Stepping Up the Game: How to Produce Profound Insights by Elevating Your Analysis

Photo by Stephen Dawson on Unsplash

Two Sides of The Coin In A Raging Warfare

Running a business is all about finding the right moments. Say that one would like to do business and launch a new product, not only that one needs to know the right product for the market, but also a full understanding on the right time to launch, the right place to distribute, the right price to sell, and the right promotion to boost sales performance. The process to unravel those right moments is perpetual, therefore one must not stop once the right moments are found. There will always be a shift in finding the right moments as customers’ needs are progressively changing. What was once an attractive feature or selling point from a product might become the customary standard nowadays. This is one of the challenges that every organization needs to overcome.

Fortunately, there are so many ways to tackle such challenges, one of which is by listening to the customers. Customers’ voices are everywhere to be found. In the era of technology, it is even easier for a business to collect those voices. The practice that usually took time until months are now can be simplified within days and a few clicks. Moreover, developments in the area of data collection make it possible for everyone to gather any kind of information from customers, be it in the form of thoughts, numbers, voices, pictures, videos, locations, etc.

With the abundant data that can be collected, every business next objective is how to properly utilize those data. Data abundance, if not handled properly, may cost an organization to fall into one of the following:

• Indecision paralysis, that is a condition where an organization becomes so drawn with the profusion of information but fails to act or go anywhere due to lack of priorities to process the data and cultivate a meaningful insight.
• Insubstantial insight ideation process, that is a condition where an organization lacks the capabilities of generating profound insights due to the shortage of resources, ability, etc.

Most organizations fall into the latter category, and your organization might be heading there too. Gartner reported that in 2017, of 196 surveyed organizations around the world, 91% had not yet reached a “transformational” level of maturity in data and analytics, despite this being the top investment priority for CIOs in recent years. Indeed, 60% of those surveyed rated themselves average or below average when it came to data maturity.

“When companies are data-rich but insight-poor, innovation suffers. Ideas for new products, services and business processes are episodic and often off-target.” — Tata Consultancy Service

There is a saying that in this digital era, data is the new oil. Take it raw and crude, it will become a large and useless numerical cascade. But once it is properly processed and refined, it will give so much power for the organization to boost and soar. To give a better understanding of the saying, let’s look at how ABB successfully process the data they gathered and deliver a numerous revenue stream. They managed to create ABB Ability™: a platform that could gain real-time data and monitor conditions of the industrial facilities (such as factories, oil wells, and power plants). Those gargantuan number of data surely would mean nothing if it couldn’t give any insights on the condition of the facilities monitored. Luckily, the story didn’t end there. Based on the data that was relentlessly recorded and monitored, the platform was able to perform a thorough analysis and showed how the operating costs of the facilities could be reduced while also improving safety and maintenance. By doing so, in 2017, ABB showed an increase in orders of the platform by 11%. They also estimated that ABB Ability™ had the potential of $20 billions of annual sales in the future. This concludes that by having the ability to produce meaningful insights from such abundant data, not only cost savings that can be achieved, but also a new generation of revenue streams.

ABB Ability™ is surely a machine that can perform numerous complex tasks to provide meaningful insight. However, that doesn’t necessarily mean that an organization needs to invest in such wondrous platform with such exorbitant cost. It is a good thing to invest in a platform that can enhance the organization’s level of maturity in data and analytics, but understanding to what extent an investment should be made is all that matters. What’s important is that an organization understands how to refine the data to create a sharp story, communicate the story to produce a profound insight, and act based on the insights given. The process surely can be done with the resources that the organization already have, that is the resources who are capable of cooking the numbers. Having such handful of resources is an ultimate ticket to elevate your data analysis. This will differentiate the organization that is capable of producing cutting edge insights from the mediocre one.

To understand how to produce a cutting edge insight and how it differs from the mediocre ones, take a look at the following example.

