Sensory Perceptive Cues to Understand Your Customer

Do you find that you are more sensitive to a colour’s contrast than your friends are? Do you feel like you’re more aware of the tick tock or subtle changes in temperature? Don’t worry, there’s nothing wrong with you!  Each one of us has a distinct way to relate to or understand the world around us.
VAK, which stands for Visual, Aural/Auditory and Kinesthetic, are different sensory perceptive modalities through which we understand our world.
For example, people who rely primarily on visual cues, tend to buy clothes depending on the colour combination or patterns, whereas people who emphasize on the texture and fitting would be relying on their Kinesthetic cues.
David A Kolb was one of the earliest learning theorists who found that individuals have a preferred learning style through their dominant sensory perception.
From a sales perspective, it’s highly recommended to keep your customers’ learning styles in mind to foster effective engagement and establish good business orientation.
Naturally, your next question is, “How will I know the customer’s learning preference?”. To answer this, it is important to look for certain cues in their demeanour and communication patterns. Some commonly observed patterns are listed below.
Visual
Characteristic features of a person who predominantly uses the visual mode:

• Fast talkers
• Loud while in conversation
• Use words and phrases such as ‘see’, ‘watch’, ‘point of view’, ‘focus’, etc., anything that draws attention to an image

Business orientation: They will be more receptive towards engaging when the interaction involves using visual aids such as charts, pictures diagrams, etc. They believe in first impressions and dress appropriately according to the situation. They might form an opinion on your product/idea based on the projections you present to them. In a nut shell, they will buy the concept if something visually appeals to them.
Aural/Auditory

• Distinctive features of an auditory person:
• They are soft spoken and slow in their speech
• They enjoy music, rhythmic beats
• Use words such as, ‘listen’, ‘ring a bell’, ‘sounds good’, or ‘rhythm’, more often. They might frequently use fillers such as ‘ah’, ‘umm’, in their conversation
• Easily distracted by noise

They are known to filter out outside noise by playing music to concentrate better.
Business orientation:  They are good listeners and learn best when they listen to someone explain the subject to them.
They base their opinions more on what they hear than what they see, hence you can make good use of words to captivate them to your discussion and facilitate business.
Kinesthetic
Striking features of a Kinesthetic individual:

• ‘Feel’, ‘Try’, ‘Experience’, are common words from a Kinesthetic individual
• They are people who learn quickly by relying on the sense of touch
• They like to experience and learn

They can be good at sports, performing arts or any task which involves physical movement
Business Orientation: They prefer trying out things before forming an opinion on them. They usually communicate at a slow pace. They respond well to demos or trial runs as this enables them to get a hands-on experience.
There are two other less-popular sensory perceptive modes:
Audio-digital, refers to those individuals who would like to “make sense” of a concept by reasoning out the rationale behind it.
Characteristic features:

• They are very formal and conservative
• They fear being illogical
• Their voice is generally a monotone with few intonations
• They appreciate concepts that have depth rather than relying excessively on the visual attributes
• They need structure to understand and articulate things
• They like to be looked at as dependable hence, they tend to verify and fact-check information

Reading/Writing - These are individuals who like to learn from their meeting sessions and written material. Some commonly observed aspects about them are that they enjoy reading and writing.
Business Orientation: To explain things or to convey a message to them, it’s best to have a written version of it for them to read.
It’s best to use all the above-mentioned strategies in appropriate context.
When you meet a set of people, it’s recommended to greet everyone with a handshake (of course to a limited audience size!) and provide a crisp presentation with pictures, data, handouts, and verbally explain the topic at a moderate pace.



Disclaimer - Do not generalize individuals by the presence of just one of the above traits. This will help maintain an unbiased approach. 

Unicorns Amassing Billions in cash, Decoding their technology spending patterns

The term Unicorns was coined by Venture Capitalist Aileen Lee in 2013 symbolizing the statistical rarity of such successful ventures. However, these organisations have now become almost commonplace, reaching 220+ in number and multiplying at incredible speed, a CAGR of 233% to be exact during the last 3 years.
The massive economic scale of the Unicorns club can be understood from their combined net worth which has reached nearly $724 billion USD in 2017, just shy of Netherland's gross domestic product (GDP). If Unicorns were an independent nation, they would form the world's 18th largest economy!!
These organisations, being digitally native, structurally lean and technologically agile have emerged across all industries designing, hacking, and changing products, structures and business models of traditional industries.
The top of the herd is dominated by some of the very big ones including the Uber, Airbnb, Chinese Xiaomi, Didi and China Internet Holdings which are among the fourteen decacorns, a term used for private venture-funded company’s worth over USD $10 billion.
What are they doing with the money? Not a lot, it would seem.
Leveraging our DRAUP platform, we estimated that Unicorns spend on technology reached a massive USD 32 bn, growing exponentially at a rate of 133% YoY during the last 3 years.


