Manhattan Marketing Maven

You & Your Data

You've probably been hearing a lot about Big Data; huge databases accumulated by companies. The challenge is to make sense of that data in ways that personalize the experience for consumers and sell more stuff. 

The other faster moving data trend is collecting different sets of data, linking them and automating them to serve specific purposes. A huge number of start-ups are based on this premise. They use Facebook, as the database of choice because it’s rich (and getting richer each day) in personal information and because Facebook has made it easy and cheap for developers to get access to its data. 

Imagine a travel company that uses your Facebook info (posts, photos, apps, games) to figure out what kind of traveler you are. They know or can infer who you are, where you went, whom you went with and what you did there. Then they marry your info to a database of travel locations and deals and serve you up offers that match your style and your inferred budget. 

The richer the data sets; the richer the value to you and to them. The underlying assumption is that people will appreciate data mash-ups because it will yield more personal, relevant and valuable ways to enrich our lives expand our connections one to each other and make our money go further. At the moment this might seem, far-fetched. But when you realize that more than 25% of the population has always lived with the Internet and is very comfortable with living out loud, sharing their actions and ideas publicly and is quickly embracing the latest technology, you can begin to see the near-term future. 

The use of data is simultaneously creepy and useful. Studies have shown that the vast majority of Americans will trade off private data for utility or value. As data sets automatically talk to each other, this trade-off will be perceived as less scary and more useful. We’ll get used to being cued and prompted easily and quickly. 

A principle tool for data collection is your mobile phone.  You may not realize it but everything you do with your phone is stored and captured. It’s easy to figure out who you talk to or text, how often, from where and at what times. 

Your phone's GPS device records and potentially tracks or monitors your whereabouts and movements. The built-in accelerometer can imply what activities you do, the speed you do it and the likely impact. There are many apps with sensors that measure and record vital stats like blood pressure, heart rate, etc. All of these technologies reveal a great deal about you that could be used to personalize offers and enrich or personalize a brand experience. 

This is data arena is where technologists and entrepreneurs are playing today.  There will be some horrific privacy breaches and some amazing new gadgets that come out of this tension between privacy and technology.

 

 

6 Roadblocks to Data-Driven Marketing

Data is being touted as the new marketing darling. Yet in spite of widespread availability of amazing, existing, proven data collection, mining, processing and automation tools mainstream marketers haven’t been walking the walk. 

Some industries like banking, insurance, financial services, airlines and telecom have to be data-centric marketers because their margins and share of wallet demand it. Yet even in many of these firms, data lives in siloes and lacks integrated analytics necessary to significantly impact business growth. In retail and grocery, we’ve been playing around with purchase histories, private label credit cards and loyal programs, but very few truly integrate multi-channel data, build impactful data marts or fully exploit the power in the data. 

So why would rational competitive marketers underutilize a suite of tools that could make them smarter, faster and richer? 

Here are 6 reasons; we need to get out of our own way. 

Math Phobia. It sounds funny, but it’s real. Most of us suck at math, are fearful of math or are still shaking off or suffering flashbacks of math anxiety from school. Couple this with the general feeling that math cancels or constrains creativity and you have an attitudinal bias amongst marketers against using the data at hand. 

Talent Deficit.  The really good math guys don’t want to work in marketing or advertising, even if it’s a target rich social environment. Agencies, marketers, e-merchants and publishers are constantly trolling for hard-to-find analysts, database administrators and database marketers and coming up short. For real math savants, advertising is baby stuff that doesn’t pay well. Plus there are few consensus metrics or best practices, much less proven analytical formulas to apply. Another hurdle is just getting the data by finessing internal technical and political structures. It is not anything that introverted analysts want to do. 

Low IT Priority.  Database projects are low priority generally for corporate IT leaders. The relatively low complexity of the tasks or systems and the absence of a perceived ROI benefit ranks marketing needs far below complex, big budget aspirational IT landscape, software change-out or architecture projects. Marketing data applications tend to require much more hands-on technical support and service because marketers are unwilling or unable to run the software themselves. So IT guys run the other way. 

