customer data

Don’t Let the “Perfect Dataset” Be the Enemy of Good Customer Experience

Providing great customer experiences requires data. The good news? Over the past decade, enterprises have collected troves of customer data.

Providing great customer experiences requires data. The good news? Over the past decade, enterprises have collected troves of customer data. The bad news? Many enterprises fail to implement CX initiatives because their dataset isn’t “complete” or “ready” yet. In fact, two-thirds of customer journey initiatives today don’t drive any action.

By Arun Theivendirarajah

Having worked with some of the most customer-centric enterprises across retail, banking, and healthcare sectors, I can attest that none of them started with turnkey, clean, complete data. Instead, those enterprises used the data they already had and to orchestrate experiences with a bias to iterate and optimize over time. For the two-thirds of customer journeys that have yet to drive action, here are three ways to demystify the notion of “perfect data” so you can cultivate happier and more profitable customer relationships.

1. Don’t Start with Data

It’s counterintuitive. From day one, marketers, engineers, and product managers alike are programmed to be data driven. When presented with a problem or an opportunity to build something new, our instinct is to look to the data first and devise a strategy second. CX initiatives are no exception. However, collecting customer data, cleaning it, and centralizing it to work together is a herculean effort. Teams often spend so much time collating that they don’t even get to strategizing, let alone pursuing action.

Instead, begin with a business objective. For example, retail banks often have KPIs for improving customer onboarding or decreasing the duration of customer support calls to reduce costs.

Next, prioritize the one or two customer journeys that, through iterative improvement, will yield the greatest business outcome-based on where customers are experiencing the most friction. The key here is to prioritize and focus your efforts — don’t try to boil the ocean and solve the entire issue of customer retention all at once.

Revisiting the customer onboarding example, perhaps you notice that a majority of customer turnover occurs after three-six months. Instead of looking at the entire dataset of churned customers, focus on retaining the subset of new customers. Then strategize how to inject proactive customer support at the one- or two-month mark before the churn occurs. Plan to build and iterate on this journey strategy over time and gradually apply it to different subsets of customers to improve overall customer retention.

2. Respect the Law of Diminishing Returns for Data

It always feels like better data is just around the corner. Whether the desire is to expand your dataset with new records or supplement with more data points per record, there is a point at which the value of pursuing “more data” won’t outweigh the expense of obtaining it i.e. the law of diminishing returns. So how do you determine if your data is “ready” or “good enough”?

Consider a retailer that wants to increase its cross-sales among high-value customers. The company has order data from past customer purchases, but they want additional granular details (e.g. which sales rep sold it) about those purchases to serve a highly personalized cross-sell recommendation. Of course, the additional details are beneficial in optimizing the personalized recommendation, but the time spent waiting for the additional data represents lost opportunity for cross-sales.

That’s why it’s important to deploy journeys early and iterate often. By adopting this agile, test-and-learn approach to customer engagement, you’ll be better positioned to capitalize on incremental revenue opportunities by orchestrating experiences in real-time.

3. Remember: Clean Data Isn’t a Silver Bullet

Often, people are so focused on resolving siloed data and cleaning data that they forget about the equally, if not more, important task of garnering cross-departmental buy-in on CX initiatives. Cleaning and integrating data is a huge effort, but lack of company-wide alignment around experience orchestration will cut any initiative off at the knees, regardless of how “perfect” your strategy (or data) may be. In fact, misalignment can make it more difficult to gather the data needed especially if there’s a misunderstanding about how or why the data is being used.

Part of getting company-wide buy-in is mapping your customer journey strategy (decidedly a marketing initiative) back to broader business priorities. Citing department-specific KPIs, like NPS, won’t earn you the support you need to successfully execute your strategy, because outside the marketing department that metric bears little value.

Progress Over “Perfect”

All to say, the tunnel vision focus on a perfect dataset can make it easy to overlook the other key ingredients needed for a CX initiative to succeed. But succeed it must. In a world where customer expectations are higher than ever, enterprises can’t afford for customer journeys to remain in the desk drawer. Whether it’s increasing incremental revenue, retaining customers, or driving down costs, leading enterprises are embracing an agile approach to experience orchestration to bring more value to their customers.

  1. I couldn’t agree with you more. Those companies that do not have inside agile support must look outside. Outside solution providers require a strong customer team to monitor that the work required is being properly. Only the customer will care the most about their data and the ultimate management and output thereof. The customer cannot afford to get lazy.

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