Ah, Summer. A time of great joy and great dread. Lounging in this beach chair, thoughts inevitably drift back to that upcoming client presentation, and I begin to think about ways to make data more interesting. Here is but one imaginary scenario. Hey, it’s not perfect, but I’m on vacation. Let your own imagination transport you now, and join me in a stuffy, crowded boardroom, where I lead the discussion…
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“Ahem. In the interest of brevity, I decided to dispense with our usual monthly updated graphs and charts, and proceed right to the Q&A, which we have found far more engaging and interesting for immediately applying our findings. Let us begin.
As you can see, the title of this session is: “Summer, a time of great joy and great dread”. Any questions so far?”
Q: Why great Joy?
A: Because we eat more ice cream cones at the beach in summer.
Q: Why great dread?
A: Because of the increased frequency of shark attacks which, interestingly, also happen at the beach in the summer.
Q: Are you suggesting it is more dangerous to wade into shark-infested waters while holding an ice cream cone?
A: No; as your resident data geek, I should quickly point out that this coincidence in no way suggests that the sharks and ice cream vendors have engaged in any sort of collusion. Next question, please.
Q: This makes me wonder…where did that shark data come from? Did we rent it? Are sharks covered by data privacy laws?
A: As a matter of policy, we at Fan Foundry are recommending to all our clients that you cover sharks under your data privacy policies. If, like me, you like to swim in the ocean, you don’t want to even accidentally piss off any sharks. Ignorance is not an excuse.
Thank you for your time and attention. Let’s adjourn. Surf’s up.
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Sorry: unlike most of our really useful posts, there are no resource links at the end of this post. That’s because we did no research. Remember, we’re on vacation. If you have a goofy data analytics vignette to share, kindly use Twitter hashtag #summerofdata2018. Or, comment below; we’ll gladly share it out. Happy summer!
Thanks to a rich online experience, buyers indeed have greater purchase influence these days, but where does the true power reside? It’s shared, really.
Marketers have made much of this “empowered customer” phenomenon. Online, you can research and get close to a buying decision – right down to vendor, product, price and feature selection – before the seller even becomes aware of your existence.
Salesreps, just a scant decade ago, guided purchases with probing inquiries about interest, budget and other decision factors. About 2/3 of buying and selling decisions today are salesrep-facilitated, but a full 1/3 of buying and selling is of the buyer-driven, “salesrep-lite” variety. We can expect to see considerable rebalancing from time to time, thanks to (a) recent advances in mobile digital profiling ; (b) a coming wave of marketing technology mergers, acquisitions and partnerships, and (c) a currently proposed standard for profile data interchange currently before the Worldwide Web Consortium – the W3C.
Profiling – It Really Is All About You
Today on the internet, so the updated joke goes, if you’re a dog everyone knows it – as well as your breed, age, gender and preferred kibble brand. Today, your online behavior – actions such as clicking “Like” buttons on Facebook pages you visit, for instance – helps marketers (interpretive algorithms, really) make inferences about your identity including gender, age, political and social tendencies, then use that info to tailor your online experience so you see ads and content that cater to your digitally harvested “buyer persona”. That preferences profile of you is continually enriched and refreshed based on your online and mobile behavior patterns.
Stated differently, “free” isn’t really “free”. It never really was. When you surf the web, you reveal (“lend”) bits of your identity to savvy marketers who trace your online behavior patterns to compile that rich profile of you that can be then used to tailor your online experience in such a way that your satisfaction from the online experience is improved and, of course, increases the likelihood you will buy from them.
Emerging Standards
Recently, a consortium of retail and insurance companies including Adobe, Google and BestBuy have proposed to the World Wide Web Consortium (W3C) a set of standards for commerce data interchange that would make it easier for us all to do business online. Merchants, health care providers, finance firms, and consumers all stand to gain from this.
Who Goes There?
As long as you consent and your privacy is protected, all is well. Increasingly we have come to trust certain online identity repositories curated by the likes of Google Wallet, Amazon, LinkedIn, Twitter etc. In the broader commerce world, however, small and midsized organizations have not built, bought or hired the depth of technical ability to make sense of all that data, let alone apply it to their business or curate it responsibly. The above-mentioned W3C Web data acquisition standard could really democratize things.
Leveling the commerce field
Larger organizations may seem more capable, but that isn’t always the case; they typically are running legacy apps (archaic programming and hardware) whose code is tough to maintain, let alone modify to take advantage of the proposed newer standards. Fortunately, companies like AppDirect, Apigee and Nexaweb Technologies – experts at modernizing all those legacy apps for large financial, trading, shipping and consumer facing companies – are hard at work on the challenge. (Disclosure: I own a smidgin of stock in Nexaweb).