Product development and Infrastructure Support are the major spend areas for all Unicorns. Nearly 47 percent of the total technology spend is consolidated on product development, being the key differentiator of superior product experience and growth enabler for Unicorns. Technology spending on digital areas such as Design, Data science and AI are amongst the core focus segments for creating a personalised experience in consumer space and intelligent products in enterprise space.
On the infrastructure-support front, the heavy technology spending is to ensure robust scale that can support a large number of customer transactions, active security requirements and analysing a large dataset of customers, products and other stakeholders within and outside the organisation. However, being born digital, nearly 95 percent of their infrastructure is based on cloud platforms which is reliant on global external vendors. The spending on Cloud hosting, network management and security & maintenance activities is a huge component, comprising of nearly 53 percent of their total technology spend. AWS maintains a significant market share lead, controlling nearly 65% total market among Unicorns. Unicorns such as Snapchat has already announced a contract with AWS for spending USD 1 bn on latter’s cloud services over next 5 years.
How do these hyper scaling organisations put up large technology capability in a short while?
The answer lies within the unconventional route which has been adopted by Unicorns compared to the traditional organisations.
During the growth stage Unicorns rely on infrastructure from 3rd party platforms, open source OS, libraries and other platform providers.
Take the case of Airbnb which has built its backend infrastructure supported by open source nginx platform and used AWS for hosting. It has also developed capabilities through partnerships, acquisitions or acqui-hiring niche solution providers such as Twilio for communication, LocalMind for navigation, Trooly for identity verification etc.
As Unicorns scale, they bring critical capability inhouse in order to provide the users with a seamless experience across the product stack. They also invest in enforcing a comprehensive product control as well as an overarching security, privacy and regulatory compliance.
Many Unicorns such as Uber has invested nearly half a billion on Mapping project to build inhouse capabilities and plans to create its own detailed maps for traffic patterns, pickup location etc. for its autonomous car project. Cloudera is building necessary data centre infrastructure in-house and Airbnb has developed a search and discovery algorithm to search 35k diverse property listing on its website.


As they develop deep expertise in their technology stack, Unicorns sometime opensource their capabilities. Airbnb has already taken a step ahead by open sourcing its AirMapView API to co-innovate and experience data from real world models. Its API enables interactive maps for devices with and without Google Play Services to support multiple native map providers such as Google Maps V2.
Given their openness to work and readiness to scale their existing capacity in emerging areas, Unicorns are good potential segment for Technology Service Providers (TSPs) to collaborate.
So, how should the TSPs leverage this hyper evolving Unicorns ecosystem? Are Unicorns a viable target segment for technology service and solution providers to target?
We have already seen instances where TSPs have partnered with scaled Unicorns. Outsourcing giants such as Wipro has partnered with Uber at its Bangalore based map improvement group. Tata Technology Services has announced plans to work with NextEV and Faraday Future to codevelop battery technology for Electric Vehicles in Bay Area.
But what are the activities where Unicorns need help to develop capabilities?
Most of the Unicorns have small existing development teams focussed on core product activities. These companies face challenges in scaling up their existing workforce and infrastructure set-up to address potential exponential growth. Non-core functions such as Dev-Ops, Customer Support and Q/A are the areas where Unicorns lack capability and leverage third party solution providers to help them scale.


