Years ago, marketing’s benchmark for success revolved around the number of leads, website visitors and revenue. How times have changed. Obviously, revenue is still a key performance indicator (KPI), but today’s marketers have evolved to leverage value-based KPIs in addition to raw numbers.
From Volume to Value
Five years ago, a marketer may have walked into a quarterly meeting armed with a count of last quarter’s new leads, website impressions and site visitors. However, these metrics are like games played for a basketball player—the player may be active on the roster, but does that participation lead to the team’s success?
Do you think that the 2015 NBA Champion Golden State Warriors measure that Stephen Curry played 80 games this past season? I’m sure that number is part of their player analysis, but the more important questions are:
How did those 80 games translate to wins?
How did they translate to an eventual NBA championship?
My point is that volume counts, but it is only a small part of the overall story.
Winning with Value-based KPIs
Today’s best practice marketers go beyond volume-based metrics and focus on value-based metrics asking, “What is marketing’s value in driving revenue?” These value-based metrics enable executives to measure marketing’s impact on the bottom line.
Businesses will never have one report that tells the whole story, rather there are several reports that provide insight into a business’s health. Here are a few your business might want to consider:
- Marketing Contribution
At a high level, the marketing contribution report puts marketing at the revenue discussion table. It answers the question “What is marketing’s contribution to revenue?” For example, marketing influenced $750 million worth of pipeline last year while helping drive 65% of last year’s $150 million revenue number. Those are powerful numbers for any marketer to share at a quarterly meeting.
- Marketing Program Success
Your company spends money on marketing, but is it spending the money wisely? This report enables marketers to gain insight on their most/least impactful marketing programs.
Success is defined on a program by program basis. For example, of the 1,000 people who stopped by a trade show booth, how many signed up for a follow-up demo and experienced success (as opposed to stopping by for the free t-shirt)? The below report is an example of a rolled up report that compares all website leads to all trade show leads. As a whole, website leads perform better than the event leads.
- Lead Funnel Analysis – The Waterfall
Do you know your MQL-to-Won rate? According to Aberdeen and SiriusDecisions, only 3-7% of MQLs turn into won deals. I’ve actually seen these rates even lower for companies that do not have a strong Sales and Marketing process.
The lead lifecycle (aka: The Funnel) refers to how leads move from their creation through to revenue. The goal is to develop a healthy funnel to optimize leads moving through the system in an efficient and timely fashion. These waterfall reports put metrics around that funnel to find gaps and improve conversation rates. They are called “waterfall” reports because leads flow from one row to the next. Just like a waterfall, once water flows over, it never goes back.
Small improvements in the process can result in massive gains. Looking at the below example, we see a small MQL-to-Won efficiency increase from Q1 to Q4 (4.0% to 5.5%). That might not seem like a lot, but look at the Won numbers—that’s 15 more deals and a 38% bump in revenue.
Funnel reporting is one of the more difficult metrics to measure as it requires excellent sales and marketing alignment and a clear definition of terminologies. These take time to put in place.
- Closed-loop Opportunity Analysis
Where should your organization invest? With closed-loop analysis, management can compare programs to see which is the most cost effective for driving leads to revenue. Here we get to insight nirvana by tying marketing investment to revenue. These reports allow marketers to make better decisions on where to allocate marketing dollars based on opportunities created and deals won. The insights also help organizations justify additional funds when making budget requests.
For example, if two trade shows cost the same but one helped convert more opportunities, a business might consider reallocating budget for next year’s events.
- Acquired vs. Influenced
These reports help businesses understand what programs are driving acquisition versus the ones that are influencing deals. To summarize, “acquired” means first customer touch—there can only be one. “Influenced” means the customer touch could have happened anywhere along the funnel process. For example, trade shows generally bring in many new names (acquired). On the other hand, webinars usually bring in fewer acquired leads but are effective at influencing leads already in the lead pipeline. Businesses should measure the success of each.
Your Steps to Value-Based Metrics
Companies have historically focused on volume-based metrics because they are easy to understand and relatively easy to obtain. Like measuring Stephen Curry’s impact to the Golden State Warriors’ success, value-based metrics are a little tougher to quantify.
In my experience, the transition to value-based metrics takes anywhere from three months to over a year–don’t expect overnight success. Once your business decides to make the switch, you can begin the journey to creating a repeatable and measurable business model.
