Why Customer 360 Matters And What You Can Do About It

What is Customer 360

Every piece of data tells a story. But we have to connect the dots to find the narrative. Customer 360, the holy grail of analytics and operations, is one such initiative. The customer 360 provides a unique, unified, and coherent view of each customer. Literally, a 360-degree, complete journey of the client with the business.

A 360-degree customer view does not mean having all consumer data in one place. It means connecting a single client’s data across support emails, payment applications, offline stores, telephone calls, website visits, and all other customer touchpoints. The same purchaser is identified uniquely through all these different interactions.

Customer 360 builds each buyer’s unique story with the business, a universal customer profile as some say.

Let us say, for example, a shopper walks into a store and purchases running shoes. A few weeks later, she also visits the e-store of the company and logs an order for walking shoes. The brand could recognize her as a repeat buyer and personalize her product feed. Further interactions with the store or the website about order delivery or repeat orders could be serviced differently, given her past interactions with the brand.

When an enterprise knows and understands its consumers — through a single profile — it can improve their experience and turn them into loyal patrons.

Why do we need Customer 360?

Let’s discuss some of the most fundamental applications of Customer 360.

Personalization

We tweet about our refund complaint to a company’s customer care only to receive a phone call asking for our details all over again. We wonder, exasperated, why they cannot fetch our information from the complaint ticket or email? Our insurance companies keep messaging us with policy offers of those policies that we already own and will certainly not purchase again. A very frustrating experience for many of us.

Even though these are all very common stories, most marketing efforts don’t have a brighter narrative. As customers, we all know we have blocked emails and calls from brands and services we have loved in the past.

Without a Customer 360 view in place, businesses cannot understand customers’ actions or anticipate their needs. When organizations don’t personalize their client’s catalog, campaigns, and calls, they end up upsetting potential consumers. Thoughtless marketing can waste huge sums of money and work hours.

But if enabled with the unique profile of each customer, an enterprise can successfully personalize a client’s experience. Knowing how the consumer interacts with the systems and products is essential to pitching to them as per their preference, time, and needs. A 2018 Epsilon research suggests that 80% of consumers are more likely to make a purchase when brands offer personalized experiences. Hence, businesses need to unify the customer data and identify how to provide an immersive experience to their clients.

Imagine a potential buyer getting an email with a discount coupon for the laptop he enquired about in the store. Or the salesperson calling us from the car dealership has the preferences we once entered on the company’s website.

Intelligent, contextualized and personalized marketing efforts guarantee better conversions and higher revenues. Only a seamless customer experience can retain buyers amidst heavy competition and put an organization ahead of everyone else in the market.

Customer Analytics and Prediction

A 360-degree customer profile helps understand customer diversity versus profitability across products and services. Consumers can be segmented using this analysis and marketing efforts can be broken down. When we unify consumer data, we also gain insights into client feedback, retention rates, and purchasing patterns. Sales outreach campaigns can be analyzed for their performance in various customer categories.

A client’s 360 profile is the single source of truth every business can hold and transfer to other products. For all new launches, a 360-view of a buyer will help the organization make educated estimates about the new product. Even in a global emergency scenario, one we recently faced, only those companies which have consolidated data can predict consumers’ needs and prepare accordingly.

Finance and Risk Industry

Customer 360 has crucial applications in the finance and insurance industry.

Imagine a lender issues a high-interest loan to a credit card defaulter, but the organization doesn’t know. Financial service firms and credit businesses can do better risk analysis with unified customer views.

As part of the Anti Money Laundering (AML) compliance, financial institutions have to do client identification and verification (KYC), transaction monitoring, screening for prohibitive lists, and reporting suspicious activity. To run any AML operation, keeping the demographic and transactional data integrated and updated is essential. With a 360-consumer view, finance firms can achieve all the above AML elements and prevent money laundering.

An insurance company can use a client 360-view to flag potential fraud cases. It can also train AI models on this data to detect claims exhibiting patterns of previous fraud. For instance, a customer may have had multiple insurance claims for the same property under different identities.

With Customer 360, finance and insurance companies can build a strong clientele by improving customer experience, detecting risks early on, and increasing their revenues.

Privacy Regulations and Compliance

Customer 360 is essential to comply with the existing and emerging data privacy regulations. For example, the EU’s General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act require companies to afford consumers the right to access their data and to have it erased.

