Posts Tagged ‘big data’

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 According to Gartner, contact information ages up to 50 percent in any year, becoming inaccurate and out of date, only serving to compound the issue.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.

24
Jul

Too Much Data Means Too Much Data

Written by Andrew Edwards. Posted in Big Data

The digital marketing industry is one of the few that talks of a surfeit of resources. This is not only anomalous but cause for real concern. What would you say about a restaurant that had “too much food” or an energy company that had “too much natural gas”?

You might scold them for whining, of course. Or you might take a page from King Ludwig of Bavaria when he told Mozart his opera had “too many notes.”

The surfeit in our case is a problem, not an asset. It is a warning sign, not a harbinger of great achievements to come.

A common definition of “too much” is “more than we need.” And if you’ve seen the slide decks at recent digital marketing events, you know that many presenters see it as a badge of honor that data is piling up at a stunning rate while the ability to use and process this data is lagging farther and farther behind. There is a gleam in the eye of the data-miner, who believes there are more nuggets because there are more mines (and certainly more work for data miners!).

Do We Already Have Enough?

The real news is that we may have already exceeded the useful amount of data being captured. Data mountains are not like Everest: you don’t climb them “because they are there.” You climb a data promontory because you need to see your assets on the field of battle. And if you can do that with a pair of binoculars and a position upon a strategic hill, you really would be wasting everyone’s time and money by hiring Sherpas and pack mules and going atop Everest where you can’t breathe and must carry your own oxygen. In fact, all you might see up there would be other mountains of data!

We certainly can have plenty of data about our target markets. And we certainly have constituencies that love the pileup of data because it allows them to deploy ever-niftier algorithms and ever-more-rapid access to petabytes of information; selling into our fear of falling irredeemably behind the data curve.

Just Another Buzz

But “big data” is really the latest buzz-meme more than it is a goal to conquer. The next most recent buzz-meme, “social media,” has been brought somewhat to earth with the realization that a million “likes” will get you on the bus only if you also have the fare. It’s now clear that social media is a campaign, and that without a tieback to conversion or sale, it’s “a whole lotta nothin” as they might have said in the old vaudeville acts.

It isn’t so different with big data. Big data thrives on the notion that ever more granular information will provide the ability to perform ever better targeting. However, beyond a certain point – a point I believe we have now passed – it becomes an exercise in futility. For instance, how much more targeted can your communications get before you simply exceed the ability of even the most savvy creative genius to craft the perfectly targeted message? And how many microsegments would you care to chase and at what cost toward what benefit? Moreover, how small a segment can you target before you simply freak out your prospect by seeming to know too darn much?

Some might argue for endless data collection with a throttle on its use. But then, the pile of unused data becomes just another bag on the Sherpa’s back as you trudge rather egotistically up the north face.

The answer today is to target not your customer so much as your data collection. Of course, you need to define your marketing goals more carefully than ever. And you’ll need to target your expertise as well, since technology and analysis can get costly. But you want to focus data collection based on need, not ability.

Half a century ago, the automobile reached a performance level that began to exceed the ability of humans to control it. It was possible but impossibly costly and even frightening to put Joe Driver behind the wheel of a 20-foot long heap of hurtling chromium that could make a thoroughbred seem hobbled and lame by comparison. Did it make sense to keep supercharging the engine for even more raw power? Or did it make more sense to refine the mechanics for efficiency and safety? With hardly a V-8 in production today, I think we have our answer.

The Big Data Mythology

Big data is the V-8: mythologized for the burble of its throughput. But beyond fabulously vertical line charts and a feeling of domain mastery, where is the benefit of this mountain of data? My suggestion is that there’s little benefit and a great deal of wasted time, money, and effort.

Never mind the sales pitch from big data specialists. Much of it is today’s equivalent of the kandy-kolored-tangerine-dream with a competition clutch and a big spoiler on the back. You can’t drive this baby anywhere but in circles on a closed track.

But if you keep it simple, you might find yourself leading a cultural revolution. Remember the little bug they called Volkswagen, and what it did to the roaring monsters from Motown?

Collect all the data you need and forget the rest. You won’t miss it. But you will miss the cost. Focus analysis on whether your audience did what you wanted them to do (this is really the heart of the matter and always has been) and don’t bother with trying to sell a different flavor of breadcrumb to every ant under the magnifying glass. They won’t notice the difference, and it will be cheaper for you.

Simplify. Concentrate. Don’t get distracted by what you might get, but focus on what you know you can have. And that is, a targeted data set used against defined goals, properly implemented, and carefully managed to achieve the return on investment that makes beautiful music in anyone’s ear.

