As a former marketer myself, I know that marketing is often marginalized within enterprises, particularly those with strong scientific or development organizations. Marketing is often viewed as being responsible for the “soft stuff” that looks pretty but doesn’t have any real impact on the business. I’m here to tell you that this view is wrong, and if you don’t realize it quickly, your competitors will.
We recently surveyed 362 marketers from around the world, across more than 15 industries, and found that Leading Marketers’ enterprises had 40% greater Revenue growth and twice the Gross Profit growth over the past 3 years when compared to the rest.
What exactly is a Leading Marketer?I’m glad you asked. We identified 2 essential traits of effective marketers: “Effective Engagement” and “Intelligent Investment”. Essentially we defined Leading Marketers as those who had a high level of responsibility forengagingwith customers across channels as well as a sophisticated approach toinvestingmarketing resources.
We then looked at publicly available financial data and found that when we correlated that to our segmentation of leading marketers, a clear trend emerged: Leading Marketers’ enterprises performed better financially.
So how, exactly, do you develop a Leading Marketing organization within your enterprise? Like most things in today’s world the answer is complex but grounded in the principles of Marketing 101. It can be as simple as the 4P’s or as complicated as developing a collaborative relationship with other functional areas within the enterprise. I’ll be blogging more about this topic and other insights from our study over the coming weeks, but get a sneak preview by reading our executive report, How Leading Marketers Outperform: Effective Engagement and Intelligent Investment.
If there is a particular topic you’d like me to talk about, please login and leave me a comment, below.
A couple of weeks ago I wrote a blog post discussing our recent paper that links Leading Marketers with financial outperformance.In our study, these Leading Marketers had 40% higher revenue growth and twice the gross profit growth.Naturally, the next question you’d ask is “how do I become a leading marketer?”And that’s exactly what I’m going to talk about over my next few posts.
To kick things off, we found that Leading Marketers engage with their customers across a variety of channels.These leading marketers are more likely to have integrated inbound, outbound and offline marketing programs in some or all channels.They are more likely to use interaction optimization technology in all of their channels.And they are also more likely to adjust offers in real-time across all channels.In short, they create a “System of Engagement” that allows them to engage each customer as an individual, across multiple channels.
So if leading marketers are creating a system of engagement to deliver targeted messaging across channels, what specific tactics are they using?To answer that, we looked closer at mobile and social channels.
Essentially, a number of tactics within these channels can be considered “table stakes.”Everybody has a mobile version of their website and delivers mobile e-mails.Everybody has a social networking page on a site like Facebook and most engage in micro-blogging (Twitter).But there are some specific, innovative tactics where we saw differences between leading marketers and others.
When it comes to mobile, we found that leading marketers were more likely to use mobile messaging campaigns, location based targeting, and mobile-specific ads.For social, leading marketers were more likely to develop apps for 3rd party networking sites (Facebook), leverage social/local group buying (Groupon), and participate in location-based games (Foursquare).All of this means that leading marketers are faster to begin leveraging emerging/trending technologies to see if they can enhance the system of engagement.Some of these tactics may or may not prove to be effective in the long run, but the leading marketers get there first… not unlike the adage “fail fast, fail often”.By being at the forefront with these tactics, they stand to benefit when they come across something that’s especially effective.
It’s also interesting to note that location-based tactics saw greater use by leading marketers in both mobile and social.When you think about a system of engagement that strives to deliver targeted, personalized, relevant offers in real-time, it makes perfect sense that location-data is a key component to enhancing that ability.
There are a number of ideas you can take away from our data, but there’s one over-riding principle that I think is worth taking to heart:Innovation.Leading marketers aren’t afraid of trying out new channel engagement technologies or tactics.They get there first and they find out what works.They don’t worry about whether a channel is completely mature… they jump in and get their hands dirty.This enables them to be proactive with their customers, rather than reactive.
In my previous blog post on How Leading Marketers Outperform, I discussed how Leading Marketers develop a system of engagement that drives customer value at every touch.Today, I’m going to focus on the other side of that equation and dig a bit deeper into what prevents many marketing organizations from becoming Leading Marketers.
