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AI has long been used as a tool to enhance and extend the in-store experience. But in the age of COVID, retailers have to find ways to keep store visits as short as possible, and in some cases eliminate them altogether. Fortunately, AI can help here, too. As retailers become more dependent on apps and websites, AI algorithms can help make products more discoverable, recommendations more relevant, online shopping more streamlined, and digital experiences more engaging.

Jake Moskowitz and his guests discuss the impact that AI has on retail, and what determines the winners and losers in terms of leveraging AI in retail environments.

The first step is understanding what AI is — and what it’s not. Jason Goldberg, Chief Commerce Strategy Officer at Publicis, explains that AI is not an outcome, it’s a tactic. He helps his clients see the difference, prioritize the outcome they want, then decide if AI is the right tactic to help them achieve it.

Guru Hariharan, CEO and founder of CommerceIQ, breaks down how AI is changing the world of retail, and the ways that it democratizes and creates efficiencies in the way we shop.

Listen as Jake and his guests cover the five steps to bringing AI tactics to retail and explore how AI is changing the customer experience, how data — what you get and the permissions you have to use it — is the most valuable piece of the puzzle, and how AI is changing the way we think about who we hire and how fast we really need to work.

The FIVE List:

Here are five steps to incorporating AI in retail.

  • Start with great data
  • Operationalize your data
  • Enable Agility (People and Processes)
  • Incorporate AI as Analytics 2.0
  • Automate

The Five podcast is presented by Ericsson Emodo and the Emodo Institute, and features original music by Dyaphonic and the Small Town Symphonette. Social media and promotional content was composed and conducted by Lyon Solntsev. This episode was edited by Justin Newton and produced by Robert Haskitt, Liz Wynnemer, and Jake Moskowitz.


Transcript of AI E4: Putting the AI in Retail

Jason:

When a client comes to me and says, Hey, I want to kick off an AI initiative. That’s kind of like saying, I want to kick off a green paint initiative. Right?

Jake:

Let’s talk AI. Welcome to FIVE, the podcast that breaks down AI for marketers. This is episode four, “Putting the AI in Retail.” I’m Jake Moskowitz.

The retail sector is going through a massive amount of turmoil and transformation. It’s no secret, brick and mortar retail in many categories has faced an incredible amount of change and uncertainty over the last several years. In its various applications, Artificial Intelligence has the potential to revive the real world shopping experience and take it to a compelling next level of personalization, automation and efficiency. Algorithms help shoppers navigate the store and find new or specific items. AI helps retailers learn customer preferences, measure customer reactions, transform the dressing room experience and eliminate the weight of the register just to name a few.

Meanwhile, behind the scenes, AI helps retailers predict demand, respond to demand changes by getting the supply chain to react and quickly restocking locally as shelves empty. But chances are some of those experiences and plans have been put on layaway indefinitely.

The events of 2020 have seen to that. Think about it, instead of drawing consumers into the store, instead of trying to keep them there, many brick and mortar retailers have had to devise ways to keep customers out of the store or if they enter, encourage them to not stick around.

Ordering online or picking up in the store isn’t a new idea. Neither is curbside pickup, but they’re certainly not the core models most retailers would have pursued under normal circumstances. For retailers, those forced moves have driven greater need, usage and dependency on their apps and websites. And that makes physical retail look a lot more like its digital counterpart. These are monumental shifts for retailers and they affect brands too like the CPG brands, the clothing brands, food and beverage and electronics brands, the products people buy in those stores.

In physical stores, brand marketers had it figured out, gaining attention through shelf position, promotional displays, Co Op deals, end caps, coupon programs. You know, the old school tried and true been around forever stuff.

AI algorithms are helping brands regain some of the advantages they’ve lost as consumers do more and more of their ordering and shopping digitally. In E-commerce, AI can help products become more discoverable, recommendations more relevant, online shopping more streamlined, and digital experiences more engaging. In fact, AI is often used to help make e-commerce environments feel more like their physical counterpart.