Cost-Value Perceptions. Too many marketers and C level people don’t see the incredible pay-off from incremental marketing data investments. In many cases, modest costs yield immediate and significant lift in awareness, traffic, engagement and sales.  Decision-makers don’t understand what the technology is or how it’s applied. Its much more complex and confusing than making ads or building websites and because it’s perceived as plumbing, data investments can be cut, delayed or ignored without fear of harming business results. Nobody gets promoted or wins awards for smart database work. Ironically the truth is exactly the opposite; by tightening up the plumbing and crunching the numbers you can add incremental value quickly and predictably.   

Vendors are part of this problem because they focus overwhelmingly on the features and functions rather than on the pay-off from their tools and services.  And while there are several ways to automate data-collection or lead generation, and countless predictive models and even more approaches to automating e-mail delivery, few vendors can clearly and simply communicate to a disinterested CFO that an investment of X will pay out 10X in a time frame that would surprise everyone. 

Data Overload.  Many brands complain that they collect mountains of data that is labor-intensive to process and make sense of. This argument says its too overwhelming so forget it.  It belies ignorance about marketing since top marketers know that in most cases a small number of variables can predict important insights. The savvy players are using predictive models that not only optimize messaging, offers and costs but get smarter over time as they integrate more and more purchase and behavioral data points. These guys are willing to trade off customer insight, practical segmentation, personalization, channel strategy, customer intimacy and message cues because the bag of numbers looks too big. 

Big Brother. Another segment of data deniers is focused on either a genuine concern for customer privacy or on a fear of being perceived or prosecuted for violating privacy rules. The reality is that we have pretty good transparent consumer privacy laws and enforcement in-effect. None of which has dampened consumers’ willingness to trade their personal data for deeper deals and relationships. 

We have useful, accessible and affordable tools that are underutilized because they aren’t sexy, understood or the digital-mobile-social flavor-of-the-week. It’s frustrating and wasteful.

 

McNamara & Metrics

Robert S. McNamara, who died this week at 93,  was the original metrics wonk. An early whiz kid he pioneered the use of  data to inform and direct the strategic bombing initiative in Europe against the Nazis during World War II. Subsequently he brought process discipline and data-driven decision-making to Ford and to the business world in ways that revolutionized industries and dramatically improved competitive postures.

As the longest serving Secretary of Defense, he brought the orthodoxy of counting, measuring and making data-based decisions to the Pentagon. The effect was to achieve some economies of scale and to reign in the desires of the uniformed services for countless toys and new weapons. But his perspective and process also led to our disastrous extended engagement in Vietnam. 

McNamara's metrics legacy has several dimensions:

1. Data Illuminates. By studying data of what actually happens, we can identify patterns and inflection points and find ways to pull costs out and built efficiencies into many systems.

2. Data is not always clear cut. The numbers can be spun many different ways to support or defend a broad range of inferences, suppositions, resource demands and ideologies. The famous Missile Gap was a politically-motivated distortion of the data.

3. Data People Can Be Neutral Observers. Sometimes the number crunchers are neutral observers. They dig deep and report out their findings to leaders who use the facts to make decisions. 

4. Data Players Can be Advocates. At other times the number crunchers have an affirmative moral obligation to use the data to advocate a point of view. McNamara was unable to persuade his clients, JFK & LBJ,  to make peace when the stakes were much lower.  

In our current fervor to measure, monitor and monetize everything we can, McNamara's experience reminds us that data mining and data-driven marketing are not pure science. They require a fair amount of interpretation tempered by context and conscience.

The B2B Database Dipstick Report

Lists are the bane of database marketers existence. Everyone thinks their own data sucks but the real disappointment usually comes when you rent a compiled list and realize they suck too. Everyone wonders why this is but few have done anything about it -- till now.

