Posts Tagged ‘convergence analytics’

16
Apr

Black Box vs. Tool Box Analytics in Digital Media

Written by Andrew Edwards. Posted in Digital Television

digital television

Media converges with digital more and more each day.

Even a year ago, would anyone have predicted that Amazon would win a Golden Globe for a show starring Jeffrey Tambor of Arrested Development fame? Or that today you can watch network television without a cable subscription (or even an antenna)? Given these new twists, can we begin to consider how this affects digital analytics as compared with the “ratings” that once drove all media sales?

Today, where media companies sell to advertisers, they continue to offer up the proverbial eyeballs even if no one wants to call it that anymore. How many viewers? How many pre-rolls of the digital video? How many tweets, shares, replays, aborted sessions, repeated sessions? In an ad-sales environment, the data can drive the transfer of lots of real dollars. And typically, media folks just haven’t as reliable a data model today as they once had; consequently making it that much more difficult to help their customers (brands) make decisions about what to buy. And that’s despite the fact they can offer far better targeting and far more depth into audience analytics than was ever before possible.

Why is there even a debate about what’s important in digital media? Aren’t the old-fashioned ratings systems working the way they used to?

Turns out they’re not. And it seems like it’s more about optics than data.

For instance, when television was unchallenged by the Internet as a media outlet, there was just one accepted metric, and that was the Nielsen rating. It was more or less gospel. If you had bad ratings, you had bad advertising sales rates and if you couldn’t sell a 30-second spot for your show at an acceptable rate, then your show got canceled.

The reason the ratings model was accepted was not necessarily because Nielsen had come up with a fault-free method of audience measurement. As likely it was because Nielsen didn’t share a great deal about exactly how they arrived at those ratings numbers. Or at least they owned the methodology and were willing to stand behind it. And media folks had no way of questioning it because they had nothing else to compare it to. Nielsen said: “here are the numbers,” not “here are some methodologies we developed to measure audiences.” I call that a black box. It was easy and it worked.

Then came the Internet and, heaven help us, you could look in your browser at the code on a live, published page yourself; almost as if you might have won a spot backstage at the sitcom set and watch them writing the script just before show time. And if you were the “publisher” and wanted to know about who was looking at the page, when, and for how long, you had no choice but to do the counting yourself. Whatever you found out you did not share; and you also could not reasonably compare. For where one sitcom versus another was pretty close to apples/apples, now it became apples/starship. Worse, no one could agree on what an apple was. Or if a starship could be made of apples.

With visibility into methodology came the right to question everything, including the methodology. It’s inherent to the Internet paradigm that no one company measures and presents results. Everyone is their own little Nielsen and they know every flaw in the model and have a thousand reasons not to believe the numbers. It isn’t that it’s less reliable than Nielsen, it’s just that it looks that way.

Somehow, media needs to get back to a model such as Nielsen had. But that might require a black box again, and that seems about as likely as a starship made out of apples.

18
Mar

Can Digital Analytics Save Itself?

Written by Andrew Edwards. Posted in Digital Analytics

digital_analyticsDigital analytics today is burdened by disillusionment and disappointment. Not that there are no success stories with digital analytics. There certainly are. But they are comparatively rare. Much more common are legions of valiant but frustrated marketers continuing to struggle with the basics:

Is data collection accurate? Once we learn what the data tells us about our business, are we in a position to do something about it? What happens when our agency tells us they’ve taken care of measurement and, behold, the campaigns are “all good” (or at least not a total waste)? What does change really look like, and can we make it happen in time to matter? How do we do that without automation? And where are the successful predictive models that drive automated responses?

The unanswered questions don’t stop there, but for the sake of brevity we shall.

No one suggests that organizations go without analytics. And many businesses do get to a place where they are comfortable measuring with accuracy and understanding. Many fewer end up being able to fix any but the most egregious “disconnects” between themselves and their customers. The vast majority settle for knowing what happened, with a moderately strong determination to do something about it “in the next release.”

How Digital Can Deliver for Marketers
Many of the most dire threats to success in digital marketing can be overcome by adhering to a process. The process is not very mysterious, and, in fact, can, with some alteration, be applied to almost any endeavor requiring rigor and results.

The process looks something like this:

1. Determine key performance indicators
2. Implement data collection and reporting
3. Review and analyze reports
4. Make content changes
5. Measure again to prove success

Following these will go a long way to avoiding disappointment and marketing paralysis, but often it proves devilish hard to get through the process.

Saved by Automation?
The toughest parts of the above process are numbers three and four.

It’s easy enough to figure out your basic metrics and get the data collected properly as long as you have a team of analytics experts. We’re pretty much overrun these days with analysts, but often it’s tough to turn what they say into recommendations. Then, the most difficult part is getting changes made. Figuring out what changes to make, and how to get them made, typically slows the process nearly to a halt.

Automation will be key in changing this from a roadblock to a starting block.

With Tealium’s AudienceStream, you can build in rules and thresholds that send out directives to content delivery systems that let you know it’s time to contact the customer with an offer (for instance). The key to its success is its timeliness and the certainty of its execution. It becomes automatic.

Conductrics deploys Artificial Intelligence to create a system of learning and action based on data. For marketers, this means that Conductrics will facilitate the creation of an “agent” that seeks out challenges and then tackles them (for example, it looks for meaningful patterns and then can direct content to be distributed as needed). Conductrics has likened their agent to a Roomba for digital analytics. It learns its environment and then focuses on doing one task very, very well automatically.

XplusOne [x + 1] markets a product called Origin. According to the firm, “Origin harnesses data to drive real-time, one-to-one interactions across all your digital channels, so every prospect and customer interaction is more relevant.” They also deploy a Data Management Platform that controls numerous customer touchpoints automatically.

