales team, and you need to help them sort it out and automated fashion. Is that right?

Bob McKinney - 15:32 - Absolutely correct.

Allison Hartsoe - 15:34 - So using CLV to help them score essentially who they should be focusing on.

Bob McKinney - 15:40 - That is correct. So as those commercial leads go into that salesperson's leads within Salesforce, they were actually ranked based on customer lifetime value in real time. So as a salesperson is going through their lead for that day, they're already ranked for them based on a customer lifetime value formula that is done in real time as those leads come from the store.

Allison Hartsoe - 16:00 - So there's really no delay. So they have like hot leads all the time. It's like the Krispy Kreme donut store, right?

Bob McKinney - 16:08 - Absolutely. That's exactly right. I would really recommend just on the call analytics is there's a number of companies out there do some great things. We experimented with IBM's Watson, and ultimately we landed on the Google Natural Language Processing API, and speech detects API and we've worked closely with Google on a lot of these projects and done alphas and Betas and things like that just because like phone and regional dialects, things like are kind of difficult to pick up from speech to text. So the technology is really great right now and just at the very least the operational nuggets that you get out of just actively listening to customer calls because of just the experience of, Hey, this is what our customers are experiencing at our stores. Can we make it better? Can we make it easier for the associates in? And I worked closely with the operations team and the vice president of stores to make those experiences amazing in the store. So that's what we're always striving for.

Allison Hartsoe - 17:03 - You know, that's a great customer first thinking. It sounds so pedantic and everybody's like, Oh yeah, we put the customer at the heart of our business, but I find that leaders like when you give your examples and last year at the conference when we had Bill Schlough from the San Francisco Giants speaking, they have the most senior executives actively listening to what the customers are saying and considering the relationship to the customers, and they really feel for the people who are interacting with their brand, they're not about product. They're really about relationship building.

Bob McKinney - 17:35 - Absolutely. You build the relationships, and the revenue will come right alongside that so you won't have to worry about your margin dollars and all the things that you'd normally talk about at a high-level executive meetings. When you start focusing and saying, hey, are we providing the best experience for our customers from an inventory, from a phone call to experience in the stores or the stores clean? Are the associates friendly and helpful? That kind of thing. All together focused on that. We're working on putting that all together in a platform right now so that we can see that every day from a customer experience standpoint and really start basing decisions around that.

Allison Hartsoe - 18:11 - That's got to be tricky.

Bob McKinney - 18:13 - Sure. So we're working with a company called Medallia, and there's a couple of them out there, but Medallia, they have a platform that allows us to kind of propagate that data out to our store manager. So phrase they use all the time. It's called the rescue in a bad customer experience, and that's basically what it does is saying, hey, tied with the stuff we've already built, and they mainly survey company, but the possibilities are so much greater than what they have available from their system so we can integrate our data into that system to say, hey, you know, from phone calls to everything, hey, we can bring that in that system, and we can rescue a bad customer experience by way of, Hey, sending an alert to the store manager and then the district manager and all. I mean, it could go all the way up to the CEO if need be based on hey we're not getting or sponsor or the timing, or we just can't answer this type of question. So it just keeps getting pushed up to the appropriate person and honestly the, you know, from the data that I've seen the ROI on it, it's unbelievable. So just if you can turn one of those bad customer experiences into a positive one as a social media and in everything that goes along with it is, is exponential. So the ROI will. We'll definitely follow.

Allison Hartsoe - 19:20 - Are you willing to disclose something x ROI, like a 4x ROI or a 400x ROI?

Bob McKinney - 19:28 - Oh, I'm not sure yet. As you said, we're actually implementing it right now, but the data that I've seen from these companies that are out there telling the platform and you know, we've verified with a lot of their customers, we have a number of employees that have worked at previous employers that use this type of system. And I think coupled with all the other technologies that we have that will be pumping into that system where we should see a pretty good result, uh, uh, either way, the revenue always follows a great customer experience anyway. So what this platform really allows us to deal with all the interconnectedness is provide that great customer experience.