Ruth Stevens and Bernice Grossman have been writing white papers on database marketing since 2005. Both are well known independent thinkers and marketers with extensive direct marketing and database chops. For their seventh outing they devised a wickedly simple test titled "Online Sources of B-to-B Data: A Comparative Analysis" to understand the limitations of compiled lists. 

They convinced ten database compilers to participate and asked them to find 10 selected executives from 10 different industry verticals in their databases. It was a dip stick exercise to quickly measure the coverage and quality of leading databases. The vendors ranged from big data houses to online cooperatives. All have online ordering capabilities which generally means they are willing to deal in small lots and/or niche verticals.

Bottom line -- when they had your data it was pretty accurate, though coverage was spotty across databases.

In addition the study turned up a bunch of surprising results:

• There was a wide variance in company and contact counts. In one category -- stone, clay and glass products -- company counts ranged from 386 on one database to 36,382 on another.
• Email addresses were the hardest datapoint to find. Unfortunately these days its the datapoint most in demand.
• When a vendor had your record, there was a good chance of accuracy.
• But many vendors didn't have the 10 executives in their database. In the worst case only 2 of ten vendors could find Jim Carey of Northwestern University. Maybe its a subtle hint about academics.
• C level names seemed to be evident in more databases than lower ranking players. This could speak to the method of compilation. 

So what's a B2B marketer to do?  Bernice and Ruth recommend these steps.

1. Quiz the vendors on what they have and how they got it.

2. Don't assume subsidiaries of large compilers have the same data or use the same compilation or cleansing techniques. They don't. Ask.

3. Be very specific when placing list or database orders. Use SIC codes or other tools to keep 'em honest and to be sure you get what you thought you ordered.

4. Check for industry or vertical specialization. Test their coverage before you buy. Shop for the vendor who has the best data on your target audience.

5. Run a data append test before you buy to test coverage and accuracy and to compare multiple vendors. Build in some house names that you know for sure as an accuracy benchmark. Better yet buy a small number of names and verify the data yourself by phone before you place a bigger order.

There's about 12 million companies in the USA and evidently getting to someone in them still isn't easy as you think it might be. These kind of exercises help us understand the realities and limits of databases which in turn drive our thinking on how best to use the data we can get our hands on.

The Dark Side of Data Mining

James Bamford’s The Shadow Factory looks inside the National Security Agency and warns us of the dark side of data mining. The pre-eminent historian/reporter of the NSA and Sigint (signals intelligence) documents the Bush Administration’s total disregard for individual privacy and their machinations to finesse the Foreign Intelligence Surveillance Act (FISA) to spy on more people more often.

 

According to Bamford, former NSA Chief (now

CIA

chief) Michael Hayden dramatically expanded the interception and processing of phone calls, signals, e-mail, Internet transmissions and satellite feeds and made a critical transition from snooping over the airwaves to snooping on every major fiber optics cable. In doing so he amassed vast amounts of data that are difficult to store and process. So he outsourced the processing of the take and spawned a specialized new industry of software and hardware developers dedicated to mining vast amounts of data and connecting disparate databases to identify likely terrorist threats and map networks of potential terrorists and anyone they know or communicate with.

 

There’s plenty of secrecy but no subtlety in this game. Privacy advocates will be apoplectic by use of a lame blanket anti-terrorism justification for this activity and for the fluid exchange of key players and the likely conflict of interests between the secret agencies and the highly paid, often no-bid, contractors that service them. Almost as scary as the civil liberties implications are the secret bureaucratic machinations, usually rubber stamped by Congressional overseers, that loosed zillions for countless spying programs that have yielded damn little warning, insight or actionable intelligence. Evidently our spying is much less effective or impressive that it is in the movies.

