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4 Critical Elements of a Customer-Centric Data Strategy

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Read more about author Hanz Qureshi.

From the humble data warehouse to the lake and swamp to potentially an ocean of data, take your pick where you want to drown yourself. The reality is the customer doesn’t care about the fancy lake or the Kafka pipelines; they care about the service they are receiving from you.

Due to poor communication and misalignment in organizations, we see poor overall data strategy execution. The data team does more engineering work without realizing the end business goal of the tasks. How is this activity moving the needle? And why am I spending so much time and energy on it if it is not?

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In a world where everything is urgent, nothing gets the proper attention. The end customer gets frustrated and moves on to another competitor with marginally better strategy execution. So how do we avoid this? How do we build with the customer in mind?

Let’s look at this in detail. Below are four critical elements of a customer-centric data strategy.

1. Show them you care

Customers want to know that their issues are being heard. You need to ensure the communication channels are consolidated. The most frustrating part is that your chatbot, your telephone agent, and your store personnel are unaware of the conversation each other is having with the customer. It is imperative to record data and disseminate it to all your customer-facing channels.

Many organizations are happy to ask customers for detailed information, including the date of birth. If you have the data captured, maybe contact the customer on their birthday and give them a personalized offer (as long as they have consented to be contacted). Avoid being creepy and sending unnecessary emails weekly with no tangible value – it will drive them away.

Please don’t consider customers as revenue-generating machines but long-term, mutually beneficial partners.

2. Repay their loyalty

How often do you see offers being available for new acquisition customers whereas the old incumbent is taken for granted? Customers know this, but sometimes it’s just too much hassle to change suppliers. If customers are loyal to you, give them reasonable periodic marketing offers. At the time of contract/service renewal, don’t make them call the contact center to negotiate a new rate; give them a reasonable renewal rate, so they can keep trading with you.

Create a loyalty scheme but don’t make it gimmicky – give tangible outcomes from it. Personalization, omni-channel experiences, preference management etc., are great ways for customers to continue their loyalty. I am a big fan of Hilton Hotels’ loyalty scheme, as they know how to treat their top-tier loyal customers with discounts, offers, additional points, desk-free check-ins, etc.

3. Don’t make them regret giving you their data

Nothing is more annoying than signing up for a service and receiving emails from some random third-party service. Is it legal? Probably, because you baked it into your three-page privacy policy that no one reads. This behavior is not cool. The data you are capturing should be for you to provide them with personalized service or for legitimate interest purposes. Nothing more than that.

Provide them options if they want to sign up for additional services. Don’t write strangely worded sentences that make it impossible to understand whether they should say yes or no in consenting their approval to be contacted. It takes a lifetime to earn someone’s trust and a minute to lose it. So, don’t make them regret giving you their data.

4. Culture Over Technology

You might be surprised to see this list with no mention of data architecture, centralized or decentralised infrastructure, or even self-serve or not. Frankly, the customer sees none of this and cares about none of this. These are simply enablers: “Tech is vizier, customer is king.”

Focus on building a data-first culture, where the organization you lead respects customers’ data, understands their privacy rights, and utilizes data in an nonexploitative way. There is too much focus on technological debates and not enough on how to make a data-first culture. Let’s change that.

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