In an exclusive interview with Analytics India Magazine, Ravi Vijayaraghavan (Chief Scientist and Global Head of the Analytics and Data Sciences Organization at 7) talks about current trends and challenges in analytics practice and how 7 is using analytics.
AIMAnalytics India Magazine: What are some of the main tenets (philosophies, goals, attributes) of your analytics approach and policies?
RVRavi Vijayaraghavan: 7, provides software and services that make it simple for consumers to connect with companies to get things done. 7 has one of the largest cloud-based, self-service networks in the world, managing more than 2.5 billion consumer interactions annually. 7’s software products drive a more intuitive customer experience for consumers interacting with our clients.
At their core, 7 products are driven by predictive analytics, real-time decisioning, and data sciences. At 7, we believe that an intuitive customer experience is driven by the following framework:
Anticipate –Predict the intent of the consumer
Simplify – Provide the customer the easiest possible way to fulfill this intent
Learn – Learn at scale from each of these interactions to build a closed-loop system of continuous improvement
- Big Data platform, called the Px Platform
- Develops predictive and machine learning algorithms at scale for the web, chat, mobile, and speech.
- Real- time decisioning
- Optimizes the algorithms for real-time decisioning on the various channels consumers choose to interact , including Web, Chat, Mobile, and Speech
- Unstructured data mining and machine learning
- Performs text mining, web mining, speech analytics and natural language processing on customer interactions to enable learning at scale.
- Fusion of structured and unstructured data
- Integrates data from web, chat, email, voice, CRM and other sources of data.
- Big Data platform, called the Px Platform
Our organization focuses heavily on building intellectual property and has numerous patents and publications. Our patents are primarily in the application of data sciences to drive a better customer experience.
AIM: What according to you is your biggest USP that differentiates your organization with similar sized players in the analytics space in India
RV: The core value proposition of our solution is that we drive business outcomes. Our business model is performance-based. We are rewarded when our clients achieve success. For example, if we deploy a prediction driven online acquisition solution then we are compensated for the incremental acquisitions and revenue we create for our client. From that perspective, our analytical models have to create tangible and measurable value for our clients.
AIM: Please brief us about some business solutions you provide to your customers and how do they derive value out of it.
RV: Our predictive analytics and machine learning driven business solutions are the engines that drive our patent pending products. These include:
PxOnline – Web self-service that anticipates what consumers want and helps them get things done.
PxSpeech -Speech self-service that’s conversational, intuitive and easily integrated with your online and mobile channels.
PxMobile –Mobile self-service that combines speech and touch to simplify interactions for your customers across devices.
PxAssist – The first solution to bring prediction and real-time decisioning to assisted service and apply it across channels and devices.
AIM: Where do you see the bulk of your business coming from? Do Indian organizations have the same affinity towards BI/ Analytics as that of organizations from other regions?
RV: Today a bulk of our business comes from North America, Australia, UK, and Europe. These markets lead in the strategic focus on customer experience. However, increasingly consumers in emerging markets such as India are also demanding a more seamless customer experience across channels and are adopting our solutions as well.
AIM: What are the most significant challenges you face being in the forefront of analytics space?
RV: If you think about some of the key trends in the world of analytics and data sciences you see a convergence of deep mathematics, advanced computer science, and several other adjacent areas such as natural language processing, linguistics, speech recognition, business and social sciences. A data sciences team that will thrive in this environment and create differentiable value would have to be extremely cross-functional with a lot of people with “T shaped skills”, i.e., depth in one of these areas and broad knowledge on the other areas. In addition, the culture should be very open to embrace rapid new developments in the field. Building such a team is a significant challenge and people who succeed in doing so can build sustainable competitive advantage.
AIM: What kind of knowledge worker do you recruit and what is the selection methodology? What skill sets do you look at while recruiting in analytics?
We have a rigorous interviewing process where we evaluate candidates on soft skills such as creativity, work ethics, and passion for the field. We test candidates on their quantitative ability, structured problem solving, coding ability, and logic. We use questioning and case studies to assess these skills. Typically, we have 5-6 rounds of interviews. We also look for participation in data mining competitions such as Kaggle.
AIM: Would you like to share any example of an Insight that generated a huge positive impact for your clients?
RV: Given that our focus is in driving outcomes for our clients, rather than examples of providing insights, let me share with you an example where we created positive outcomes.
One of the world’s leading PC manufacturers wanted to explore online sales chat as a channel to drive incremental revenues for them. We engaged with them and deployed our Px solutions to drive better performance of the chat channel. Within a period of 8 months we grew the chat incremental revenues by 3X. This was accomplished using various intent prediction models for real-time targeting of the right online customer with the right offer at the right time, Machine learning models were used to drive better agent performance, text mining models to understand the “Voice of the Customer” and recommendation algorithms for right cross-sell and up-sell recommendations to the agents.
AIM: How do you see Analytics evolving today in the industry as a whole? What are the most important contemporary trends that you see emerging in the Analytics space across the globe?
RV: Analytics is embedded in all products and services around you today. Starting from how you look for information on the web, to how retailers target you, to how your credit worthiness is determined, to how social media sites recommend friends, everything is driven by analytics and data sciences. Analytics is transforming diverse areas such as banking, travel, retail, social sciences and biology.
Some of the key trends in this industry are –
- The emergence of Big Data – Data signals pour in from various human and machine sources in different forms – databases, weblogs, text, audio, videos. The ability to gather, process, understand and model this data to drive business action is a key differentiator for companies across industries.
- Personalization – The ability to target customers as individuals using data about their device, history, identity, location, context and intent and tailor the interaction to transform their customer experience.
- Machine learning at scale from structured and unstructured data
- Decisioning in real-time
This includes the ability to control-test-optimize.
- Leveraging social networks
Finally, there is a premium for the ability to driving outcomes rather than just provide insights.