6 Steps for Using Customer Data Analytics to Optimize Your Customer Experience
Improve your customer experience with a CDP
What’s so important about customer experience? Why does it matter to your bottom line? Today, companies are increasingly recognizing that a fast, streamlined, and engaging customer experience gives them a significant competitive advantage. However, a large percentage are still stumped as to how to make their customer experience generate positive results. Part of the problem: most companies struggle to measure customer experience in a meaningful way, which makes improvement a major challenge. And that’s partly because customer experience can be conflated with customer service. If we don’t know how to measure it and we don’t know how to define it, how can we improve it?
Although companies don’t realize it, in most cases they already have the data they need to optimize their customer experience. Unfortunately, it’s divided in various silos across their organizations. Thankfully, advanced customer data platforms (CDPs) have arrived on the scene, capable of unifying data, pinpointing drivers of customer satisfaction, and providing actionable insights that simply haven’t been possible with traditional measurement tools.
What is customer experience and why is it important?
At its core, customer experience is perception. It’s what customers see, hear, and feel when they interact with your brand. It happens every time customers visit your website, pay a bill, call customer support, visit your store, use your products, etc. Every touchpoint across the customer journey helps to shape it. How well do your customer service reps solve problems? Are your services delivered efficiently and on time? Do products perform well and live up to expectations?
Customers are constantly observing your brand’s performance and comparing it to their expectations. Each interaction leaves a lasting impression, which is used to form an opinion about your brand. So basically, optimizing customer experience means putting customer needs first — prioritizing customer service and satisfaction at every touchpoint.
The current focus on customer experience isn’t hype. It matters to business success, and the results of a recent Forrester study prove it. Its conclusion: “CX leaders grow revenue faster than CX laggards.” And there are more benefits than just growth. According to a Harvard Business Review study, delivering great a customer experience means:
- Increased customer revenue. The study showed that customers who had the best experiences spent 140% more compared to those with poor past experiences.
- Improved customer loyalty. There was a 74% chance that customers of subscription-based businesses would remain members for at least a year compared to only 43% of those who had poor experiences.
- Reduced costs.While there may be upfront costs to improving the customer experience, these costs are often less expensive than managing the fallout from bad customer experiences and customer churn.
A significant portion of the business world is already convinced. In a Harvard Business Review Analytics Services study, 45% of companies surveyed listed customer experience management as a major strategic priority, and 53% said that it provided a competitive advantage. Unfortunately, it’s one thing to measure customer experience and another to tie it to business outcomes. Nearly half of the participating companies in the study said that they faced challenges when trying to achieve correlation. A big part of the challenge to measuring outcomes stems from how customer experience is typically quantified.
How to measure customer experience (and why current methods fall short)
Currently, there’s no one-size-fits-all way to measure customer experience. However, these are three common ones in the marketer’s toolkit:
- Net promoter score (NPS). Customers are asked to rate the company on a numerical scale in response to a single question (“How likely are you to recommend us?”).
- Customer satisfaction (CSAT). Customers are surveyed after interacting with customer support and asked to rate their level of satisfaction (“Not satisfied at all” to “Very satisfied”).
- Customer effort score (CES). This determines the efforts required by customers to accomplish a task by asking them rate to a statement (e.g., “It was easy to find information I was looking for”) using a defined scale (“I agree” to “I strongly disagree”).
Of course, there are others (Customer Churn Rate and First Response & Average Handling Time, to name a few). However, what’s important to note is the simplicity of all these metrics — and their substantial limitations.
Take, for example, the CSAT. Many companies use it to indicate the customer experience throughout the customer journey. But what if a customer has different experiences and different needs at each individual touchpoint? Say a customer calls a company asking for a shipping update for a product they’ve ordered. Before hanging up, the customer is asked whether they’d recommend the company. However, they haven’t even received the package yet! Just like many customer experience metrics, in order for the CSAT to provide any insight, it should be calculated and analyzed across multiple touchpoints.
Even using a relatively complex approach like this, you’re still not going to get a full understanding about where to invest your money into improving customer experience overall. All survey results are complicated by the fact that customers often say one thing and yet do something else. They also don’t all behave the same way, and they don’t have an equal impact on the bottom line. Ultimately, surveys are great tools for collecting data about your customers’ perceptions of your business, but really, they should be regarded as more of a backup plan. What’s truly valuable is the knowledge gained from customer data over their entire history.
6 Steps for Using Data to Influence Customer Experience
Using a customer data platform (CDP) to unify customers’ data into a single profile and track their behavior across every channel over time ensures companies know exactly how their customers interact with their brand. Ultimately, detailed customer data ensures companies can make strategic choices about where to focus on customer experience improvement and tie those initiatives to outcomes. Here are the steps for putting that data to use:
Step 1. Inventory the customers you have.
