Marketers often wish a wondrous machine existed that could tell them exactly which lever to pull at exactly what time to get the ideal customer to make a purchase.Magic dust or machines may not exist, but marketers do have access to powerful data dashboards to drive their marketing intelligence engine.
Here is what the webinar covers:
The needs and requirements of high-performing marketing teams
How best-practice leaders have developed their view of the customer and intelligence dashboard
Other insights from current marketing and industry leaders
LIZ: Hello everyone who has joined us on this day before Valentine’s Day, a day that we’re going to say thank you to all the marketers out there, at least this is the CMO Council’s Valentine for everyone. We wanted to gather together for the second in an ongoing conversation that we are having around analytics. It tends to be that analytics is a giant midi-topic that we are both thrilled and excited about, but we’re also challenged enough and frustrated about whether we are under-resourced, under-budgeted, disconnected, all of the things that we tend to hear when it comes to advancing analytics. So we want to thank you for joining us. I want to thank our partners in this conversation, they’re really the group that we turn to, thanks to their long track record in the business intelligence space, and we’re consistently asking them how we apply some of those tactics into really providing that space where we can advance these analytics. So thank you all for joining us. We’re going to go ahead and get started.
Let’s lay some ground rules down. What are we looking to do? We want to be able to understand where and how analytics should shape the customer experience. We want to identify those best practices for getting us there, and we’re going to hear from experts who can actually get us started. We’re going to hear from a marketing leader who is working within a very advanced and innovative organization at L’Oréal and who is changing the way their customer experience is being viewed not only internally and externally, but we’re also going to hear from an expert in business intelligence, and we’re going to get a very special look inside how an organization that specializes in this area of conversation is actually applying this. They’re drinking their own champagne, shall we say, and really looking at how their own marketing programs and their own processes are being stacked. So we’re going to have some really great case studies today.
The number one thing I want to ask of all of you to do who are joining us live today is ask questions. There is a question button at the very top of your screen (you should be able to ask questions now that you can no longer hear background music—I know that we’ve had that question come in), but we want you to ask questions. Write them into the question dialogue screen, and we’re going to get those answered at the very end of the session. I’ll be fielding those, so please, please, please send in those questions. We’ve also got a Twitter account, so stay active on social media. You’ll see the hash tag at the very bottom there; it’s #DomoCMOC. We want to be able to keep the conversation going well after this webcast. So let’s get moving.
How are we using data? That’s the big question we want to start talking about, and as we’re trying to leverage data to actually better connect and communicate and engage with our audiences, what we have found from some CMO Council studies is that 72% of marketers that we’ve spoken to are looking to create more timely, targeted, and relevant messaging to their customers. 58% are looking to seek richer segmentation so they can actually engage in those relevant communications far more efficiently and effectively. And 52% would like to be more effective at cross selling and up selling to their existing organizations.
What we’re also seeing is that we’re having a lot of challenges in executing some of these big goals. In fact, culture and internal politics often do the most damage to marketing goals to advance the customer experience. 42% of marketers to whom we spoke believe that corporate culture and internal politics actually subvert marketing processes, structures, and approach, and 41% actually blamed siloed data and limited feedback loops between functional operations across the organization as a key challenge and holdback. Only 4% of marketers that we’ve surveyed are enjoying a very high level of satisfaction with their current visibility, accountability, and output of their marketing operations. But unfortunately, 84% of marketers believe that they operate in a change resistant environment within their organization. We firmly believe, and we see this across all of our engagements with our membership, that it really is because they lack the visibility in a single song sheet, or that single dashboard so to speak, to be able to understand not only their marketing operations but also their customers.
The question is always asked of us, do the customers really care? Well, when we asked consumers (and we asked well over 2,000) why they disconnect from brand engagements, 46% of consumers said they stopped engaging with a brand because the communications that we were sending were simply not relevant to them. 73% were irked over receiving a promotion or some type of material about a product that they had already purchased—and of that group (the 73%) 57% said they purchased it for a higher price. So that really made them want to disconnect. 39% are actually ignoring messages because they’re flooded in both their mailbox and inbox and also through their social media connection.
So what’s the actual reaction to irrelevance? 41% of consumers say that they would consider halting all business and all transactions and no longer purchase from a company that continues to send impersonal and irrelevant clutter.
So how do we get there? When we have these conversations with our CMO Council members we get a mix of feelings as to whether some of these issues are a fairy tale, if they’re a far off reality, or if they’re a mandate that marketers feel are just around the corner. I would say the four things that I hear the most are (1) that brands internally want to have all of the organization sing from a single song sheet. (2) They want to see a 360-degree view of the customer, but more often than not they feel they’re only getting the marketing or even marketing and sales view of that customer. (3) Marketers are pushing towards that customer-centric enterprise, and (4) most importantly I always hear from marketers that we’re looking for that lever to pull. We want to be able to walk up to that button that says “this is the most relevant customer group” to send the most relevant engagement to, that is going to action on what we’re sending and what we’re delivering in our messaging.
So with that, that’s where I want to launch this conversation around how are we maximizing the marketing intelligence engine? How are we advancing that and pushing it forward? Our experts today are Abhay Patel, who is with L’Oréal, and they’re doing some amazing things, so we want to thank Abhay for being with us, and also Tom McConnon, who’s the Senior Director of Product Marketing for Domo. Both of these gentlemen are going to provide us with some terrific insights. Abhay I’m going to go ahead and turn this over to you.
