DiG is looking to “conscript” someone to join our DiG Tactical Unit, a team of analytics and data professionals working to help our clients derive insights from their data.

For more information on how to apply, check out the full Job Posting .

And to learn more about the DiG Tactical Unit read last month’s blog post .

The three most common options -  hiring FTE, contract workers and external suppliers – all come with pros and cons:

• Hiring –  Obviously hiring your own analytics workers keeps skills in-house and close to the business; workers develop a strong understanding of and connections to the business.    The biggest challenge is in finding  and keeping the right skill set – and providing them with the right mentorship and growth opportunities.
• Contractors:  The benefit of contract employees is that they don’t impact FTE count and you are not committed to these skills in the long-term; as your needs change, so can the resources.   By their nature, however, these workers are transient and any business learning they’ve developed is be lost when an individual’s contract expires.
• Suppliers – Suppliers come in all shapes and sizes.    Off-shore teams can be found with very attractive cost-structures (working in India or China, for example); these relationships are put to the test however as it is difficult to communicate exact requirements (especially if you are not an analyst yourself , and when there are language and time-zone barriers).     Local suppliers can provide specialized skills like predictive modeling and advanced data mining;  this is especially attractive when your needs for a statistician ebb and flow so you are not paying for someone full-time with skills you only need part-time.    Most good suppliers are able to bring experience from other clients to bear on your projects which can be a great advantage; however, having an arms-length relationship with the company means they often miss critical nuances and details of the data and the business.    In addition, the knowledge and learning about both analytics and your business stays with the supplier.

At DiG we’re pioneering another approach.  Our DiG Tactical Unit^TM is a hybrid model that brings a team of resources to work alongside our clients – as an extension of their internal team;  the Unit includes senior thought leadership and planning, embedded analysts working inside client teams to complement their existing resources, and analytic specialists (S.W.A.T. Teams) for key projects.   This approach provides our clients with an analytic partner, not a supplier or a contractor.  It provides flexibility, experience and hands-on knowledge transfer  and mentorship – and has been particularly successful for clients who are building or expanding their analytic capabilities.

There is no right answer for all companies; however, companies should ask themselves some key questions in addition to all the usual FTE ones:

1. Does my company have other workers for this worker to learn from?  Organizations building this competency often find that sole analysts struggle with finding mentors, growing their knowledge and seeing a career path.
2. Will the analyst be an “island of one”?   Despite its reputation, analytics is rarely a solo pursuit; the best work comes from the collaboration between skilled workers who share past experiences and new ideas.
3. Where are we on our analytic roadmap?  Are the skills we need now the skills for the long term?     Or do we need skills now (perhaps data manipulation skills or advanced statistical skills) that we won’t need once our project is up and running?
4. Do you know what you are looking for?   If analytics is a new area, it can be very difficult for HR and hiring managers to really understand the competencies required.  Mistakes can be costly and all analysts are definitely not created equal.

We’d love to hear what has worked in your organization?   Who does your analytics today?   What works? What doesn’t?  Have your needs evolved over time?

The author of a recent article on the late Nora Ephron in Forbes Magazine recently described another scene where she:

….saw the divine Ms. Ephron dump out the contents of her purse (on purpose) at a conference in New York City, and the mish mash of random objects that fell out caused the audience to howl with knowing laughter. …. Her message to women that day, and on most days, seemed to be: I know you better than you know yourself. I know you look like a million bucks to passers by, but inside that nice purse of yours is a mess of empty gum wrappers, loose change, a fuzzy aspirin, three-year-old receipts and hair balls.

Nora Ephron knew what was inside your purse. And data-driven marketers know what what’s inside your purse too (or your backpack, briefcase etc.). Once we’ve looked inside enough purses of “people like you”, we too can tell what’s inside yours with reasonable accuracy.

But that doesn’t have to be ‘invasive’ or ‘scary’; when Ms. Ephron did it, it made everyone laugh and nod with recognition. Well targeted marketing messages should also create that nod – how did they know I needed that? What a great offer? Where do I sign up?

So much attention is paid to the fear that marketers know too much – I admit, despite my profession, I get sucked in sometimes too. But a marketer knowing that you typically shop in women’s fashion and have never bought from the children’s department, so that they send you a catalog with only women’s clothes isn’t such a bad thing, is it? Them knowing that your purse likely has loose change and gum wrappers – or that people of your demographic profile typically own their own homes – is just smart, like Nora Ephron, not invasive or threatening.

Ms Ephron turned her own purse out – she didn’t embarrass someone in the audience by revealing her birth control pills or secret stash of vodka. She didn’t really want to know what was in everyone’s purses, she knew what was likely in most people’s purses. She wanted to know what would make the most people laugh.
She didn’t need to know if you ‘fake it’, only that most people have at one time or another!

