Today CMOs have so many big-data marketing and media analytical options that they are able to drive superior growth.
Some become the super predictve CMO (like Tom Cruise above). Other CMOs however, seem to drown in the big-data options to become paralyzed.
For this group and the CMOs that call big-data a marketing hype, I suggest you read along.
Marketing used to be an art, now it’s a science.
Learn how big-data can drive superior growth.
Big-Data Gives CMOs Decision-making Firepower
Better analytical tools, new methods and approaches in recent years has given CMOs significant new decision-making firepower. Just see an example of Gatorade here:
Click here to view the embedded video.
The advanced analytics provide the ability to increase growth and marketing return on investment (MROI). But for some reasons CMOs stick to their old methods.
The diverse activities and audiences that marketing dollars typically support and the variety of investment time horizons demand a more sophisticated approach.
Some tangible examples from my period as global CSO at WPP? Here I worked with global CMOs and CIOs on big-data marketing and media strategies.
Some of the brands I worked on? Amazon, Nike, Unilever, Ford, AmEx, Nestle, Coca-Cola etc.
I discovered 2 new big-data methods that drove superior growth: big-data in media and big-data in marketing.
I will explain them both to you here.
Big-Data Driving Adaptive & Liquid Media Planning
The media example is pretty straightforward. Suppose you are Unilever and your global media spending’s are around $5 billion per year.
Can you imagine that the global Unilever CMO demands a higher ROI on his media investments? Indeed, try new methods!
That could be either be:
1. Achieving the same revenues next year, but now with a media budget of $4.5 billion. Not the $5 billion from last year.
2. Achieving 5% higher ROI next year, with the exact same budget of last year: the $5 billion worldwide.
How can this be achieved?
Both 1 and 2 above, can be achieved by machine made, liquid media planning. Big-data is optimizing the media-mix in real-time.
I know all media owners don’t like this method. They want to know their media budget (revenues) for the coming year, upfront.
Liquid or adaptive media planning however, decides in real-time if more of the budget should go from TV to online or mobile, for example.
But let’s be fully honest. Which CMO is looking to be the largest sponsor of the media owners?
Just create a new big-data driven media metrics dashboard, and connect it to your media planning architecture. Now let the machines increase your media ROI.
The other easy method?
Turn your old skool media-mix upside down. Switch from a Paid media towards a POE media ecosystem.
That could be either be:
1. Start to see Owned and Earned media as important part of your POE media mix.
2. Be a badass and try to work on Owned media first. Next earn attention and blow it up by paid media.
How can this be achieved?
Both 1 and 2 above, can be achieved by doing it the Nike, Coca-Cola or Red Bull way.
Claim your domain! I don’t care if it’s running, hiking, happiness, football or whatever.
Create your owned media platform like Nike+. This way you’ll be relevant 365 days a year, even in between your campaigns your platform / community will keep growing organically.
Yes you could ignore my idea and sponsor the media owners as well. But being entitled to advertising, I just hope you have the deepest pockets.
Next work on earned media. Like Old Spice, Oreo or Red Bull. Start your content marketing program and earn massive attention.
When you use this method right, you can make things as big as you want by using Paid media.
So use the POE media ecosystem. Or rather, use it the OEP way.
It will give you superior ROI on your media investments.
McKinsey & Company on Marketing ROI
In the experience of McKinsey & Company, the best way for business leaders to improve marketing effectiveness is to integrate MROI options in a way that takes advantage of the best assets of each.
The benefits can be enormous: our review of more than 400 diverse client engagements from the past eight years, across industries and regions, found that an integrated analytics approach can free up some 15 to 20 percent of marketing spending.
Worldwide, that equates to as much as $200 billion that can be reinvested by companies or drop straight to the bottom line.
Here’s one example. A property-and-casualty insurance company in the United States increased marketing productivity by more than 15 percent each year from 2009 to 2012.
The company was able to keep marketing spending flat over this period, even as related spending across the industry grew by 62 percent.
As the chief marketing officer put it, “Marketing analytics have allowed us to make every decision we made before, better.”