We buyers can tell who is “with it” and who isn’t, based on whether the ads that get served to us, or our repeat visits to favorite sites, are tailored based on our browsing behavior or our location. For example, I recently visited a jewelry website, after which my visits to other websites, including Google Search, became peppered with jewelry, wedding and dating ads. With the recent accelerating consolidation among solution providers in the marketing automation, sales CRM, email marketing and web analytics space, those web commerce architecture elements are becoming knit more tightly. Expect the next few years to bring an expansion of already existing analytics, buyer profiling and content tailoring solutions, more broadly affordable to midmarket and smaller enterprises with whom you regularly do business.
Do the benefits outweigh the risks?
If you consider the ability to track user behavior narrowly through the buyer / seller lens, Consider the implications. Will buyers’ online preference profiles tailor each netizen’s digital experience so greatly that the reinforcing effect of a profile-driven, tailored on-the-fly web experience merely helps bring relevant online information conveniently into sharper focus, to your benefit? Or, could the online experience become so digitally mutated by profile-driven content tailoring that its “echo chamber” effect distorts your online experience in ways that prevent you from viewing alternative information to consider broader options and render well-informed decisions? Will the rich have a different web experience than the not-so-rich, based on their profiles, harvested data, and access to speed? In other words, how much is too much?
If you broaden your focus beyond commerce and consider the ability to track population behavior to detect and help resolve anything from traffic congestion (like, say, Waze) to disease spread, then the benefits become more clear.
Shut it down if you want to
Do you know how to “shut down” your behavioral profile and surf the Web anonymously to obtain a more random, unfiltered experience? It’s possible, you know, without a lot of geekery. Tools abound, such as Google’s InPrivate Browsing feature and other tools that let you assume a random IP address (Google that boldface phrase to see some options) when surfing. Your mobile experience can also be made private if you know how to turn off geo-location, but you’re still registered on a network when your phone is on.
This delicate balance of individual privacy, public disclosure, information gathering and sharing between big firms, security agencies and other firms is now being played out in the world headlines. The NSA and other entities regularly approach Google, Facebook and Microsoft, as well as telecommunications companies, to obtain customer activity information for the purposes of national security and law enforcement.
Our Best Behavior
If we marketers hew to the goal of providing a more useful, satisfying experience to you while keeping your privacy sacrosanct, that’s all to the good. As tools become more broadly available and powerful to enable deep customer profiling and tailored online experience, you may come to expect a more gratifying relationship with your favorite brands. After all, consumers already have heightened expectations. They don’t want every interaction with the same business to feel like the first date.
How do you feel about the coming boom in digital profiling and data exchange? Comments welcome here.
In preparing case studies for my talk titled “Be a Big Data Voodoo Daddy” at Boston’s October 2012 FutureM conference, I noticed that almost half of our 40+ client projects over the recent years had to first devolve from “Implementation” projects to “Readiness” projects – equally valuable, and absolutely necessary. How’s yours going?
Is your marketing automation, CRM, analytics, email marketing or other automation project going to deliver your desired payback? Here are my top 3 warning signs that it may take longer to pay off than you think.
Stated differently, here are 3 must-do’s to ensure near-term ROI.
1. The Right Stuff (Value based Goals).
Let’s first assume that you’ve already connected with the concept of Marketing as Moneyball. Still, you may find that you are not gathering useful, relevant data to help you accomplish your stated strategic goal and implement the right CRM or analytics solution. This may stem from having broad, imprecise goals. For example:
“Grow revenue” is a great goal, but the paths are varied and nuanced.
“Increase Partner Channel Revenue” is, well, getting warm.
“Double Partner Channel Service Contract Revenue” is more like it. Now you have a specific channel, identified players, and a specific product/service element attached to a numeric goal. Specific, measurable goals and then measuring the right things are both essential elements if you are to to yield any meaningful analysis to motivate and support change. No matter how efficiently you automate the wrong data, you risk stretching out the time horizon for any meaningful payback or, worse, running in multiple or wrong directions and wasting effort. Strategy comes first.
2.) The Stuff, Right (Data Analysis and Process Maps).
Typically, your data is not homogeneous and some necessary processes don’t exist yet. Data often exists in a variety of formats ranging from locked spreadhseets and various departmental databases to unstructured documents, such as paragraph text and visuals. Processes that don’t yet exist can’t be mapped to a system; you can’t automate a vacuum.
Significant effort is involved in standardizing and preparing data for upload into your new automated solution, as well as selecting the right tools to enable you to access and mine insights from unstructured information. At Fan Foundry, we are familiar with an array of powerful tools, and can develop custom, reusable upload frameworks to help clients address current and future needs for unstructured data.
This is where the scope of a project almost always expands, as additional valuable information repositories become included, because we often discover additional insights using all available data that just would not be possible otherwise. You never know where the breakthrough “aha” discoveries may lie. If you don’t have the luxury to expand your analysis, though, then rigorously insist on only analyzing the most salient data.
3) The Players (People).
The talent shortage is legendary. If you are inadequately staffed or trained to assume the role of data manager, analyst and strategist, or transformational leader, let alone carry on administratively after implementation, you shouldn’t start the project. The time to assign roles is up front. Get any necessary talent aligned first so they can be involved in the project. Some of your team can adapt; sometimes you need to extend your team to include a capable partner. The single most effective way to stretch out the payoff time horizon is to not involve its eventual owners and primary users, or not have the stomach to lead a transformation effort. Be prepared to change, or else don’t start.