Example: Elevating Customer Satisfaction (CSAT) Analysis

Qualtrics defined Customer Satisfaction (CSAT) survey as a tool to understand customers’ satisfaction towards given product, service, and/or experience. However, apart from understanding the satisfaction level, the CSAT survey can also be used to understand problems encountered by the customers when using the offered product and/or service. The survey often uses a combination of Likert scale (to ask customers’ satisfaction level towards certain attributes) and open text/pre-coded choices (to understand the reason for their satisfaction/dissatisfaction)

Figure 1. Example of CSAT Survey
(source: www.tokopedia.com)

It is reported that a high level of customers’ satisfaction is strong predictors of customer retention, loyalty, and product repurchase. Customers stay loyal because they are satisfied, hence wanting to continue the relationship with the organization (Curtis, 2009). This can be shown by customers’ willingness to promote, word of mouth, repurchase intention, etc..

It is generally easy to make use of the data collected from a CSAT survey. Take the top 2 boxes and mean scores, then you have the satisfaction index that shows the satisfaction level of your customers. Issues and pain points can be seen by further analysis of the reasons for those who are dissatisfied with the product and/or service. Track the scores and the reasons over time and the impact of every improvement can be determined. Voila! The job is done straight. Still, it is a common way to analyze your CSAT. There are so many ways to elevate the analysis aforementioned, hence producing a profound insight. The following mentions some of them:

Exhibit One: Analysis by customer segments.

Customers’ needs are very unique, shown by varying degree of needs across different customer segments. For example, urban customers probably need faster delivery of goods they are purchasing, but rural customers still need assurance for their product to reach at their doorstep once they make their transaction. Analyzing the CSAT score and index from these segments can give richer insights for the improvement as users’ needs and dissatisfaction might differ from one segment with the others. There are several ways to analyze CSAT by customer segments, including the addition of segment related questions in the survey, crossing the recorded response with internal data, etc. Segments that can be analyzed are also varying, such as geographic segments (e.g.: urban and rural), demographic segments (e.g.: age, socioeconomic status, etc.), psychographic segments (e.g.: lifestyle value, etc.), and behavioral segments (e.g.: usership, purchase decision-makers, etc.).

It is not a bad idea to analyze your data by total, but when there is a chance to slice and dice the data, the produced insights would be more meaningful and targeted. However, careful considerations need to be undertaken when doing analysis by customer segments, one of which is that the sample size must be adequate and representative enough to portray the whole population. Consequently, the correct segment definition must be established. Sometimes, this means that you need further research to define the segments (as in the case of psychographic and behavioral segments).

Exhibit Two: Analysis by using problem solving techniques.

Users’ issues and pain points can be gained by looking at the reasons for their dissatisfaction. We can actually take further steps to understand what causes them to state those reasons, hence understanding the problem from its root cause. There are so many tools to do so, one of which is by Duncker Diagram.

Figure 2. Example of Duncker Diagram
(source: umich.edu)

Duncker Diagram helps us to define profound solutions from identifying problems from its root cause. By using Duncker Diagram, it is possible for us to solve the present state problem and generate solutions by both achieving and not achieving the desired state. There are two types of solutions that can be generated by Duncker Diagram: Functional Solutions and Specific Solutions. Functional Solutions are the ones that tell us what we can do to move from the present state to either achieve or not achieve the desired state, whilst Specific Solutions tell us how to implement those Functional Solutions.

For example, it is found that the reason consumers dissatisfied with a product is due to its taste. It is also known that product freshness correlates with the taste, meaning that as the product becomes stale, the taste will degrade. Hence, manufacturers need to come up with a solution to market the product while maintaining its freshness. By using the Duncker Diagram, solutions can be generated as follows:

1. Achieving the desired state: get the cereal to market faster: In order to get the cereal to market faster, manufacturers can build more plants closer to market locations or by improving the transportation system. Doing the latter will be more efficient, both in terms of time and cost, hence the former solution is rejected. Next, to improve the transportation system, manufacturers can hire former race car drivers, ignore the speed limit, build an integrated supply chain management system, etc. Generated ideas will be evaluated with all supporting data until proper and targeted solutions are achieved.
2. Okay not to achieve the desired state: it is okay not to get the cereal to market faster: To maintain the cereal’s freshness by not getting it to market faster, manufacturers can make cereal stay fresher and longer. This can be achieved by adding the chemical to slow down the spoiling process, make tighter packaging and increase water and air impermeability, etc. Again, evaluation is made with all supporting data until correct solutions are achieved.