Anchoring Analytics to Strategy
A company’s overarching strategy should ground its choice of analytical options. Without a strategy anchor, we find companies often allocate marketing dollars based largely on the previous year’s budget or on what business line or product fared well in recent quarters.
Those approaches can devolve into “beauty contests” that reward the coolest proposal or the department that shouts the loudest rather than the area that most needs to grow or defend its current position.
A more useful approach measures proposals based on their strategic return, economic value, and payback window. Evaluating options using such scores provides a consistent lens for comparison, and these measurements can be combined with preconditions such as baseline spending, thresholds for certain media, and prior commitments.
The other prerequisite in shaping an effective MROI portfolio is understanding your target consumers’ buying behavior.
That behavior has changed so radically in the past five years that old ways of thinking about the consumer—such as the marketing “funnel”—generally don’t apply.
Where the funnel approach prioritized generating as much brand awareness as possible, the consumer decision journey recognizes that the buying process is more dynamic and that consumer behavior is subject to many different moments of influence.
Five Questions for Maximizing MROI
One home-appliance company, for example, typically spent a large portion of its marketing budget on print, television, and display advertising to get into the consideration set of its target consumers.
Yet analysis of the consumer decision journey showed that most people looking for home appliances browsed retailers’ websites—and fewer than 9 percent visited the manufacturer’s own site.
When the company shifted spending away from general advertising to distributor website content, it gained 21 percent in e-commerce sales.
Making Better Decisions
While new sources of data have improved the science of marketing analytics, “art” retains an important role; business judgment is needed to challenge or validate approaches, but creativity is necessary to develop new ways of using data or to identify new opportunities for unlocking data.
These “soft” skills are particularly useful because data availability and quality can run the gamut.
For instance, while online data allow “audience reached” to be measured in great detail, other consumer data are often highly aggregated and difficult to access.
But such challenges shouldn’t impede the use of data for better decision making, provided teams follow three simple steps:
1. Identify the best analytical approaches
To establish the right marketing mix, organizations need to evaluate the pros and cons of each of the many available tools and methods to determine which best support their strategy.
When it comes to non-direct marketing, the prevailing choices include the following:
Advanced analytics approaches such as marketing-mix modeling (MMM). MMM uses big data to determine the effectiveness of spending by channel.
This approach statistically links marketing investments to other drivers of sales and often includes external variables such as seasonality and competitor and promotional activities to uncover both longitudinal effects (changes in individuals and segments over time) and interaction effects (differences among offline, online, and—in the most advanced models—social-media activities).
MMM can be used for both long-range strategic purposes and near-term tactical planning, but it does have limitations: it requires high-quality data on sales and marketing spending going back over a period of years.
It also cannot measure activities that change little over time (for example, out-of-house or outdoor media); and it cannot measure the long-term effects of investing in any one touchpoint, such as a new mobile app or social-media feed.
MMM also requires users with sufficiently deep econometric knowledge to understand the models and a scenario-planning tool to model budget implications of spending decisions.
Heuristics such as reach, cost, quality (RCQ). RCQ disaggregates each touchpoint into its component parts—the number of target consumers reached, cost per unique touch, the quality of the engagement—using both data and structured judgment.
It is often used when MMM is not feasible, such as when there is limited data; when the rate of spending is relatively constant throughout the year, as is the case with sponsorships; and with persistent, always-on media where the marginal investment effects are harder to isolate.
RCQ brings all touchpoints back to the same unit of measurement so they can be more easily compared. It is relatively straightforward to execute, often with little more than an Excel model.
In practice, though, calibrating the value of each touchpoint can be challenging given the differences among channels. RCQ also lacks the ability to account for network or interaction effects and is heavily dependent on the assumptions that feed it.
Emerging approaches such as attribution modeling. As advertising dollars move online, attribution becomes increasingly important for online media buying and marketing execution.
Attribution modeling refers to the set of rules or algorithms that govern how credit for converting traffic to sales is assigned to online touchpoints, such as an e-mail campaign, online ad, social-networking feed, or website.
Those credits help marketers evaluate the relative success of different online investment activities in driving sales.
