Embracing Product-Centric thinking within a services organization
Today’s world is driven by products! Not just physical ones, but equally so by digital products and goods. Seems like they are changing some business models too.
Self driving cars. Revolutionary!
Virtual reality goggles. Bringing reality to your fingertips, but not really.
Instagram. Designed so that a human devotes, on average, 53 minutes of their time swiping through the lives of others.
The world has been constantly hammering the anvil in an attempt to create the perfect product that will act as the black-hole for the desired outcome. Time. Money. Clicks. Swipes. To do that, product companies hire the smartest people that walk the face of the earth and task them with creating the right conditions that is designed for making the product sticky. Identify the right messaging for people to adopt it in their repertoire of choices. Develop the right gamification elements to psychologically tailor the experience that an ideal user has to go through.
Alright, but what does this have to do with the other side of the business spectrum? The outsourcing (or) services industry.
Just like the rest of the world, the services industry is at a watershed moment. The views and opinions published here pertain more to the Analytics & Data Science outsourcing industry, but the broad ideas should be applicable to most companies in the general services sector.
Now, what did I mean by ‘Watershed moment’ for the industry.
While the analytics industry is projected to grow to about $275 Billion by 2023 — which is a staggering amount by itself — there are a few considerations to be taken before jumping onto the $275 Billion bandwagon. This projection is for any sort of investment in the field of Data Engineering, Business Intelligence, and Machine Learning — External or internal. With analytics and data science functions becoming more mature and the talent gap becoming wider, services companies need to rethink the way they approach their businesses, specifically the way they approach project delivery. There are aplenty of causal factors playing an invisible hand and acting as the master of puppets, the industry can straight-off-the-bat get some inspiration from how product companies approach and solve use cases.
Companies that designs products to be used by their consumers take into account a plethora of factors right from which color should the dial be in, all the way till what channel should they use to tell my target that they have just received a once-in-a-lifetime discount of 10% if they purchase the product in the next 15 minutes. While the life cycle of analytics and data science projects take a different route within the world of a product company, there are a few inspirations which services organizations could, and should, adopt to ensure that the value provided is that much more.
- Think solutions, not projects:
Being twice removed from the actual consumers, the tunnel vision that is developed by teams delivering a project is confined to the bells-and-whistles of the project and ONLY the project. This distilled thinking takes us down a rabbit-hole that will shroud the larger picture at play and how the “project” that is being executed is going to fit into the larger scheme of your customer’s delivery value chain. Take for instance, a sprint team that is developing machine learning models for identifying personalized offers for their customers. The typical project planning and measurement activities focus on the core scenarios surrounding the success, risks, and opportunities within the silo of this context. Project managers will plan around the complexity of model development and data connectivity issues into their estimations. Data scientists pore over the ideal algorithm that will be able to identify customers who are likely to churn. Analysts will work on setting the threshold values for the different campaign possibilities. These activities, while vital, will act as blinders to the larger potential opportunity available to the organization.
How could the same situation play out with a bit of solution thinking laid in?
By training the mind to break the barriers confining the project, teams will be able to question certain assumptions and contextual infrastructure and develop a strong thinking that can be generalized to any potential business that might have similar problems that has to be solved — but at different levels of complexity. Extending the situation that was referred to earlier, a solution-centric thinking will enable the teams to assess the foundations needed for a personalization model to be built i.e Are customer intelligence parameters such as Churn risk, Lifetime values calculated? What is the most common IT architecture on which these models are to be built? Could there be different teams using the same information (Campaign managers vs Market managers vs Insights managers)?
When approached with the right rigor and consulting-led thinking, identifying solutions invariably opens up more doors for ancillary analytical work streams to open up!
2. Ingraining user experience based thinking:
Design is key to the adoption rate of a product that is introduced to the market. Product companies have dedicated teams of individuals and designers who mull over the smallest details before including or excluding from the product specs. Should the color be red, green, or blue? Should the buttons be rounded or square? Extensive analysis of customer preference data and focus group feedback is taken into consideration before arriving at the end design.
So why can’t this be institutionalized within a services organization?
Answer: It should. Considering the scenario and persona of the individuals affected by the custom solution should be the de facto line of thinking — even during solution designing. While a lot of the teams focus on what model to use and how to feature engineer the right variables to build the best performing model, there needs to be dedicated focus on the ‘Last mile delivery’ mechanism before the solution could be classified as completed. At Brillio, the engagements are structured to weave in data scientists, analysts, and user experience/designers as a part of the working team to constantly remind ourselves of how the insights or the model is to be consumed once it has been productionized. Next time you or your team is working on a solution, try answering these questions and structure the solution to fit the responses.
- How much time would the end user spend in looking at/consuming the solution?
- What action would the end user want to take with the information provided?
- Would the user associate a feature with a “Form” mindset or with a “Function” mindset?
- If the user acquires the solution without a manual, how easy or difficult would they find while trying to use it?
Such questions, when answered, provides valuable insights into some of the limitations in reality, which might not have necessarily been uncovered while approaching the solution from a technology or algorithmic lens. But most importantly, well designed solution-products make a mark on the customer and one that will likely last for a considerable period of time.
3. Go AGILE with Analytics:
AGILE has traditionally been associated with IT and Application development from the inception of the concept. At a certain tipping point, taking the AGILE route with sprints capturing features and sub-features became the norm while developing a full-fledged product where multiple teams are focused on building their pieces of the jig-saw puzzle.
Analytics teams, on the other hand, are still functioning on traditional waterfall methods of execution which mandates a requirement gathering phase early on in the life cycle and is generally inflexible towards a large quantum of change — thereby making the process generally inflexible and rigid.
While the bugle horn has been sounded by many practitioners in the industry, AGILE for Analytics is still at a nascent stage and would require a collective effort to attain critical mass for adoption.
Moving Analytics teams to AGILE should still be approached with some steps of caution. The natural tendency to fall into gut-based estimations and identifying sequence of work based on available resources will be strong and the force required to move this inertia-filled body should be strategically applied.
- To begin with, some teams within the larger project could adopt AGILE and execute their work streams using story points and sprints as the unit of effort and time respectively. This gives a runway for the other teams to follow suit and will quickly be able to adapt a framework that might be applicable to the context of the project.
- The other step could be to forcibly make teams to think parallel activities to overcome the waterfall mindset. While model development happens, teams could work in parallel for building the data pipeline, designing of the right views and technology for visualization, identifying user personas and tailoring type of insights accordingly. This exercise will help in easing the processing of shifting the paradigm of serial planning.
- Think MVP (Minimum Viable Products) — Even if not perfect, the intent is to create a base working version of the model/dashboard/analysis and let users test the functionalities and features. This ties in the original solution-centric thinking to delivery and will also help the teams be more aware of user feedback and expectations, thereby making them more nimble to changes and aid in better planning
Of course, traditional AGILE processes and methods might not be directly applicable given the ambiguous and repetitive nature of analytical solutions, but the core concepts are still in-line to creating high performing teams which are quick to react to changing market conditions or customer needs
In conclusion, I believe that services, as an industry, will start to blur the lines with their counterparts in the business spectrum. As analytical functions within organizations become more mature, a need for self-serve tools or self-serve products will become the need of the hour. And while the industry will always hold the trump card of being able to build bespoke solutions that makes it difficult to replace, the marching orders to change and revolutionize the way service companies function and deliver their solutions have been laid down.
Do you have any processes that you follow to make sure you deliver a holistic solution to your customer? Let’s talk!
