Wavicle Insights, Opinions, Commentary, and More.
Your Customers Like You, They Really, Really Like You
Date: Tuesday August 25, 2020
How to Achieve Customer 360: 3 Best Practices
Have you ever come across a customer name in your database, like an Elizabeth Ames from Boise, Idaho, and then discovered you also have a Liz Ames from Boise, Idaho in another database? Are they the same person, and if so, does she go by “Elizabeth” or “Liz”? Or have you ever reached out to a customer for the first time, only to leave them frustrated because they were contacted earlier by someone else from your company?
Lack of clarity on simple details like this can easily cost you hard-won customers. In fact, 80% of consumers have stopped doing business with a company because of a poor customer experience, according to a study from CRM software provider Hubspot.
To maintain contact with your customers, deliver awesome experiences, and keep customers happy, it’s critical to know who your customers and prospects are, what they like and dislike, how they want to interact with you, and what their value is. This is called having a 360-degree view of your customers, or Customer 360.
What is Customer 360?
Customer 360 is the method of consolidating customer data captured through various touchpoints across their entire journey – including in-store purchases, phone calls, social media responses, mobile apps, and Internet interactions – and merging it into one unified record.
Customer 360 offers a complete, accurate, and holistic view of the customer, giving you a comprehensive understanding of who your customers are and how you can better leverage their past, present, and future engagements with your brand.
With Customer 360, you gain deeper insights that enable you to create more personalized interactions with your customers, increase upsell and cross-sell opportunities, and improve retention and loyalty through better customer experiences.
The Hubspot study also revealed that happy customers remain loyal to the businesses they love: 90% of consumers more likely to purchase more and 77% are more likely to share positive experiences with their friends or post on social media and review sites.
Read on to explore the challenges and benefits of Customer 360 and three best practices we recommend to achieve a holistic customer view. For more, check out Illinois Technology Association’s recent webinar, “You Have the Data, Now Get the Insights – The Benefits of Customer 360” with Duane Lyons, Wavicle practice area lead and Mark Chapman, senior solutions architect at Talend, a global leader in data integration and data integrity.
Three Best Practices for Successful Customer 360
Achieving a 360-degree view of the customer is a challenge, as customer data often exists in multiple formats; is fragmented, inconsistent and incomplete; and is stored in different legacy systems and data siloes. Plus, data is constantly changing as customers move, get married, divorce, change jobs, etc. It’s difficult to glean any insights and improve customer interactions if you can’t bring quality customer data together, analyze it, and act upon it in real time to reach customers on their terms. Only 3% of companies’ data meets basic quality standards, according to Harvard Business Review. These issues point to the need for a data quality action plan. With this in mind, here are the three best practices to enabling Customer 360.
1: Get Your Master Data in Order
Whether you’re dealing with consumer or business customers, master data and data governance are critical first steps for successful Customer 360. You need a solution that can bring together data that is spread out across multiple systems and compile a “golden record” – a single, accurate, and complete version of a customer record – that can be consumed by the enterprise.
This goes for product information as well. When marketing to customers, it’s critical to know which products they already buy, so you can more effectively cross-sell and upsell.
Some examples of problems created by disparate data include:
- A CRM database lists a Tony Stark from Manhattan, NY, while an ERP database lists an Anthony Stark from New York. Are these the same people?
- A customer purchases multiple insurance policies – do you know which policies they hold?
- A patient visits multiple providers and facilities in the same system – do you have a complete view of that patient’s experience?
A well-designed master data solution gives you a system to clean, verify, standardize, and update customer data at the point of entry. This means collecting the data (and metadata) from multiple source systems, then using business logic to match and merge the data to create the golden record. When the business logic is unable to conclusively arrive at a golden record, data stewards can step in to identify and fix challenges with the data. Through this process, you can train and re-train machine learning models to get progressively better at matching and merging data.