2020 may go down in history as the year the world changed so many things in so many ways and never changed back. Retail is likely one of those things. There will always be advantages to real world interactive experiences. But the increased blending of physical and digital is a change that’s likely to stick.

Let’s talk about how AI will help marketers win in this hybrid physical digital world of retail, both now and in the future. With so many areas ripe for innovation, how do you get started? If you’re a retail marketer, how should you think about AI as part of the solution? What determines success? What will separate the winners from the losers in terms of leveraging AI in retail environments? Think of this episode of FIVE as your mini playbook for AI in retail. And if you’re wondering how many plays fill a mini playbook? Easy, that would be six. I’m kidding, there are five. 

Here are five steps to incorporating AI in retail. Start with great data. Operationalize your data. Enable agility through people and processes. Incorporate AI as analytics 2.0. Automate. And I’ll say up front that for this five list, I’ve brought in the A team, two great guests that will definitely bring the depth and expertise.

All right, number one, start with great data. I’d like to ease into that first step by touching on a little context first. For that, let’s start with Jason, retail geek Goldberg. Chief commerce Strategy Officer at Publicis. 

Jason:

All right, I just switched microphones. Can you guys still hear me?

Jake:

Oh, yeah, you sound great. Thanks for joining the pod. Alright, Jason, you work with a lot of retail marketers, how has COVID changed the adoption of new technologies, particularly AI in retail?

Jason:

There are some technologies that were probably looked at more favorably before COVID, right? So if you think about like experiential retail is a common thing we talk about like this is the notion that people want to have a visceral experience inside of a retail store, nobody’s really interested in dramatically increasing the dwell time in a store, like we’re not, you know, putting coffee shops in stores to get you to hang out in the store and more, we need you to get out of the store to make room for the next customer. I might only be allowed 25% of my former maximum capacity. I need to install a very reliable system to measure how many people I have in the store at any one time. And the best technology for monitoring store traffic happens to rely on artificial intelligence and computer vision.

So you know, these are camera based systems. And at some point, I have to close the store to new visitors until someone leaves and so I need to interface those traffic systems with stoplight systems at my front door. So a lot of retailers are investing in that. How I fulfill inventory is wildly different. I would now rather you do curbside pickup than come into my store, because I have a limited number of slots in my store. But all my inventory is still in my store. So I would love it if you would shop online, buy things from me and drive by and pick him up in my curbside so that you deplete my store inventory but you don’t use one of my precious slots to go in the store. And that creates a lot more supply chain complication. How many widgets should I have in the fulfillment center that I use to ship to your house? Versus how many widgets should I have in the store for people to take off the shelf? Versus how many widgets should I have available for curbside pickup? And so you know, again, artificial intelligence and machine learning is one of the best tools we use for inventory allocation and optimizing the efficiency of where I put all that inventory and predicting consumer demand.

Jake:

I imagine that part of your job is talking to a lot of folks that are not AI experts. And let’s face it, most of us aren’t, about their need to adopt AI and what role AI should play. What are some of the key aspects of AI that you find are the most important to explain to non experts?

Jason:

I’m not a fan of talking to clients about AI in their need for AI because to me, AI is not an outcome. AI is a tactic to achieve a bunch of outcomes. And you know, I like to talk to clients about prioritizing those outcomes, and then picking the best solution that achieves it. And very often AI might be an important tactic in achieving that. But you won’t win or lose because of your choice to use AI or not, you’ll win or lose based on you making smart decisions about what outcomes and how well you execute against those outcomes. So when a client comes to me and says, Hey, I want to kick off an AI initiative, that’s kind of saying, I want to kick off a green paint initiative, right? Like green paint might be you know, something really useful for your store, it might be something totally irrelevant for your store.

Jake:

Which of these is more true, AI is more beneficial to larger companies with more data like Amazon, because more data means more effective algorithms. Or alternatively, AI is more beneficial to smaller companies with less data. Because AI uses small high quality training data sets to extrapolate wider to compete with those that have more data.

