Music, marketing and mathematics can combine beautifully to create a million possibilities

As I reflect back over the year, this is one conversation that has left some deep impressions on me. The challenges that are there in rewiring our skills, learning from different streams and applying them in our day-to-day working is becoming more and more critical.

Marketing today is on the threshold of change. In the past, marketing as we knew it was largely dominated by 30-second TV spots and other mass media such as print, outdoor, radio and so on. The number-crunching only came into play while deciding which medium to back in the advertising campaign and for what price to buy the media.

But, look around today and there are the likes of Google, Facebook, Twitter and others who apply complex algorithms such as Page Rank, Adsense, marketing mix modelling, content marketing and so on along with technology (analytics, digital marketing, search engine optimisation (SEO) and search engine marketing (SEM) to make marketing a lot more data-driven. Similarly, in music the magic of maths plays a huge role.

As a musician, Padma Vibhushan Umayalpuram K Sivaraman has an accomplished career of nearly 71 years – he debuted at the age of 10 and has been seen and played with the best musicians across the years. His son has been one of the earliest adopters of marketing analytics in India. cat.a.lyst brings you a conversation between the legendary mridangam vidwan and his son S Swaminathan, Co-Founder & CEO of Hansa Cequity, a major customer marketing & analytics firm, on the cross-learnings from music, maths and marketing.

 

Here's the full link to the conversation

 


"When you implement a new technology in your organization, look at it thro' a customer lens"

Recently, I had a chat with Kate Visconti, Managing Director of  Acumen Solutions, USA who takes care of the Sales Acceleration and Change Management Practice. She is also an Adjunct Professor with Santa Clara University, USA. It was an interesting & engaging conversation and here are the highlights of the discussion.

What was interesting to me was the points she made for successful adoption of technology in an enterprise - the importance of business process re-engineeering, change management as much the software or technology selection & upgrade itself and looking at the implementation itself thro' a customer lens.

I have believed for many years that these were the most critical aspects when it comes successful technology adoption & usage and often enough importance & attention is not paid within the organization and the stake holders to this area. Kate brought this out beautifully and reinforced this very well during our conversation.

Here is the summary of our chat:

Swami: How do you approach a technology implementation and what do you believe are key differences that you or your organization focuses on?

Kate: We always start any technology implementation with a first principle approach - how is the enterprise and their stakeholders currently thinking, feeling and doing with their workflow right now. We strongly focus on process innovation and not a run-of-the-mill implementation like other system integrators. We conduct multi-day workshops, build customer personas, enable collaborative  conversations across cross-functional groups to understand current issues and identify opportunities for optimization and automation. For us change management is as much important as the technology implementation itself. That's a key difference we believe we bring to the table & where I have seen successful technology transformation happen. 

Swami: When it comes to selecting or shortlisting technology platforms or software etc. and adopting technology across the organization, which are areas that are normally missed by them in your experience?

Kate: Most of them don't have an 'outside-in' approach and we bring that to play when we work with organizations. When a tech platform or software is selected, there is a lot of discussion on features, benefits etc. but very often during implementation, they don't see the technology from a customer's lens. We do a lot of shadowing to know how the current processes work, do customer research, customer experience benchmarking and often these are areas that are not often not given enough attention or missed most often.

Swami: Enterprises spend millions of dollars on acquiring licenses for tech & software and you have seen many successful technology implementations, adoption and transformation across enterprises, what do you believe is the secret sauce for their success?

Kate: What I have seen in enterprises where there have been successful technology transformation or adoption is that if there is an Engaged Executive Sponsor, the chances of success improve by at least 2 times! An engaged leadership committee which defines the vision & organizational priorities makes the next difference in the success as the technology road map, business outcomes and priorities get defined well. Engaged Stakeholders make the next difference - end-users, managers, executives, customers as they influence adoption and validate user experience across the enterprise. These I believe are the secret sauce to success and where I have seen this happen in organizations, things have been successful most of the time.

Swami: You also emphasize a lot on hand-holding the enterprises which your organization does after you implement the technology or software. That's a very interesting point that you make and in fact what kind of metrices do you track and for how long do you suggest one must do this?

Kate: I normally suggest we do this for 60-90 days ramp cycle depending on the scale and complexity of the project and implementation. We track a lot of metrices post implementation like:

  1. Project Success - Both by way of budget and time
  2. Adoption -  Quality of inputs that go into using the software or technology within the enterprise after roll-out - Timeliness, Completeness of the information, Not just no. of log-ins but demonstration of new user benefits and referrals etc.
  3. Business before vs Business After - Benchmarking and looking at % increase in agreed business metrics, % decrease in, say, sales cycle or service cycle reduction etc.

These are some of the sample key metrices one should look at.

Swami: There is often an underestimation of the services costs which enterprises need to spend to make the technology transformation successful. There is a lot of focus on licenses fees, infra needed, maintenance & renewal fees etc. but much less attention is paid to services & costs associated with it. Right?

Kate: I totally agree with you, Swami. In my estimate, these may vary by project scope, complexity and these are directional just to give you a perspective that enterprises need to factor these services cost for a successful technology transformation - up to 30-40% factor for change management, engaged leadership, customer research, building alignment workshops, post implementation program adoption costs etc. These are over and above license fees and customization costs they would incur during the course of a 3-4 year project or program.

Swami: I saw that right at the start of our chat, you mentioned don't treat it like an IT project. What did you mean by that? 

Kate: I often quote from the point made by Forrester Research Chairman and Chief Executive Officer George Colony made on technology projects, that in this age it is transforming from IT projects to Business Technology projects thinking. This is a key difference to successful technology selection, implementation, adoption and usage. I also say - Don't treat it like an IT project but treat it like a customer project!


Deep Learning, Personalization & Privacy

These three - Deep Learning, Personalization and Privacy are indeed oxymorons.  The fact that when they come together for contextualization, relevance and differentiated customer experience - privacy takes a back seat!  When Privacy takes centre stage, then Deep Learning & personalization take a backseat! The real question, is from a customer point of view, can they work in harmony? How do companies use them effectively?

What deep learning can do is well documented. When you listen to this talk by Jeff Dean on Large scale deep learning at Google, you will realize the power of its capability. The most important difference you will see is how Google has embedded that in its products and services.This then brings the issue of privacy to the center of the debate, as this is the data of the customer that Google analyzes & monetizes. However, when it comes to the synergy between Deep learning and personalization, all the data that is used to understand this customer, effectively means personalization can be done like never before. So, the next time you walk-in to your favorite store, or your bank, or your online store, or your health care provider - the personalization possibilities thro' virtual assistants can create a phenomenal experience to the customer. Hence, if deep learning helps customers make their life simpler, then what's the problem with privacy? I think if this is done with the permission of the customer, then puts to rest the debate.

However, this has a lot of learning for the other industries - as the customer pays and owns their products, unlike Google where products are sometimes used for free. Take for example an automotive brand - the applications of deep learning, personalization is phenomenal. With most cars fitted now with navigation systems and telematics devices, the data this can throw about the customer, their driving habits into their automotive platform is phenomenal. The driving habits, the engine data, travel data, innovative use of public data all represent great deep learning application opportunities for the automotive brands to drive personalization & differentiation. Here, privacy is something of little consequence or debate as any assisted customer service will only enhance the customer experience. The same goes with banks - where the usage of the mobile app, online banking, payment apps, shopping data  thro' credit cards, public financial data etc. will only create opportunities for deep learning applications and personalization for enabling personalized financial solutions.

The question really is not will they but they will have to do it soon.