Analytically-driven marketing is not just about the data and the math. Successful firms of the future will be those that create an analytics culture for marketing that allows the human element to shine through. It’s as much a question of creating the right culture as it is the right data and algorithms.
I recently caught up with Dr. R. Sukumar, CEO of Optimal Strategix to discuss the future of analytics. Despite the overuse of terms such as “Big Data” being thrown around with looser and looser context and the onslaught of analytical solutions guaranteeing a solution to every marketing challenge, Dr. Sukumar sees the human element as ever-present: “There will always be a need for human interaction, particularly with new sources of data. And there will always be a need for human interpretation and application.” …Continue reading
Continuing from yesterday’s edition of Myth Busters (Big Data Edition), I now present you with myths 5-8. You can’t believe everything you hear…
Big Data Myth #5: Big data means that analysts become all-important
It is often said that Big Data will see the rise of the analysts, “the new gods of the Information Age”, as O recently heard someone call us. But the rise of the analytics team is exaggerated. The dramatic increase in data velocity means there’s no time to “brief the analytics team” now. Actually, what is required are tools to cope with velocity, volume and granularity of the data quickly. What’s really needed are analytics tools to empower marketers to do their own analyses. The intersection of technology and analytics means that Big Data is not about a shed full of analysts working away. It is all about a small group of master-analysts leveraging technology to empower marketers to do more of their own analytics and scenario-modeling and decision-support. …Continue reading
Although analyzing “big data” has the power to transform your business, the ease of doing so has been over-stated. In reality, harnessing big data is still a messy and labor-intensive business. As an analytics professional, I am incredibly excited by what we can do with data, but I think some of the hype is doing us a disservice, because it creates a false expectation of how easy this work is going to be. Most things in life that are important and worthwhile are difficult, and the analysis of Big Data is no different. The solution is to take small steps, get started now with analyzing data with very specific objectives, accept that this is still very much a manual model building process, and build a staircase of successive small projects that build steadily over time into a transformative program. To begin with, don’t believe these commonly heard myths…
Big Data Myth #1: It’s Big
Big data isn’t big. And not only is “Big Data” poor English, but it’s also misleading. What we’re talking about is a large volume of data points, updated at high-frequency, with short lag to the actual event (real or near real-time). It’s very granular. It’s individual transaction data; it’s a certain credit card, paying for a certain amount of gas, at a certain gas station. Big Data is actually lots and lots of very small data. It’s not a landslide of data, it’s a sand storm. And sandstorms can blind and disorientate you. The Bedouin said a sandstorm could drive a man mad in 6 minutes. So, to help see in the storm, what other myths do we need to debunk? …Continue reading
Big Data: Wow, what an over-used expression this past year. 2012 was the year of headlines such as “Barack Obama’s Big Data won the US election,” “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did” and “Big Data knows when you’re walking past a pharmacy and texts you a coupon.” Blah, blah, blah.
The application of analytics and mining data is indeed part of a very powerful revolution, but it’s only part of the story. Analytics can tell you a lot of about the who, the what and the when – but what happened to the why? We need to combine analytics with primary research to understand more. This combination of big data analytics and primary research is the solution behind marketing analytics 2.0. …Continue reading
At some point over the past two and half years, most of us have handed our credit card over to some proprietor of a small business – whether to a cab driver ferrying you to the airport, or a food truck vendor down the block from the office – to have have them swipe it through a small card reader attached to their smartphone or tablet.
For the past few years, products from Square, VeriPhone and Intuit (GoPayment) have been offering something basic but previously inaccesible to many small and micro business owners: the ability to accept credit cards. When launched, these offerings were going up against giant financial institutions and forced to compete for customers who were thought of as “undesirable” and therefore traditionally ignored by banks – those too small and with too little or poor credit history to get a merchant account. Now that they’ve established themselves and are handling billions of dollars a year in transcations, the question is: What’s next?
For Square in particular, there are two big things on the horizon that could be bigger game changers than the initial offering: …Continue reading
For organizations seeking to create sustained business performance improvement, managing and improving customer experience is generally considered essential. Commonly, many companies track and manage customer experience using “satisfaction” surveys. Such surveys can be linked to commercial performance, and sophisticated analytics can optimize investment in customer touch-points to create improvements in prospect conversion, revenue and reduce churn. Detailed research and analytics can be specifically tailored to address business issues that create remedy plans for problems such as poor conversion at the first touch, the need for more up-sell, etc.
