How to Craft a Data-Driven Customer Experience Strategy

Written by Ganesh Narayan  | 

How to Craft a Data-Driven Customer Experience Strategy

Devising a strategy, especially a data-driven customer experience strategy, is no mean task. There are so many moving components and uncertainties to overcome. Here are five steps that will make the task easier for you.

It is almost a decade since the British mathematician and data science entrepreneur Clive Humby coined the phrase “data is the new oil.” It is more relevant today than ever.

The truth is, businesses no longer have a problem in sourcing data. There is an abundant supply of it in all imaginable forms. From website to website chat and voice analytics, today, data can be sourced and analyzed in myriad forms.

However, for most businesses, devising a data-driven customer experience (CX) strategy is still a challenge. If analyzing data based on hard evidence and taking corrective actions was easy, every business would be doing it.

So, what’s stopping them?

Customer experience is a relative concept. What is construed as delightful and positive by customer X could be considered as a given for customer Y. No two customers are alike, even if they have identical demographics.

However, to quote Ronald H. Coase, the renowned British Economist: “If you torture the data long enough, it will confess to anything.” Data does have answers to most of the pressing questions about delivering great customer experiences. However, it is not a walk in the park.

It is a path that is riddled with challenges.

The purpose of this blog is to take a crack at demystifying those challenges in devising data-driven customer experience strategies. By the time you scroll through the last section, you should be knowing how to go about crafting a data-driven CX strategy for your business.

The Pillars of a Data-Driven CX Strategy

A well-thought CX strategy should enable a business to deliver value at the right time. There are three pillars that support a CX strategy:

  1. Customers
  2. Data
  3. Customer service process

1. Customers

  • Who are the customers?
  • What are their pain points?
  • What are their demographics?
  • What digital habits and buying preferences do they have?

These questions should help in getting a 360-degree view of the customer. Of course, you can still add more questions to get a comprehensive customer view.

Nailing the customer persona and their traits is the starting point of crafting a data-driven CX strategy.

2. Data

Having data is just the beginning of creating a data-driven CX strategy. A data analytics platform is also necessary to build and scale analytics throughout the organization.

3. Customer Service Process

  • How does the company provide customer service?
  • What channels are covered?
  • Are these channels served separately as in multichannel customer service, or does the company have an all-connected omnichannel customer service process?

Outlining the customer service process will help in two ways. First, it would give clarity about the customer’s journey in finding a resolution. Second, it would also help understand which channels customers would take when they reach out for assistance.

It is with these three pillars that a CX strategy is crafted. Now, let’s take a look at some of the steps involved in crafting a data-driven CX strategy.

Steps to Crafting a Data-Driven CX Strategy

1. Collect the Data

The first step to devising a data-driven CX strategy is to collect data. Data should be collected from all possible sources and from all devices that your prospects and customers could be using. This includes web, mobile, and tablets. Collecting data from as many sources and mediums is necessary to avoid creating a bias in the data population.

2. Set up a Data Analytical Model

Setting up a data analytical model is crucial because it determines how the customer data would be processed to arrive at actionable insights. It is necessary to be aware of the mistakes when building analytical data models and staying clear of them.

The data analytical model should dissect data to recognize patterns in customer behavior. It should help in framing the repeated wants of customers as well as identify and predict key drivers of customer support. An ideal data analytics platform should be easy to integrate with the most-used business tools such as helpdesk software, CRM, marketing automation tools, etc. It would also allow for customizing the reporting dashboard to fetch periodical reports effortlessly.

3. Identify Customer Preferences

As mentioned in the beginning, the customer wants and CX experiences are diverse and subjective. However, there always remains a common thread in customer preferences. In B2B software, it is usually the convenience of using a tool; in B2C e-commerce, it is affordability and free shipping, and so on.

A quick analysis of FAQs, repeating customer tickets, and a survey of churned customers would help with understanding the most common customer preferences. These preferences can then be used as a north star to set up the data analytical model. The model can incorporate KPIs for customer service to measure how customer preferences are met and whether they are delivering positive customer experiences.

4. Map the Customer Journey

A digitally native customer’s journey, be it for support or sales, is no longer linear. A customer could take multiple paths before they arrive at the end destination. They might begin a customer service journey with a chatbot and resume it with an email. Phone, social media, and in-store visits could also become part of this journey.

While crafting the CX strategy, it is necessary to keep in mind the various customer support touchpoints that the customer would pass through. At each touchpoint, steps must be taken to deliver a positive experience. Again, data should be collected as to what kind of experience customers expect at each touchpoint. The CX expected from a website chat support would be drastically different from that of phone support.

5. Test and Improve

It goes without saying that no strategy is ever perfect. It needs periodical reviews and refinement to ensure that it remains relevant and compatible with the times. Ensure that you revisit your CX strategy every six months or a year. If the data is revealing new actionable insights about customer preferences, do a double check if a change in CX strategy is necessary. Testing and improving the strategy is the only way to keep delivering great customer experiences.

Introducing GoodData for Freshdesk

The Freshdesk and GoodData integration will enable users to use advanced statistical metrics for monitoring their customer function. It will give customer support managers, admins, and agents the ability to identify and predict key levers of support quality and use them to deliver better customer experiences. The integration would also become handy if users want to create custom metrics, set up benchmark performances, compare data, or set internal goals. In other words, the integration would enable businesses to enjoy both worlds made up of the best helpdesk software and the best analytical platform there is.

Check out GoodData for Freshdesk integration in action.

Bringing It All Together

Good customer experience makes customers feel appreciated and valued. It makes them stay loyal and continue contributing to the business growth. That makes devising a CX strategy challenging.

Creating a CX strategy is no mean task. The steps outlined above should help you get started and progress steadily in creating an effective data-driven CX strategy.

About the author: Ganesh is a content marketer with Freshworks, the business engagement software that helps businesses deliver delightful customer experiences.

Header photo by Priscilla Du Preez on Unsplash

Written by Ganesh Narayan  | 


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