The first question you might ask is “why doesn’t everybody just establish a system of engagement?”The short answer is because it’s not easy.A look at the barriers both Leading Marketers and others face in implementing marketing technology is very telling.
To begin with there are a set of barriers we found that are common to virtually any technology decision: cost, ROI, and organizational structure. If we continue looking, the additional barriers for leading marketers are ease of use and lack of appropriate user skills.Alternatively, we found that some others are more concerned with alignment/collaboration within the organization – particularly with IT.In many cases, marketers may not even have ownership of marketing technology decisions.
In short leading marketers are collaborating with IT to implement the technology framework that supports a system of engagement and are focused on issues that enable them to improve the effectiveness and scale of their activities. The others are struggling to coordinate effectively with IT and other functional areas within the enterprise. They aren't at a point yet where ease of use or a lack of user skills could be a barrier.
This leads to the second question, “okay, how do you collaborate more effectively with other functional areas (especially IT)?”This is complicated, but our data suggests that Leading Marketers are able to collaborate effectively at least in part because they’ve established credibility within the organization.
There are lots of ways to establish credibility, but a part of it is being able to demonstrate the value that you bring to the table.To that end, our study found that 88% of leading marketers attribute business results to marketing activities. They use a variety of different systems, ranging from spreadsheets to complex software suites, but the common thread is that they attribute results regardless of methodology.
And of that 88%, 93% of those leading marketers have a set process in place for determining which marketing activity receives credit for the business results.Again, the specific methodology varies – first touch, last touch, results distributed across multiple touches – but they have a set process in place.
This measurement allows leading marketers to invest resources intelligently.They know what works and what doesn’t, and this allows them to maximize the impact they have on the business and focus only on the most effective activities. This in turn builds credibility with the rest of the enterprise. Marketers can finally speak in the same financial language as the rest of the business.
So in summary, it’s very difficult to become a leading marketer without measuring the results of marketing activities.Measurement not only informs operational spending decisions, but also impacts the role of marketing within the organization. Leading marketers’ ability to attribute results helps them not only invest intelligently but also build credibility and the financial justification needed to construct an enterprise-wide system of engagement.
I'll be back next time with a discussion of the overall characteristics of leading marketers and how they illustrate a road-map forward for marketing organizations. In the meantime, if you have any questions, please feel free to leave a comment!
I've previously written about our research of leading marketers, both their correlation with improved financial performance and what exactly they do differently than everybody else. We recently sat down with three leaders from our Enterprise Marketing Management team, Yuchun Lee, Elana Anderson, and Jay Henderson, and asked them to discuss our research and the implications of that research in more detail. Check out the video to get their take on why marketing matters, and how you can continue to engage with customers effectively and invest your marketing dollars intelligently.
Leave us a comment here or on YouTube to let us know if you're seeing similar trends in your enterprise.
The era of “Big Data” presents a variety of challenges and opportunities for marketers. With the increase in volume, velocity, and granularity of data, marketers can become much more precise in how they interact with both the marketplace and individual customers. But the same time, when you’re dealing with large volumes of data, it’s easy to over-fit your models and mistake “noise” for “signal”, to borrow a concept from Nate Silver’s excellent book, The Signal and the Noise.
This is something that we’ve been dealing with internally at IBM for a while now. In response, we’ve developed a framework internally that we think may help others refine their own approach to generating insights from data.
We call this framework “Marketing Science”. This is a 3-step framework consisting of “Architecting Data”, “Applying Science”, and “Influencing Action”. The fundamental idea is to apply the scientific method to developing insights within a business setting. This presents unique challenges in and of itself. But there are some basic concepts to keep in mind:
"Architecting" (or collecting and structuring) data is extremely important. The rest of the process depends on getting access to the right data from a variety of sources and if you haven’t done a good job of dealing with data across your enterprise, it’s like trying to run a 100m race with your shoes untied.
A hypothesis-test-refine approach to data analysis is central to the concept of Marketing Science. Developing and testing hypotheses is one of the main ways you limit your exposure to over-fitting data.
