Storytelling with Data is becoming much more common today because of both vast amounts of data being available in the public space & also the emergence of a newer breed of younger, more “social” professionals who consume such data with far more ease!
But is the corporate world looking at data like this? Are analytics teams creating powerful data visualizations to tell their stories! Can they afford not to?
At Cequity, our model is unique because it tries to integrate very contrasting dimensions into one entity where the sum is larger than the parts! Having a designer’s sense with data may contrast with a statistician’s dry look at numbers! We seek “intersection” skills-intersection of Creative, technology, data & business! Not easy to do with highly talented people & we are attempting it!
The interesting thing is that journalism is getting far savvier with data! I see visual data based story telling in the New York Times that is absolutely mind boggling. Even here in India, I see some lovely data visualization in the Mint!
But are analysts getting creative with their story telling? Visualization of data is getting democratized & it is not very difficult for analysts to be creative about this. Today we as consumers are getting far savvier about technology in our personal lives & that will impact our expectations at the work place. I am sure that savvy consumers will make data presentation so much more fun even within “enterprises”.
Turning data into understandable, graphical communication is a difficult task but today you have a plethora of free tools that you can use! . Visual.ly is building some of these interesting tools. I tried to use my creativity with Visual.ly & looked at my twitter behaviour & compare it to my favourite Bollywood film actress-Gul Panag! It took me two minutes to produce this Infographic! Imagine if we presented data in interesting ways at work!
Here is something wonderful from Stanford from GEOFF McGHEE. He is an online journalist specializing in multimedia and information graphics.
Journalists are coping with the rising information flood by borrowing data visualization techniques from computer scientists, researchers and artists. Some newsrooms are already beginning to retool their staffs and systems to prepare for a future in which data becomes a medium. But how do we communicate with data, how can traditional narratives be fused with sophisticated, interactive information displays?
Can a company Market so effectively, that customers actually perceive it as a service..."Marketing as a Service"? Today when consumers get innundated with so many junk messages, wouldn't this be way too aspirational!
Consumers are leaving their "data" behind as footprints for companies to unravel not at "leisure" but in "real time". When I buy a high value item at a jeweler using my credit card & my bank calls me,just to check if it is really me !! That's an example of data equity driving brand equity at that point in time! There are many more opportunities like these for myriad set of businesses! And this intersection of the "correct data insight" with "marketing action" transforms marketing into a service! Analytics captures this but at a later time, so the challenge is to respond to the customer at the "time" which they are "ready" to "recieve" your mesage! Banks & Telecom companies are so well positioned to do this!
Can Marketers read that data so effectively that they reach out to customers just when they are ready! And isn't that a service!! But to do this effectively you need the coming together of IT, Marketing & Customer service!
I spoke at an INSEAD forum a while back & shared some thoughts on this. You can download the presentation at the link below
It’s quite amazing that companies who have the most data about you as the consumer find it particularly hard to create “Loyalty” basis that information.
Banks have truckloads of data! But banking is incredibly silo driven! This is coupled with the difficulty that consumers have in “switching” a banking relationship! So how do you leverage this data to be able to make a compelling proposition across the bank, is possibly a million dollar question!
I was looking at some data from the US & found the Temkin Loyalty Ratings. They are based on consumer feedback of companies with whom they’ve recently interacted. They asked consumers to rate three elements of their loyalty—willingness to buy more, reluctance to switch business away, and likelihood to recommend—on a 7-point scale. For each element, they take the percentage of consumers that gave a rating of 6 or 7 and subtract the percentage that gave a rating of 1, 2, or 3. This results in a “net loyalty” rating for each of the three elements. The overall Temkin Loyalty Rating is an average of the three “net loyalty” percentages.
As the scores below indicate, Banking has a problem! Customers are not "loyal"!!
Closer home, based on interviews with some 20,000 people across 13 Asian markets, McKinsey had some interesting conclusions. They found that since the survey was last conducted in 2007, Asians on the whole now have more banking relationships than before the crisis. For example, the mass affluent segment now banks with 3.3 banks on average compared to 2.7 in 2007, a 22% increase. McKinsey also found that on the whole, Asians are less likely to recommend their bank to others. They found a dramatic drop in willingness to recommend, from 91 percent in 2007 to just 71 percent in 2011.
Banks need to consider how they can create a Loyalty framework across the bank. Here are a few thoughts about Loyalty in banks:
- Bank wide loyalty is easier in concept but very hard to implement. Fundamentally it needs to be linked with the bank’s intention to grow Profitability per customer
- Important to create the concept of “lead products” while designing a bank wide loyalty initiative. Loyalty can be led by Credit/Debit cards & the Direct Banking channels ( ATM, Mobile & Internet). Important to navigate across product group to get buy in for them to see value in this strategy. Especially Assets(apart from credit card) businesses which by nature will be cautious about an incentive to a customer before a valid assessment of his risk profile.
- Critical to not depend only on a card based program. Rather use the Mobile as the primary instrument for driving bank wide loyalty
- Profitability Analytics per customer can be measured & can form the basis of creating an evaluation framework. Customers can be classified in bands basis profitability & a structured series of Campaign experiments can be run to devise an appropriate program framework. This analytics is at the heart of any new Bank wide Loyalty initiative. Without this, you will end up constantly trying to defend whether the program is working or not.
Since setting up their crack personalization team in 2009, clicks on Yahoo’s “Today” box have increased 270%.So personalization makes us four more times likely to click on a link. Internet companies are way,way ahead of the game when it comes to using algorithms to power personalization. When was the last time you got a personal experience when you called in a company’s “help line”? Possibly never!
But can analytics lead a company astray with “too much personalization”. I wrote earlier about how analytics can be done in ways which are not ethical. Increasingly, with consumers becoming far more conscious about their data, the analytics community will need to understand how to respond to these real fears!
I wrote about this earlier here: "The dark side of Analytics"
A few weeks ago, the New York Times published an article that revealed how Target uses consumer data to identify shoppers who are going through major life changes like pregnancy. Target then sends out tailored coupons to encourage these shoppers to buy more products—stuff that they may not have bought there—such as groceries, vitamins and clothes!
Eli Pariser, author of “The Filter Bubble” delivers a compelling TEDTalk on the dangerous unintended consequences of web companies personalizing “news” basis an analysis of our data! He argues that: this personalization is the internet showing us what it “thinks” we should see, as opposed to what we should or need to see.
What are the lessons here for us?
- With the responsibility of carrying customer data comes "accountability" for all companies. Urgent need for being conscious about consumer privacy!
- Analytics can help drive personalization that improves the "customer experience". Focus on measuring that value & make changes!