However, TSPs need a proactive sales approach and thinking while planning for collaboration with these hyper evolving companies.
Unicorns needs are dynamic and short lived. Secondly, it has been observed that their decision-making is widely different from traditional organisations. The Product ownership and responsibilities in many Unicorns is distributed across new age stakeholders such as CMOs, CROs, CAOs, CInOs etc., unlike the traditional organisations where decision making is confined to senior stakeholders such as CIOs.
1. TSPs need to be proactive in selling and need to engage early with young Unicorns to offer integration services for scaling latter’s core product capabilities
2. This would require TSPs to develop capability in digital areas to explore new age partnership in segments such as User Experience, API management and integration services etc.
3. Decision makers in Unicorns have diverse roles across emerging markets, products and business segments. TSPs need to establish deep connections with new age stakeholders as compared to restricting engagement with only CIOs
4. Most of the Unicorns does not have global engineering presence. TSPs need to engage through an on-shore delivery model to sell customized services by understanding the technology stack and dependencies of the prospect. This would require deep understanding of the pain points by working closely with the in-house engineering teams.
Many events such as large VC investment, M&A and leadership change are some of the major triggers which could lead to a potential outsourcing requirement for Unicorns. An event such as a senior leadership hiring from a global outsourcing organisation could be a signal for strengthening existing product capability through investment in newer technologies or talent.
Draup’s Signal Processing tracks and interprets subtle and obvious changes in organizations to recommend proactive actionable decisions for long term sales strategy.

Autonomous Vehicle: Driving Digitization into Auto Industry

The traditional automotive industry has undergone a massive transformation with digital native technology providers across different industry segments playing a critical role. Smart Mobility, Autonomy, Connected Car and Electrification are the major technology-driven trends emerging across the industry.
The autonomous segment in the automotive industry is seeing a major upswing due to factors like reduction in cost of Transport as a Service (TaaS), lowered government regulations related to testing and operations of autonomous vehicles in the US and significant advancements in Machine Learning technology. The current Autonomous Vehicle ecosystem has been rapidly growing through a rich infrastructure of network, cloud & insurance providers enabling new age business models.
A DRAUP study estimates the Autonomous industry to be worth $87 Billion by 2020 and enable potential savings of $2.2 Trillion in the areas of fuel efficiency, cost of life and productivity gains.

Digital Transformation of the Automotive Industry
“With increasing focus on digital engineering within Automotive, OEMs are prioritizing initiatives around connected and autonomous vehicles”

Around USD 8 billion is the Technology spending by the top 25 players in Autonomous vehicle segment. In-house R&D is focused on developing core software capabilities, leveraging deep learning for computing, vehicle control and vision-based perception.
The automotive industry, in general, and the autonomous vehicle segment in particular, is moving toward a new customer value proposition. Today, digital platforms are at the core of how customers figure out how to get from point A to point B. This makes it imperative for automakers to digitally transform their internal operations, service models as well as external partnerships.
While the automotive industry has always used information technology to achieve efficiency and scale, it is ripe for a digital disruption since the New age customer now uses a smartphone, is social media savvy and environment-conscious, and has become much more demanding in terms of speed and convenience.
This has led to the entry of AI / IoT native platform providers like Google and AImotive with strong AI capability that leverages deep learning algorithms required to make advanced driving systems safe and predictable. OEMs now have strategic focus on developing critical safety and driving systems in-house. OEMs such as Daimler, BMW and Ford are establishing partnerships with technology providers to collaboratively develop software capability for vision and perception systems.
Semiconductor giants such as Intel and Nvidia have developed specialised Autonomous Vehicle SoCs for processing and computing large amount of vehicle datasets using Machine Learning algorithms. While Traditional suppliers such as Bosch and TomTom have enabled advanced vehicle navigation and monitoring through specialised telematics equipment, new age suppliers have built capability into Advanced vehicle control using deep learning, sensor systems and connectivity services.
Well-funded start ups like Nauto, Argo AI and Drive.ai are the top players investing in full stack-Autonomous Vehicle solutions.

Changing Industry Structure
“Digital firms have disrupted the automotive sector across the value chain and as a consequence R&D focus has shifted to software”

In the traditional industry structure, Tier 1s and Tier 2s worked together to provide the Full-Stack of automotive solutions to the OEMs. This is now changing, with Tier 2s disrupting the traditional supplier relationship model to position themselves as a direct Full-Stack supplier of AV solutions.
For instance, Semiconductor giant Intel was traditionally a Tier 2 supplier who created ML/Deep Learning based microprocessors and sensor chips, and depended on Tier 1s like Continental and Bosch for sensors like LiDAR and RADAR, and 3rd party integration. In the new age industry structure, Intel directly offers full stack solutions related to autonomous vehicles – cameras, in-car networking, sensor-chips, roadway mapping, cloud software, machine learning and data management, to OEMs.