Allison Hartsoe - 20:00 - But you know, Fader would say great customer experience is important, but a great customer experience for the right customers is more important that you know, honoring their heterogeneity. So do you find a way to create different kinds of experiences for different kinds of customers based on CLV?

Bob McKinney - 20:19 - Absolutely. I'm glad you asked that because this is one of those points that I always seem to miss. One I'm talking about. This is one of the other things that we're building into this platform that didn't exist previously is from a bad customer experience is great and all but to rescue that. And we're 100% focused on that. But one of the other angles, if a high customer lifetime value customer comes into a store and make the purchase, we also get an alert from that as well to the store manager, the district manager so they can reach out and just them for their business and what else can we help you with? And we have a great new solution, not necessarily selling them a product, it's more providing a solution to their problem saying, hey, we do onsite inspection for lighting and all these other services that we have available and being able to reach out to that customer, just knowing that customer x customer lifetime value and our top core tile came into the store today. Just getting that alert is a big deal. Right? So you can reach out to that customer from. It could be, you know, an automated message. But we really liked in these cases, an actual human being reached out to that customer. So, and just thanking them for the business at the very least.

Allison Hartsoe - 21:21 - So is that alert coming in at the end of the day or is it like real time in the store manager is like, you know, they get a little ping, and they see somebody media picture, and it's x, y, z person is in the store and I only ask this because when I was a, a young in my twenties and I was working in New York City and there were these people that used to come in to the Bombay Company and I remember this guy coming in and, and he looked at one of our lamps that had a horse on it, and he said, oh, that's the logo of my company. And I was like, oh my God, who is this person and you know, you have this moment of should I know who this is? And it turned out to be the CEO of Jordache that was in our store, but I never knew that. You know, I certainly didn't get a text on my phone in the 1990s. How, how does that work with your store managers?

Bob McKinney - 22:07 - That's exactly that. So just like rescuing a bad customer experience, right? You don't want to call them the next day because it's kind of, they've already diffused, and it's really hard to rescue that experience. All of our messaging is within five, 10 minutes of that experience. So one of the things that this platform allows us to do is have that APP available on all those store manager phones, and you know all the way up the chain and so now rescuing, in this case, providing that awesome experience for the customer that has a high customer lifetime value is really simple because it'll hit an alert within that APP and give them all the data they need to see like, hey, back to the customer is this kind of commercial account. They spend this kind of money, and this is the products they typically buy and we can actually do prescriptive marketing within that message to say, Hey, you should talk to them about this or that. Right. We haven't gotten that yet. We haven't gotten that far with it, but we built that capability into it.

Bob McKinney - 23:02 - So you think that like a recommendation engine on the website but a little bit more advanced based on customer lifetime value in saying that the biggest value for this customer and hey, maybe our recycling program, right? Did you know they buy all these batteries from us, but they don't do any recycling with this? Where's the recycling going in and we can provide that service for them which might say sometimes we might not make anything on that interaction, but it's more about the experience and you know, their relationship with the customer.

Allison Hartsoe - 23:29 - Do you have to train the front line not to be creepy or is there some light sensitivity training here?

Bob McKinney - 23:38 - Sure. But there is to a point, but at the same time, I think you've talked about this before. If I come into your store a lot and I come in, and you treat me just like everybody else, that kind of goes against heterogeneity first of all. And it's kind of annoying to me because I don't want to answer your same questions about your normal customer.

Allison Hartsoe - 23:56 - You consider that recycle program.

Bob McKinney - 23:58 - Yeah. You know, customer routine with me that everybody else goes through. Even though I got through the doors of your store once a month or twice a month, right? Like, you know, I act like you would know me instead of treating me like just anybody else that comes through the door and I think that goes all the way back. I grew up in like, you know, mom and pop kind of retail where you knew your customers, right? So it wasn't the creepy factor now is I think kind of a fallacy and the fact that like, especially in our stores, because our store is, for the most part, are locally owned by somebody in that community and it really does go back to those relationships of hey Bill, you know, how is, how's your daughter Susie doing, you know, that's great and that interaction and, and we don't want to lose that. I said the creepy factor is kind of negated by like just good old fashion retailing of knowing your customer base.