Within a business setting, insights are only valuable in so far as they’re able to inform decision-making and/or influence action. At the end of the day, driving business outcomes is the goal of Marketing Science. Keeping this in mind helps to keep you focused through the first two steps. And it means that once you’ve uncovered a nugget of insight, the real work may just be getting started as you take that insight back to the business.
Marketing Science is a fascinating topic that we’ll be talking about quite a bit more moving forward. We’ve conducted some market research that I think will be very enlightening and have started collecting some use-cases of how we’re applying these principles in a practical sense. In the meantime, if you have any comments or thoughts on developing insights from data, we’d love to hear from you.
We recently put together a nice video that provides an overview of Marketing Science. What is Marketing Science exactly? Well you can either watch the video or take a look at our Whitepaper. But the short version is that it's a way for marketers to deal with the challenges that "Big Data" presents by using a more rigorous scientifically grounded approach to develop insights and then using those insights to impact the business.
The concept itself really isn't very complicated. We've boiled it down to 3 steps: Architect Data, Apply Science, and Influence Action. However, the application of these concepts isn't always easy or straightforward. So over the coming months, I'll be posting about some of our own internal examples of applying Marketing Science to give you a better feel for what it looks like in practice.
And if your company has been engaging in Marketing Science, we'd love to hear about it. Who knows, maybe your example could be the subject of a future blog post.
I came across this Smarter Commerce video a couple of weeks ago and I really like because it gets to the core of what Smarter Commerce is and why you should care about it. If you take a few minutes to watch it, what you’ll notice is that it keeps coming back to the customer as a central theme.
And at the end of the day, that’s really what Smarter Commerce is all about. It’s taking all of that data you’re collecting, helping you develop insights about your customer and the marketplace, and then applying all of that to every aspect of your business.
We recently conducted some research on a couple of specific areas with our colleagues from IBM DemandTec. Specifically, we looked at retail merchants and CPG sales organizations. The retail merchant research was conducted in collaboration with Planet Retail and the CPG sales org research was conducted in collaboration with Kantar Retail.
As we looked at the data from these research projects, we kept coming back to the same central theme as the video above: the customer. Everybody talks about being customer-centric. It’s almost a cliché. But what we found was that while everybody talks about it, most aren’t actually doing it.
Merchants tend to be product and category oriented, and CPG sales orgs tend to focus on their customer, the retailer, rather than the end consumer. Now we’re not saying that merchants should forget about product categories or that CPG sales teams should ignore the retailer, but we found that groups that placed a strong focus on the customer (or more specifically the consumer when talking about CPG) tended to outperform those that didn’t.
These “leaders” were customer-focused and collaborative, both within their enterprises and externally with vendors and partners. And they also made much greater use of analytics to uncover insights about their customers and the marketplace. The differences were really quite striking. For example, Leading Merchants were 1.6x more likely on average to use analytics to drive merchandising decisions, while Leading CPG Sales Organizations were more than 1.7x more likely to use analytics to improve product innovation.
At the IBM Center for Applied Insights, we’re always searching for new best practices to share with IBM, our clients, and the rest of the world. Which is why, at a recent team meeting, we gathered to discuss a new article from McKinsey. In “The do-or-die questions boards should ask about technology”, McKinsey outlines nine questions all boards should be posing to their company management in order to be “technology winners”. You’ll probably notice that few of these questions focus on the technology – they focus on how to get business value from the technology. These nine questions fit so well with what we try to accomplish at the Center, I thought it would be a good exercise to pull key insights from some of our studies to see how we are helping to address them:
1. How will IT change the basis of competition in our industry?
As we’ve seen in many industries, technology is radically changing the competitive landscape, allowing new companies to gain significant market share from established players. In our 2012 Tech Trends report, we segmented over 1200 respondents into 3 groups based on their organizational stance on emerging IT. What we discovered was that the leaders (Pacesetters) were ahead of their competitors in the mobile, analytics, cloud, and social business spaces. These Pacesetters believe emerging technologies are critical to their business success and are using them to enable new operating and business models to improve their competitive position.
