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You sure want to give a better customer experience every time. You might also set some goals to reach that ideal customer service of yours. But there are certain ways and methods you follow or inculcate in order to achieve those goals. This journey from “goal” to “achievement” is known as a 360 customer journey.
With proper customer data at hand, you can make better products by knowing the customer’s purchase history and preferences. With a way of reaching out to them in a better way, you are basically providing them good customer service. It allows you to take a look at your customers on an individual basis and design your products accordingly.
Conducting exploratory research seems tricky but an effective guide can help.
The very first step to understanding your customer is to capture the data that they are sending to you. Most organizations these days have an omnichannel approach to dealing with customers. Meaning there are many possible channels open for the customers to reach out to the business and interact. It makes it difficult to capture all the data from such channels as phone calls, SMS, social media, emails, etc. Even more difficult is their analysis.
It is important to capture the right and important customer data. It can be a call customer made for a product inquiry or review, social media comments, shopping app purchase, email for a replacement, anything that counts as an interaction.
Now that you have captured the data from all the sources, it needs to make some sense. Since the customer data comes from various channels, it will have different nature too. Some might be quantitative, some might be qualitative. So depending on that, you can bifurcate the data based on type and nature. For example, sales data is mostly in the form of graphs and numbers, so you can keep it together. Whereas the reviews might be more descriptive and qualitative, you might want to keep them separately for different analyses.
As an organization focusing more on business intelligence, there is no way it can be done without automation. There are various data analysis tools that run through AI and automation which helps you in analysing any type of data.
Just capturing and organizing the data isn’t enough, for the data to make sense and talk to you, it needs to be analysed properly through such data science tools. It helps you draw out the demographics of the available data and evaluate it to get some insights that are useful to your decision-making process.
After analysing the data, you will have the needed ideas and insight on customers and their product preferences. The data that was flowing through the channels will now start making sense and the teams can act on the conclusions that were derived through data analysis.
Example, you get a result of analysis indicating the sales of products are going low for a couple of months. Similarly, you see many social media reviews say that they would prefer the other brand over yours for cost reasons. As an action to the findings, you can link both the results and state that people are preferring to switch to another product due to pricing issues. So when you next launch a new product, you will look out for such constraints.
Once you implement the changes and start interacting with the customers, you can gauge their behaviour. Keep track of the customer journey and 360-degree view of customers throughout the organizational works.
Identify the best customer journeys and their maps for future references. Do the gap analysis from time to time to keep track of the gaps and loopholes in your systems.