The full list of must-do’s is extensive, but if you tend to these three first, most of the rest will fall in line, and you’ll enjoy a successful implementation.
Toward a “Measurement Culture”
You’ll know you are succeeding when you have established a “culture of measurement” in which the right things get measured, the data supports meaningful analysis, all meaningful data is reflected in a single, integrated, centrally accessible “record of truth”, and you are using the insights you have gained to achieve transformations like improve margins, speed to market, pricing accuracy, supply chain efficiency, sales growth, and other incremental and transformational improvements.
Finally, it must be stressed that human judgment is not taking a back seat to data. Interpreting analytics in light of pragmatic experience and using that knowledge to take calculated risks is a hallmark of success.
A recent IBM survey found that over half of business leaders today realize they don’t have access to the insights they need to do their jobs. They just aren’t harnessing the data.
Today, surrounded by sensors, apps and systems, we have ability to generate and store data cheaply like never before. Ironically, as the data piles up, the ranks of organizations able to process it is declining, and the talent shortage for managing it is increasing.
The ability to harness and leverage data is now a big competitive advantage. This doesn’t mean that experience, judgment and intuition are less relevant; on the contrary, you need those attributes to evaluate data.
You do not need a million dollar budget or a Ph.D.in Computer Science to be a Big Data Voodoo Daddy – one who knows where the data is, can harness it, and mines it daily to delight customers and boost revenue. Devise a basic plan or framework, select a few tools, apply them to your most important data, and you’re started. Just be sure you’re improving on existing data and processes; you can’t automate a vacuum.
Basic Framework
Ask: Where is your data? What does it look like? It generally has 3 characteristics:
Volume – nobody is scared anymore by the prefix “giga” as in gigabytes (GB). The average hard drive stores hundreds of GB. So, the average U.S. household has a place for it. Of course, there is always the cloud computing and storage option, as long as you don’t mind internet and power outages bringing your business to its knees.
Variety – here it gets a little hairy. Data is everywhere, but not organized. Some of it lies dormant in locked spreadsheets; some of it is in silos, like Point of Sale receipt reports, accounting ledgers, and customer records. Some of it is “unstructured” i.e. not contained in nice neat rows and columns, but rather in text records and notes. You need tools to organize and standardize data so it will fit nicely into your main database. This is tedious and messy, but worth doing. Fortunately, most major software providers understand this too and have enabled their products to interoperate with one another.
Velocity – some data is historic; some is collected periodically and batched; some data is streaming live (example: your location in a GPS app, your favorite mobile shopping app). Does live streaming data matter to you? It should if your customers are mobile, and these days who isn’t?
Basic Tools
You need 3 things to harness the volume, variety and velocity of your data:
a database / storage place (love that cloud storage option!)
software to help you access, manage, report, cleanse, update, analyze and act on it;
helper apps to get help standardize data from multiple “feeds” so they can enter your main storage place.
While some of this may sound geeky, be assured that the tooling and resources are becoming more usable. For some large organizations, however, expert talent and technology remain the best option.
One Big challenge Remains
The single biggest challenge of data these days is its quality. In that huge haystack of data, there are some gems and usually a bunch of stale (call it historical) data. Stale and historical data are okay, and even valuable for trend spotting and progress reporting. You can easily take steps to update it via customer surveys, sorting, web / email response forms, etc. Most people and related data sources will happily keep their info in your database current if it helps you stay relevant to one another. Just be sure to keep that process easy, and clearly spell out the benefits. Consider sharing your data discoveries with them, too – at least to the extent that you do not infringe on people’s privacy.
Best Uses for Big Data
According to McKinsey Global Institute, big data has five broad opportunity areas: increased transparency and use, improved performance management, better decisions, greater precision in meeting customer demand, and more targeted R&D. If you are in Sales and Marketing management, web and social media analytics, financial reporting, call center reporting, fraud and security detection, energy usage, safety management, risk and opportunity management, inventory, assets, logistics, agriculture or health care, you have the opportunity to excel and lead in your marketplace by leveraging available data.
You must use these powers only to do good. Concerns about privacy, access, security, intellectual property rights and liability must be factored into our thinking, policies and practice concerning the use of data.
Got data challenges? Drop a comment. I’ve listed a few helpful resources below.
Cheers, ~Ed
Additional Resources (see sidebar on this site for tool ideas)
Pluris Intro (Pluris Marketing) – OCDP (omni-channel dynamic profiling) so big orgs can treat people as individuals
Aryng (“A-ring”) – Analytics help, e-zine, training etc. for big data people
“Ahem. In the interest of brevity, I decided to dispense with our usual monthly updated graphs and charts, and proceed right to the Q&A, which we have found far more engaging and interesting for immediately applying our findings. Let us begin.