2. What will it take to exceed our customers’ expectations in a digital world?
Customer expectations are as high as they’ve ever been - customers demand an experience that is convenient, immediate and hyper-personalized. In “Why leading marketers outperform”, we found that leading marketers deliver targeted, personalized messages to customers in real-time through channels such as social media and mobile. These marketers encourage innovation, measure every customer interaction and touch point, and collaborate regularly with IT. Compared to traditional marketers, these leaders have a three year CAGR that is more than 40 percent higher.
3. Do our business plans reflect the full potential of technology to improve our performance?
Investing in technology is expensive, but it can yield incredible returns and boost performance. We asked over 1500 IT decision-makers about their attitudes in the Platform-as-a-Service space in “Exploring the frontiers of cloud computing.” We found that the leaders, or “Pioneers”, were adopting PaaS as a way to drive innovation and improve application lifecycle across the enterprise. For these pioneers, benefits included increased resiliency, efficiency, data management integration, and optimization. According to one respondent, a VP of IT, utilizing PaaS can make a company “more nimble and cost-effective, with consistent performance and faster roll outs.” Sounds like a pretty good payoff to me.
4. Is our portfolio of technology investments aligned with opportunities and threats?
A technology portfolio must clearly reflect current opportunities and threats, change regularly, and balance short and long-term technology investments. In our Sourcing study, we looked at CEMEX, one of the world’s leading suppliers of cements, as an example of company that leveraged opportunity and minimized risks with its long-term sourcing strategy. CEMEX realized it needed to accelerate its transformation and become more agile to respond rapidly to new opportunities and threats. It engaged with a strategic sourcing provider to cut costs, improve productivity, and deliver transformative innovation. CEMEX built innovation into its sourcing contract by requiring the provider to invest annually in innovative projects that helped CEMEX achieve desired business outcomes.
5. How will IT improve our operational and strategic agility?
Across industries, customers expect new customized products and services, faster than ever before. In order to decrease time to market, companies can leverage IT to improve operational and strategic agility. We studied operations strategy decision makers from financial markets firms in “Beating market mandates: How winners are re-engineering financial markets operations” to better understand the characteristics of leading companies. The leaders in the study excel at meeting both regulatory and marketplace requirements, and typically introduce new products and services in 3 months or less. These leaders are extremely agile - focusing on improving access to analytics and reducing complexity.
6. Do we have the capabilities required to deliver value from IT?
At the Center for Applied Insights, many of our studies are about identifying and understanding the groups that get the most value from IT. Whether it’s Chief Information Security Officers who have a mature security strategy, CMOs who act as Marketing Scientists and deeply understand their customers through analytics, or CFOs who accelerate performance through analysis and prescriptive insight, we want to understand the capabilities necessary to get the best possible value from IT.
7. Who is accountable for IT and how do we hold them to account?
Who “owns” IT is becoming increasingly difficult to determine. Leading organizations have clear operating models that determine accountability for IT activities - it’s not just the CIO who is accountable anymore. In “Accelerating performance: The evolving role of the CFO”, we discuss how the CFO must contribute to the company’s IT strategy as well. This study looks back at the 2010 IBM Global CFO study, to see how the 2010 leaders are performing today. The outperformers excelled in finance efficiency and business insight, and continue to outperform financially today. However, in order for these leading CFOs to accelerate the performance of their organizations, they must now expand their influence beyond financial decisions to broader, strategic choices about business and operating models.
8. Are we comfortable with our level of IT risk?
With explosive growth in connectivity and collaboration, information technology is becoming increasingly complex and difficult to manage - managing risk from IT must be an enterprise-wide priority. In our article series “Security Essentials for CIOs”, we define an approach to manage all forms of IT risk, whether it is cybersecurity risk, IT compliance, risk to the supply chain or technology impacts to business transformation efforts.
9. Are we making the most of our technology story?
The McKinsey article could not have ended on a better question. We aim to bring stories to life - to show how leaders are building and advancing their businesses with IT. We use data to identify best practices, and communicate an IT story that addresses competition, strategy, value, performance, and risk.