Allison Hartsoe - 24:46 - I love that.

Bob McKinney - 24:47 - But yeah, and what the platform allows you to do is have a quick summary because actually, you don't see here every interaction with the store owner, store manager that you know, but it just keeps you up to speed on like those relationships.

Allison Hartsoe - 24:59 - Yeah. It's basically taking the classic maybe 1950s style mom and pop, like you said, moving that to digital and digital scale.

Bob McKinney - 25:10 - Absolutely. It's just a scale. That's all we're talking about here is scaling. I'd tell you the best experiences you can have is having that local Deli or in your neighborhood or the major or something like that that you interact with, and the person knows you and knows what you want, and you know, that way you don't have to go through this whole routine every time you go in. And that's great. And we want to scale that. We want to have that experience throughout our whole system, and that's what we're working towards.

Allison Hartsoe - 25:35 - Oh, that's fantastic. So let's say that I have decided that I want to try it out, arm the frontline and I love these examples, and I'm going to integrate my technology so that I can use CLV to push things forward. How should I start? What should I do first, second, third? What are your recommendations to somebody who wants to do this but hasn't been able to roll it out yet?

Bob McKinney - 25:58 - I would say I'm listening to one of your previous episodes. Each episode, you know, you said to get all the data and put it into a central place so everybody can look at it, right? Because I don't know what I don't know. But you know what, if I had a sample of those phone calls in a place where operations could listen to them, the value that they would get off. Those are huge. So I guess my point is to start with an actual problem and start working your way out from there are an actual problem would be something like we have all these phone calls going to the store and we feel that our own skills could be improved on. Right? And there's always room for improvement and how do we start? So if you could get in the store and listen to one side of the conversation and saying there's got to be a way to make this interaction easier for the associate and the customer or you can deploy technology at scale and do the same thing and it's a hard problem is we want to make a phone call the best possible experience with Batteries Plus Bulbs and how do we do that and how do we do that through technology.

Bob McKinney - 26:54 - And that's what I would say to anybody is start with an actual problem that you have and we just get an enormous amount of phone calls in a day because our business is needs based, right? So your car battery dies, or you need a battery for something or a light bulb. You're coming into the store today to have the best experience on that phone. It's a big deal for 

Allison Hartsoe - 27:12 - But I want to call out what you're saying here is that when you start with an actual problem to fix your specifically emphasizing that it's a customer problem to fix, not accompany problem. So the company problem is we need to sell more x product, we need to push more product here or there. That framing is whole upside down and what you're specifically saying is you need to listen to what the customer problem is first and then the revenue follows.

Bob McKinney - 27:38 - Absolutely. Our customers in particular kind of in a point of vulnerability, right? Like if your car battery dies or something along those lines, they're already frustrated. They're already having not a great day probably. Right? So it usually starts the day out. I had a really, really cold day. Absolutely. We don't want to add to that problem. Right? By having a poor experience in stores. And that's what this allows us to do is say, how can we make that experience better for our customers? And like I said before, I mean you make that experience better, the dollars will follow that. And so we don't focus 100%. How do we get more EBITDA dollars? We're more focused on how do we make a better customer experience because those dollars will follow.

Allison Hartsoe - 28:16 - And have you found anything that you went to solve a particular customer experience? And the EBITDA dollars didn't follow?

Bob McKinney - 28:23 - I would say the fail fast model is kind of our mantra here to say because you can always learn something from that experience. I would never say like that EBITDA dollars don't follow eventually because you're going to learn something and it goes back to why you learned in the first place and how you learn is nine out of 10 things you're going to try probably not going to be that great, and you'll get to that 10th one, but you wouldn't have gotten to that 10th one if you wouldn't have tried these other nine things. Right? And that's kind of how we work here is we're working our way down a path, and there's some dead-end back, and we back up and go a different route. And that's the beauty of having like real-time data systems is we can put something out and try it for an hour or two hours with the volume that we have in our stores. And if it doesn't work when we pull it back and try something else.