That’s what we do….we look for patterns in data, evaluate the likelihood that, if you have purchased these three things, you’ll buy the fourth. We don’t really want to know what you buy – only what those purchases tell us about what you might buy next so that we can be the one to offer it to you.

As data-driven marketers, we need to remember this. When we use information, it should be to provide a better customer experience, to make a relevant offer , or to avoid sending someone an offer for something they are not eligible for. We should not use information to look smart, to embarrass anyone, or for nefarious intent. Perhaps marketing isn’t quite as much fun as Sally had, but we can still surprise and delight our audiences!

RIP to a great data-driven marketer! That scene still makes me laugh!

While these models do a fantastic job of helping companies figure out who to target with their churn offers and tactics, they don’t answer all the questions. Most organizations think of those of “low risk” as “loyal” customers – likely to stick around and therefore of little consequence to their churn strategies.

I argue that the opposite of churn is NOT loyalty. The opposite of churn may really be inertia ….customers don’t leave because it is difficult to or takes some effort on their part. Have you ever tried changing credit card companies and had to remember all the automatic payments you had associated with your card and all the places where your credit card is stored for you? Do your investments automatically renew when they come due?

However, inertia works both ways; the inert may not churn but they are also unlikely to purchase new services, upgrade their products and increase their value.

An “engaged customer”, on the other hand, is likely receptive to your marketing advances; he is invested in his decision to buy your product and interested in hearing more from you. Engagement means receptiveness to your messages – both interest and opportunity. He is interested so he opens your emails, takes your calls, follows you in social media.

Few people walk down the aisle without a period of engagement first.

We’ve worked with a number of clients recently to help them identify these engaged customers and test this as a metric for targeting. This metric can also help you assess whether your marketing efforts are working – moving the customers closer to further commitment.

Interested in understanding customer-level engagement? Give us a call or join us next week at the Predictive Analytics Toronto.

Strong SQL, SAS and Excel skills are a must…as strong communication skills, a can-do attitude and passion for data!

For more details, go to the posting (or click on the JOBS link above).

Thanks for any recommendations you can provide.

So is Direct Mail, like the payphone, a scarcely-sighted relic from a previous era? Or are there times when your cell phone breaks or you don’t want the call traced, that the old payphone is still very useful!

Last month, I was asked to speak at the Toronto Chapter Meeting of NAMMU (National Association of Major Mail Users – yes there are some!); the topic that I and my fellow panelists were asked to discuss was the Outlook for Mail in 2012. The question got me thinking.

There is no question that direct mail is not what it once was; my mailbox is much emptier these days than in the heyday of direct mail – when every credit card company was sending daily entreaties for my business. But smart direct marketers are replacing that quantity with quality – carefully choosing when to use the mail and who to send it to.

I got a piece of mail last week that illustrates this.

Starbucks Canada recently launched a rewards program. They have been tracking my purchases on my prepaid card for the past little while and recently sent me an email telling me that “EMMA, you’ve made it to the Gold Level! Now let the free drinks begin.” Great gimmick, which I, as they’d hoped, tweeted about!

But while the email was nice, receiving a card in the mail with a shiny GOLD card was much more exciting. Borrowing from American Express’ “Member since” , my card is embossed with my name and “Cardholder Since” date. Definitely the most exciting communication I received this week, in any channel (OK, I admit it, I’m a Starbucks addict!).

Putting that card in my hand makes me feel part of the club. Now, as a data expert, I am well aware that Starbucks could have activated my GOLD membership on my existing card, sent me an email, and saved the postage and printing costs of this package. But sending me the personalized (very shiny) card makes me feel important in the way that no email ever could.

In addition, it has the added benefit that every time I pull it out in a Starbucks store, other people want one too. I had one friend get quite indignant that she hadn’t got one despite being a regular visitor.

By delivering it by mail, they have enabled me to be an offline advocate in their retail locations – and to ensure others aspire to the same status! I may have tweeted once but I pull that card out at least once a day (don’t tell my husband!).

The most common argument for why we don’t use direct mail is that it is too expensive. While there is no question the cost of my GOLD card is more than the cost of an email communication, ROI is about return not just investment.

I hope that Starbucks is tracking the return on sending me that card.

They targeted me by looking at my spending patterns over the past few months. Will I spend more? Will I come back more often? Will I use the free drink coupons to try a more expensive drink – and will I keep on buying that drink going forward? Hopefully they have a control group who only got the email for comparison purposes.
Analytics can help marketers evaluate the return on different channels – including mail – as well as target their messages more appropriately.

What do you think? Is there a role for direct mail in 2012? What about in 2020?

Liban Ali Yusuf, a chemical engineering student at the University of Waterloo, has explored “the idea of statistically analyzing music lyrics and gaining insight into music with numbers.”

He’d be following the discourse in sports (think Moneyball and SabreMetrics) and the fact that statistics are being used frequently to evaluate players and describe the game. Yet, he found no such objective measures existed in the world of music; people’s opinions were rarely backed up with facts.