ABHAY: Thank you Liz. Good afternoon everyone. I’m excited to have the opportunity to share with you some of the great work that we have going on here at L’Oréal, and I want to touch base on a few things before I get into this first slide. I think Liz touched on a couple of key pieces that we have to deal with, which is the cultural element that I think a lot of organizations have to deal with. And I think part of what I’ll be able to frame up, which I’m sure many organizations have to deal with, is overcoming some of those internal barriers because—for as much as we love the data and we love the analytics—socializing and actioning the results and the insights for the organization is the most important piece for driving that through.
The first slide here speaks to where we’re at. It’s “however beautiful the strategy you should occasionally look at the results.” It’s a perfect quote from Winston Churchill that speaks to what we as an organization need to take a step back and say “We’re very successful as a company in L’Oréal, we have great assets from our brand standpoint. How can we continue to evolve and make ourselves even better?” And what we found was as we were engaging with customers is there is a lot that we did not know. And that’s where the role of analytics has come in to play in terms of what we’ve been looking to build, in terms of helping define success for the organization.
And there’s been a lot of things that we’ve been working to do to help build on this current situation. I think, in the interest of it being the day before Valentine’s Day, I’d like to say that we’ve started to truly love the data, and that’s the first piece that is the most important as it relates to the work that we do from an analytics standpoint. Understanding that every activity and action that we do in terms of reaching to the customer generates some version of data. And understanding whether we’ve been capturing that, how we’ve been capturing that, and at what level we’ve been capturing that has been a fundamental part of what we’ve been trying to build internally, so that we can then actually build the analytics as part of that, so that we can drive the insights for the organization.
The next piece is to overcome feeling overwhelmed, and I think this touches on what Liz was talking about in terms of a lot of organizations not feeling like they’re satisfied with the visibility and the accountability of the work that’s being done from an analytics perspective. Analytics for folks who are within the industry is something that isn’t necessarily easy, but it’s easier to understand. For a lot of folks within the organizations, going through the statistical work or understanding the details of the analytics can be very daunting. Part of our job as folks on the advancing audit side is to ensure that we communicate and tell the story and drive the insights so that the organization knows how to leverage the data that’s there and hopefully overcome that feeling of being overwhelmed and instead utilize it as an asset for the organization as we try to move forward in terms of transforming that experience for the customer. So these are some things that we’ve started to lay down as an organization in terms of the work that we’ve been doing from an advanced analytics standpoint.
When we go to the next slide this is essentially the general framework that we look at. And it’s more specific to all of the work that we’ve recently been doing; it’s more on the digital side for the organization. The reason that this is important is that the simplicity of the approach that we take is very important in terms of getting buying and socialization within the organization; it helps folks understand and appreciate what we’re trying to achieve. What we’ve really been trying to drive from why the importance of tracking everything and driving to the transformation of the customer is really round, first, understanding what happened, observing and generating those hypotheses and having our business teams ensure that we’re aligned to the objectives of what we’re trying to achieve. In every campaign that we run, there has to be a reason and objective for why we’re running it because that’s how we’ll be able to understand what we need to measure and then what we need to be able to observe to provide insights on.
Why did it happen? One of the key pieces for working internally with our agency partners is really understanding what’s happening for the campaigns that we’re launching and knowing what the different types of activities that are there are. We’ve built a robust platform on the back end to have data sources that allow us to track and understand the behaviors associated with and linked to the different campaigns that we’re running. And this, as we see it here, is a good indicator of learning. What we do is we take that information to help us learn and improve our planning process as we’re going through in future campaign development.
And then finally, so what if it happened? Once we’re able to understand what the measurement is telling us, we are enabled to learn from that in the future measurement planning process. We remove some of the uncertainty, but the other piece that’s important for us is that we don’t use this data as a crutch. We use this as an opportunity to innovate because as you’re learning from the insights and what you’re taking away from the measurement, you have to help the organization make smarter decisions. L’Oréal as a company did not become as great of a company as it is based on just simply doing the same thing over and over again. We’re in a trend-driven business. We’re the ones who are trying to reach the customers and find innovative ways to do it. That’s something from an analytics standpoint that’s very important for us as we do the work in terms of the output in helping the organization to view this as a tool to help us make smarter decisions and not using the data as a crutch in terms of how we go into planning and activation mode.
And then the final piece. So this links back to that initial framework in terms of what we’ve been doing. It’s what we call our mantra internally; it’s planning, monitoring, and optimization—linking back and making sure folks are aligned. I’m sure many organizations have to face this also, and it’s usually the simplest piece that’s out there, but getting an organization aligned given the stakeholders is one of the more challenging pieces that’s there. What we’ve done is talked to the processes in place to really get folks aligned on what the objectives are and also on what the benchmarks are that we need to be able to measure against. Before we get into any of the studies that we’re doing we want to make sure that there’s an action standard that’s linked to a specific benchmark so that we can help address and answer the result right off the bat. What we don’t want to get into is a back and forth debate that fits into a story that someone wants to be able to tell; what we’re trying to be is the objective voice associated with the analytics that’s being worked internally.