However, over the last decade, Net Promoter Score® (or “NPS”) has gained popularity as a simple alternative, thanks to the work of the author Fred Reichheld, Bain & Company (a consultancy), and Satmetrix (a software vendor).
NPS® is founded on a single question: “On a scale of zero to 10, where zero is extremely unlikely and 10 is extremely likely, how likely is it that you would recommend Brand X to a friend?” Respondents are then categorized into one of three groups based on their responses. Those who scored a 9 or 10 are termed “promoters,” those who gave 7 or 8 are “passives” and those scoring 6 or less are “detractors.” The percentage of respondents that fall in to each of these categories is calculated and the NPS® is found by subtracting the percentage of detractors from the percentage of promoters.
But why has this simple question been adopted so readily at the expense of more traditional satisfaction-based series? …Continue reading
I was driving in circles yesterday morning looking for a WiFi signal (feeling quite untethered and naked in the world) when I pulled up next to an Iron Mountain van. It occurred to me that Iron Mountain is a great example of sustaining innovation. It’s not as sexy as the transformational innovation people think of when considering Apple and Google but, according to HBR, 70% of all innovation is sustaining rather than adjacent or transformational.
We’ve all heard the stories about companies that failed to innovate as the world changed under their feet (who’s been to a Blockbuster lately?). This is the story of a company that understood its core value proposition rather than defining itself by its products. Because it was focused on its value proposition, it evolved with the market and has become a leader in its space.
Iron Mountain’s story is not well known story – but it is a true story. A guy buys a defunct mine to grow mushrooms on the land. The mushroom business declines, and he decides to use the mine for its next best purpose – as an atomic bomb shelter. Rather than shelter people he decides to use it protect information, which in the 1960s was all on paper and microfilm.
The company has evolved from a single mine purposed to protect paper and microfilm to an international company focused on electronic data. But it’s core value proposition has remained the same, regardless of how they fulfill it. This is the secret to their success. As the world evolved toward electronic data, Iron Mountain evolved by innovating new services to sustain its core value proposition. The founder is rumored to have once said, “this business will mushroom.” He was right. Thanks to unsexy sustaining innovation, Iron Mountain is now a $3B company, with 139,000 customers in 139 countries…including 95% of the Fortune 100.
People love hidden innovation stories. So think about using Iron Mountain the next time you need an innovation example. I guarantee your audience will prefer to hear about the story of a $3B mushroom farmer rather than another story about how Apple created the iPhone 5.
photo credit: Marisa | Food in Jars via photopin cc
I was recently shopping on the Lands’ End website for a new tote bag to lug all our kids swim gear in. The next day, while checking the news I got a large, somewhat obtrusive pop-up advertisement reminding/encouraging me to buy this tote.
This isn’t the first time it’s happened, and in fact it’s happening with increasing frequency – I shop for shoes on Zappos and then see “recommendations” and pop-up ads for similar shoes on other different websites. I know that marketers love the power of digital because it allows them to be even more targeted. However, this ad was a great reminder of how lots of different entities are tracking every move you make.
To me, it feels a bit creepy to have advertisements follow you from a targeted shopping experience on Amazon to, say, the experience reading an article on the Huffington Post.
Furthermore, Microsoft Kinect recently applied for a patent to serve up ads based on your emotions, using the Kinect sensors to analyze your facial expressions and body language while playing. I don’t know about you, but my friend always looks grumpy and angry when he is playing video games – I wonder what kind of ads he will get?
Where is the line between creepy and convenient? For more on how your digital shadow is helping companies track you online, click here. For more on Microsoft’s plans for sensory advertisements, click here.
TNS has analyzed the way in which consumers “eat” data and created five consumer segments based on their readiness to absorb data:
- Fast Foodies consume the easiest, lightest data they can find
- Supplementers devour as much information as they can
- Carnivores consume only meaty chunks – whole books and in-depth research
- Fussy eaters are loath to consume information from any source
- Balanced dieters never consume too much information; what they do take comes from a variety of sources
According to TNS, these ‘eating plans’ are a good way for marketers to target resistant consumers: by understanding the predominant “eating plans” brand managers and CMOs have a tool for maximising the reach, resonance and values of their campaigns.