Jason:

The current implementations of AI that are in the wild, have served to slightly democratize some customer experiences, right? So 5 or 10 years ago, Amazon and Walmart, you know, had the resources to build their own search engines. Today, third party vendors have been able to build search using much better AI that was much cheaper to build than what Walmart built, right? So you could argue that Walmart’s search engine now has a lot of technical debt that some, you know, two year old search engine vendor doesn’t have and that search engine vendor, as I previously mentioned, sells that search engine to a bunch of small players that each help improve and train those algorithms. And then Abercrombie and Fitch gets to be the beneficiary of all that. So it’s absolutely true that AI has democratized some customer experiences in that, I can very easily buy a cheap search engine off the shelf that will be fabulous. I can very easily buy a product recommendation engine off the shelf that will be fabulous. And I can launch a new digitally native brand tomorrow and plug all those into Shopify for a few $100 a month and have an amazing solution that, you know, legitimately competes on a customer experience level from Walmart.

So I think that is true. But if you said like, where do you want to place your big bets for 5 or 10 years down the road? I would argue the customer experiences are going to continue to evolve. And the things that will allow some of those players to differentiate from others will be their ability to build unique things that are based on unique data that you know, isn’t available as a commodity. And so whoever owns the best relationship with the customer, in my mind, is the best bet to have the most differentiated data in the long term.

Jake:

Alright, let’s talk about step number two, operationalizing data. It’s not just about having the data, it’s about harnessing it, centralizing it and structuring it in a way that’s conducive to machine learning.

Jason:

A lot of the AI we talked about in retail is machine learning, it all needs data to train it, and retailers in general have an awful lot of data. So on the surface, that’s good news. The bad news is in the modern environment, we can only use that data for specific purposes that we’ve disclosed to the consumer before we collected that data. So there’s a lot of new data privacy rights that have been enacted in various geographies that retailers have to comply with. And very often, it means the eight years of data that I would like to train that machine learning model with, I don’t have the rights to use in that way. Because I didn’t disclose that in a privacy statement.

One of the first things we do is say, let’s clean up our data policy. And let’s make sure we’re on a go forward basis. If we kind of imagine ourselves three years from now, there are going to be clear winners and losers in executing machine learning. And the biggest differentiator will be access to data that they have the rights to use. And so we can’t wave a magic wand and go back and fix mistakes that were made in the past. But a high priority right now is to fix those mistakes as quickly as possible so that you have good, valuable data on a go-forward basis.

Jake:

Jason, thanks so much. I really appreciate you joining us.

Jason:

Jake. It’s been an absolute pleasure. I really enjoyed the conversation. Thank you very much.

Guru:

Yeah, that availability of data is very important in this day and age. What is commodity is the actual algorithm itself, you can have the world’s best algorithm, doesn’t matter. Computing power commodity again, you can have the most powerful computer.

Jake:

Guru Hariharan is the CEO and founder of Commerce IQ, a company that helps consumer brands like Kimberly Clark and Nestle grow their e-commerce presence through automation in AI.

Guru:

Those two things have started to become commodity. What is right now super valuable, is the data that you have, and the diversity of data that you have and the labels around these data. What worked, what did not, what scenarios, what context can you provide, all that stuff.

Jake:

Guru, how would you describe and characterize the impact of AI on physical retail as compared to digital?

Guru:

There are two specific use cases or a broad areas where I feel where we have seen that AI can play a very significant role in terms of driving business value, and shopper value. One is marketing. And the second is supply chain. So let me describe both of these.

Marketing is one where there’s just a tremendous amount of data, while we are going out to the market with investments in our ad dollars. For instance, if you’re on Amazon, or walmart.com, or Instacart, most of these retail sites, they give you an ability for you to go out and invest dollars to win shoppers. And you’re looking at something like say 2 million bid changes that you’re able to do in any given day. Every single day, I’m getting smarter with that much amount of more data and starting to see which one worked, which one did not. What was the context around it? What was my inventory position when I was making this bid? What was the time of the day? You started to create all these permutations and combinations of scenarios that existed. And because we are feeding this into a machine, the machine starts to put this on, quote unquote back of its mind, so that the next time you start to see a scenario which is similar to this, it starts to make an informed decision. What that means is we’re now starting to figure out how to predict the next order that’s going to come in, where is it going to come from, which shopper segment is going to order, which warehouses are going to be fulfilled out of and all that. So again, we are starting to create prior information and feed it to the machine so that way, the next month if I want to create a demand planning model, a forecasting model, I have so much more information, I have 100 million more data points to be that much smarter.