 

However in spite of countless revelations, the tough issues are never resolved. We need to know what the bad guys are thinking and doing. And we need to keep the government from spying on us -- its citizens. We need to understand, anticipate and prevent terrorism without selling out the Constitution and becoming worse that the terrorists we seek to defeat. Bamford notes Obama’s flip-flop and capitulation on the issue but offers no concrete way to thread the needle moving forward.

 

As a data miner and data marketer, I understand how powerful these tools can be to reveal patterns, connections and behaviors. Connecting disparate databases and marrying them to secret intercepts, a program started, stopped and morphed under the direction of former Iran-Contra felon John Poindexter, has the potential to institutionalize a malevolent and voracious Big Brother.

 

Bamford’s right. We need much more light and my much scrutiny on this topic so that we get the data we need without suffering the down side of data mining.          

Trigger Follow-Up Messages Within 48 Hours To Maximize Prospect Yield

Every direct marketer makes offers. The difference between the successful direct marketers and the also-rans is follow-up. Fast, personal, on-point follow-up turns a random offer or a blind pitch into the beginnings of a meaningful sales conversation. More great campaigns have been killed by the lack of fast-acting follow-up than anything else.

The most effective marketers build follow-up communications into the initial plan.

Why?

Two reasons.

The Politics of Data

I got an invitation from Oracle to a webcast titled “The Politics of Data” and it occurred to me that they hit the nail squarely on the head -- politics have taken over the issues of data in organizations dependent on data quality to grow their businesses. That’s why it is so difficult for companies to get the data right and keep the data clean, even though there are many available technical solutions to this persistent problem.

This is a real problem that requires a a process-oriented solution. But in most cases it becomes subject to internecine politics which derail attempts to put all the data in one place, clean it, up date it and manage its use.

Consider the typical data landscape:

1. Data changes constantly. People move. They change titles, jobs, phone numbers, e-mail addresses, locations, responsibilities, etc regularly. On an annual basis 30% of b2b data goes bad and 15% of the b2c population moves their primary address. Add to that people and companies buy things, return them, get credit, pay late, need support, etc. So on top baseline data changes, transactional data changes are constant since relationships are fluid and dynamic.

2. Data is often held by different players. In a typical company bits of data are held in different departments. Legal has the contract details. Accounting has the credit application and the payment records. Marketing has contact names and addresses of customers and prospects and is constantly collecting more of each. Sales knows what they bought and who they sold it to. Customer service has names and details of issues raised, problems solved and technical matters.

Each department tends to hold onto the information they have. They share only when asked and usually in the context of a specific incident or problem. In big companies each department might be in a different location and/or have its own system of filing and its own software for storing the data. Meanwhile nobody has a complete view of the customer so opportunities to better engage and sell that customer are lost and opportunities to annoy the customer abound.

3. Data control means job security. Salespeople, managers and marketers think that if they know critical stuff about the business, they can’t be fired. So they hoard information as a hedge against the zigs and zags of corporate life. Since everyone knows information is power, there is a second incentive to gather and hide information -- to attack rivals or to defend yourself in on-going corporate battles.

In too many organizations today, many messy and changing details are dispersed among competing people and organizations, often in different places using different data formats or systems, each of whom have practical incentives not to cooperate. It is the picture of Dorian Gray.

Yet with top-down leadership (or if I were king) this state of affairs can be easily reversed. Consider this formula based on best practices …

CEO decrees all data will live in a single data warehouse. Sets negative incentives for non-compliance and a timetable for implementation.
CIO selects and implements an enterprise data software system. Creates and administers a secure access and permissioning regime. Trains everyone and creates positive and negative incentives for rapid compliance.
CMO appoints a data czar/czarina who owns data collection, aggregation, privacy and integrity functions and devices routine updates and data hygiene processes. Marketing also is charged with analyzing data and sharing relevant insights across all departments.
Administrative help is hired to insure salespeople input data and commission payments become dependent on data compliance.

Its almost that easy, if you can filter out the politics.