On this episode of Data in Depth, we dig into how sophisticated data and analytics are transforming logistics and changing the way companies manage their supply chains. We talk with James Lumb, CEO of Zenkraft, who offers cutting-edge examples of how logistics data can be used to avoid waste, improve customer experience, and even improve your product line.
4:48 – Supply Chain 360
6:05 – Internet of Things
6:54 – Product Improvement Feedback Loops
8:55 – The Power of Data Integration
9:57 – 10x ROI
12:03 – Customer Engagement Opportunities
13:10 – Artificial Intelligence and Data Sharing in Logistics
14:01 – Quoting Shipments in CPQ
15:34 – Win a Pair of Bose Headphones
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Announcer: Hi, and welcome to Data in Depth, a podcast where we delve into advanced analytics, business intelligence, and machine learning, and how they’re revolutionizing the manufacturing sector. Each episode we share new ideas and best practices to help you put your business data to work. From the shop floor to the back office, from optimizing supply chains to customer experience, the factory of the future runs on data.
Andrew Rieser: Hi, thank you for joining us for another episode of Data in Depth, the podcast exploring the world of big data and its role in the manufacturing industry. I’m your host, Andrew Riser. Today, we are lucky to be joined by James Lumb. James is the CEO and co-founder of Zenkraft, a Salesforce app exchange package to help companies manage shipping, packing, and returns.
I’m super excited to have him on the show because we want to dig into some more sophisticated uses of data and analytics and how they are transforming the logistics space, and really just changing the way that we view and manage our supply chains. So welcome, James.
James Lumb: Thanks for having me, Andrew. I’m excited to be here.
Andrew: Let’s get started a bit by learning more about your journey and Zenkraft. So can you give us the 30-second elevator pitch on Zenkraft?
James: Yeah, great. So Zenkraft builds shipping applications for all Salesforce clouds. This includes return management, tracking, quoting and label generation. So we’ve natively connected over 50 different shipping carriers, including FedEx, UPS, and USPS. And as of today, we’re about just under 1,000 live customers and about 38,000 daily active users.
Andrew: That’s awesome. That’s some great growth, and one of the things I always find interesting is how your personal journey led you to where you are today. So maybe you can tell me what led up to founding the Zenkraft.
James: Oh God, yeah, great question. So my first real job was working for a software startup in the UK, and I was immediately tasked with figuring out what Salesforce is. So they sent me to Dreamforce in 2006, and about six months later we launched this address validation app. It was one of the first apps. In fact, I think it was the 13th app on the AppExchange. And in 2010, I and my two co-founders of Zenkraft decided to launch our own business on the AppExchange.
Andrew: That’s awesome. That’s also great that you were kind of one of the early adopters of the AppExchange. Did you have any idea of how it would grow so fast and what this ecosystem would grow into?
James: I would say yes and no. Back in 2010, Salesforce was more of a Salesforce automation product, which is today Sales Cloud. But in the last few years, we’ve really seen Salesforce grow into the middle and back-office as well. So every department within the organization, including service, commerce, and logistics, is really using Salesforce as well, which is really helped grow ZenKraft.
Andrew: So Zenkraft, technically, is the second app that you have been involved with or built on the AppExchange, is that correct?
James: Yeah, although I’m not really involved in the building of the package. I’m more involved with the kind of launch and growth of the business.
Andrew: I see. So kind of the marketing strategy and the go-to-market of how you’re going to promote Zenkraft.
Andrew: Very cool. So what was the pain point or the gap there, white space if you will, that you saw in the ecosystem that drove the creation of ZenKraft? What problem were you trying to solve?
James: Yeah, so Zenkraft’s really about completing that 360-degree customer view. So we’re bringing shipping data inside of Salesforce. So a business will typically manage their customer data inside of Salesforce, but the shipping data’s managing third party system, often a desktop application that the sales and support team doesn’t have access to. So it’s a really inefficient process.
So Zenkraft brings all the shipping capabilities, including label generation, tracking, quoting inside of the Salesforce platform to really complete that 360-degree customer view. And we did it on the Salesforce platform because we believe that bringing shipping data alongside customer data was the best solution for our customers.
Andrew: That’s great to hear. So on this podcast and in general, we kind of think of three different pillars or buckets, if you will. So the customer 360, which you alluded to, the shop floor 360, about all the interactions and data that flow on the shop floor, and then also the supply chain 360.
So for those that are following along, you might be thinking, “All right, so I thought the show was going to be about data. This sounds like we’re just talking about logistics and operations.” So maybe James, you can dig a little bit deeper into how Zenkraft is actually leveraging this data and analytics, to help not only the shop floor 360 and the customer 360, but really getting that visibility and insights into the supply chain.
James: Yeah, absolutely. Yeah, so Zenkraft’s customer base is really concentrated around three main verticals, which are retail, manufacturing, and then healthcare, life sciences. But I’ll provide the life sciences example.
