Customer World

In this second episode of my podcast ContraMinds, we explore how mediocrity is one of the greatest epidemic facing people and organizations. It explores how a mediocrity mindset sets-in, what differentiates people who strive for excellence versus people who want to be mediocre and what needs to be done to quarantine ourselves or organizations from mediocrity.

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!

I loved this article by Jeff Leek on how the era of data has evolved over time.

1. The era of not much data This is everything prior to about 1995 in my field. The era when we could only collect a few measurements at a time. The whole point of statistics was to try to optimaly squeeze information out of a small number of samples - so you see methods like maximum likelihood and minimum variance unbiased estimators being developed.
2. The era of lots of measurements on a few samples This one hit hard in biology with the development of the microarray and the ability to measure thousands of genes simultaneously. This is the same statistical problem as in the previous era but with a lot more noise added. Here you see the development of methods for multiple testing and regularized regression to separate signals from piles of noise.
3. The era of a few measurements on lots of samples This era is overlapping to some extent with the previous one. Large scale collections of data from EMRs and Medicare are examples where you have a huge number of people (samples) but a relatively modest number of variables measured. Here there is a big focus on statistical methods for knowing how to model different parts of the data with hierarchical models and separating signals of varying strength with model calibration.
4. The era of all the data on everything. This is an era that currently we as civilians don’t get to participate in. But Facebook, Google, Amazon, the NSA and other organizations have thousands or millions of measurements on hundreds of millions of people. Other than just sheer computing I’m speculating that a lot of the problem is in segmentation (like in era 3) coupled with avoiding crazy overfitting (like in era 2).

What is interesting to me is that how this will impact the world of analytics thro' application of new methodologies like AI and Machine Learning is one thing. The other one that I see is that how does this 'mass of data that is being generated' represent the right population that one is developing insights on. There is a lot of potential biases that can happen given the kind of people who have access to the net.

So, the future is about an era of mixing lot of samples offline along with a lot of data that is being generated online. The power of data fusion techniques will be required to build meaningful insights and predictive actions by various industries across the world.