So going back to your question, I sort of look at it as the intersection of availability of data and value to be created. And from those two perspectives, marketing and supply chain have been extremely impactful in terms of being able to utilize the power of AI.

Jake:

Number three people and processes. How do you put the key pieces in place like a company structure, a workforce, a set of processes that is set up to optimize the potential of AI? Guru, one of the key threads of this show is how people in AI work together. There’s so much transformation occurring in the retail space, how does all of that disruption impact the people picture?

Guru:

So first of all, the bad news here is that it is a do or die situation. It is a grow fast or die slow situation. Look at retail, 20 years ago, it took 20 years for a JCPenney to constantly decline and eventually filed for bankruptcy. On the other hand, companies like the Home Depot or Best Buy immediately caught on to that technology bandwagon, and they grew and they grew rapidly in their market capitalization. I do think there’s good news, especially in the decade that we are entering 2020, which is the power of SaaS. Technology is very accessible right now, it is not the privilege of just a few companies who can afford to have the promise to do it, you can pretty much pay or rent software, which is again, called SaaS, and all this ecosystem of technology companies coming up are able to essentially provide you the ability to at least to establish the right technology stack.

But again, now it’s up to you in terms of how do you sort of put the right processes around this? How do you change in order to become a lot more agile and nimble? Of course, technology can guide you towards that. But again, your internal processes have to adapt to that. But also finally, what are the type of people you’re hiring? Like for instance, in the yesteryears, sales in a retail world, you would be looking at Ivy Leagues or negotiators to be managing being the head of sales. Today managing sales on say, Amazon, I would argue that you should go hire somebody who’s math-driven, may not even be able to show well in a meeting, but doesn’t matter, right? It’s how fast is the person able to break down the problem into pieces and solve them in a very methodical manner. Those are the types of things that really matter at this point. So it really behooves you to think about things from all the way from ground up. And think about the people and the processes and the technology that you’re deploying to go out and win this new battle.

Jake:

When we think about AI, we think of new technology, enabling a bunch of things. But what you’re describing, it almost sounds like it’s the opposite. It’s like the world has changed. And the only way to keep up with the new world is AI. And maybe it’s just dumb luck that AI is hitting its stride at just the moment when the world needs it.

Guru:

It has completely democratized selling. What used to be if you wanted to buy a detergent, you go into Walmart and P&G had 50% of share of shelf. It wasn’t given, right? Now, if you want to buy a detergent, go on e-commerce. And it’s not like a 50% share anymore, you see all these third parties and smaller brands coming up, which are starting to sell those sort of products. AI is moving the world into a more democratic world and hence a more efficient world where the shopper is going to win, not necessarily any particular brand with a certain halo.

Upper funnel marketing is still going to be more of a guided and supervised type of an AI. Bottom of funnel, you have an opportunity to make it extremely hands off the wheel and automated. Top of funnel like Facebook and others, you’re still making decisions. The role of AI is for you to inform you as a marketer to say, you know, the types of segments that are working for you, you know, the times a day or demography or geographies which are working out for you. You know, the types of creatives that are working, and so on. So then you ultimately start to sort of marry that with your strategic investment and media plan and start to allocate dollars towards and provide guidance to your creative company or creative team members to go launch more of the campaigns that are working out for you. It’s still a very much a guided experience. And there’s a lot of human judgment involved in the top of funnel.

Jake:

Wow, I want to put a pin in that and come back and ask you about the lower funnel later.

Number four is AI analytics 2.0? How would you characterize the relationship between AI and analytics? Why do you need to look at data about what happened in the past if an algorithm already fixed the problem in real time?