So when life science shipments are going around the world, typically they’re going to have to stay within a temperature range. Things that might be shipped are, I don’t know, raw materials to produce particular drugs or precursors to pharmaceutical products that are being manufactured around the world. And if these shipments fall outside of the range for a certain timeframe, it’s called an excursion, and the contents can no longer be used. So with these temperature-controlled packages, internal and external thermometers are attached to them, and these are transmitting data in real-time back to the customer every 15 minutes. So when these excursions occur, data really comes into place. So firstly, the shipper and the recipient needs to get alerted that now these shipments and the contents is now unusable, but also a real order has to be made.
And this is really where data comes into play. In addition, what we’re seeing is these co-chain companies, what they’re doing is, they’re actually learning about where within these consolidator’s networks, these excursions are happening. So this could be happening potentially in a warehouse location, or this could be happening on a particular type of plane. And now what they’re looking to do is sell this data back to the consolidators, to actually say, “You need to improve this area,” because actually, potentially these consolidators, they’re being penalized when these shipments fall outside of this range, and this helps improve the consolidator’s product and improve the use of data sharing.
Andrew: Yeah, I think that’s an awesome real-world scenario, especially in the life sciences world. And another thing that that really tells the story about is the internet of things. So we hear all these buzz words in this space, and the internet of things is definitely one of them. And so I think you just hit the nail on the head when you talked about the sensors that monitor temperature, and there’s real consequences if things fall outside of those ranges. So that real-time monitoring is super important.
James: Yeah, absolutely.
Andrew: I love hearing these real-world scenarios that put the topics into this kind of context, that becomes relatable because you can now really understand where this data is making an impact and where it becomes important. So capturing this information not only gives them insights into their logistics processes, but also into the product or service being offered by the carriers. Is that right?
James: Right, exactly. And this works for manufacturers as well. So I think an example, in e-commerce for our power retailers, returns count for up to 50% of orders, and this is a crippling cost to retailers. So what we’re doing now is, we’re actually working with retailers to collect data about these returns before the shipping label is being generated. And they can either schedule a pickup or drop it off.
So one of the customers we’ve been working with recently was a shoe manufacturer. So instead of just accepting a return, what they do now is ask questions, such as if the shoe was too small, where on the foot is the shoe rubbing? Is it the heel? Is it the toe? Was the material not stretchy enough? So we’re capturing all this data and then as soon as that data gets pushed into Salesforce, the magic really starts to happen because we can create reports and dashboards, which can then get fed back to the merchandising teams, the product development teams, to then improve the product in the future.
Andrew: Yeah, that’s a really good use case, again, that you shared. So this becomes kind of a reoccurring theme that has shown up multiple times on our show. The concept of a feedback loop that helps you improve your product and also promotes iterating that product development faster and better.
So when we talk about this, it all boils down to data, right? And integration and how you promote that information sharing. So making sure that your systems are aligned and that you communicate that data across these different divisions like you alluded to. So sales, service, marketing, engineering, product development, and really ensuring that this feedback loop exists, and you’re breaking down these internal silos that often get generated within organizations.
So the power of integration is definitely that reoccurring theme and insights into a single dataset is one thing. But when you’re able to cross-reference all this data and then dive deeper into this metadata that you just described and alluded to around the shoe and those additional questions that are being asked, that’s really powerful information. So can you give me another example, maybe, of where you see all that coming into play?
James: Yeah, absolutely. I can give you another example, but probably a healthcare example this time.
So when a patient is having an operation, typically they’ll like to have it on a Friday, so they can recover over the weekend and then go back to work on Monday.
So the parts and tools for these operations can actually run into the thousands of dollars. So most hospitals, they don’t really have the space or even the budgets to store all of this inventory on-site. So what they’ll do is have them delivered in a day or two, just before they’re required. So the window of time for the package to arrive is very narrow and crucial. So we’re working with customers now to combine various data sources to predict and mitigate shipment delays, including volume. Is this a peak period? So let’s order a day or two earlier than normal.
But also weather data. Are planes going to be delayed? Are trucks going to be delayed? Will there be congestion on roads? So when you combine this data, you can start predicting if a shipment is likely to be delayed during a certain period and then mitigate that issue before they occur.
Andrew: Yeah, so I think your examples really tell a solid story around how there’s all those various data, and making sure that it’s being analyzed and understood really helps kind of drive these efficiencies, but also plays a critical role into some of these scenarios. Especially when you talked about that example, where there really is truly a small window of opportunity to get those products delivered.
So, James, we’ve talked a lot about how better data can yield better insights into your business, but your work also illustrates how data can support major gains in efficiency. So when we talk about efficiency gains, I think that’s a key topic that people want to know. If you’re digitally transforming or investing in new software, new platforms, where do you really see those returns on your investment?