Time and again, we’ve identified leaders in our studies and determined that these leaders are asking and getting answers to the nine questions above. If “digital technologies are disrupting industries,” then to be a technology winner in any industry, companies need to ask the right questions about IT strategy, and, more importantly, act on the answers they receive.
The Guardian's Media Network recently hosted a live chat around the topic of how CMOs can align and use digital marketing and data analytics - two areas we've taken a close look at since the inception of the IBM Center for Applied Insights.
The Guardian notes:
Big data (and analyzing that data) means that marketing professionals are now getting even closer to the customer – they know more about audiences than ever before, with pinpoint precision. At their fingertips a marketer now has detailed facts and figures about consumer browsing habits, their favorite brands, how they use social media. It means that campaigns can be targeted, analyzed and proved better than ever.
It becomes the job of marketers and CMOs to make sense of all that data and not get lost in the noise. Doing this, takes an analytical and curious approach to data. It's easy to find the "big numbers" but more challenging to find the "right numbers." As Surjit Chana, CMO of IBM Europe, has said, the core principles of marketing haven't changed. What has changed, dramatically, is how those principles come to life in today's marketing campaigns, customer experiences, and business results. In our paper, Marketing Science: From descriptive to prescriptive, we found that only 23% of marketing professionals use tested analytic approaches to understand the vast amount of data they have access to. More traditional marketers, using data to describe outcomes but not determine actions, consistently use data at face value - without applying data models or scientific thinking.
When technology and analytic skills don't exist in the marketing teams, it makes perfect sense to build partnerships with those who do. The closest partner in most organizations is IT. Thus, the renewed focus on CMO + CIO collaboration. We're continuing to watch, collaborate, and recommend approaches to our C-Suite colleagues. Check out "Understanding leading retailers" to see how the retail industry is collaborating with IT and partners to serve customers better.
If your enterprise is working with Big Data, or at least beginning to stick your toe in the water, and you're not thinking about the concept of "signal", you're about to make a big mistake. Identifying the signal is what will enable you to leverage Big Data effectively. And if you don't, you're going to spend a lot of time and money chasing red herrings.
When we rely on data for decision making, what qualifies as a signal and what is merely noise? In and of themselves, data are neither. Data are merely facts. When facts are useful, they serve as signals. When they aren’t useful, data clutter the environment with distracting noise.
For data to be useful, they must:
Address something that matters
Provide an opportunity for action to achieve or maintain a desired state
When any of these qualities are missing, data remain noise.
I like this definition. It fits hand in hand with the concept of Marketing Science that we proposed earlier this year. Insights (aka signal) are only valuable in so far as they drive business outcomes. And if you're developing insights that influence action within your enterprise, you had better make sure that what you're looking at is actually signal.
This is where Big Data is presents challenges. In his post, Few makes the absolutely correct point that data are noisy. And when data increase dramatically in volume, velocity, and variety (aka it gets BIG), that noisiness grows right along with everything else. All of a sudden, it becomes that much harder to correctly identify signal. As Few points out:
Finding a needle in a haystack doesn’t get easier as you’re tossing more and more hay on the pile.
If you listen to some of the discussion around Big Data, you could easily walk away thinking that if you can capture it, all you need to do is run it through some sophisticated analytic software and "boom" you've got new insights.
The problem with this approach is that pesky noise. As you start dealing with huge data sets, it becomes relatively easy to find "statistically significant noise". You may think you're looking at signal, but instead you're just finding random patterns in the noise that happen to look like signal. This is what can happen when analysts are given lots of data and told to go find something.
How do you combat this? Part of it, as Few points out, is having data analysts that have a deep understanding of how to detect signal and the associated challenges that Big Data presents. The other part, is in how you approach data analysis in the first place.
Again, I'll reference our Marketing Science framework and propose that by applying a scientific approach to data collection and analysis, you improve your ability to correctly identify signal. Instead of randomly looking for patterns in the data, by developing hypotheses and then testing and refining them, you're able to focus on signal that (a is more likely to actually be signal and b) will help drive the business forward.