Providing or deleting customer data can be cumbersome — and error-prone — when it is fragmented across systems and has not been reconciled. Compliance failure is a high risk in organizations that avoid integrating their data systems.

Training AI/ML Models

Clean and consolidated customer data is also essential for training AI and ML models. Models trained on dirty data will be faulty. If fed poor-quality data, even good models will churn out incorrect results.

So, for instance, we train a model on images labeled with traffic lights and pedestrian crossings. But if most of our photographs are marked wrongly, our model would give high false negatives and false positives. And if we feed age in decimals to a stable model, we can not guarantee if the buyer categorization would be accurate.

From the Anna Karenina principle, we understand that each faulty dataset will be faulty in its own way. And this faulty data would result in a uniquely inaccurate model, and thus uniquely wrong results.

Customer data is mostly fragmented and inconsistent. It is stored in multiple billing, support, application, and product databases, POS, Customer Relationship Management (CRM) platforms, and across other disparate offline and online systems. These input sources don’t follow the same formats and definitions.

The input sources also keep changing as IT systems update. Social media platforms and their customer interaction points are ever-evolving too.

Dealing with dirty data is analogous to trying to solve the wrong problem. Without the right data, resources are wasted, costs are higher, and root-cause analysis is impossible.

To gain a single and coherent customer view, all the customer data from various sources, channels, and representations need to be brought together, cleaned, matched, and unified. Customer 360 solutions guarantee this unification while eliminating the scope of human error and avoiding redundant costs.

Challenges in building a Customer 360 View

As we have said in Entity Resolution, buyer records can seem impossible to reconcile just on the basis of demographics. Throw in information about consumers’ website visits, app logins, retail store visits, general inquiries over phone calls, and so on. Now try connecting a consumer with her interactions with the business.

One can imagine the myriad of data inputs and touchpoints that have to be reconciled here. Matching and unifying the data across such diverse data storage platforms and inconsistent data formats make Customer 360 a very hard problem to solve.

Given the difficulty of the problem, not many products that claim to do Customer 360 actually succeed in doing so. Customer Data Platform (CDP) and Master Data Management Platform (MDM) are two categories that are said to enable Customer 360. But they both come with their limitations.

First of all, CDPs can not unify all the offline and online data pouring in such diverse formats. The CDPs expect clean and high-quality data as input, which is a challenge most of us are familiar with. Aligning our user data with the CDP data model is non-trivial. Even when we get there, the CDP data model makes representing relationships like households a challenge.

The unification or identity resolution by the CDP is limited in its capabilities too, and it mostly does deterministic matching with known identifiers. On top of that, organizations have limited insight into the external CDP’s unification model and can not control the data matching process.

A third-party CDP is a separate system of record with duplicate data. For companies that have been investing in their modern data stacks, a third-party CDP with partial and duplicate content quickly becomes a redundant and very expensive proposition.

And there are grave privacy concerns. Customer data is the most sensitive data for an enterprise, both from a business and a regulatory point of view. A third-party sharing of any kind, where an organization has no control over the access to the data is a threat to data security.

Gartner once said about CDPs: “rather than a new technology, CDPs can be understood as a repackaging of already existing features that are inconveniently distributed and thus untapped across various alternatives.”

Before investing in a CDP, all organizations should ask themselves if onboarding an expensive and high-maintenance CDP separate from their data systems while risking their data’s security and quality is worth the effort.

While CDPs come imbued with new technologies, MDMs are old systems many of which promise Customer 360. But most MDMs have outdated technology stacks that can’t be integrated with the latest data stacks employed by most enterprises. Given the infrastructure incompatibilities, thinking of MDMs as solutions for Customer 360 could end in a roadblock.

How to Build Your Customer 360

Instead of deploying an external stack away from a fully-functional data warehouse, a better approach for organizations could be to use the data lake or the warehouse itself to unify the data. Through reverse ETL, customer data can be pulled from the various in-house data models into a unification tool, joined, and fed back to the systems.

With this unbundled approach, the organization will have full control over its data throughout the unification and cleaning process. Needless to say, this customer 360 solution is scalable and keeps the costs and efforts at a minimum.

When we made Zingg open-source, unifying the data and building customer 360 was one of our main goals. Zingg runs on top of an enterprise’s data warehouse while taking data from their input sources, running the matching on their systems, and feeding the data back to where it belongs.

Let us know if you try Zingg and have any questions.

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