Mass personalization is dead; welcome to the age of nano segmentation !

Attending this conference around analytics, marketing and understanding the customer behavior using segmentation as a discipline, almost all the speakers talked about targeting improvements as the segment sizes got smaller. Complexity of algorithms and the availability of hardware resources on the cloud as well as lower cost of acquisition has made it possible to churn data – a lot more data – than it was possible not too long ago. Marketing effectiveness is waking up and is moving away from the conventional world.

Companies that engage customers directly and those with high volumes of transactions have already begun to garner benefits from these technologies. Even others with a few degrees of separation between the brand and the consumer are exploring alternative strategies and business benefits. Almost every conference also has a mandatory presentation from a research house. The analyst on stage is expected to shower thought leadership on the audience; it is a rare occurrence when they actually make any practical sense.

One such session started with anecdotes of individual attention to the consumer in the pre-technology days and predicted the return of individualization in the near term. Given enough data points, oodles of hardware and highly optimized algorithms around machine superintelligence (the next level after deep learning according to the speaker), the engine will be able to segment the large volume of data into buckets of one, i.e. identify and predict behavior and thus target individuals and capture wallet share.

It has been an aspiration of software and solution builders to use technology to predict the future; we have all seen demand forecasts and revenue predictors with Sales & Operations Planning, most of which hovered in the 70 percent range with a swing of 10 percent on either side. Definitely better than the binary state of tossing a coin or 50 percent, these solutions removed error rates when 70 percent was off target to 70 being on target ! Predictive analytics has had better success than fortune tellers on accuracy levels.

Artificial intelligence at some levels is already influencing our behaviors with our every keystroke and click being recorded by some websites and mobile apps whose terms and conditions we hardly read and comprehend. Changing privacy policies and updates render our choices ineffective with every update leaving us exposed and vulnerable. Data privacy coupled with sieve like security allows the good, the bad and the ugly equal opportunity to use our digital footprint to coerce and nudge us to buy at one end and pay up on the other end.

Enterprises mired in Paleolithic technology, archaic mindsets and rusted skills are struggling to deploy and use such solutions effectively ethically, at times veering away from virtue. Consultants and technology providers are effectively creating Fear Uncertainty & Doubt with a promise to bring the Holy Grail to them. Despite their inability to deliver any credible design or strategy, many large consulting companies continue to rake in the moolah; CXOs and Promoters are shy of accepting they have been taken for a ride.

On the sides of the conference a conversation thread discussed the waste of precious resources and lost time with one of the premier consulting companies. The angst of the CMO was palpable, the drinks brought together a few other victims of the same company whose voluminous reports were gathering dust in their cupboards. Common sorrows bring feelings of brotherhood that only misery can keep together; they found light at the end of the tunnel with success that one of them had with a frugal startup solution.

Nano-segmented Earth’s billions, does provide individual as the atomic unit who can be observed and experimented upon to identify triggers that work. As the number of attributes rise, effectiveness improves, but we have known this for a long time and that contemporary solutions did not have the ability to provide the actionable insights in a timeframe that makes the action relevant. Lack of data integrity and clean data rendered analytics ineffective; algorithms did not know how to overcome these limitations.

The future of consumerism, customer behavior, segmentation, marketing, engagement, targeting, influencing, shadowing, nudging, bombarding, and swamping the hapless customer is closer than we think. Look around your behavior across life stages, daily innocuous decisions, and how they are being shaped by invisible forces. As an enterprise there are too many choices and options; the company with better quality data, compute and algorithms will be the winner albeit with a short window until competition catches up.

As an individual I am worried that have limited choices !

Will bots kill the call center outsourcing business ?

I was at a conference where an animated, heated, passionate discussion was happening between members representing the technology players’ leadership team and some customers. It was quite obvious that there was a difference of opinion leading to a disagreement on the subject. There were multiple points of view which added to the liveliness of the debate. The conference organized by an independent organization was on one of the currently hot technologies – artificial intelligence and its manifestation in the form of bots.

The Structure of words determine segmentation methodology and parsing for semantic analysis, tasks that can now be done with higher accuracy though complete sentences require additional facts and external world knowledge. For written text in a chat session, the bot is able to hold fort quite successfully when addressing well-defined tasks and decision trees. Many online portals and businesses have already deployed chat bots to supplement agents who step in when the bot is unable to parse and respond to a question.

Eliza was seen as a breakthrough, so are personal assistants who respond to and act upon simple tasks using basic language parsing tools. While the technology is nascent, attempts to make computers chat with humans have been around for over half a century. Current experiments indicate that they can be purposed for specific tasks. Excitement revolves around automated interactions to improve efficiency and reduce cost in comparison to current models of humans talking to customers for customer support issues.

The Natural Language Processing (NLP) journey that began with SHRDLU has improved significantly with Machine Learning and Deep Learning. The ability to pass the Turing Test (the test investigates whether people can detect if they are talking to machines or humans) is still some time away, though we are getting closer to the milestone. Though some may claim that Eugene passed the test a couple of years back, many do not agree with the results. Artificial Intelligence barrier is yet to be crossed convincingly.

As bots mature and they are evolving fast, their ability to manage specific tasks is already giving organizations the benefit of consistent responses to basic and mundane queries; use cases around query and response of HR systems on leave balances, tax options, and others have been successfully deployed. Externally airlines and hospitality industry have taken the lead while inside our homes personal assistants are making an appearance; the biggest driver however has been the smartphone listening, responding and taking actions.

Call centers and IVR systems continue to drive customers crazy with long decision trees and stupid obvious questions; wait times add to the woes of the frustrated customer. Offshore call centers did reduce the cost but faced backlash on other social impact it created. Bots promise to take up the challenge and address the customer with agility and surprisingly improved outcomes. The rich repository of information and past interactions helps the rules engine train effectively and take decisions objectively.

As the cost of deployment continues to fall with mass development and niche players, companies are slowly embracing this revolution. Which brings to fore the point that will they be able to replace humans in call centers as the bot learns and graduates to the next level of interactions ? Recent times have seen the rise of hullabaloo about jobless growth and the disappearance of low end jobs to be replaced by machines; as the gap to the Turing Test reduces, the probability that they can starts staring us in the face.

The job loss is indeed going to impact destinations that build their business models around calls and low end business process outsourcing (BPO). Many existing players have already started upgrading skills as well as retrain staff for other roles; enterprises will ruthlessly choose efficiency over distant loss of employment even if it involves initial high investment. Are there options available to current players ? It is a race against time for incumbents to move up the value chain or become irrelevant to their customers.

Unless, unless they embellish their services with bots and attack their own business before someone else does. A handful of providers with strong technology backing from within or their parent companies are beginning to offer value added services using bots thereby changing their customer acquisition and retention strategies aligned to the new reality. They are using their rich knowledge repository to train the technology solution towards addressing existing and new problems thus opening up opportunities.

A system is as good as the people who build it; in the end it will be humans who will continue to create the differentiator !