We've seen some really interesting and impactful results internally with the Marketing Science framework. We've developed insights that both drive business outcomes and challenge conventional thinking. I'll be highlighting a few of these examples in future blog posts. In the meantime, I'd love to get your feedback on what challenges you've experienced with identifying signal within Big Data.
What do you do when your insights challenge prevailing beliefs? That’s the question we faced at IBM in 2007 when a brand new forecasting tool we’d developed started spitting out projections that conflicted with our other forecasts.
Large companies, like IBM, can really struggle with pulling together complete and relevant data to create accurate forecasts. We initially developed the new forecasting tool when IBM missed an earnings target in 2005. After studying the forecasting technology we had been relying on, the Market Development and Insights team discovered that its focus on the longer term had failed to pick up on a shift in the market. That meant we had to improve the accuracy of the longer term view with some sort of early warning system to flag abrupt changes in market direction.
The new tool was designed to track over 2,000 market indicators in the G7 and BRIC countries, including oil prices, key manufacture goods and transportation, on a monthly basis. It uses correlation techniques, regression testing and principal components analysis to identify the best indicators, given the current market climate. Then it combines them to produce a forecast of the market for the next three quarters.
By 2007, our early warning system was ready to roll out to handle short term forecasting and supplement the long view.
There was just one issue: No one knew whether to trust it. Because it was forecasting that black clouds were gathering on the horizon. But our long view and all the third-party sources we used said the sky was blue.
IBM’s top management was understandably dubious of the predictions of an unproven new forecasting tool. So we decided to take the time to track economic data on our own very carefully for two quarters and compare it with forecasted findings.
The result? The alert tool was more accurate. In fact, it was so accurate that we were able to predict the recent global recession two quarters before third-party sources detected it. This six-month lead gave us time to restructure our cost base and realign our investments.
We now use a short term approach to predict the state of the hardware, software and services market by country for the next six months. We’ve also closed the loop by feeding the projections from this into the long term tools to continuously refine our long-term forecasts and monitor downside risks as well as upside opportunities.
Of course, no forecasting tool will be 100% spot on all the time. But as companies battle test and tweak the array of tools at their disposal, they get closer all the time.
Market forecasting tools can tell you how business your company is winning and your aggregate market share. But what if you want to know how much of each client’s business you’re winning?
That’s the question we at IBM set out to answer several years ago. And it’s a case study that we analyzed as part of the Center for Applied Insights study, “Marketing Science: From Descriptive to Prescriptive.” We thought that the data issues that IBM had struggled with might shed light on how other companies can use marketing science in their businesses.
In this particular instance, IBM had to figure out how we could measure our share of each company’s tech budget.
We started out on square one, figuring out which organizations we could serve. This meant identifying every company – and every one of its subsidiaries – in all the markets we operate in. Pulling together this information was a big job, but it turned out that we faced an even bigger hurdle (you can probably guess it).
We had lots of customer data: 10 terabytes, to be precise. And all of that data was sitting in different databases throughout the company, each built by individual business units over the years. And the data definitions they used varied widely. The top 164 customers alone had more than 600,000 unique identification codes. To get a coherent view of IBM’s business at the client level, we would have to reconcile millions of customer identifiers before cleaning up and consolidating the data.
A few months later, we finally had a structured database. The last step was to work out how much each customer was spending on IT.
We did this by using another forecasting tool we had developed that estimates the size of the overall market along with mathematical algorithms to assign a share of that expenditure to every company, whether they were customers or not.
It was a huge effort, and one we've refined many times since, but one that’s now paying handsome dividends. With this view, our sales and marketing teams can detect buying patterns and target holes in IBM’s market coverage. And by integrating the insights the view provides with other customer data, we can identify new opportunities. We can also explore why specific customers buy (or don’t buy) particular solutions, why some don’t spend very much with us, and what we can do to serve individual customers better.
That’s lets us anticipate and respond to our customers’ needs and allocate our resources more effectively. And we think it’s an example that demonstrates just how powerful marketing science can be within a company.