And then the monitoring piece. We ensure that in every campaign that we’re running we have all of the right data sources on the back end that are allowing us to monitor the activity that links back to the objective that we’re trying to achieve. The final point is the optimization piece. This is something that we continually look to do where we find the insights, we find the learning, and we apply that to future campaigns. It’s a fundamental part of why you do this, part of this is to reduce down the uncertainty and risk and hopefully find new innovative ways to be able to engage the customer as you move forward.
LIZ. Terrific, I think that’s a huge process that you guys have been undertaking, and we’ll have some questions for you at the end during our Q&A session, but thank you for that.
LIZ: Tom, we’re going to go ahead and turn it over to you to see how Domo’s actually tackling some of these analytics problems and challenges and opportunities within your organization.
TOM: Fantastic, thank you, and first off thanks to Abhay. You definitely had some interesting insights from a perspective of what L’Oréal is doing.
So again, my name’s Tom McConnon, and here at Domo I’ve had the privilege to work closely with our CMO Heather Zynczak. Recently, we’ve collaborated on a series of case studies that illustrate how crucial data is to today’s marketing executives. I think the findings are pretty fascinating, and I’m excited to share these with you.
When people ask me how to become a data-driven marketer, I have one overarching piece of advice. It is to get all your data in one place in real time. Admittedly, this is easier said than done, but if you can pull it off you’ll be able to operate more efficiently than 99% of your peers and, it goes without saying, 99% of your competitors as well. People ask me what it looks like to get all of your data in one place in real time. Here’s an example. What we’re seeing here are examples of KPIs, or key performance indicators, that help measure and drive a marketing organization.
What is important to note here is that all of these metrics are being pulled from a wide variety of data sources. We’re getting metrics from sales, from social media, from paid search, email marketing, web analytics, etc. And by seeing all of this data in context and seeing it all in real time, you can obtain insights that would have otherwise been impossible to see. So I’d like to jump into a few of our examples now. These are case studies from Domo’s CMO. There are four examples of actual marketing data that we’re tracking and what we’ve learned from it. I’m also going to share an example from a customer that’s authorized us to share some of their information.
Here we have Domo case study number one. The chart that we’re looking at is Cost Per Lead by Web Channel. This is something that we’re tracking here internally, and what we’re focusing on is this the green bar, Google Banners. This a lead source for us. You’ll notice back in November of 2012 this green bar was extremely high. We were paying a lot per lead for this lead source, and we have a great online marketer who was able to work through this problem and get our cost per lead way down. You’ll see in the following two months it was below that red line, which was our goal. We were patting ourselves on the back; we thought, “Hey, Google Banners is a great leads source for us. We have a high lead volume, and these leads don’t cost very much.” It felt like success. But then we did a little bit more digging.
On this next slide here we’re looking at Cost Per SQL by Web Channel, and for those who don’t use the same terminology that we do in house, an SQL is a sales qualified lead. So we’re seeing here that in December the cost per sales qualified lead from a Google Banner was extremely high. The red line is our goal of where we wanted the cost per lead to be, and it was way beyond that. We found that these leads cost a whole lot to turn into actual prospects that our sales team could work, and we weren’t producing very many of them, so we found that we had some trouble on our hands.
We decided to dig into this example a little bit more. What we found was that the perfect storm, basically, had occurred. We had increased our spend for this lead channel because we were getting lots of leads and they weren’t costing a whole lot. At the same time, Google, who was serving these banners, found a lot more properties to serve our ads. They were delivering 10 times the inventory that they had been previously. We bumped up spend, and Google found more placement. The problem was, we had the wrong audience. You can see in this screen shot on the left an actual example of what happened to us. This fppt.com is FreePowerPoint.com. Our ad was served on this website, and it made it look like, if you clicked our ad you’d be downloading a Christmas with Santa Claus PowerPoint template. And so we were getting all kinds of leads from extremely irrelevant websites, and this was killing our conversion rate. It was producing leads that didn’t produce sales.
So what was the data-driven decision that we made from this? We decided to cut our Google Banner spend. We decided that form fillers really aren’t worth it if they don’t turn into revenue. We don’t care how many conversions we’re creating if they don’t actually make us money. So that was case study number one. And it illustrated the importance of looking at multiple sources of data to get the real big picture, the full picture.
Domo case study number two. Back in the day when we were just beginning as a company, we had one landing page. We were driving all of our leads there because we had limited resources. We didn’t have a whole lot of time to build out landing pages per customer segment. But then we decided to create these customized landing pages. Previously, with the old generic landing page, we were averaging about 100 conversions per day. Then we launched a new landing page targeted towards the executives—you can see in the chart on the left—and we were looking at executive landing page conversions. We were expecting to maintain at least 100 conversions per day, what we had been doing previously, but when we launched that page we saw something really interesting. We saw that our marketing automation tool, Eloqua, registered only 80 leads on a page that we thought should at least be averaging a 100. So we were starting to wonder what’s going on here—we have a custom landing page for a custom audience. You would think that it would convert much better. We thought that we really blew it in this case. But then we looked at a different data set, or a different source of data. We looked at Adobe Site Catalyst, Omniture at the time, for our web analytics, and what we saw is that Adobe was looking at all of this traffic coming to the landing page, and they were treating that landing page as an entry page. Adobe was reporting that we were getting 140 conversions from the same data, from the same traffic source.