Allison Hartsoe - 29:09 - And you might even try it again later or try it under different circumstances. I always remind myself that it wasn't like that Ford didn't invent. The first thing he did wasn't the right car. It was essentially the model t that became a fantastic success and wasn't the first one out of the gate. So every invention, every great solution in our lives, whether it's lighting or whether it's cars or something else, seems to have gone through the classic fail fast model, whether it's 100 or a thousand times to get to the right combination. So I often feel like we lose that online because we get such instant feedback. We sometimes forget to keep going and keep going. It's not just trying it once and, oh, that didn't work and let's not ever do that again. It's let's modify and twist and turn and keep going.

Bob McKinney - 29:54 - What I would say to that, and just make sure you have a plan for the data that comes out of that real-time system. A lot of times you're just waiting for it, and then you see it and then you say, okay, that's great, but there's no plan for it right back these results, and if it doesn't go this way, then we're going now, and we actually have a path laid all the way out. Everybody makes fun of me from a marketing standpoint over here because I'm an engineer and so all my drawings are like data flow diagrams or some kind of engineering diagram, but it helps you build up that feedback loop. That way you're not just, hey, this is our campaign, and it stops, and hey, we have a whole bunch of data, what do we do with it now? And so we really map out where does that data go and how many different sources should it go to, should it go to operations or product management or whatever it may be to help the company as a whole provides a better customer experience.

Allison Hartsoe - 30:43 - Got it, got it. So after you get the data together and then you have your customer problem to fix, is there a third piece that you. And maybe the third piece is really mapping out the flow of what you'll do with it. Is there anything beyond that that you want to call out?

Bob McKinney - 30:58 - No, I think that really is dead. It's mapping out that flow and what to do with it and getting it into the right post hand is honestly the most valuable thing and saying a lot of people are calling about this particular product, and there may be a problem with it, or this product is great, and we should stop more of it or in getting that data in real time to somebody that they can actually do something with it is really, really valuable. Instead of saying all that data pertains to product management and that data became the operations. There's talk that aside and we'll really focus on the conversion rate on that. A website or something like that and nobody ever sees that other data and we really, really tried to get away from that.

Allison Hartsoe - 31:35 - That makes perfect sense and I think that's the heart of using customer-centric information is putting it into the lens of the customer and not into the lens of the organizational business units.

Bob McKinney - 31:46 - The other funny part is you start getting intuitive question, so we built this model for customer lifetime value within the marketing department, and we built a function within code and we sent it over to it department to implement as part of our Salesforce implementation and then it start asking questions like, so what you're saying is the number of employees and a company doesn't have an impact on customer lifetime value. So they started asking like how the equation actually worked, which is great because the more understanding that they have the better off everybody is. That's what we like about it.

Allison Hartsoe - 32:20 - Yeah. So it gets everybody aligned under a new focus. It's not how can I protect my IT data and keep that away from the product or how can I protect my turf? It's how do we make a better experience for the customer, and I think everybody can get behind that. I mean, isn't that why we joined companies in the first place? Is because we're inspired by what they do, they offer and what they can grow into and how we can help become something more valuable for customers in the first place.

Bob McKinney - 32:46 - Absolutely.

Allison Hartsoe - 32:46 - Okay. Well, let's summarize. So in this podcast, we talked about a couple of different things. First we talked about why should you care about arming the front line with the old data and what I really liked here was, I've always suspected this, but it's great to hear it from someone like yourself, Bob, who are actually implementing it, but the CLV provides a singular focus for all the tactics, experiences and other elements to kind of get in line behind because ultimately that's what we want for our organizations as we want them to be healthy and financially positive. So all of that should line up into what does the CLV mean, but we don't stop there. It's not like everything is driven based on you have this score. I'll talk to you. You had that score. I won't talk to you. You, you haven't cut it in that fashion. You've just used it to drive the nuances behind the relationships. Knowing that all customer relationships are important, right?

Bob McKinney - 33:44 - Absolutely.

