Bridge Briefings: Panel discussion on ‘Driving Business Action with Analytics’
21st April’ 2016 at BRIDGE School of Management, Gurgaon
- Anubhav Goyal – Vice President & Head - Analytics, Snapdeal
- Saurabh Sharma – Founder & CEO, Indus Insights
- Saurabh Bansal – Principal Consultant, PWC
- Dipyaman Sanyal – Founder & CEO, Dono consulting
- Keynote address by Naveen Yadav- Analytics Solutions Leader, Genpact
This panel discussion was conducted to understand the situations mentioned above, to create a sustained conversation around the need for effective analytics implementation in India and to know how a trained, attuned workforce can drive business outcomes.
Following key topics were touched upon during engaging panel discussion:
Where does analytics come into play?
There are several areas where analytics can be involved. For example, in an e-commerce business, analytics can be involved in
- Pricing: How to price the product right so as to get an edge over the competitor, what is the tipping point for the consumer and many more such questions can be addressed.
- User growth: Each person has to be sent a different notification or reminder. Here is where analytics comes into play.
- Supply chain: There is a lot of optimisation done in supply chain. For example, addressing a question like what is the right route to take to deliver a product? Analytics comes into play here.
So, essentially where there is a constraint or a problem to solve, that is where analytics is used.
A huge demand of analytics is coming in the manufacturing sector as well. For example, with analytics, one can actually plan when is a machine going to fail? So around that, the production can be planned.
Another big thing is internet of things. It is being used in the aviation industry. For example, a lot of data is carried with one flight. To use and analyse that huge data, analytics is being used. So, this enables the pilot to get real time data that helps him make decisions.
The Journey of analytics
There is no one model used for analytics. In most organisations, analytics was started with a centralised unit. For example, in Snapdeal, analytics was first centralised. Then, with time and need it was decentralised and now again it is being centralised. This journey was also seen in Maxlife and would have been the trend in other organisations as well.
The question that needs to be asked is what is the hunger of the management for data?
If the hunger of management for data is with one of the CXOs, then it is decentralised to that person. If the CEO owns that hunger then it becomes centralised because he is taking the big decisions related to it.
What is the visible change in analytics?
The big change is merging of strategy and analytics. In the earlier days, the advisors had a more sane understanding of ground realities and had more to guide. It was more of a gut call by the advisors. That role is now changing. Now information has begun to depend more on data. So that is the role analytics professionals will slowly begin to play in the corporate hierarchy. It will no longer be a gut call, but an informed decision which analytics professionals will be able to guide everyone towards.
- The scenario in consulting industry
The merging of strategy and analytics is a fundamental shift even in the consulting industry. The consumer earlier used to be the CIOs. Analytics assignments were sold to them. Now it is the CMOs, CEOs, CHOs who are buying data and are the key stroke-holders. So now analytics is not only a technology issue. It is a business strategy issue. And these are the issues which the management wants to solve using analytics. There is a clear demarcation now from before. Earlier it was a pure-play technology project and now, analytics is more of a strategy project. This approach and shift will help build analytics across an organisation.
- The scenario in a marketing function
Analytics is a function of what is the size of the organisation. If there is not enough need for analytics project, then it can be started with a centralised unit. As the need for analytics grows, it can be decentralised. In the marketing function, there is a strong emergence of Chief Customer Officer (CCO) who would own everything around the customer and not just be a part of the marketing unit. Here, the CMO is more likely a candidate for a CCO role. We see a lot of e-commerce players carving the role of CCOs because at the end of the day, all these channels and customers being at the centre of it has made customer experience the prime driver of growth. From push marketing, the industry is moving to ‘demand generation’ marketing. And with this shift, a big part of decision-making will depend on data and analytics.
Examples where analytics is used in a marketing function
- The classic contact policy
This is - don’t touch a customer more than thrice a quarter. Most of the banks have this policy else it leads to customer fatigue. So wherever this policy is being used, analytics comes into play because through analytics it can be ensured that this policy is more driven by data and not by gut feeling.
- The card folio (Support system of GE card folio management)
In earlier days, there was a contact policy of not reaching out to people more than thrice a quarter. Now, let’s take two axes. On the horizontal axis we have ‘number of contacts’ and on the vertical axis, there is ‘response rate’. Here is where analytics is used. If you try to find out the ‘inflection point’ (the point where you get the maximum response), that is the point which really decides what should be your contact policy. Therefore, on the basis of analytics, each contact policy for each customer needs to be unique.
- Customer experience method
Most of the traditional banks have not given it the focus that it needs. Retail organisations have tried to. There has been a huge transformation in the last 10 years, which means that not only the channels, but also the navigation of customers over devices is becoming more challenging for marketing professionals to trace. Earlier, there was the traditional marketing method where mailers were sent to customers. Then came digital paid search. But now, with various devices being thrown into the market and into the hands of customers, technology cannot be depended upon completely to give information. Technology may have rule engines, but to find the coefficient of rules engines, the information comes to data centres. And then analytics comes into the picture to evaluate and analyse the data that has been gathered. Only with the analysis of data, customer experience can be enhanced.
Key challenges of data scientists
- Getting the right analyst in the team
There are different skills used in analytics - technical skills, industry knowledge, marketing functions and more. What’s difficult is to get the right mix of people with all of that mentioned above.
- The need of data scientists who are good at storytelling.
There is a need of people who can present an idea or a concept to business leaders. A business leader will need a business solution, not an analytics solution. Therefore, there is a need for analytics guys to make the transition. And, of course, one needs strategy guys to help in making the transition.
- Talent Acquisition
Talent acquisition is tough. And to get the right people, here is what needs to be addressed – the ‘why’, ‘how’ and ‘what’. At mid to senior level, people end up speaking more about the ‘how’, ie, how they did the project or how they solved a particular analytics problem. This doesn’t ring a bell because this is the job of a person who is probably starting out in his career. Those starting out should definitely know the ‘how’.
But as one moves up the ladder, one will be rewarded for the ‘why’ and ‘what’. The ‘why’ is - why is this problem important? Why am I solving this problem? Why is it important for the business? The ‘what’ is - now that I know that this is the solution, what will I do with it?
Analytics is about micro-innovation. It requires that questioning mindset and that innovative thought process to exist at the ground level too. To be able to look at the data each day and figure out what can be made out of this data is what is needed. And data scientists are facing a challenge in finding such people.
- Change Management
The challenge here is how to convince people that analytics will actually help. The fact that one has to convince the division and then make them the creator of an analytical journey as well as show them how this will help in their scope of work is challenging.
There is tremendous future of HR analytics. Here’s where analytics is being used and will grow:
- Attrition prediction
- Understands training needs.
- Succession planning
In all these areas analytics was used earlier too, but the difference now is the amount of data that is being consolidated and also the usage of specific models to develop that required data. So, the data that is being received is much more accurate now. HR analytics will become big and organisations within India and outside will slowly realise its importance.
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