So basically we were looking at two different things. Eloqua, who was measuring the conversions on the landing page, said we were getting 80 conversions. Adobe Site Catalyst said we were getting 140. Who was right in this scenario? Digging into it a little bit further we found what was going on. Adobe was registering all of the conversions that were happening across the entire site. So maybe somebody clicked on an ad, came to our landing page, and instead of filling out the landing page they navigated to the home page and then later filled out a resource form. Adobe was capturing all of that additional activity while Eloqua was only capturing activity on that specific landing page. So we found that our new landing page wasn’t so bad after all. Our data-driven decision from this was to pump out more custom landing pages because we didn’t really care where prospects were filling out forms, we just wanted them to fill out forms. And we found that once we had these custom landing pages people were navigating deeper into the site, and we were actually getting more conversions than we were previously, so, again the importance of looking at multiple data sources to get the full marketing picture.
Onto case study number three. This is really interesting. We’re looking here at Average Deal Size by Lead Source, and I’ve numbered these lead sources here. (Sorry I’ve had to scrub out some of these—some of it was proprietary information in case we get competitors on the line.) But if you were looking at this chart and you knew that this was the average deal by lead source, you would think that lead sources two, three, and four were probably really profitable. These are all above our red line, or our goal, so these deal sizes were much better than average. If you didn’t have any other information, where would you invest? I personally would invest in two, three, and four since they’re above the line. But then we started to look at another source of marketing data. This next key performance indicator is the Return on Marketing Investment for the same data source. Now we’re looking at different information. We’re asking ourselves, what is the best return on investment by these lead sources here? If you look at one through five, it looks like one and three are the clear leaders—for every marketing dollar spent they’re producing a whole lot more revenue for that dollar.
What I want to do now is look at these two charts side by side. So we have Average Deal Size by Lead Source and the Return on Marketing Investment for these leads. And you see something interesting occur. Whereas in the first graph you might have invested more in lead sources two, three, and four, in this next chart you would definitely want to invest in three. Three is a clear winner. Not only does it have a huge deal size but it also produces a huge return on marketing investment. I would also invest in lead source number one since it does have a positive ROI even if the deal size isn’t so large. The really tricky one though is number two. If you look on the left you’d probably want to invest more in that lead source, but if you look on the right you see you’re actually losing money. On every dollar spent you’re just bleeding cash for that lead source. If we weren’t looking at both of these charts side by side, if we didn’t see this data in context, then we could have had a real problem on our hands. We might have invested more in lead source number two and lost ourselves a whole heck of a lot of money. So the data-driven decision there was to invest in lead sources with positive return on marketing investment, the ROMI metric there, and beware of lead source number two.
Moving on to case study number four. This was kind of fun. When we did a refresh on our website we decided to implement a live chat feature. Because we’re Domo, because we care about business intelligence, and because we track everything we do, we have metrics around a lot of different live chat data. On the chart here we’re looking at Chats per Hour, i.e., how many people visiting our website engage with us via chat by hour of the day? And you’ll notice here in this data, we saw two clear spikes from 9 to 11 in the morning and from 2 to 4 in the afternoon; before and after lunch we were getting the vast majority of our traffic. But with that additional traffic we had, we found that we had less time to take care of these customers that were engaging with us via chat. The staff that we had was spread pretty thin, and we felt like the experience that these people were having was not as good as it could be because we weren’t taking the time to understand what their needs were and answer all their questions.
So we decided to make a change. You can see here on week four we made a staffing change. We decided to put more people on live chat so that we could accommodate those spikes in traffic and especially staff during those two peak hours. We also implemented some new training. We told our staff, “Hey, now you have more resources and there are more people on live chat. We want you to take more time to really engage with these people, find out what their needs are, what their challenges are, what their questions are, and spend a lot of time with them. Make it the best white-glove experience that it can be.” And something really interesting happened. We found that when we did that, we experienced a 22% boost in leads that came from chat. So basically people came to the website, maybe it was customer support, maybe it was questions, but because of this increased engagement we turned more of these looky lous into potential customers, or potential revenue essentially. And that all happened by focusing on the customer and improving their experience. That was a really positive development for us.
So now we’re looking at case study number five here. This case study is the example from one of our customers. This customer is a retailer, and they were planning a search engine marketing, or SEM, campaign. They were using PPC, Pay-Per-Click, to move some more of their inventory. As you can see from the chart, they had dedicated 33% of their ad spend to push their digital cameras—they were serving as to sell digital cameras. They set up this campaign to last for about a week, and they were going to let it run and see what the results were. But then something interesting happened here. The supply chain for digital cameras was disrupted. They were anticipating receiving more digital cameras to their warehouse, but something happened with that supply chain and it didn’t happen. So they found themselves four days into this week with 57% of their ad spend left, and they determined that inventory would run out in two days, so they had all this money on their hands and they didn’t have inventory to push. But because they were able to see these metrics in real time, they were able to make an agile marketing decision, and that data-driven decision that they made was to shift a portion of their ad spend to their overstocked memory cards.
This never would have happened were the online marketers, or the search engine marketing managers, not able to see the supply chain. We’ve seen this happen time and time again, where people plan big marketing pushes, big marketing initiatives, and they don’t know what’s going on with the product or they don’t know what inventory is like. But these marketers had access to inventory levels in real time, and they were able to adapt very quickly because they had access to this data. As Liz mentioned, it’s data that’s beyond marketing, it’s beyond sales; it’s operations, or inventory, data. So that was a big win for them.