Too much data is too much distraction. Keep your compass in hand. The near hills are full of low-hanging fruit, and the picking is good.

Convergence_Analytics_Report

 

24
Jun

Marketing’s New Rules

Written by Rand Schulman. Posted in Digital Marketing

During the last few years I’ve had the opportunity to meet lots of marketing executives during my travels. I hear the same storyover and over and a pattern is emerging that I’ve tried to address in this post.

The marketing profession is always changing and we know that the “Mad Men”-style era of advertising that favored creativity over analytics is gone. Some see this move from creativity to data-driven actionabilty as two distinct and separate disciplines. I don’t, and they’re not. It’s critical that we learn these new rules.

Rule No. 1: Merge Your Left and Right Lobes

The new marketer needs to be multi-talented, using both left and right brain functions. Those who can shift seamlessly between these two lobes will give their products the best chance for success. These days we need to be part artist and part data scientist to excel. We need to be both analytical and creative – truly a content engineer.

Rule No. 2: Creativity Without Conversion Equals Zero

It’s the same both online and offline. We need to sell products, and to do that we need to communicate and convert, both with micro and macro conversions. Today it’s critical that we create compelling content for a pre-defined business purpose. And if you don’t agree with me, you’re really only writing for yourself, so save it for your diary.

Rule No. 3: Learn the New Marketing Four Ps

Remember the old marketing Four Ps: product, promotion, place, and price? Well, there’s a new set of Four Ps today, driven by mobile: people, process, purpose, and platform.

People

It’s not about Math Men, Mad Men, or Sexy Data Scientists. As we know, roles and responsibilities across the business need to realign and meet the needs of our new paradigm. There’s now a direct line between, and often a single person who owns acquisition marketing, conversion marketing or “on-boarding,” and product user engagement, as volumes of data drive outcomes very quickly – sometimes in less than a day.

“Gamers” drove much of our notion of app engagement around user experience in the mobile world. Apps just had to be good and a crappy app would die a quick death. Fast fail. And since so many users at any given time were using a game, we could let the “law of large numbers” drive our decisions and we learned a lot about user behavior and retention rates in mobile applications. Zynga is, after all, an analytics company masquerading as a gaming company, and it clearly taught us a lot about app engagement over time.

Process

As we are moving toward one person owning all three levels of responsibility for acquisition, conversion, and retention (more like a producer in the game world), we now need to evolve our processes about what messages and media we use, by segment. We have to look at retention rates over lifetime value (LTV), so the message, content, and media can be A/B tested against app performance goals, cost, and ROI.

We need to define our process about what data we collect, how we collect it, and who is responsible for the optimization. Some say, “In the right time.” New roles, like chief data officer, will replace the old notion of CMO and champion new process.

Purpose

Today, the identification of purpose is missing from many mobile applications, just like during the earliest days of the website deployment. I hear the excuse, “We’re doing it because our competition is doing it.” That’s not enough.

The understanding of purpose was key to building online business. And, like in the late ’90s, mobile is now in a very early stage of accelerating adoption, yet few organizations have much discipline around definition of goals. Analytics then and today is key.

Platform

And once you have your people on board, have identified your purpose, and have addressed the process, it’s time to think about the platform. Or, what tools and applications do you need to reach your goals? I’ve seen too many companies do this in reverse, usually falling prey to vendor hype compounded by not having the first three Ps set.

Then, when you have all of your Four Ps in place, start with a small and measureable project. If you don’t, your platform will likely become shelfware.

Rule No. 4: ABC – Always Be Clear!

We need to define our terms/words, as too many people are trying to measure everything, across every channel in real time, when few seem clear on the definition of much of anything. I’ve recently coauthored a report on convergence analytics, with Incisive Media, which included our survey about the definition of common terms like “real-time” and “multi-channel.” And it’s not surprising that there’s no clear definition of what the terms mean. Some think real-time is sub-second, while others think it is hours. Sometimes multi-channel means online and offline channels, while other times it means only digital channels or cross-enterprise silos – more marketing operations-like. Be careful and make sure you’re on the same page. Few are.

Summary

We need new hybrid skills to help drive mobile and online business – people who create process and apply numbers to words that convert, and who understand new engagement and conversion models. We need agile product developers schooled in both mobile-first design and the latest ways to take advantage of the underlying technology, both in hardware and software, and who are analytical. They will know about how augmented reality creates brand engagement; how the phone’s multiple signals can be tapped for conversion data; and where artificial intelligence works, and where it doesn’t. Next time I’ll write about the new marketing roles and how “big data” is making a big difference.

Convergence_Analytics_Report

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