Intel accelerated its AV push through acquisition of Mobileye, bringing later’s core AV capabilities inhouse. Intel has now positioned itself as a full stack system integrator across vision systems, sensor technology and computing platform, and has also partnered with companies like Harman for its connectivity platform and Velodyne for its LiDAR technology.
The industry is also seeing more partnerships such as the one between Intel, Ericsson and GE to launch an open industry platform – 5G Innovators Initiative.

Challenges and Opportunities for Traditional Automakers
The automotive business is undergoing a transformation, leaving turmoil and uncertainty in its wake. Traditional automakers are dogged with concerns such as declining car ownership, flat revenues, new competition from non-automakers like Google and Uber, a new ecosystem of OEMs, suppliers and dealers, and changing government regulations. Auto sales are seeing a slowdown in all major markets including Europe, US and Japan.
This presents both challenges and opportunities for traditional automakers. In addition to investing more on profitable segments, culling declining segments and expanding into emerging geographies, it has become essential for OEMs to invest in “riskier” new age technologies such as autonomous and connected vehicles, ride sharing, electric vehicles etc.

DRAUP estimates the Engineering Services market in the Autonomous & Connected Car segment alone to be $215 Mn – $225 Mn.
A lot of automakers like GM, Toyota and Daimler are investing in ride-hailing companies, which act as a great platform to test their autonomous prototypes on.
Ford is an example of an automaker who was struggling with declining stock prices, flat revenues and decreasing profits. Ford was seeing low sales of their “Car” product line and declining growth rate even in their top geography, Northern America, due to a change in the customer mindset. This led to an R&D spend of $7.3 Billion in 2016, an aggressive increase of 9% over the previous year, on high growth areas such as Autonomous, Connected and Electric vehicles. Ford realigned its vision and strategy in 2016 to evolve from being an automotive company, to being an automotive and mobility company. While Ford is spending $4.5 billion to expand its EV portfolio by 2020, its investment of over $1Bn in AI, LiDAR, 3D mapping technologies and talent hiring from Silicon Valley, puts it ahead of the others in the Autonomy Landscape.

“Automotive firms will continue to gear up investments around autonomous capabilities as it becomes more mainstream “

Analyst firm IHS Markit predicts that autonomous cars will reach global sales of 600,000 by 2025 and 21 million by 2035. It is becoming apparent that those auto companies who focus on R&D spending and patents in non-traditional Business Units, realign their engagement strategy with the ecosystem, connect with new age digital suppliers and invest in engineering talent in new age technologies, will be ones who will stay competitive in the years to come.

NFV is a solution and a problem!

Priorities of Telecom Service Providers (TSPs) are changing – They are driven by ever changing customer needs such as – high availability, high elasticity and highly reliable networks – resulting in their rapid adoption of next generation and virtualization software technologies. Industry giants like AT&T are setting a target of achieving 55% network virtualization by the end of 2017 and many of its peers such as Telefonica, NTT, CenturyLink, Telecom Italia and China Mobile are also taking steps towards implementing NFV (Network Function Virtualization). But all this is not without unexpected challenges.

What it used to be?

Historically TSPs have been dependent upon vendors who provide proprietary hardware and software black-boxes. Network elements that provide functionality for 3G/4G wireless networks were all based on vendor specific proprietary solutions. These ‘proprietary black boxes’ offer network functions like evolved packet core, network address translation, firewalls encryption, domain name service, caching and others. Introducing new services for end customers using this infrastructure was time consuming, costly and hence ready for disruption. Further drawbacks were low rate of feature innovation and feature introduction and a costly oligopoly system.

Disruption – The drivers for NFV

Innovation by web-scale companies changed consumer expectations. Companies such as “Google”, “Netflix”, “Amazon” etc. used virtualization technology, automation, AI, etc. to roll-out software updates to millions of people in minutes. These technologies have changed how consumers work, learn, live and play! Proliferation of smart and real-time devices like smart – phones/TVs fuelled customer needs. Demand for elasticity of network services, automatic network and service provisioning, service agility and monitoring and management based on real time analytics has become critical.

Changing consumer demand forced TSPs to incorporate business model changes, they shifted focus from:

• Voice to data
• Quality of bandwidth to Quality of customer experience
• Individual plans to shared family plans
• Bandwidth centric plans to application centric plans
• Monthly interaction with customer to real-time interaction with customers

Multitudes of start-ups and smaller vendors dis-aggregated the vertically integrated black box solutions to address the service providers challenges mentioned above. Shown below is a comparison of a vertically integrated traditional architecture versus an open, horizontal, disaggregated Eco- System.

