In the past few months, I have met with a half a dozen big data companies. With each successive meeting, it becomes increasingly clear how the merits of information will transform the way companies look at customers, and slowly evolve processes to streamline, tightly target and create more efficient systems.
In previous posts, I have talked about eliminating the guess work that marketers traditionally faced with limited information that was available. I still shake my head when I speak to agencies who still want to integrate more robust information into existing targeting schemes to fit the structure they’re comfortable with. It doesn’t work, but it’s the way things are done.
We’ve discussed the limitations of relying on Demographic information. Age, gender, income levels, employment status are identified personal information that fall short of giving us the information we need to connect identities to behaviour.
Can Cognitive Computing be the panacea?
You’ve read the information from many of my colleagues in this space: Bryan Kramer touts #H2H Human to Human interaction, Ted Rubin effectively promotes #RonR Return on Relationship. These are the soft KPI’s that have eluded many organizations because this has never been part of the organization DNA. Understanding the drive to revenue has always been mechanical: a plan to drive visibility and demand for products and manufacture a need among consumers. With a combination of influenced focus groups and available data (Remember PMB scores?), the right market would be chosen to market against.
I had a discussion with Rob High, VP and Chief Technology Officer of IBM Watson where he stated,
Behaviour of buyers is shifting. Consumers don’t want to be classified as consumers. They want to develop an experience
IBM Watson is making strides to use technology to develop faster ways to achieve this. Through Cognitive Computing Watson seeks to develop this deeper understanding of people and the experience they seek. Rob emphasized that this is not about replacing the human mind, but rather automating human cognition or amplifying reasoning. By definition,
Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.
Rob relayed the 4 characteristics of cognitive computing in its current role
- It’s a style of computing that learns behaviour and gets better the more knowledge it amasses.
- It seeks to understand human forms of expression: written language, visual language –the ways we express ourselves to each other.
- It derives evidence from more human style of expertise: what we know, how we interpret, intent, goals and agenda, and inevitably –>trust.
- Cognitive computing is not binary: It is not 100% right or wrong. There are degrees of each. This allows the system to attempt to mimic the ambiguity of context.
In its early state, humans have to adapt to the constraints of the computer.
Why Context is important
Social networks and mobile continue to build a picture of each one of us, aggregating and attempting to contextualize and build more reliable predictions as each new truth surfaces. The goal beyond demographics is to piece together fragments of behaviour and user propensities like the ones mentioned below and develop the optimal consumer experiences through personas:
- geographic location
- lifestyle;
- interests;
- who influences purchasing decisions;
- personal goals;
- how they respond emotionally to events;
- past behaviours;
- why they interact with your company;
- what they want from your company;
- where they look for product information;
content consumption habits.
With this information, it’s not unheard of to use these personas to target people based on common traits and achieve a better result. We need to keep in mind, however, that these clusters will continue to morph as more information is compiled. The speed and the volume of the data will make these clusters much more dynamic in nature.
The reality today is that relationships are not static. Technology is moving at a faster pace. The information we relied upon to make sound business decisions is no longer relevant. I’ve seen 3-month data being dismissed in place of more recent real-time insights. While this can be a knee-jerk reaction to put more emphasis on the here and now information, it’s just as irresponsible to rely upon historical information that may bear no relevance to the current state. It’s a learning curve that is still nascent at best.
We are progressing… in healthcare.. in business… in people ..
In it’s early stages, Watson is working alongside partners to expand clinicians’ treatment options for Leukemia. If successful, this program can be widely redistributed to cancer institutions.
It is also partnering with prominent banking institutions to understand the dynamics of supply chain management, targeted at different reasoning problems.
Personality insights can be gleaned with 70% accurate approximation. Watson can, for example, scrape the content for individual authors: blogs, tweets and surface a personality map. This is the result of overlaying over 52 points of personality insight.
Data Can Be Creative
As a marketer, I am already witnessing amazing examples of data being used to develop awesome experiences for the consumer.
Everyone remembers this campaign from British Airways: the #lookup campaign that used data that identifies a BA flight and its destination as it flies in proximity of a digital billboard.
While data stops the guesswork, the importance is being able to leverage these nuggets of information to create more personalized, inspiring and emotional experiences to forge connections with people. It’s this human to human connection that businesses need to scale today. This quote says it all,
The ability to collaborate and iterate over a single version of the truth will be the new mantra for data-driven organizations.
I saw this compilation of ads from Cannes 2015 and I’ve referenced a few that have succeeded at doing this.
“The 3D petition for World Animal Protection that printed over 20,000 people’s names onto a life-size 3D printed Elephant and people could watch the action via a webcam:”
“One of the most stirring campaigns: Huggies that used ultrasound data to reproduce a 3D model of an unborn child, so that a visually impaired mother could feel and embrace her baby.”
As we begin to see this increasing well of data, the frustrating banters of privacy will persist. However, the constraints of disclosure aside, how we use the information will be telling in how its acceptable use moves forward.
Image source: Deviant Art
Founder at ArCompany, and Director, International Council on Global Privacy and Security by Design Hessie is a seasoned digital strategist, and intelligence analyst having held senior positions for top ad agencies including Ogilvy, Rapp Collins, ONE and Isobar Digital. She also has extensive start-up experience in AI technologies, social tech, online publishing and artificial intelligence like Yahoo! Answers, Overlay.TV, Jugnoo and Cerebri AI. Hessie is the co-author of EVOLVE: Marketing (as we know it) is Doomed! She is also an active writer for Forbes, Cognitive World, Towards Data Science and Marketing Insider Group.
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