 

Allison Hartsoe - 33:45 - And then when we look at what kind of impact we talked about, what the example I really liked was the commercial scoring example you've got what was it about 2000 leads or so coming in on any given day and trying to sort that out and figure out which one's your commercial team should go after and what's the value of that particular customer and how do you get them to understand a little bit more about all of your services and in fact in this conversation I didn't know you had a recycling program, so I'm actually quite inserted to go down because in our household we actually. We have a little box where we put all of our batteries but what we would do is we would take them to Intel where my husband works to recycle it at Intel, and now I know, hey, I could just take it down the street to Batteries Plus Bulbs. So nice little conversion for you in the podcast.

Allison Hartsoe - 34:38 - So the commercial scoring is really a nice application for the frontline, and we see more of these applications in the front line too, but I also like your application of rescuing the bad customer experience and the sense that it's flowing throughout the organization and it could even flow up to the executive level if it were a bad enough customer experience so that you're constantly triaging what the customer needs, looking for ways to help them, looking for ways to satisfy their relationship and not just meet their expectations but meet and beat their expectations. Whether that involves the product that you're selling currently or whether that involves something that you don't sell but might consider selling in the future. Maybe if you got enough demand,

Bob McKinney - 35:21 - just a view into that customer is a big, big, big, big step forward. So

Allison Hartsoe - 35:26 - you know, and that's one of the things that we don't see on a website when we're looking at digital data is we have no perspective of. We don't have already except maybe through search. Sometimes we get keys through what are searching on the site or what keywords they came in on. But if it's not present, we sometimes tend to be very myopic about what we can see in the data and listening to those customers is so important and that kind of brings us down to the third part of what should you do next. It's not just getting the data together so that you can get that customer-centric perspective, you know, putting together a product and call center and website and that can be a little bit of an iterative process, but it's really emphasizing the importance of the voice of customer data, whether it's chat or call center, that that's such a gold mine to get people pivoted to the desire to build a stronger customer relationship and what kind of actual customer problems could you fix knowing that hopefully the dollars will follow and if not, the dollar is at least a solid learning experience that let's think about that as maybe opportunity cost or dollar spend prevented.

Allison Hartsoe - 36:32 - It didn't work, but you know, we didn't spend money that way and figured out $15 million later it didn't work. We figured it out quickly. And then finally, the piece that you mentioned toward the end was once you have those results, then mapping out in greater detail what will you do with it? I often see this where people set up tests and get results, and it's somebody else's problem to take advantage of those recommendations. There's not enough flow around that. So mapping that out in advance is a really great tip. Thank you for that. Is there anything else you'd like to add? Is there anything else I missed?

Bob McKinney - 37:07 - Just the final note would say, what are you using do for my cross-functional partners and say, don't look at what we have right now. Tell me what you'd want to know. And that usually starts a great conversation because there'd been looking at reports for years and that's what they think all we have. Just what would you want to know and start those conversations with your marketing partner that way?

Allison Hartsoe - 37:27 - Great advice, great advice. I've heard that in other more advanced organizations as well. They become great problem solvers of which data is the tool to help get to those problems. Once they know what those key problems are, they just start going after them in the organization, and it's such a powerful play. It's fantastic. Thank you so much for joining us today. It's been an absolute pleasure to have you and here are all your tips and tricks.

Bob McKinney - 37:52 - Thanks for having me today.

Allison Hartsoe - 37:54 - Remember everyone, when you use your data effectively, you can build customer equity. It's not magic. It is just a very specific journey that you can follow to get results. Thank you for joining today's show. This is Allison. Just a few things before you head out. Every Friday I put together a short bulleted list of three to five things I've seen that represent customer equity signal, not noise, and believe me, there's a lot of noise out there. I actually call this email the signal things I include could be smart tools. I've run across articles, I've shared cool statistics or people and companies I think are doing amazing work, building customer equity. If you'd like to receive this nugget of goodness each week, you can sign up at ambitiondata.com, and you'll get the very next one. I hope you enjoy he Signal. See you next week on the Customer Equity Accelerator.

Ep. 22 | Where Academics Meet Reality with Joe Megibow Ep. 20 | AI with Ash Dhupar, Chief Analytics Officer at Publishers Clearing House
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