Now, you might be thinking about your own organization and what you should be tracking, and I would assert that everybody in attendance today still has to deal with two problems. One, you have lots of data. All marketers do, especially now in this digital age of marketing. And then the second problem is that it’s coming from lots of disparate sources, lots of different data sources. Some of these sources might have their own dashboards or they might have their own analytics. Other data sources might allow you to export data to spreadsheets that you can consume in Excel or other tools like that. My recommendation, what I’d encourage, is that whatever tools you have at your disposal, use them. Your careers, essentially, might very well depend on your ability to demonstrate ROI from a data-driven marketing organization. The days of fuzzy numbers in marketing are long gone. We as marketers need to be able to produce demonstrable results. As the CMO, you will have to report to your CEO. Your CEO needs to know why this event took place and how it contributes to the bottom line essentially.
But if you’d like to see all your data from all of your different data sources in a single dashboard, that’s also an option. And this is where Domo comes in. What we’re doing at Domo and what we’re so excited about today is we’re able to deliver all of your marketing data in a single dashboard in real time, and this can be across all of the multiple devices that you might use, smartphones, tablets, etc. And so I’d like to talk about why exactly this is valuable to marketers and just chat about Domo for a second, about what the product highlights are and what the benefits to marketers are.
So first off, with Domo you can connect directly to all of your data sources, and these aren’t just marketing data sources, although that’s a massive part of it for your organization. You can connect to financial data, HR data, operations data, whatever else it is from spreadsheets to data bases to cloud applications and pull that all into one view. Why do marketers love that? Essentially what you get is a single dashboard for simple centralized reporting. You can see all of the information that you care about and make it accessible to your team, your C suite, your CEO. It’s really easy to share all of that information and get everybody on the same page.
Second, like case study number one, if our CMO only had been paying attention to cost per lead source she would have developed a serious executive blind spot. Instead, the cost per lead source was viewed in context with cost per sales qualified lead, or cost per SQL, and that enabled her and our team to make changes that really maximized the ROI from the budget that we had at our disposal. So we were able to see our data in context and make changes that made us able to get the most out of our budget.
Third, as marketers, you know how quickly campaigns can change. It’s important to get these real time insights. Domo helps you measure all of your efforts so you can make agile decisions to feed what’s producing and essentially starve what isn’t working.
Fourth, another point that I find really important is management by exception. Some CMOs feel that they should be looking at every number in every report, but that level of engagement doesn’t really make you data-driven. It just makes you exhausted at the end of the day. Instead of looking at every metric, Domo can alert you to situations where actual results differ significantly from planned results. That way you can manage by exception and focus on priority activities instead of being in the weeds or managing all the other details that end up eating up your time.
And lastly, I’m sure everyone in attendance today has had some kind of argument over who has the right numbers. This happens all the time in every organization. Well, with Domo, everybody’s looking at the same data. If you’re B to B, maybe that’s sales and marketing looking at the same information from your web analytics and sales force. Of if you’re B to C, maybe it’s marketing looking at the same data as quick as accounting is looking at it from QuickBooks—everybody’s on the same page. So basically. we here at Domo think you should be able to ask questions of your data and get immediate answers. Just like we have search engines today like Google that help us access any information we want almost instantly, we feel like that should be possible for the data in your organization as well—not just within your department but across your entire organization. And that’s what we’re passionate about; that’s what we care about.
I really appreciate everyone in attendance being here today. If you’re intrigued or you want to learn more we’d love for you to schedule a free demo of Domo so you could see how it could work for your organization and add value to the way that you run your operations. If you’d like to contact us email email@example.com or call 801-805-9500 or visit our website at www.domo.com/marketing. We’d love to hear more from you, but at this point I’d love to turn it back over to Liz and see if anyone has questions.
LIZ: Great, great, Tom, that’s great. I always say that I get really, really nerdy when it comes to dashboards and numbers because I love seeing them, because being able to correlate some of those really big decisions about where we can be spending smarter and engaging smarter and actually substantiating all of the things and all of the great creative ways that we’re connecting with our customers, is, I think, probably one of the most exciting things that I consistently see over marketing, so thanks to you both.
To our audience, we are still taking questions so please, please, please go ahead and type those in and write them into the question box that you’ll see at the top of your viewer. We have had a couple of folks write in. I think some folks might be experiencing some audio issues on the line, we do have some folks from BrightTALK trying to troubleshoot that right now so, but I do apologize if you’ve had any issues with the audio for the webcast thus far.
I’m going to go ahead and launch into some questions that have already been submitted. Abhay, the first question is going to be directed at you. With all the things that you’re doing at L’Oréal, (and I love the process that you guys are going through—there seems to be a lot of thinking from the strategic standpoint of setting up this type of insight and setting up this type of objective view into marketing) did you guys start with a strategy and a process in place before you started down this path or was this something that grew organically? What was the thinking and the setup behind this to get some of the key stakeholders also involved?