I think at one point you told me that you tracked the impact with one of your customers, or maybe spread across multiple customers, that Zenkraft showed, in some cases, the shipping processes that used to take around 10 minutes, now are reduced to about one minute. That’s a pretty major gain if you think about that reoccurring process happening hundreds or maybe even thousands of times a day. So all that definitely adds up. What’s the key to that efficiency?
James: Yeah, so again, it’s really about data integration and using the power of the platform. So we’ve literally integrated into every corner of Salesforce. Zenkraft has connected over 100 different carrier APIs to automate the transition of data, from the customer Salesforce organization, to the carrier.
So once that data is inside Salesforce, the magic really starts to happen. With reports and dashboards, you’ll start to notice and correct inefficiencies in your shipping that can be implemented in minutes. For example, I carry on my offer two day shipping and ground to the same area. And over time you’ll start to realize that deliveries from your warehouse to that region already arrive in two days if you ship it ground. So you can start to take advantage of the cost-saving of ground shipping, instead of choosing the more expensive two day option and achieve the same result.
Andrew: Yeah, that’s another really good example. And I think a lot of that can lead to improvements in the overall customer experience as well.
James: Exactly. So at peak times, about 80% of customer service inquiries for a retailer are, “Where is my order?” Or, “Where’s my questions?” So what retailers have started to realize is, this is a huge opportunity to engage with customers more. And I’ll give you an example. So same crowd of customers using Commerce Cloud, they’re sending shipping, tracking emails at the four key stages of a delivery. And that’s in transit, out for delivery, delayed and delivered. And research shows that shipping emails, although considered transactional emails, get up to 4.5 times the open rate of convention emails. So if you add Einstein recommendations to these emails, you can start to upsell and cross-sell opportunities, as well as reducing the number, as with most calls to your call center.
Andrew: That’s great. So this is where it’s all starting to come together kind of full circle. We’re able to tie everything out and leverage this information and the insights that are being gained, to really take advantage of the data that these customers have at their fingertips.
So James, where do you see the future of logistics data going? What problems do you anticipate, or are you going to need to be solved, and what opportunities do you think we can take advantage of that are out there?
James: Yeah, so AI is critically important for building the most cost-efficient models in logistics. But I think probably even more important is data sharing, both inside and outside your company.
For example, about 50% of truck return legs are empty. If this capacity data could be shared more easily, the cost-saving could be huge. I think, also, demand forecasting could be shared more readily with third-party logistics companies. If three PLs can move inventory closer to the customer before the order, they can take advantage of less expensive transfer modes, such as rail and sea, versus air freighting goods last minute.
Andrew: James, it’s been a pleasure talking with you. I always like to wrap up these conversations by offering you a chance to get in the last word. So what’s one thing that we may not have hit on that you think is important, or one key takeaway that you want to offer our listeners?
James: Yeah, so aside from thanking you, Andrew, for the opportunity, I’d like to mention one more area where we do lots of work, and that’s around shipment quoting.
So for manufacturers using CPQ, we’re seeing a ton of interest in parcel and freight quoting capabilities integrating into customer’s workflow. So if you imagine obtaining a quote from a free PL, typically is a multi-day email exchange endeavor. For manufacturers especially, I’d definitely encourage you to look at the efficiency gains in bringing automated quoting to your CPQ cloud.
Andrew: Yes, that’s another great insight and common pain point that we see within this industry. Oftentimes, I think in that process you just described, these manufacturers will just include a write-in line item to where they estimate this or just put a placeholder in there, but having real-time access and leveraging these APIs to bake in that third-party logistics and freight and shipping cost would definitely be a huge cost saver, because you’re leveraging actual data and you can get a more accurate quote out the door in an efficient manner.
So James, we really appreciate you spending the time with us today and with our listeners.
James: Thank you for having me, Andrew. It’s been a pleasure.
Andrew: Absolutely. And so for those of you listening and like to learn more about ZenKraft, I’d encourage you to visit zenkraft.com. That’s ZenKraft with a K. zenkraft.com. And we’ll also link James’ profile and other relevant sources in our show notes for this episode.
If you enjoyed the episode, please take a moment to rate the episode and subscribe to Data in Depth, available on iTunes, Google, Spotify, Stitcher, and pretty much anywhere else you might listen to your podcasts.
And finally, thank you to our listeners and supporters. We’re giving you a chance to win a pair of Bose QuietComfort Noise Canceling headphones, those super nice headphones that everyone raves about. That way you can listen to our podcast in style. To enter for your chance to win, visit our website at dataindepth.com, or find us on Twitter or Facebook. We will have all the details listed online.
Thanks again James, and thanks to our listeners for joining us.
James: Thank you for the opportunity. Cheers, guys.
Announcer: Data in Depth is produced by Mountain Point, a digital transformation consulting firm, focusing on the manufacturing sector. You can find show notes, additional episodes and more, by visiting dataindepth.com. Thanks for listening, and be sure to subscribe wherever you get your podcasts.