His algorithms include measuring such concepts as rhyme density, line length variability and syllables per word – and something that looks at how narcissistic the artist is, the “I to you ratio”.   He examines a variety of hip hop and rap artists through this lens. Unlike the batting averages of baseball players, these statistics are not used so much to rate artists, as to understand the different genres.

While I suspect the exercise is more a labour of love – and perhaps a fun way to get through his university thesis – there are real-world applications of his work. Yusuf cites two rappers, MF Doom and Notorious B.I.G., with very different styles. He examined the Rhyme Density™ [(Total number of syllables that are a part of a rhyme) ÷ (Total syllables)] in songs by these two artists; MF Doom was found to have one the highest rhyme densities of all the artists he studied. Notorious B.I.G. was far less “efficient” in his use of rhymes – “an efficient rapper uses all parts of a line in his rhymes”.

Notorious B.I.G. however has had far more commercial success than MF Doom. He suggests that record producers can look at measures like these to determine whether young up-and-coming artists were more similar in style to one of these artists or the other?  Perhaps they’ll be more willing to throw more marketing money at the act that they feel has a greater chance of being more commercially viable.
These statistics could also be used (and may already be) to power recommendation engines. If you like MF Doom you are perhaps more likely to like Eminem (also has high Rhyme Density™) than Notorious B.I.G.

This is really just a very specialized form of text mining – customized to the idiosyncrasies of music, rather than the call centre or social media space.

And naturally it was only a matter of time before ‘analyzing rap lyrics’ turned into using analysis to create them; on his blog he refers to his Drag-Drop rhymer. Using the tool you can create ‘rhymes’ that leverage the work he has done. I wonder when we’ll see the first commercial success generated by an algorithm. For the sake of art, I hope it’s not for a long time!!

More on the topic:
The interview: http://www.cbc.ca/spark/2011/02/spark-138-february-20-23-2011/
More on Rap Metrics™:
http://rapgenius.com/posts/Introducing-rapmetricstm-the-birth-of-statistical-analysis-of-rap-lyrics
Other examples of music lyric analysis:
http://www.nytimes.com/2011/04/26/science/26tier.html?_r=1

Those of us who work with data sometimes have to be reminded that we cannot always take data on blind faith. Data can sometimes mask the truth or intentionally mislead. But, it’s NOT the data’s fault. The way we collect data is just one of many reasons why the numbers sometimes lie.

I was reminded of this fact this morning at my local Starbucks. A difficult customer was before me in line; I’d seen her there before – you know who I mean, the one asking when the prices had increased (they hadn’t) and saying that she’d meant to order soy milk in her latte (she hadn’t) all the while looking disdainfully at the Barista’s spacers in his ears!

When it was my turn, I ordered my Grande Bold with a smile and was handed a slip of paper and told “you have been selected to answer the survey; go to the website and tell us about your experience today and you’ll get a free drink.”

What a lucky break for the Barista that it was me who got the survey, not the complainer before me. Luck or not? Strangely, my ‘survey’ did not come out of the till with my receipt but was already printed and on the cash register. Hmmm… perhaps this survey was intended for the previous customer, but the Barista overrode the random selection in favour of someone who might give a more glowing review? Maybe I’m a cynic (I can be, sometimes!), but maybe getting those good reviews is directly correlated to the Barista’s performance reviews.

I remember another case where post campaign analysis indicated an unexplainable very large response group in New Brunswick. It was only after many questions that we discovered the reps in the call centre were using their own postal codes in a mandatory field on the database!

Careful planning and training – along with a clear understanding from everyone, especially the front line – of the reason for and value of the data can avoid many of these problems. Put on your cynical hat, understand the motivations on the frontlines and make sure they understand your motivations. The Barista may simply have wanted to “reward” a good customer with a free drink – rather than taint the survey results.

I bet you have a few stories of your own of when data “went wrong” due to something that happened in the field. We welcome yours here!

We struggled with renaming our company for some time; Emma Warrillow & Associates Inc. was unwieldy and didn’t reflect the team that really does the work! And we realize that the work we do is more than just data mining, more than just consulting, more than just analytics and reporting…

At the end of the day, we realize that we are, first and foremost, all about:

Data – helping our customers gather and synthesize the data they have about their customers; making connections between the sources, getting it into the hands of those who need it, interpreting it…
Insight – making sense of what the data is saying about our clients’ customers; whether through predictive analytics, reporting, post-campaign analysis or profit calculations, it’s about telling the customer story and using it to change the business…
Group – a team of professionals who work as an extension of both the technical and business folks in a business, bridging the gaps between them so that we can solve key business problems…

So we became Data Insight Group, better known as DiG. We hope through this blog you’ll continue to get to know us a little better.

We welcome your comments and feedback!