ABHAY: What we say here is crawl, walk, run. For a company as big as us having wholesale change takes a long time and needs to be done the right way. I think in terms of the process, in terms of where we’ve gotten to, I would probably say we had a good general strategy on how we wanted to approach it, but as we’ve gone through the process it has evolved. The way that we started to look at it was really to first prioritize what the key pieces within the organization were that we wanted to address from an advanced analytics perspective, because there’s so much that you can go at and address.
After first prioritizing those pieces we then worked internally to identify who the key stakeholders are or, as I like to put it, the key allies, because given the investment on the analytics side it’s not a small expenditure. What we looked to do was develop strategic pilots to give us the opportunity to learn and prove out, or what we like to call proof of concept, that what we’re trying to build is going to add value for the organization. Because pushing these things and going to your CEO and saying, “I need five, ten million dollars for infrastructure to get these things done,” is not a great conversation if they and the organization organically doesn’t feel like there’s value associated with it.
So that’s how we’ve looked at it, and we’ve really looked at it as collaborative-bringing-the-folks-along-for-the-ride process because if your marketers do not appreciate or understand the value of what you’re trying to achieve, they’re not going to stand up and say, “we should go and be investing in this.” But if they see that value and they know how it’s helping them in terms of driving insights in their day to day business, then they’re the first ones to stand up and say “I need this, let’s invest, and let’s figure it out.” So that’s how the process has been built.
And in the final piece also associated with that, which is fundamentally important, was getting the organizational structure aligned around those priorities and what we wanted to achieve. All of these things, and a lot of what Tom was talking about, can’t be done by just one group. It’s done by multiple groups, and so that’s one of the things I’d say I seen done a most in a tremendous job of, is getting the right structure put in place to allow for it to happen more readily versus trying to work to pull resources together.
LIZ: Absolutely, absolutely. Tom this next question is probably going to be a fun one for you to tackle. When people start to look at everything that Domo has been able to have views into (as shown in the client case study that you shared with us) it seems that you could probably integrate an infinite, if not overwhelming, list of data into that type of dashboard. Are you seeing success with key points of data or a list of data sources that end up being that must have type of view, or is it really more on a customer by customer, a case by case, or maybe even an organization by organization level?
TOM: That’s a fantastic question. I think I’d like to start answering it by referencing what Abhay was talking about. Really, you need to have a strategic focus on the data that you want to measure. You need to look introspectively as an organization and figure out what the questions are to ask. Maybe go to your CEO and ask, “What questions are you asking yourself every day?” or “What questions are you asking of your organization?” And from those you’ll determine what your key business requirements are. Then you’ll be able to match key performance indicators to your key business requirements.
So my recommendation is to really look internally. Find out what your key business requirements are, and then figure out how to measure those things. I do think that it differs for every organization, but if you’re a CMO I would definitely say that there are a handful, perhaps a very large handful, of metrics that you should be looking at on a very regular basis. There are a lot of things for a lot of different sources that you should keep an eye on. I think that’s why management by exception is so important. You could have all of this data, I mean there’s an infinite amount of data to be pouring over, but if you’re just looking at the exceptions to the rule or things that aren’t going as you intended them to go, then that’s where you can focus most of your effort and focus most of your time: (1) on the key business requirements and (2) where things are going array with those key business requirements that you’ve established.
LIZ: Excellent, excellent. And Abhay this question has come in for you. Regarding these insights, because of the totality of everything that you guys are looking at and being that objective resource for you organization, how are you sharing these insights? Are these things that are readily available to folks across your organization? And what feedback and what access are they also providing you to identify new sources of insight and access that they may be developing? Do think of it almost like a task force or a group? It sounds like it’s really coming together.
ABHAY: Many of us have way too many meetings already, but with the structure that we have in place one of the things that we’ve done is identify a few strategic quarterly meetings where we provide each other with the key priorities and the work that we’re doing. It’s very beneficial from a cross-sharing standpoint because everything that we’re doing in terms of developing a great product and delivering it to the customer, is truly a cross functional experience. What we tend to find is that when we have these updates, we find an opportunity to be able to work together against those key priorities.
The other piece is that I see involved is a great job of linking everyone together, when we find that there are opportunities that could be linked together between the different groups. It’s just about keeping an open dialogue, ensuring that we don’t allow silos to occur, and that allows us to truly partner and build the infrastructure together and then drive the insights for the organization.
LIZ: Yeah that’s a terrific point. I think that that’s one that probably across so many different functions, across marketing that you’re probably enabling far more improvements just beyond the insights and intelligence by being able to have that type of sharing.
Another question has come up that I’d like for the both of you gentlemen to try and tackle because we’ve got a very brave soul somewhere out in this audience, and it looks like this very brave marketer is about to engage in some types of marketing automation and some new operational efficiency improvements, and they’re beginning to pilot these types of programs. Kudos to you for making that step! But is there any advice, guidance, suggestions of where to start? Piloting tends to be a very big process, I know that when we have a facilitated pilot between organizations (as we’ve been looking at new advancements and as we’ve been looking at things just from the CMO Council) often times what we hear from various organizations is, “Well, won’t piloting show us that where we’ve thought we’ve been doing all the right things is actually where we’re doing all the wrong things?” That can be a bit scary. Can you each provide what your learnings have been from your own experiences piloting and where you would suggest for this very brave soul to start? Tom I’m going to go ahead and start with you.
