The Rise of the Super Intelligent Marketer | CDP Summit 2022 (India)

  • August 5, 2022
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Lemnisk hosted a Customer Data Platform (CDP) In-Person Summit for the India region on June 15th, 2022 at Hotel Sofitel, Mumbai. The CDP Summit’s aim was to make enterprise marketers understand how they could create exceptional customer experiences using CDP-led hyper-personalization and increase their digital engagement and conversions. It comprised of two insightful panel discussions with leading industry leaders. This article focuses on the first panel discussion titled: The Rise of the Super Intelligent Marketer.

 

The Panelist details are as follows:

 

  1. Somesh Surana, Head – Digital Business Group, HDFC Ergo General Insurance
  2. Vivek Gupta, Executive Vice President, Bajaj Allianz Life
  3. Jayant Pai, Chief Marketing Officer, PPFAS Mutual Fund

 

Panel 1 | CDP Summit 2022 (India)

 

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Here are some of the main takeaways from the discussion:

 

The Rise of the Super Intelligent Marketer

 

1. Customer data in the last decade has really become this most critical currency of customer experience as a whole. Is there a framework in which you guide your teams to sort of organize and activate customer data?

 

Vivek Gupta: 

There are three areas that are seeing considerable growth and that are always defining how organizations work:

 

  1. The explosion of customer data – Brands are subjected to dealing with loads and loads of customer data. If they can utilize this data, it’s well and good or it can go to waste as well.
  2. Growth and commoditization of analytical solutions – As solutions are now commoditized, basic analytics can be done very easily. 
  3. Lowering of cost of technology and storage of customer data – You can store trillions and trillions of data at a fraction of the cost.

 

At our organization, we use a way of doing extract, transform, and load. That is the ETL solution of data that we use, where we extract the relevant information, transform it by doing sanitization and deduplication, and then load it for specific targeting. We do it across the customer life cycle.

 

The insurance industry is not something where there is frequent usage of the product. How do we reach out to the customer without being intrusive and tell him/her that it’s a need that he should purchase? This is where customer data and platforms like CDP come into the picture.

 

They help me understand what is the right time to reach the customer. Whether the customer has a product affinity, when he/she visited my website or digital channel, whether he/she actually liked the product or has an affinity, and most importantly whether he dropped off because of not understanding the product and what point he dropped off. So this is where I think customer data and CDP platforms will help us.

 

Jayant Pai:

As a mutual fund company, today we have around 25 lakh folios. The first-party data that we accumulate will be where the CDP also comes in. That’s where the base will be for our next round of investment. 

 

Somesh Surana:

Some of the challenges that all of us as an industry need to solve is to stitch all applications and the data together to have a one view. This is where a CDP will play an important role. Only then you will be able to provide an experience to the customer which they will appreciate. 

 

Once the one view is there, then what do we do with that data is something that marketers, the management, and the product teams need to sit together and think of. There are multiple use cases in insurance as a product category and there are multiple use cases outside of it as well.

 

One of the most important things is obviously personalization and hyper-personalization. When I say personalization, it is not only restricted to communication. Most marketers feel that if a user is coming to their website or mobile application, they will show him a different creative, and if another user is coming to the same application, they will show another creative and the job ends there. 

 

I think personalization to an extent of the proposition to be shown should also be different. Because I know where the user is coming from.  Whether he’s an existing customer or he’s a new customer. What are the characteristics or persona of that customer? And I can have cohorts and the proposition is also very much similar to that. Not only on customer acquisition but also on the service side there are multiple things that can be done.  For example, if I’m aware that the user is an existing customer and prefers English as a language whenever he calls, why do I need to ask him in the IVR that he needs to press two for English?

 

There is a lot that needs to be done for that one view of the consumer and especially in categories like insurance, there are other challenges as well. The data quality that is coming from different acquisition channels is not something that really gives the right view. From a direct channel, I have all the customer details. But if I go to an agency as a channel in insurance, I don’t get customer details because agents prefer putting their own details. Hence, even if I want the one view, what do I do with that data because that will not give me the right information.

 

So there are multiple challenges that we as industry marketers and business people will have to solve for. But yes, this is the way forward, this is something we’ll have to do to survive. 

 

2. What’s your approach with respect to customer insights? How do you build this sort of healthy experimentation and test and learn kind of a pipeline at your respective organizations? 

 

Somesh Surana:

It’s important to kind of not only have data but have meaningful data. Meaningful data should result in meaningful insights that marketers or business teams can implement flawlessly. 

 

A lot of people do A/B testing or drop-off analysis or they pull data and probably give leads to their call center. Can doing these tasks result in a profitable business at a lower cost? Are we doing it in the right manner is the question that we need to ask all the marketers in the business.