This means looking at their referral sources, their customer support records, their tastes and preferences, their unique differentiators, real-time behavior, website visits, etc. As you start to dig in, you’ll reveal all the ways they interact with your company (people, processes, and technology) and you’ll learn the value of ‘rich’ data sources. The more data you have, the better, with the best data being continuously generated and coming from touchpoints such as loyalty apps, website analytics, customer portals, point of sale, and support call logs.
Step 2. Unify your data to analyze patterns of behavior and a put together a complete view of each customer.
This means pulling all of the information together into unique customer profiles so you can look at each customer journey on its own — knowing that the same person who came in through the promotional email is the one who browsed several products and added one to their shopping cart. For a number of reasons, the process of data unification can be tedious. You may have incomplete profiles or duplicate records, and you may have multiple systems which represent customers with different models. You’ll need to determine the best way to get a custom view of each customer that works best for your business in order to begin the process of journey mapping.
Step 3. Augment the data.
Once you begin to piece together an initial customer journey, you can identify potential gaps in the data. Your internal data sources will likely give you only a partial picture, but you can pull in other sets of data from third parties, partners or affiliates to ensure that you have all the right information for analysis. For example, a hotel could use third party data to target its ads to prospective customers who have not visited its website, but who have searched for flights in the area. Potentially demographic information (age, gender or income) could make a difference in your understanding of their journey too.
Step 4. Map it and analyze it.
Start to understand who your best customers are by mapping the data and comparing different sets based on your complete customer profile. Look at who buys the most frequently or who spends the most, and then discover more about those segments by comparing their attributes (gender, locations, affinities, etc). You can then work backwards to look at their behaviors or such details as how often they interact with you, whether they search for coupons or where they come from.
Subhead: Step 5. Create and test a hypothesis.
This entails finding a group of people that share similar attributes to a set of customers, but took different actions. Marketers often pay attention to segments who look like their best customers and segments who look like they are most likely to churn. Armed with your best customers, you can zero in on the important questions: Who of my newly acquired customers is most likely to make a purchase every month and how can I encourage this behavior? Which customers will purchase once and never buy again? Modern predictive modeling tools can help to provide additional information that can help you target and engage specific groups of people with similar experiences and attributes with the right messages and offers at the right time.
Subhead: Step 6. Measure with it.
An important part of keeping your data up to date for better CX is to commit it to measuring your performance with deep CX analytics (which can be delivered by a CDP). This creates a virtuous cycle of data collection, action and measurement that ensures your data is accurate and up-to-date. Given the actions you’ll be taking with your newfound knowledge of your customers’ experiences, you should make sure that every action has an objective and is measured — using the same metrics and processes you have today for measurement, such as email opens and clicks. Consider enhancing your methods of measurement. For example, if you find that your best customers are frequently calling support soon after the sale, you may want to initiate a CSAT score for that experience, or initiate a help campaign with the intent of reducing support calls and measure call deflection. Only by taking action intelligently and closing the loop with measurement can you know that you’re having a significant impact on the customer experience.
How a CDP Can Help Optimize Your Customer Experience with Personalization
Tracking and unifying a range of customer data — such as website clicks and email and advertising click-through rates — can demonstrate exactly when marketing strategies are leading to sales for particular customer segments. Moreover, tracking customer geolocation data also gives companies unique opportunities to target customers when they’re visiting physical stores, boosting sales by offering extra discounts and promotions as they shop. A good example of how companies benefit is Japanese lifestyle retailer Muji.
During a phase of rapid growth, Muji struggled to improve sales. Although the company ran plenty of marketing campaigns, it was unable to merge the physical and digital experience. Customers often searched for products on their website, later visiting physical stores for purchases. Another issue was that Muji wasn’t able to transfer its data and perform data analysis of online traffic in time to make relevant recommendations to in-store customers. A lack of engineering support prevented the company from building the scale and performance needed to capture clickstream data from the mobile app and join it with existing data from the web and point of sale (POS).
Using Treasure Data, Muji combined online browsing data and in-store purchase history to create complete customer profiles. Treasure Data provided a dynamic, scalable way to ingest and aggregate the fast-moving data streams. By merging customer information with real-time store inventory data, Muji executed on data-driven incentives, such as personalized coupons and timely, well-targeted in-app push notifications. Muji was also able to leverage Treasure Data’s machine learning (ML) capabilities for location-aware promotions. Results: 46% increase in in-store revenue and 100% coupon redemptions.
Take the next step
Every organization is unique. There’s no single customer survey or metric that can provide enough data to transform the customer experience alone. The key to getting it right is paying attention to the things that matter most to your customers, drawing actionable insights from your customer data, and then putting those insights to use. If you’re interested in this topic, learn how shopping app Wish used real-time data to personalize its customer experience and grow an $8B eCommerce business.