The most widely used scoring methods take a basic rules-based approach, such as “last touch/click,” which assigns 100 percent of the credit to the last touchpoint before conversion.
But newer methods that use statistical modeling, regression techniques, and sophisticated algorithms that tie into real-time bidding systems are gaining traction for their analytical rigor.
While these approaches are a step up from methods tied to rules, they still typically depend on cookie data as an input, which limits the richness of the data set and consequently makes it difficult to accurately attribute the importance of each of the online touchpoints.
2. Integrate capabilities to generate insights
€1 million invested online generated 1,300 new consumers, the same investment in TV, print, and radio helped the company retain 4,300 consumers (40 percent of whom were likely to stay loyal to the brand over the long term).
Those insights helped the company understand where to best focus its spending and messaging for both attracting new customers and keeping existing ones.
In fine-tuning the mix, it can be tempting to allocate money to short-term initiatives that generate high ROI.
That bias is fed by the fact that so much data comes from consumers engaging in short-term behavior, such as signing up for brand-related news and promotions on a smartphone or buying a product on sale.
That short-term effect typically comprises 10 to 20 percent of total sales, while the brand, a longer-term asset, accounts for the rest.
Businesses need to ensure their mix models are capable of examining marketing effectiveness over both time horizons.
One consumer food brand almost fell into this short-term trap. It launched a campaign using Facebook advertising, contests, photo-sharing incentives, and shared-shopping-list apps.
At a fraction of the cost, the approach delivered sales results similar to those generated by more traditional marketing, which included heavy TV and significant print advertising. Not surprisingly, the brand considered shifting spending from TV and print advertising to social-media channels.
Yet when long-term effects were included in its calculations, the impact of its digital efforts was cut by half.
If the company had proceeded with significantly cutting its TV spending, as traditional MMM suggested, it would have reduced the net present value of the brand’s profit.
3. Put Analytical Approach at the Heart of the Brand
It’s not uncommon for teams to outsource analysis or throw it over the wall to an internal analytics group. When the findings come back, however, those same teams may be reluctant to implement them because they don’t fully understand or trust the numbers.
To solve that problem, marketers must work closely with data scientists, marketing researchers, and digital analysts to question assumptions, formulate hypotheses, and fine-tune the math.
Companies also need to cultivate “translators,” individuals who both understand the analytics and speak the language of business. One financial-services company, for instance, set up councils within its marketing function to bring the creative and analytical halves of the department together.
The councils helped analysts understand the business goals and helped creatives understand how analysis could inform marketing programs. We’ve seen such collaboration cut the duration of MROI efforts in half.
Speed and agility are also important. Insights from the consumer decision journey and the marketing-mix allocation should inform the tactical media mix.
Actual results should be compared with target figures as they come in, with the mix and budget adjusted accordingly.
Attribution modeling can be especially helpful with in-process campaign changes, since digital spending can be modified on very short notice.
Our research shows that the best-performing organizations can reallocate as much as 80 percent of their digital-marketing budget during a campaign.
The pressure on business leaders to demonstrate return on investment from a diverse portfolio of marketing programs is only increasing. The data to make smarter decisions are available, as are the analytical tools.
We believe that taking an integrated analytics approach is the key to uncovering meaningful insights and driving above-market growth for brands.
My Opinion
I loved the data and examples of McKinsey&Company a lot. That’s why I had to share all their info. Credits go to them.
I tried to summarize my extended experience and examples a short as possible. Hopefully making it more accessible and actionable for CMOs.
Why do I tend to be less detailed and more outspoken? Probably because I am a pro speaker.
I need to hit the core of each topic in the slot of a keynote speech: I am gone in 60 minutes.
Most of the times, one hour is enough to inspire action and to trigger ambition.
What About You?
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About the Author
Igor Beuker is an acclaimed trendwatcher & pro speaker, serial entrepreneur, marketing consultant and board member at several disruptive media, technology and entertainment firms. Book Igor as keynote speaker, follow Igor on Twitter or contact him via LinkedIn.
Sources: WPP, GroupM, Reuters, eMarketer, McKinsey&Company.
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