You can’t improve the customer experience without a clear, data-supported picture of what that experience is like.
Personalization is more important than ever. Enterprises are under pressure to provide customers with unique, valuable experiences that cater to specific tastes and preferences.
These initiatives are often the cornerstone of customer loyalty. According to a 2019 Forbes survey:
- 70 percent of enterprise executives say that personalization was critical to ongoing customer loyalty and retention.
- 13 percent of survey respondents said their organization focused specifically on personalizing customer loyalty programs.
- 80 percent of consumers say they’re more likely to buy from companies that personalize their experience.
Until recently, creating a personalized experience for every single customer was impossible at enterprise scale. The technology and infrastructure simply did not exist.
Even now, many organizations struggle to deploy customer personalization strategies successfully. Maintaining a coherent and scalable customer loyalty strategy is an incredibly challenging task.
Bad Personalization Hurts the Enterprise Brand
Personalizing tens of thousands of unique customer experiences comes with risk. Any organization that automates personalized messaging may end up sending the wrong messages to the wrong people.
When warning enterprise executives against the pitfalls of bad personalization, there are plenty of high-profile customer personalization mistakes that illustrate the point:
- One airline offered a credit card with thousands of frequent-flyer miles to a two-year-old because of his “admirable credit history”.
- Shutterfly accidentally sent emails congratulating new parents to users who had no children. These users included people coping with painful experiences like miscarriage and infertility.
- Adidas congratulated Boston Marathon runners in 2017 with an email whose subject line read: “Congrats, you survived the Boston Marathon!”. Users were quick to remind Adidas about the 2013 Boston Marathon terror attack that killed three people and injured several hundred.
Any one of these initiatives could have been a revenue-generating boon for their companies. Instead, they serve as embarrassing, painful examples of what happens when customer personalization goes wrong.
Enterprises that qualify customer personalization initiatives and verify them before deployment can avoid these mishaps. But verifying tens of thousands of different messages and customers is easier said than done.
Enable Personalization Through a 360-Degree Customer View
Each of the personalization mistakes mentioned above could have been avoided. A brand that has access to excellent quality, highly structured customer data is far less likely to send the wrong messaging to its customers.
This requires enabling a 360-degree customer view. This is a unified, end-to-end picture of the customer’s experience with your brand that contains all of the customer data relevant to that experience.
Some of the types of data that enterprises must collect in order to establish a customer view of this depth include:
- Identity Data. A customer’s name, date of birth, gender, address, and social media profiles are all examples of identity data. Personal relationships and even payment preferences all speak to who the customer is, and how that identity stands in relation to others.
- Quantitative Data. Web analytics data, transaction histories, and customer support logs are all examples of quantitative data. Enterprises must leverage this data to provide experiences that are contextually relevant to the customer experience.
- Descriptive Data. Most of the data that you get from surveys and questionnaires are descriptive. This might include market-oriented buying preferences, seasonal behaviors, and the length of the customer cycle itself. It may also include details about the customer’s family, career, income, or education.
- Qualitative Data. Behavioral predictions are largely based on qualitative data. These data focus on customer choices, interests, likes/dislikes, and other preferences. Social media companies often use qualitative monitoring tools to predict user behaviors, but they are not the only ones.
Once your enterprise begins collecting this kind of data, you can start using it to develop highly targeted customer loyalty campaigns. The better you know your customers, the more successful your personalized outreach strategy can be.
Customer Data Must Be Accessible to be Useful
But it’s rare for any company – let alone one with thousands of employees – to easily obtain large volumes of customer data in an organized, accessible way. Usually, the process is fragmented between multiple departments, and the data gets stuck in various organizational silos throughout the company.
Your sales department may be collecting customer data in a format that isn’t immediately compatible with your marketing or product division’s software. Your application developers may not know where all of your customer data actually lives, or whether it is accurate or not.
Most enterprises address this problem by deploying gigantic data warehouses to store all of their enterprise data into a single place so that anyone who needs access to customer data can get it – in theory, at least.
In practice, warehouses rely on the assumption that your organization already has structured high-quality data at its disposal, ready for migration. Just because you put it in a single place doesn’t help you understand that you have 5 versions of John Doe when they are all the same customer. It doesn’t provide any governance or stewardship policies, and it doesn’t make it accessible to all of your real time enterprise applications.
But this process only addresses your current data. It doesn’t actually address data capture and governance, which is the actual problem. A consolidated enterprise data framework that draws from fragmented, under-verified sources will continue to experience customer experience disruptions well into the future.
Those problems will continue to interfere with customer loyalty initiatives. Some of the major things to look out for include:
- Data Access. Just because you store your data in a single location does not mean that it is accessible in real time to your various customer facing and enterprise applications. The data simply isn’t valuable if the systems that use it can’t access it when it’s needed
- Duplicate Records. Customers who have multiple records to their names may receive multiple messages when targeted by customer loyalty initiatives. If the duplicate records have conflicting information in them, the customer may receive contradictory messages.
- Data Survivorship. Without a clear governance policy in place, enterprises cannot easily determine how to deal with duplicate records once identified. Instead of having a clear-cut data survivorship policy in place, the enterprise must resolve duplicates on a time-consuming case-by-case basis.
- Hierarchy Management. Customer’s career and organizational relationships often play a key role in retention and loyalty initiatives. If marketing and sales professionals don’t know what company a customer works for (or what their role in the company is), crafting personalized messaging for them might prove impossible.
Master Data Management solves each of these issues by creating a single, unified point of reference for customer data. This improves customer loyalty outcomes and protects enterprises from reputation-damaging personalization failures. A customer database filled with contextual, 360-degree data enables best-in-class loyalty and retention initiatives at scale.
Reltio is a Master Data Management vendor that specializes in consolidating customer profile data for loyalty and retention initiatives. Find out how your enterprise can improve the customer experience and leverage personalization with our help.
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