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Easing the Adoption of A Customer-Centric Product Development Process with Data

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Easing the Adoption of A Customer-Centric Product Development Process with Data

How do you develop a customer-centric model? Data, and lots of it. Solve some of the issues with your development process with actionable intel.

· Agile Zone ·
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In search of a sort of customer-centric product development Nirvana (and the organizational tenants that allow it to flourish) known as high-tech anthropology, executives are willing to pay upwards of $20,000 to spend time with the founders of Menlo Innovations, according to an article in Forbes. The Michigan-based software design consultancy has achieved Apple-like mystique with its unique philosophy that guides both how it works and the work it completes for its clients.

In fact, according to the Forbes coverage, a full 10 percent of Menlo Innovations’ $5 to $6 million in anticipated revenue for 2018 will come from the fees it charges for tours and consulting.

This thriving business borne of educating others on its philosophy and implementation thereof is evidence of just how challenging it is for organizations to truly adopt customer-centric product development processes. Product managers and engineers will list the reasons why with ease. Development requests for big accounts take priority. There are too many fire drills in response to support requests, bug fixes, or potential big net-new customers. And there is a constant struggle to balance the expectations of marketing and sales, and deliver software on time.

As such, the journey to customer centricity is a journey in every sense of the word. But along the way, there are enablers that can ease the process of getting there. One of these is data. By further infusing usage data into our product development processes, we can begin to build customer centricity into engineering in a way that is more easily evangelized, or simply less easy to dispute, across the organization.

First, having data on how the software is used makes it easier to push back on projects that will divert resources from other projects that engineering knows will benefit the broader customer base. Being able to see, for instance, how many users are on an older release, and break down that use by a number of parameters that lend evidence on their engagement with the software can be valuable information on pushing back on a request from sales to engineer backwards compatibility with a new release. It also provides a springboard to offer solutions. These could span data-informed discounts to upgrade, to simply developing more educational content on how the new release will enhance how users currently accomplish the process with the legacy functionality.  

Secondly, usage data gives developers another channel to “talk to” customers, a way to augment the essential, but sometimes difficult and time-consuming process of in-person customer conversations. For instance, by tracking events and feature usage, the team can develop different user profiles. The team can integrate this data with a messaging framework, to pull and push information to the right audiences. In such a way, usage data can also help engineering evolve functionality in the way that users actually accomplish their process.

Consider a beta testing strategy that integrates in-app messaging and usage data. It enables the tester to customize and automate messages to unique user profiles and actions – sending information based on user profile, license status, time elapsed since installation, frequency of usage, configuration settings and more – to ensure that the messages users receive are relevant, impactful and drive value for the business. These tools make the beta process more efficient by shortening the feedback loop and providing specific insights to inform the next revision with needed improvements.

One of the goals of Menlo Innovations is to eliminate software that requires a technical manual – or even a help desk, because it is so intuitive to use. In such a way, access to usage data and analysis can play in important role in helping our own organizations move toward this ideal – by helping us discover not only what customers say they want, but also getting to the root of what they need.


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Topics:
customer analytics ,product development ,software analytics ,data driven proces ,data analysis ,agile

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