Article written by Jeff Coveney for icrunchdata News Boston, MAJeff Coveney, founder of the RevEngine
The Big Data Gray Area: Dos and Don’ts
I’ll be the first to admit it: If you’re looking for a perfect answer to the big data ethics dilemma, you’re not going to find it in this article — or any other article, for that matter. That’s because there is no perfect answer. If there were a perfect answer, an obvious answer, or an easy answer, we wouldn’t be facing a dilemma — there would be no gray area.
Perfect solution or not, in business, as in life, there are certain things we should and shouldn’t do. There are certain general standards to which we should hold ourselves and our companies, regardless of the fact that there may be no laws requiring us to do so, or that the principles behind them may not apply 100 percent of the time. There may be no perfect solution, but it is our responsibility to try nonetheless.
Big Data Ethics Dos and Don'ts
With that in mind, and with the full understanding that these recommendations are anything but perfect, here are what I believe to be the dos and don’ts of big data ethics:
Do work hard to become a data-driven organization. Considering the technological advances that have made it both possible and (perhaps more importantly) affordable to more easily and efficiently manage, integrate and analyze data, there’s really no reason for any organization not to embrace a data-driven culture. Large or small, organizations that wish to remain competitive in the modern business world must have a willingness to share information and to allow decision-making to be guided by the insights and behavioral patterns hidden within big data.
Don’t always assume data is right. As we just established, there’s no question that it’s time for businesses to start embracing the power of big data analytics. But that doesn’t mean that logic and intuition should be cast aside completely, and it most certainly doesn’t mean that companies no longer need to exercise common sense about what is and isn’t ethical when it comes to big data. Just because you have collected and analyzed data, you needn’t follow those findings blindly. Any number of issues, including poor data quality, a lack of metadata or an unreliable data model, can cause data to steer you in the wrong direction. To ensure no ethical lines are crossed, develop a culture in which analysis and intuition both have a place at the table.
Do secure explicit consent before adding people to your prospect database and delivering them content or promotional offers. The good news is that most companies that actively market to prospects with content and promotional offers are already doing this, if only because they’re usually required to by law.Andy Taylor, Author
Business Intelligence improves the bottom line
Healthcare industry focuses on driving better outcomes
Business intelligence is finding myriad ways to improve the healthcare organization’s bottom line, whether it’s through clinical improvement, greater operational efficiencies or enhanced revenue cycle functionality, system designers say.
Take, for instance, medical device integration into the electronic medical record. The focus of the project from a BI perspective is to process clinical information at the point of care, where the data can be used to drive better clinical outcomes, says Dave Dyell, founder and CEO of Panama City, FL-based Isirona.
“Making better decisions with data-driven care is the BI angle,” he said. “It is about clinicians using the data to treat patients.”
Making existing staff more efficient is another bottom line benefit of BI, Dyell says – especially in light of the nursing shortage that many providers have experienced. Automated point of care information saves valuable nurse time on taking patient vitals, which can easily add up to a couple of hours per nurse, per shift, he says.
“Another benefit we see on the data analytics side is the ability for early identification of potential problems and earlier intervention,” said Dyell.
To be sure, access to critical information in real time is the hallmark of a BI system, says Eric Mueller, services president for Seattle-based WPC.
“BI is about making good decisions with accurate information in as close to real time as possible,” he said. “That is important because if the information in front of you is 40 to 60 days old, that isn’t business intelligence, it’s business history.”
Newton, MA-based Curaspan Health Group connects providers, payers and related suppliers through a web-based transition network that CEO Thomas R. Ferry calls a “synchronized patient management solution that is interoperable with health information systems in the acute and post-acute sectors.”
The Curaspan platform serves as a foundation for accountable care organizations, health information exchanges and the evolving bundled payment and models of care standards.
“One of our tenets is ‘you can’t manage what you can’t measure’ and it takes an IT platform to help you do both,” said Ferry. “Our approach is to use the combination of content, service and workflow tools to address mission-critical processes. The key is transition management from one care setting to another for patients. By applying those tools to a very intensive process, we have been able to drive best practices.”
Chor-Ching Fan, senior director of analytics product management for McLean, VA-based LogiXML says BI is “exploding” and healthcare is seeing “the third wave of its BI evolution.” Adopting BI in healthcare is a bit different from other industries, Fan says, because “healthcare is a combination of art and science.”
In terms of product requirements for healthcare, a BI system “needs to be pervasive,” Fan said. “It can’t just be for executives – there is so much opportunity for benchmarking the clinical workflows and processes.”