Expected benefits of NFV

• Low lifecycle operations cost
• Service agility
• Scalabiity
• Faster Innovation

TSPs do not have to rely anymore on any single vendor peddling “black boxes”. They could now build their own NFV based architectures to achieve the benefits mentioned above. A new landscape with changed technology and business parameters have evolved using these ecosystem players.

Problems in scaling NFV based solutions

The biggest challenge is Systems Integration – the industry now has hundreds of players making competing software on standard hardware. New challenges emerged in getting all of them to work in a cohesive high-performance environment.With so many permutations and combinations of software products, interoperability, carrier grade uptime (99.999%), life cycle management, and performance requirements all became difficult to meet. Vendor accountability and single neck to choke is also a major issue.

The Future – How will these problems be addressed?

Systems integration expertise, availability of talent and life cycle management are problem areas. Use of web scale technologies to automate life cycle management becomes key to reduce operational cost and increase rapid deployment of services. Software service providers are beginning to address the systems integration problem too by providing value to both equipment providers as well as TSPs. TSPs should invest in:

• Automation of Life Cycle Management
• Leveraging web scale technologies like micro services and containers
• Adoption of DevOps process and technologies
• Integration, inter-operability testing and QA of multi-vendor solutions

Although NVF has provided many benefits, unless the above are addressed quickly adoption at web scale will continue to be a challenge.

How do we harvest, process and analyse millions of data points every day?

As I look through the past year, there has never been an uneventful or boring day at Draup. Each new day brings in some challenging yet interesting problems to solve. Our entire stack is divided into four important components and each of them is a complete product by itself. We will go through an overview of each without diving into much detail.

Harvesters - We have data from 1000s of different sources flowing into our database. Each data point has its own refresh cycle ranging from real-time to quarterly refresh. Volumes have sometimes gone up to 300 million records a day (200 GB) based on seasonality. Data processing time should not be affected by data volumes, irrespective of its size as decision making is affected with even minor delays.

The data might not have a well-defined schema, this is where MongoDb helps us. Its simple yet dynamic and extremely scalable document store model fits correctly with all our varied needs. We have an internal python-based tool that helps us onboard new sources quickly.

ETL - Getting the data into the system might be an easy task but processing them, which includes joins that go into 100 mil * 100 mil, is a very memory-intensive as well as compute-intensive task. We use the Databricks platform to ETL, decomplexify and merge vast volumes of related data. Databricks is a cloud-based managed spark ecosystem which allows us to concentrate on solving business challenges without worrying too much about the infrastructure and scaling. We have developed some proprietary algorithms which help us deduplicate similar data points across various sources, translate all data into English and make it ready to be consumed by Machine Learning models.

Gateway is the brain of Draup, all important business logics/decisions are made here. This is also a place where data from different machine learning algorithms, rule-based engines, psychological models and manual human intelligence converge. It follows a micro-services architecture with multiple apps, each solving a different problem and communicating through APIs. The apps generally have well defined data schemas to work with. Given the relational nature of the data, MySQL is the natural choice as our database engine. We use Django as it helps us create smaller development cycles, is easy to learn, and helps us control quality and maintain a well-defined MVC architecture. We also use celery coupled with Redis as the broker for a lot of our long running async processes. All different data points along with model results are available for our internal subject matter experts and analyst teams to review and correct through the Gateway web interface. All corrections made by them are fed back to the Machine Learning algorithms as learning data set.

Finally, we have the application which is our front facing SaaS product. It has DRF (Django Rest Framework) at the backend with all the data being consumed by a ReactJs application. To make the user experience better, by allowing our users to scoop through our proprietary heuristics and data much faster, elastic search is used.

We are primarily a Big Data and Machine Learning based startup, so scalability and efficiency is the key for us. All the components are hosted on managed cloud services. The provisioning of the architecture is automated through Terraform, we can bring multiple machines up in seconds based on the load. The deployments are managed through ansible scripts run on Jenkins.All the applications have their own Dev, Qa and Production environments that helps us test our features thoroughly and make sure we deliver bug free software.

I hope this was useful. If you are part of a budding startup and need guidance on solving similar problems, feel free to reach out to us.