Enriching this data with added information like demographics, psychographics, marital status, household income, and occupation can yield even deeper insights about customers, so you can create and target granular audience segments, strengthen your messages, and personalize your offers to achieve a higher ROI.
2: Focus on Data Privacy and Compliance Regulations Early
Companies looking to achieve a 360-degree view of the customer also need to address the issue of data privacy and compliance, particularly with regard to requirements of the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Organizations can’t afford the luxury of thinking about privacy as an afterthought, as the cost of non-compliance can be fairly significant.
In short, these regulations bring back the control of data to the customer – empowering them with the right to know what personal data is collected and how their data is being used. As part of the regulations, companies must allow consumers to access their personal data, say no to its usage, and make requests for their data to be deleted. It also states that companies must follow strict protocols for data security to protect their customer data.
Using Golden Record and match/merge tactics described above to match and eliminate duplicates is imperative, especially if a customer specifically requests to have their data removed. You would want to make sure that you find and delete all variations of your customer’s contact data, including those with any data quality errors in the name, address, email, phone number and other contact elements. This is where applying data quality solutions that verify name, address, phone and email address are key to ensure you can consolidate and link all the records into one and stay in compliance.
To accelerate this process and ensure compliance, Wavicle’s data engineering experts have created a framework that establishes data privacy logic on personally identifiable information so that updates to privacy information can be deployed automatically across all of an organization’s current and future systems. This not only saves our clients the time and effort of manually coding changes in every table and file that contain customer information, but it also saves them from potential non-compliance and fines.
As an example, we worked with a Fortune 500 client that has more than 40 applications globally, which contain customer data, including personally identifiable information, or PII. To comply with GDPR and CCPA, the customer had to be able to understand what data about each consumer is in each of those 40 applications. With this solution, we gave business users a metadata layer that tells them which of those systems contain a customer’s PII and enables them to delete those records and remove them from the system.
3: Accelerate the Move to Predictive Analytics
With a complete Customer 360 view, you now have a clear picture of your customers to perform predictive analytics and deliver a more personalized customer experience – as well as the knowledge-base to sell to them based on their preferences and actions.
The key to success with predictive analytics is to start with a particular use case, integrate the data needed to support that use case, build the predictive models, and demonstrate success. This creates a scenario whereby early successes fund further investment in predictive analytics solutions. A more traditional approach is to integrate all customer data, build a data warehouse, perfect historical reporting, and then focus on predictive analytics. This is a lengthy process that delays the most valuable output.
Customer use case: Customer 360 and micro-segmentation
We recently worked with a global fast food restaurant chain to build a Customer 360 initiative using predictive analytics and machine learning. The customer wanted to develop a micro-segmentation strategy with highly targeted, relevant offers that match the preferences of specific customer segments – particularly higher value customers – based on purchase patterns, menu items, frequency of visits, sales channels, and more.
The client worked with Wavicle to capture customer data from multiple sources, including point-of-sale transactions, digital analytics, loyalty programs and third-party delivery platforms, and consolidate and merge the data into one place. The company was able to identify which customers spend the most now and those that are most likely spend more in the future.
The result: The company leveraged its micro-segmentation effort and developed more personalized offers and loyalty programs that increased engagement, customer retention and ultimately, their bottom line. To read more about micro-segmentation, click here.
Customer 360 is Your Path to Success
In closing, without a solid, holistic, accurate, and complete view of your customers, you won’t be able to uncover the truth about them, their wants, needs, and preferences. Customer 360 empowers you to gain deeper insights into who you customers are, know how they interact with your brand in the past and present, and predict how they will engage with your brand in the future.
You’ll be able to make better business decisions and personalize your customer interactions – improving the customer experience all around. But to achieve a 360-degree customer view, organizations need to implement some dedicated processes into their operations. This includes developing a data quality action plan to get rid of bad data, addressing new compliance requirements, and tapping into new technologies like machine learning and predictive analytics to build even more robust Customer 360 initiatives.
The end result: Customers that really, truly, like you.