Guru:

Look at it as a stepping stone and version two auto, AI will start to take over and replace analytics wherever there is an opportunity. AI the way I think, at least the way I look at it is, it is something that is designed to draw conclusions from data, right? Understand concepts, become self learning in nature and interact with human beings and even automate processes.

Analytics on the other hand, was the stage before this, which is these are essentially technologies that help us study the data and draw patterns and stop at that, that’s it. There was no self learning. There’s no interactions with humans, there’s no automation involved. And so to some extent, I think we’ve essentially pushed analytics and made it grow, giving it some muscle to go out and graduate into something called AI, interesting thing quoted in a different way. I guess an example would be helpful here. So for instance, if you’re looking to go from point A to point B, ways can understand the traffic patterns and say, take to it in sort of one on one. And even though it’s five miles longer, you’re going to get there sooner. So that’s one of analytics and trying to play some rules out with speed and distance and overlaying that with real time data that’s coming from traffic, all that stuff. That ways, machine learning, and AI is Tesla, which is it’s not only doing that it’s also self driving, you’re giving obey control, and it’s starting to stop at red lights, take the right turns and get you to San Francisco from Palo Alto without having to intervene, maybe at some point, you’re guiding it, but it’s driving it. So that’s the difference between analytics and AI.

Jake:

Does AI enable marketers to use different KPIs than they have in the past? Like, do you recommend that marketers or agencies start thinking about different KPIs that AI makes possible that may not have been possible in the past?

Guru:

Yes, I think it’s not just AI making it possible. It’s also that the world is changing, which is again being driven by AI, that starts to bring out a higher bar in terms of holding yourself too for the effectiveness of some of the efforts being spent. Give you an example for instance, if you think about advertising on an E-commerce property, it used to be that the last decade was all about return on ad spend as defined by how much revenue am I getting for every dollar that I’m spending? Can you measure that and tell me? And that was a big deal. Like we went from sort of a mode where we used to spend money on TV and billboards, and we didn’t know what that was generating, and in which pockets. We started really getting the return on ad spend on a dollar by dollar basis. It was awesome.

But now the bar is raised, it’s not just about return on ad spend. Guess what, I can show you a fantastic rowers by bidding on branded keywords. Like for instance, you go to Amazon, and you search for Kellogg’s cereal, the conversion order is phenomenally higher for a Kellogg’s product than just a simplistic search on cereal, or keto cereal or protein cereal and stuff like that. So return on ad spend is no more good enough. It is: Do I have the revenue incrementality that I want to get? Can I get a share of voice incrementality through my investments? Can I start to not just win the Kellogg’s cereal keyword but can I also win protein cereal, kids cereal, toddler cereal, our keto cereal, those sorts of files. What does that share a voice incrementality that I can get?

So these are sort of some areas specifically that I’ve seen, especially when it comes to performance marketing in brands, that we’re starting to see a higher bar in moving towards measuring share of voice and revenue incrementality, profit incrementality and so on. Another angle is also looking at are your sales profitable? Are your sales leading to a virtuous cycle? Which again, feeds into a share of voice incrementality. A good example on that is, say if you’re in a price war situation, that is say Amazon has matched Walmart’s price on a certain budget, both of them are selling at a low price point, guess what, elasticity of demand has kicked in and you are going to make revenue incrementality. But is it truly profitable, because you’re selling at a low price point, maybe it’s an opportunity for you to just pause that investment, take those dollars out and put it in another product, a similar one, which might actually be a lot more profitable, so that we are giving visibility to the higher profitable item.

So you can see here that profit incrementality or profit return on ad spend starts to become a thing. So these are all sort of improvement or upgrade in terms of what is the realm of possibility now that I would highly recommend whether it be a brand manager or a leader or whether it be an agency to start to consider in their effectiveness of their programs.

Jake:

I get revenue incrementality. I get profit incrementality. Those are self explanatory. But the Share of Voice, I wanted to ask you about that. When I think of Share of Voice I think of what percentage of ad impressions were my brand as compared to competitive brands? But I get the sense that’s not what you’re talking about. What do you mean when you say share of voice?