TOM: Great. That is an excellent question. We love technology here at Domo. We adopt technology regularly when it makes sense for our business, and so we do find ourselves in the situation where we pilot very often. I guess the best piece of advice that I’d have is to, and this is kind of a buzz term now, be an agile marketer. Go with your best guess; go with your gut, and then roll something out. The key here is to test it as often as possible. Roll something out, collect some data, analyze that data, then go back and make adjustments. For me it’s just been a constant trial and error effort. The problem that you run into with pilots is letting them run too long when you haven’t set them up right in the first place, and then you get too far down the path. You’ve invested time and resources and money, and you don’t have what you’re hoping to find on the other end. So really, in line with what we’ve both been talking about today, it’s establishing the right metrics and then paying attention to them at the right intervals.
LIZ: Great. And Abhay, we’re going to have you give some of your advice as someone who’s also been through these battles before.
ABHAY: Absolutely. Tom touched on quite a bit of what I was going to say too. But maybe even taking a step back from what Tom was saying in terms of the KPI’s and all those things that are fundamental in terms of delivering on this, the big thing is to first align it with the business objective and make sure to articulate the value of what you’re trying to achieve. It sounds very political, but often these tools are political because at the end of the day you’re asking for money. Most organizations nowadays don’t have a money tree sitting in the back yard that allows you to just go and say “I need to go and use this tool, and I need to invest the money.” For us, it is first really understanding who the people are that need to be convinced, who’s the audience that needs to have this problem addressed, and understanding how to be able to influence and persuade them. So that’s the first piece, because that links then back into how to start strategically thinking about the pilot you want to conduct. It has to be a very strategic thought process in terms of what you want to conduct because for us it’s really about finding key wins, and it’s not about showing success. It’s about showing the learning and insight that can show value to the organization. And it may not be one pilot; it may need to be two pilots; it may need to show different categories, different brands; it may need to show differentiation of what’s possible. Then you’re able to come back to these individuals and show them how it’s been actioned within the organization.
And that leads me to my final point, making sure you’ve got the right internal partners. You start internally, with the grass roots organizations and the brands that are actually activating and actioning that work. Have them on your side, sitting with you in a meeting from a cross functional standpoint saying “Here’s the path that we used in our business; here’s how I activated it; here’s how it helped me from a financial standpoint or with the customer,” whatever those key metrics are—that gets people to take notice. And that gives you the opportunity to show that that pilot is now something that we may want to look at from an operational standpoint. You’ve illustrated the ROI of what can be achieved, so going and spending seven digits isn’t as much of a barrier now, knowing what’s possible given the pilot.
LIZ: Excellent, excellent. That’s great advice, that’s great advice. So another question has come in, and Tom, I’m going to have you tackle this one. The question is, is it better to outsource your analytics then try to fight the uphill battle of resources, IT projects, and the complexity of bringing all this data together? I’m sure that you see this and probably get asked this question time and time again in your work, and I think it’s something that we certainly hear a lot. It is great to have all these great plans and be able to have that visibility—that’s a great idea to have, and sure it’s on the wish list of things that we would love to be able to do, but is that part of the fairy tale? Or is it something around the corner? Outsourcing tends to come up as a way to make the burden go on to someone else and be able to get that all back in one nice package. But are there any suggestions or any kind of cases and examples that you can share with us of how folks have really been able to push through some of those issues and then integrate some of that outsourced analytics and insights into what they’re currently doing?
TOM: Absolutely, and first off, since I’m rather biased, I’d like to think that Domo makes this challenge not so much of a fairy tale, but this is a reality for lots of people who don’t have a solution like this that brings all their data together in one place in real time. And really, it has to be a decision that’s made on an organization by organization basis. In some cases it, you really just don’t have the resources, you don’t have the head count, you don’t have the ability to do that. But I would argue for, wherever possible, finding the talent and bringing it in-house. I mean, you’re likely going to be spending a lot of money outsourcing to individuals or organizations that maybe don’t understand your business and your core values the way that you do. They don’t understand quite as well that there are key business requirements and how they’re measured. And so with outsourcing you always have those risks. Sure it takes something off of your plate, but you don’t have the same level of control that you would if you were doing it internally. I guess I would recommend, wherever possible, finding somebody with that skill set and bring them on board, because I think they’ll be able to add value not only to that initiative but also to other initiatives across the entire organization. I can’t say that one is good and one is bad. Every organization needs to choose for themselves, but I think keeping it in-house there are a lot of benefits.
LIZ: Great, great. We’ve got probably just about five minutes left, so if anyone has any last questions please feel free to send them in, but this question is really one we get asked consistently across the board, and it’s one that I love to subject all the speakers who join us for CMO Council webcasts too. It sounds from the both of you like there has been a lot of hard work, this has not certainly been a fast, overnight road, but it’s been one that has had a lot of strategic planning and a lot of assistance and support from your senior leadership to be able to get this done. Looking out over the next 12 months, what’s going to be the next big elephant in the room that’s going to help us really substantiate why we need to advance analytics and why we need to take this next step? Marketers right now have got it down. When we talk about ROI, advertising effectiveness, kind of campaign, individual channel effectiveness, we’re pretty comfortable with those conversations, but we’re looking to take that next step and advance that view. So from two folks who have been able to do that within organizations that are committed to that, what’s the next big hurdle, trend, issue that you see coming down the road that we as marketers really need to be prepared for and aware of so that we can continue in our advancement of our own analytics and advancing those within our organization? Abhay I’m going to go ahead and turn that one over to you.