These products help conquer the challenges of what many today call omni-channel marketing. They help address how customers can be reached in various “states,” as Rand Schulman has pointed out.

Automation is moving ahead rapidly. It may save analytics by embedding it into an automated process — which probably is where it belongs.

16
Apr

Your Small Data Just Sucks

Written by Rand Schulman. Posted in Convergence Analytics

I was sitting in my office last week working on a targeted email when I realized something so fundamental – it’s a bit embarrassing to admit. As a data-driven marketing guy, you’d think I’d realize the most fundamental building block of any conversion starts with accurate “top of the funnel” CRM contact data. With garbage in you only get garbage out.

There’s a lot of talk about convergence of all things. There’s the convergence of all the systems we use, and the convergence of new roles – especially those of marketing and sales and building a culture of measurement.

As marketers struggle with all of the new tools, we need to review the most fundamental component of marketing, yet one of the most overlooked – quality information, our small data. Without good contact information these systems are just plain dumb, and they cost us more than they help. According to Gartner, contact information ages up to 50 percent in any year, becoming inaccurate and out of date, only serving to compound the issue. The top of the funnel data just has to be solid. And we have to be agile and act on it quickly.

A Typical Day
At our consulting company, Efectyv Digital, we use a number of tools to help us target and engage our current customers, and find new ones. We use a marketing automation system, in our case HubSpot, and a bunch of Google tools, SEO, pay-per-click (PPC), analytics, and Viralheat for social analytics. We also use various email products so we can test and optimize the send and open rates with tuned messages.

Like many of our clients, we are a B2B firm; we build marketing lists and segment and send targeted emails about our services to specific personas from those lists. The messages vary by role, industry, and need. They contain calls to action and other things you’d recognize as conversion events. We track our funnel and outbound conversions – and let me say we could do much better. Our list bounce rates are high and our open rates are low. We’ve hired an outbound lead generation person, and we’ve seen similar results.

We wondered if the issue was with our contact data or perhaps it was our offers and messages or timing (we just started using alerts)? To test, we decided to start one step at a time, and look specifically at the contact information contained within a few of the popular lead-generation tools to see why our conversion rates were so low.

It didn’t take us long to confirm, as we suspected, that our data just sucked and we needed to start making it better. Here’s our analysis. At least step one. We’ll always work on our messages.

The Simple Test
people-search-shutterstock-128521253While there are scores of products on the market, including LinkedIn, Zoom, and One Source, and some great new start-ups that have various degrees of content mash-ups like Tempo and Refresh, we chose to test three of the more popular systems that come integrated with CRM systems, including D&B 360, which has contact and company information – mostly generated manually; Data.com, the roots of which are crowdsourced with Jigsaw data acquired by Salesforce; and InsideView, which claims to rely on technology to deliver results. The levels of integration vary, depending on the CRM system, Dynamics, Oracle, Sugar, or Salesforce.

We used a real person and a real institution, in this case Krystin Mitchell, senior vice president of human resources at 7-Eleven Inc. Since 7-Eleven’s revenue is in excess of $80 billion and they’re public, we thought they might be a good test to see how we can find her in our test systems.

The results. So, where is Krystin? According to their current company Web page, she is indeed at 7-Eleven, but according to Data.com Krystin Mitchell is not included in a “Find Contacts” 7-Eleven search results. When we broadened it, we found there were 16 wrong results with her name, company, and email address. That’s crazy and not acceptable. I can see why our emails bounce.

We then tested the trusty old saw, D&B 360. Since much commerce is based on its data, it has to yield accurate results, right? D&B is the gold standard of contact data, the truth, built with human editorial control so we thought we’d get correct results. But, even with D&B, Krystin Mitchell is not included in “Build a List” of custom search results…although this time 65 wrong contacts came up instead.

To find her we needed to do a “general people in search,” but like in Data.com it yielded multiple/duplicate results, and different types of wrong contact info that tends to defeat the whole purpose of a contact tool. Interesting and again not acceptable. More bounces and wrong numbers.

We then moved to our free version of InsideView, which works with Salesforce.com. They are one of the companies that include data from multiple sources (thousands), and then validate it through a technology they call “entity triangulation.” Using analytics, this process is designed to determine the relative “truth” about people, content, and key event information. For our test about Krystin they got it right, listing her correct title and correct company and contact information, which match the company website and public disclosure. It was CRM Intelligence and we now use their Target product to build our lists and are getting much better results.

All in all we’ve analyzed vendor results many times, testing with different contacts, and companies and a great percent of the time fundamental results were different between vendors. I admit it’s hard to do, to really know where someone is, but that’s what we need and I am optimistic that there are new tools just coming to market, like marketing automation company Autopilot, releasing a new prospecting tool for sales and marketing that they claim is generating up to 42 percent reply rates on cold emails.

Conclusion
So, while this column is about conversion marketing and analytics, and I usually write about more meta subjects, I thought I’d share some personal real world issues that impact marketing, and ultimately sales. In this case our sales. We expect to at least double our conversation rate by spending more time creating quality data and lists.

money-falling-shutterstock-98093063We are drowning in data. It is no simple feat to filter this sea. But, it seems to me that we need to get the basics right about “small data” before we talk about optimizing big data, real-time data, and the impact of attribution models. B2B or B2C, quality contact information is fundamental. It’s best to walk before we run and finally sprint to the holy grail of real-time conversions, and revenue falling from the trees.

Images via Shutterstock.

About Efectyv Digital

Efectyv Digital is focused on strategy for two distinct markets: digital analytics end-users; and marketing strategy for technology companies.

Click here to learn how how we can help your business grow >