Guru:

Share of voice is how often or at least the way that we’re looking at it considering this and E-commerce, is how often are you appearing in terms of the various shelves on an e-commerce website.

Jake:

And finally, number five, automate. This has to do with use cases about how we need to embed AI into our day to day operations in order to extract full value and enable capabilities never before possible.

Guru:

Let’s take an example of Procter and Gamble and Kimberly Clark, they both sell diapers and at 2am or Saturday morning 8am, whatever, Pampers goes out of stock, right? On a particular item. Let’s say it’s a four year old, or a three year old diaper for a toddler. And it’s a 36 pack that you’re looking at. This is a great opportunity for Kimberly Clark or the owner of Huggies to go and say you know what, this could be an opportunity where the shopper is looking for a particular type of diaper. And we also know that once you make a change, a shift in human behavior from going from a Pampers to a Huggies, it’s extremely sticky, or at least as an opportunity to be very sticky and really get the lifetime value of that mom who’s coming and shopping at that point. So a great opportunity for you to consider not just the immediate value of the sale, but start to think about like what is the potential lifetime value of the shopper? And bake that into the bidding strategy for either ad or a promotion that you might want to start immediately at 8:01, as soon as your competitor goes out of stock, to now start making your product a lot more appealing, and start to push that.

So as you can see, there’s a lot of real timeness that has happened, the machine figured out at 8am that you’re out of stock, the machine also has now computed the contextually in terms of what the lifetime value and what the bid amounts need to be. And also has, in real time figured out what is the equivalent item of the competitor’s item and starts to now place an ad for it. This is not fiction, this is exactly what we’re doing for many of our customers. And in doing that what we’re doing is, we’re moving especially in the CPG categories, where the behaviors are so sticky, that you just are looking for that one vulnerable moment where you can sort of go and suit that customer and give them a taste of what your product looks like. It may be a loss making transaction in itself. But then over a lifetime, very quickly, starts to make a tremendous amount of sense to be able to have in conquest at that shopper in that instance.

So these are the types of things that you are able to do with a automation AI driven approach to advertise.

Jake:

What key decisions do companies need to make now to ensure that they’re on the winning side of the future of AI?

Guru:

We fundamentally believe that you’ve got to really put artificial intelligence to bearing and get a head start on it, it may not be a situation where you can catch up, there might be situations where it’s just almost impossible to catch up. And so starting to dip your toes into some of these things, I would say the winners, if I look at look into the future, and we look back, the winners would have done the following things. One, made sure that they have a kosher and highly hygienic single source of truth in making decisions.

The data is not lying here and there in silos. It’s not polluted, it’s not old. But they are all the time up to date in real time, and it is completely normalized and you got it in a single source. On top of that, we would have seen that companies that are winners are starting to not just look at insights and analytics on top of it, but also start to make some decisions on top of it, which is, can you tie an inventory data, which in 2019 might have lived in some ERP software somewhere? Can you tie that with the advertising investment which is living in, say, a retailer website, and so on so that the right hand is talking to the left hand.

The same I’d say from a real time perspective, as opposed to the losers who would still be doing, say a monthly, weekly or daily decisions and are deploying hands on keyboard in decisions which truly need to be automatable. For instance, going back to our competent or conquest example that I gave, that you cannot wait till Monday morning to come back and capitalize on that 8am out of stock that your competitor had, you had to have acted on at 8:01. Maybe 8:02 is fine, but not beyond that, Monday morning is too late, because then by that time, they’re already back in stock and you lost just an amazing amount of opportunity to have conquest at so many of the competitors’ shoppers. So I think it really boils down to a good kosher data source and then interconnectedness in your decision making and finally automation and real timeness in your operations. These are the three things I would say would truly differentiate the winners from the losers.

Jake:

Really well said. This has been a great insight. Thank you, Guru, for joining us.

Guru:

Absolutely. Thank you so much, Jake for having me.