Generated solutions from both streams will then be analyzed for the feasibility, both in terms of technical and economical, to achieve a sound solution.

Figure 3. Duncker Diagram for Cereal Problem
(source: umich.edu)

Exhibit Three: Analysis by using Kano Model.

Not only unique, but customers’ needs are also progressively changing over time. Therefore, a better understanding of how to answer customers needs, make them satisfied, and turn them into delight is needed. To do so, enter the Kano Model. Kano Model is an insightful way of understanding, categorizing, and prioritizing customer needs and requirements for a product and/or service based on the likeliness to promote satisfaction. To do so, one question module needs to be added apart from having the current satisfaction module, that is the functionality module. Satisfaction module will ask whether customers are satisfied with the product and/or service, while the functionality module will ask the experience that the customers are having with the offered product and/or service. Both question modules can also be asked at the feature level, hence gaining a deeper insight into what feature works and what feature doesn’t.

Afterward, customer satisfaction and product and/or service functionality can be crossed to produce a Kano Diagram, shown in the following figure:

Figure 4. Example of Kano Diagram
(source: mindtools.com)

Based on the Kano Diagram, it can be seen that the offered product and/or service will fall into these categories:

• Performance. The satisfaction of the product, service, or feature is linearly dependent on the product and/or service functionality. In other words, if the product, service, or feature has good performance, it will drive customer satisfaction. However, failing to do so will make customer dissatisfied. For example, users’ satisfaction on their phone/gadget will be very impacted by its battery life. Long-lasting battery life will make users satisfied with the product. The opposite will happen if the product has a short battery life. Another example is getting a fast response from a customer care representative.
• Threshold. The satisfaction of the product, service, or feature will be impacted the least as the functionality increase but will be severely impacted if it loses its functionality. In other words, customers simply expect the product, service, or feature to work. For example, phones should be able to make calls, customer care representative should be able to solve our problems, etc.
• Excitement. The satisfaction of the product, service, or feature will be impacted the least as the functionality decrease but will be boosted if it gains its functionality. In other words, it will be a delight if customers have the product and/or service. For example, phones that have such a fluid touchscreen interface will surprise and excite users. Another example is having a proactive service by giving education to users about certain issues, so they know what to do if they find similar issues in the future.
• Indifferent. The presence of the product, service, or feature will simply have no impact on satisfaction. For example, phones that come with silicone protectors, etc.

The data collected in this manner can also be tracked over time to see how customers’ needs are changing over time, shown by shifting categories for each attribute.

Leveling Up The Game

The exhibits aforementioned are a few examples to elevate your analysis. There are so many ways to level up the game. To sum up, the following steps can be done to reach a new level of analysis:

• Be creative. This means that you can look at the data from every possible angle, be it as a whole or sliced in parts. Total data will give you the background of the story, whilst sliced data will give more meaning towards what’s happening.
• Be open to resources. Maybe there’s another better way in refining your insights. Don’t let your current way put you inside the box as there’s so many ways to fabricate insights.
• Be mindful. Cross-checking the data, method, etc. are needed to make sure it all sound and representative.
• Be hungry. Always crave for more as knowledge will always expand beyond any limitation whenever humanity still exists.

The proper way to close this is by quoting one of writer’s favorite manifestos:

“It bears repeating: Intelligence is a gift, not a right. It must be wielded not as a weapon but as a tool for the betterment of others”