ABHAY: That is a great question. There’s a lot of different ways that we can address this, Five years ago the media landscape was very different, and as we see how customers engage with brands, with businesses versus just the traditional TV and print, it’s constantly evolving. The biggest piece is to understand how all of the different media and marketing spend and tools that we’re utilizing and leveraging are working best for us. Whether it be a social, whether it be a mobile, whether it be a tablet, all of these other incremental reach tools, and if they’re differentiated by the given targets and how your business is lined up. Companies are needing to get leaner and leaner We need to drive profits, and that’s one of the things that analytics is able to help us balance, not address, but balance in terms of how we’re looking to go and spend those dollars and identify that next great innovation that might be the right place to put our money and help drive further incremental volume. So it’s continuing to evolve in measuring the different tools that are out there and appreciating the fact that, in reality, it’s the consumer behaviors continuing to evolve as is the multi-channel world that the consumer has in front of them today.
LIZ: Absolutely, great point, great point. Tom?
TOM: It really is a good question. I think that one of the challenges that marketers are facing now and will continue to face is a lack of confidence from the executives that they report to, perhaps the CEO that they report to. We’ve seen some scary statistics on lack of confidence from CEOs aimed at their CMOs and their marketers. One thing I think will help resolve this pain is for marketers to really ,as Abhay was saying, understand what these metrics are, to justify everything that they’re doing at every turn. Two specific examples I’d like to share. One, because we’re a B to B organization we have a marketing team that generates leads and a sales team that hopefully closes those leads into revenue. As we think about that we feel that there are a lot of marketers in the B to B space that aren’t thinking as much as they could or should be outside of their own department or their organization. They need to be looking further down the pipeline to look at how the activities that they’re engaging in are performing all the way through the sales cycle into revenue, or the bottom line. And right now I feel like marketers are challenged with that. They’re very good at looking at their own territory and seeing what their own metrics are, but once they jump outside of that and move into sales, it’s really difficult to determine metrics like return on marketing investment or which lead sources are creating the best revenue.
Another point that Abhay touched on is that I think marketers are still having a really hard time measuring social media. I mean we’ve been talking about social media for years now. We just performed some research in-house and the early results are looking like social media is still a really difficult thing to measure and to determine the ROI that we’re getting out of all these initiatives. Do we engage with it more? And how do we measure it? I mean all of these are really interesting questions that will become ever more important as we have these new touch points, as Abhay mentioned.
ABHAY: And actually Liz, one thing as you were speaking that came up in my mind that is probably the big elephant in the room is that often a lot of these things that we’re looking at (like piloting was brought up, which is I think the best way to go) are not small investments, depending on the size of your company, depending on what you’re trying to achieve. These are pretty big investments that you’re talking about in terms of being able to capture and measure that data. And that’s something that I think will continue to be the big challenge, especially as more new advanced tools continue to come out on the market place, allowing you to continue to be more in the forefront of these things. The level of investment and involving those things and being agile to ensure that you’re not buckled down by your current infrastructure is one of the things that’ll be interesting to see, and there will be a challenge as we move forward in the next few years.
TOM: I completely agree.
LIZ: That’s a great point. Yeah, yeah definitely. I want to thank you both for joining us and thank you for your honesty in presenting what you guys are both doing and what L’Oréal and Domo are both doing as far as looking into some of these really difficult subjects. I know that for a lot of us on the line this can be a real challenge and we see it day in and day out.
This presentation and this webcast is actually going to be available immediately through this same link. We will not be sharing a copy of the deck, and I’m sure you’ll understand that what we’ve shared today has really been very contextual, and we want to make sure that it’s held in that same regard and certainly respect the context in which all of these things have been delivered. So feel free to come back and view the link, share the link, all of those great things. We want to thank all of you.
One of the things that I certainly have heard and that both of you have helped me really solidify in my mind is that we can’t, as marketers, afford not to have these types of conversations, and we can’t, as organizations, be willing to stay with where we were yesterday, or for some organizations, from a technology standpoint, it may feel like where we were five years ago. This is an ongoing dialogue that we have as far as advancing where our performance is going and where we’re taking analytics and innovating these new ways and realizing that is important for all of us across the marketing ecosystem, because if there is one big thing that I always say, if I look out 12 months from now and see what I feel is the big elephant in the room, it is an inherent paradigm shift from being a B to B or a B to C organization, and now we are all existing in a world where we are all C to B organizations. Our customers, regardless of what side of the fence you’re on, are now making an active decision on where and how and when to engage, and we have to be far smarter about meeting those expectations and guiding them to continue their positive customer experiences with us. So we are going to keep talking about this subject. We want to keep this dialogue going, and the big question that I like to pose to people is, “How Social Is Your Data?” That will be our next webinar where we’re going to be talking about everything from continuing these analytical discussions, continuing to deep dive and continuing this discussion. We’re going to be back with another conversation on April 10. A registration link is going to be available on our site very soon. I expect to see all of you back here for it. But thank you all for your questions; it’s been fantastic, and now a Happy Valentine’s Day to all of you and hopefully you’ve enjoyed the present that we’ve been able to get for you. So thanks to Abhay and thanks to Tom, and we’ll see you next time.
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