Jake:

Before we go, Jeremy Lockhorn and I got together to talk about our own personal experiences with AI in the retail environment. Hey, Jeremy, today, I was thinking we could talk through personal experiences in which AI has made retail more efficient for us. What do you think?

Jeremy:

Yeah, that sounds fun. Let’s do it. So I think one of the most profound examples of artificial intelligence in brick and mortar retail is what Amazon has done with their cashierless and checkout line-less ghost doors. You know, they refer to the technology that powers it as just walk out technology, or J watt. And it’s effectively you enter the store by scanning a UPC code or QR code on your phone, that is linked to your Amazon account, that sort of authenticates you and logs you into the store. From there, there’s an array of cameras that follow you everywhere you go in the store, track what you pick up and put into your basket that is even smart enough to track when you put something out, put it in your basket, and then put it back on the shelf, it removes it from your virtual cart. And you just walk out when you’re done. It’s a really interesting intersection between using the phone as a login and then the cameras sort of take over from there to track you and the items that you pick up from around the store. And then, in my personal experience, when I walked out, I got maybe a block and a half away, and I had a push notification from the app with my receipt and, you know, details around everything that I thought I purchased, and it was 100% accurate. It was really interesting.

Jake:

I’ve never done it. Like, did you feel like you were shoplifting? Did it feel weird?

Jeremy:

No. I mean, it felt very different. You know, they have guards posted at the turnstile, whoever, they’re kind of both as security people, but also almost like tech support if you have issues. And yeah, I mean, it just felt kind of magical to be honest with you.

Jake:

So for me, I feel like I’m going to all talk about the car buying experience and how AI is taking over one layer behind the scenes where it’s significantly impacting the consumer facing people. But it’s not necessarily something that the consumer sees themselves. I’ve both gone to a dealership in a very traditional way, but also, like used online lead generation tools that go off to a bunch of different dealerships. And that’s where it’s been really fascinating, especially during COVID. Like, being there on the lot is not the biggest deal. It’s really about the relationship building online and working toward the transaction going, you know, lower funnel as fast as possible. Like on one end of the spectrum, you have these dealerships that are clearly like so focused on their online experience where they can go all the way to the bottom of the funnel and give you a great deal right off the bat. No haggling just right to the point. And on the other end of the spectrum, you have people that are trying to do it old school, but in an online way. So you have folks who were just reaching out to me and say, hey, when do you have time for a phone call? Or when do you have time to come down to the dealership? And that felt to me way less effective, like, I already saw the car, I already knew that I wanted the car. And now I want to go to the bottom of the funnel.

Jeremy:

I feel like every time I’ve gone through the car buying experience and have tried to use any of those sort of online lead generation tools. It’s an interesting exercise in the sophistication of the individual salespeople as well. You know, lots of times the individual seller is eager to get out of that CRM system and text me directly or call me directly or you know, email from a different address. It makes me wonder like, are they really seeing the value out of those systems?

Jake:

Yeah, it’s like the ones who are trying to keep it traditional are missing the whole point.

Jeremy:

Right.

Jake:

And the ones that are embracing efficiency are the ones who are going to get my business. 

Jeremy:

Right.

Jake:

Anything else you wanted to add?

Jeremy:

Aaaahhh, AI is cool, man.

Jake:

I’d like to thank my guests, Jason Goldberg of Publicis, Guru Hariharan of Commerce IQ, and of course, our own Jeremy Lockhorn. 

On the next FIVE, what role does AI play in the world of agencies? And what role do agencies play in the world of AI? And what is that like today? And what will that be like in the future? Hey, and if you like the show, please write us a comment and give us a rating on your favorite podcast listening platform. We’d be super grateful. It definitely helps more people discover the podcast.

Thanks for joining us.

The FIVE podcast is presented by Ericsson Emodo and the Emodo Institute and features original music by Dyaphonic and the Small Town Symphonette, original episode art is by Chris Kosek. Social media and other promotional stuff was composed and conducted by Lyon Solntsev. This episode was edited by Justin Newton and produced by Robert Haskitt, Liz Wynnemer and me. I’m Jake Moskowitz.

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