Using marketing analytics to drive
superior growth
Companies
have so many analytical options at their disposal that they often become
paralyzed, defaulting to just one approach.
There’s no question that the development of better analytical tools and
approaches in recent years has given business leaders significant new
decision-making firepower. Yet while advanced analytics provide the ability to
increase growth and marketing return on investment (MROI), organizations seem
almost paralyzed by the choices on offer. As a result, business leaders tend to
rely on just one planning and performance-management approach. They quickly
find that even the most advanced single methodology has limits.
The diverse activities and audiences
that marketing dollars typically support and the variety of investment time
horizons call for a more sophisticated approach. In our experience, 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.1
(A sidebar, “Five questions for maximizing MROI,” highlights additional
considerations.)
Sidebar
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 nondirect 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 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
Although some companies rely on just
one analytical technique, the greatest returns come when MROI tools are used in
concert. An integrated approach, which includes pulling in direct-response data
and insights, reduces the biases inherent in any one MROI method and provides
business leaders with the flexibility to shift the budget toward activities
that produce the most bang for their buck.
So how do these techniques work
together? A company may find, for instance, that TV, digital, print, and radio
make up about 80 percent of its marketing spending. Since those activities generate
audience-measurement data that can be tracked longitudinally, it makes sense to
use MMM. But digital spending can be refined further using attribution modeling
to pinpoint the activities within broad categories—such as search or
display—that are likely to generate the most conversion. The company could then
use heuristics analysis such as RCQ to monitor the remaining 20 percent of its
spending, which may go toward sponsorships and out-of-home advertising to
capture the company’s non-TV-watching target audience.
Developing common response curves
across analytical techniques helps marketers put the values of different
approaches on common footing. The organization can then use a decision-support
tool to integrate the results, allowing business leaders to track and share
marketing performance on a near-real-time basis and course correct as needed.
An international power company, for
example, used RCQ analysis to adjust its out-of-home and sponsorship mix,
efforts that increased reach within its target audience and raised the
efficiency of marketing communications by 10 to 15 percent. The company then
turned to MMM to get a more granular MROI assessment of its spending on digital
versus traditional media. It found that while each €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 the analytical approach at the heart of the organization
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.
Rishi Bhandari is a senior expert in McKinsey’s Chicago office, Marc
Singer is a director in the San Francisco office, and Hiek van der
Scheer is a consultant in the Amsterdam office.
http://www.mckinsey.com/Insights/Marketing_Sales/Using_marketing_analytics_to_drive_superior_growth?cid=DigitalEdge-eml-alt-mip-mck-oth-1406
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