 

It’s not only collecting the data or deriving insights. You need to implement those insights in a manner that you can do all of this on a real-time basis. There are certain things that change faster than we expect them to change and hence it’s essential that real-time data and consumer insights are being looked at and revalidated again and again. You need to then implement the changes in such a manner that they can benefit the business and the organization on an overall basis.

 

Jayant Pai:

In a mutual fund, I don’t think real-time data is so important to us as compared to some other sectors. But it is still important because if somebody is trying to invest with us and are facing some problems, you will know immediately. Mutual fund forms are very long. There are a lot of things to be filled up and there are many drop-offs. When a user has dropped off, our call center immediately gets a notification and that has really helped because that immediacy is required. 

 

The first-party data that we have is the foundation of all my marketing so far. So for instance, if we offer a product suite of only four schemes, I know based on what I see that if somebody has a particular scheme, I will not send them a communication saying that please invest in this or add it to your folio because already they haven’t. Similarly, in a tax saver, there’s a limit of one and a half lakhs under 80c. So if I see that my unit holder already has crossed that limit, then I will not send them a mail again to invest in a tax saver fund.

 

If you keep on sending communications that they really feel are not relevant to them, they will treat you just like some other sort of provider and ignore the communication or put you in spam. Second is the nature of the communication which is of course personalized to a large extent. Our main aim is to reduce customer friction and so that’s why I was looking for a CDP because of the unifying feature with respect to data from various sources. The CDP will definitely help to reduce some of the angst at our end.

 

Vivek Gupta: 

We are currently at CDP 1.0 and why I say 1.0 is currently we are trying to have a unified view. I have not seen any company which currently has a completely unified view.  We still have certain silos, we still go with modeling, profiling, and persona creation. 

 

At my current organization, we use personas, we have digitally silent customers, digital customers, and offline customers. We have created segmentations and use them for targeting. But can I actually bifurcate all my customer base? Not exactly. So we use sharper targeting nudges and extract the data, use it, analyze it, and put it into use. It is not real-time. So it’s actually CDP 1.0.

 

There will still be a stage that will come very soon where we will be able to quickly modify this and target the customer accordingly. When this 2.0 and hyper-personalization happens, I believe strongly that these two will marry, and then we will see the actual benefit of a CDP coming across. But we have to put the groundwork from now itself for that to happen.

 

3. Within your individual experiences and the kind of practices you are trying to put up at your organizations, how do you focus this discussion around Artificial Intelligence (AI) and Machine Learning (ML)?

 

Jayant Pai:

We don’t do any AI currently. We are reliant on Google for providing us with suggestions which then we could filter out. AI may be far away for us right now. It will definitely help us to sharpen some things and to reduce some of the drudgeries later on. How you will harness it and use it in your business is up to each one of us. 

 

Vivek Gupta: 

There’s been a sea of change in how AI works now compared to the past and I have seen it across products. It’s still changing instead of evolving completely. The important part here is how we embed it into our systems. This is currently happening and I don’t foresee any financial organization relying completely on AI. But we are seeing changes in what information AI and ML are providing to take decisions in real life. According to a Gartner study, 65% of the decisions being made by businesses have become more complex than what they were before and those decisions can be taken by AI.

 

There are areas such as underwriting, fraud investigation, speech analytics, etc. where actual practical implementation of AI-ML is happening. My firm belief is that over the next two to three years, we will practically see decision-making in the boardroom with the help of information provided by AI-ML.

 

Somesh Surana:

For me, AI-ML could be a mischaracterization or overhyped or whatever we call it, it will become a reality. We will have to have specific teams working on this. At HDFC Ergo, we have a special task force that only specializes in AI-ML and works on it.

 

We have 10 plus bots for Conversational AI. We need to do machine learning on a regular basis and understand the consumer psyche. Every 10, 15, and 20 kilometers, the dialect changes, and even if you want to implement the voice bot or if you want to implement something, you need to understand what a consumer wants. We’ve done experiments where we have used an application. We are now hearing what people talk about and then we are making the machine learn over a period of time so that we get the right set of results that we are looking for.

 

It’s not only for acquisition but for us industry use cases are renewable. If it is just a payment such as a policy renewal, there are not many changes there. Hence, the bots and the cognitive boards or the conversational AI really will help us in making that manual process automated or remove the manual intervention. 

 

When it comes to vehicle inspection, a surveyor used to go do it earlier. Now, we ask the customer to just make a video and send it to us. Most of our self-inspection is now looked over by AI-ML. All our cases are approved within seconds or minutes and the accuracy is excellent. We’ve reached a level where the damage in your vehicle can also be looked at by AI-ML.

 

So I think there are multiple use cases. We need to break them and have the right set of people working on them whose day in day out job is doing the problem. Any machine learning or AI platform needs to be fed the right data. In our industry, we are not feeding in the right data and we are expecting the results to be there. So these are the problems that we need to solve and it will be a reality that we’ll have to rely on AI. 

 

 

By Bijoy K.B | Senior Marketing Manager at Lemnisk

 

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