Healthcare organizations need to know the details of what goes into the provision of services, such as how long services take and how long patients have to wait for service, Fan says. A holistic approach using dashboards, charts and analytics allows managers to see how long things are taking in the care process, he said.
Revenue cycle management is critical for all provider organizations – none more so than cash flow-challenged physician clinics, says Taylor Moorehead, partner for Carmel, IN-based Zotec Partners’ west region.
To help physicians collect the maximum amount of revenue from payers and patients, the Zotec Partners BI system uses analytics to look at pay patterns so that physicians can negotiate better contracts with payers and establish payment terms with patients, said Moorehead.
To illustrate how BI works for claims processing, Moorehead uses this example: if a clinic sends in 1,000 claims and the insurer pays 700, there is a 30 percent denial rate, and it is up to the provider to figure out why those claims weren’t paid. Using BI principles, the accounting staff can track CPT codes, ICD-9 edits and all the carrier-required elements to figure out why those claims weren’t paid.
“If you’re not tracking, slicing and dicing, you can’t improve or educate anyone about the process,” he said. “Without it, you’re DOA.”
Primary care physicians are among those providers at greatest risk of revenue loss due to reimbursement cuts and a growing number of uninsured patients, adds Jim Rose, senior vice president of business development for Burlington, VT-based Patient Engagement Systems.
He summed up the situation this way: “How can primary care providers improve their revenue position? The only thing they can do now is fill their appointment books and charge people for canceling appointments. It is an airlines type of mentality – people better show up or we’ll lose money. So if physicians are adapting those practices, they are going in a bad direction.”
To achieve the goal of a more “comfortable” reimbursement position, Rose says practices need to use BI to “create meaning out of loose, disparate data.” That means harnessing and presenting data to payers to prove quality patient care.
Indirect ROI benefits
Since adopting a BI system from Orlando, FL-based Pentaho, Loma Linda University Health Care in Loma Linda, Calif., has upgraded its decision support capabilities “without breaking the bank,” said analyst Duncan Henry. While the health system expects to see upfront and ongoing cost savings with the system, Henry points out that he has seen some indirect benefits to the ROI as well.
“Being able to download and evaluate the product was also beneficial,” he said. “We looked at one proprietary product that had a very siloed approach and would have required significant tailoring to our existing setup to get it to fit, whereas Pentaho hooked right up to our data warehouse without a hitch or having to resort to lots of service calls to either IS or the vendor.”
As Henry and his team begin to automate many of Loma Linda’s manual processes, they have started to increase their data output to match growing demand without having to add extra staff, he said.John Andrews, Consultant
If you are like most companies, you are making a big investment on market research hoping to gain insights. And these data are used to make very important decisions in various areas, from product development to customer segmentation, or from marketing campaigns to product strategy.
But how can you be positive you are relying on accurate and unbiased data?
In the recent years, the field of behavioral science such as behavioral economics and decision making research are discovering that data can be easily biased based on very subtle cues, such as wording of questions or the order of questions. We’ve been also learning that people (including your customers and ourselves) are not always rational and therefore, we provide inconsistent responses based on the environment or the state we are in.
The implications for researchers, though, haven’t been well documented.
In order to avoid collecting biased data, and hence relying on inaccurate data, what do you need to do?
The first step is to understand how your consumers really make decisions.
For example, do your consumers tend to make decisions mostly based on fast thinking (e.g., intuition, gut feeling, “what feels right”) vs. slow thinking (e.g., deep/elaborative thinking that requires weighs pros and cons from all the alternatives available to you)? I cannot tell you the answer without knowing your customers and your products/services. But I can tell you that they are relying more on intuition than you might think – or you might want to believe.
In fact, some behavioral researchers including myself believe that 75-85% of decisions we make are mostly based on intuition and not based on slow thinking.
That is because we simply do not have enough resources, such as time, energy, cognitive resources, and willpower to make all the decisions based on slow thinking. For example, how did you decide what to have for breakfast? Did you weighs pros and cons of all the possible options from your pantry and decide which one to have? The answer most likely is no. You grabbed whatever that comes to your mind first.
But wait, you might say, I know most people don’t think much before they decide what they’d have for breakfast but our customers do when it comes to our products. After all, your customers in your focus groups tell you all the reasons why they chose (or didn’t choose) your products over your competitors. Isn’t that evidence that they are thinking when they are deciding?
Or you might say that your products require careful thinking because your products come with consequences, such as financial and health/medical services.
Decades of research in behavioral science tells you otherwise.
Namika Sagara, Ph.D., President