We are currently in a world where Customer Experience (CX) is at its pinnacle. Customers have come to expect a certain standard of service and will not settle for anything less. In this era of digital transformation, it is crucial to know your customers and build strong, lasting relationships with them. Offering a strong CX ensures a higher customer retention rate.
Most companies direct their focus on acquiring new customers, rather than retaining existing customers and ensuring continued customer loyalty. However, from a business perspective, this approach is short-sighted, as on an average, the cost of acquiring a new customer is anywhere between five to seven times more than that of retaining an existing one.
You may be thinking to yourself, how would changing our focus help us generate more revenue, increase profits, and be more successful in retaining customers? This is where implementing a well-defined Data Quality (DQ) strategy comes into play.
What is Data Quality (DQ)?
DQ is the process of ensuring your data is accurate and complete, to serve its intended purpose. Your customer data is your enterprise’s greatest asset. Having high DQ will help you derive the value of your customer data, by presenting a complete 360-degree view of your customer’s profile, which will enable a stronger relationship. On the other hand, poor DQ or lack of data management can hinder the CX completely.
This article will focus on how ensuring DQ excellence will help your organization achieve a 360-degree view of your customer, opening opportunities to enhance CX and consequently, attain high retention, loyalty, and eventually, broaden your customer base.
DQ Gives a 360-Degree View of your Customer
Having a unified 360-degree view of the customer, or a single customer view, gives you the foundation to overcome DQ challenges. According to Quadient, 6 out of 10 marketing professionals say that the lack of a single customer view is an obstacle to their success.
You may be thinking to yourself, if I just link all my customer data together via names or IDs then I will get a single customer view and be able to improve CX. Unfortunately, it is not that simple. By having “dirty” customer data, you will be deriving the wrong value and might end up targeting the wrong customers because of such discrepancies. Companies do not realize the impact of poor DQ and how much it can hinder business performance.
Most organizations have customer data fragmented across various source systems with varying degrees of integrity. Customer data is constantly growing within an organization and without appropriate DQ processes in place, the business will continue to lose the true value of their data.
The lack of integrity across multiple source systems is just one of the many DQ issues that could exist in your systems. A strong DQ framework serves as the foundation for achieving a single 360-degree customer view, and the path forward to defining the framework can be identified by answering by the following questions:
- Are my customer records complete? (Eg. Both first and last names are populated)
- Are their inconsistencies? (Eg. Name: John, Gender: F)
- Are the values valid? (Eg. Canadian Postal Code follows A1A 1A1)
- Is the data accurate? (Eg. 123 Main Street, New York, NY 10030 is found in an official reference base of addresses)
- Are there duplicate records? (Eg. Customer found multiple times in the same dataset)
If these issues are not fixed, it can be extremely hard to derive the right value from your data.
Duplicate customer records that span across systems can be a roadblock and need to be cleansed to be able to perform unification using matching rules. Matching rules only work if Data Quality rules have been implemented properly, otherwise you will end up undermatching or overmatching customers and this will be a hindrance in performing reconciliations in reporting. DQ issues can be fixed in the source system, in transit, or downstream. If you have been maintaining your data using spreadsheets, or other manual tools, it is extremely difficult to manage your DQ. As new customers are acquired, manual data entry inevitably results in mistakes such as input errors, improper structure, or duplication. This is usually the case for most applications or processes that lack validation.
So, how do you get started?
First, you need to understand your data. This can be performed by data analysis or profiling. Based on your analysis, you will then be able to identify important issues in your data and create a remediation plan to fix these issues. It is ideal to start small and scale up, enabling you to figure out the initial issues before you invest in a suitable tool to clean up your data. With the right tool, you can cleanse, standardize, validate, match, and merge all your customer data across different silos to achieve a 360-degree view. For example, if you have customer information in every line of business, such as marketing, product, sales, and finance, the aim of achieving a single view of the truth would be to link and conflate these source systems together.
By following a methodical stepwise process, you will achieve a complete, clean, and consolidated 360-degree view of your customer profiles and can derive the true value of your customers by improving CX.
Importance of Customer Experience (CX) in a Digital Era
Organizations that treat their customer data as an asset are able to leverage great insights and enhance their competitive advantage.
According to Genesys, 9 out of 10 consumers value when a business knows their account history and current activities with the company. This is because we live in a digital era where customers are more demanding than ever before. So much so that 32% of all customers say they would stop doing business with a brand they loved after one bad experience (PWC). Being able to connect with your customers and give them a personalized experience will ensure they receive a great CX and remain loyal to the brand. Companies who do not embrace the strategic value of CX will get left behind.
So how does one derive strategic value from customer data?
Once all your data has been unified to get a 360-degree view, the next step is customer segmentation and using the data to provide personalized experience to your customers. Being able to understand the customer journey across all touchpoints and collecting the right type of data will allow you to derive measures, KPIs, and insights, and take the right measures, through customer profile analysis. This includes analyzing customer behavioral patterns and identifying buying patterns, trends, and assessing who your high value customers are and the customers likely to churn. After performing these analyses, you would need to build processes to target customers on a personal level. This is done using Machine Learning by building models that make predictions and recommend possible courses of action in real-time.
It is shown that businesses that make efficient use of their data and implement data-driven customer experiences are achieving 1.4x growth in revenue over other companies. Organizations want to make sure that their customers feel appreciated, and by actioning insights from high quality data, they can determine the best offers and service levels that will make their customers happier and keep them loyal.
Rewarding Your Customers
Customer acquisition has been a key business priority for decades, but in this digital era, customer retention has become the focus because of the value it brings. Not only does acquiring new customers require more time and revenue, but a lack of effort in customer retention also gives the customer an opportunity to switch to your competition. CX is the key to increasing retention and by extension, safeguarding and increasing your company’s revenue.
One of the many ways in which organizations can use customer data to reward their customers is through loyalty programs. 56% of members prefer one loyalty program over another because it is easy to use, 50% because it offers rewards that are relevant, and 43% because the program is trustworthy (LoyaltyOne).
Companies implement loyalty programs to give back to customers and make them feel appreciated. By collecting and analyzing various types of data, we can determine customer buying preferences, the most efficient channels and methods of interactions, and make customers feel like a part of the organization, by giving them as a sense of involvement and identity, supporting their needs and rewarding their loyalty. This enables organizations to build stronger connections and provide customers with a one-to-one experience.
Whenever you buy a product, the loyalty program will build and enhance your customer profile and leverage insights to provide you with personalized offers that match your profile. This personal touch is what will keep a customer coming back. Keeping customer data up-to-date and accurate is how loyalty programs continue to be successful and increase customer retention.
All of this is possible because organizations have a unified customer view and accurate, enhanced customer profiles, with a high level of trust and confidence in the data, KPIs, and derived insights. This trust translates into better decisions and actions to improve overall customer satisfaction and happiness.
That being said, loyalty programs are just one example of how DQ can help keep your customers engaged and loyal. A holistic view of the customer, thanks to DQ, also forms the basis of accurately delivered marketing campaigns, targeted cross-selling and other initiatives that not only convince your customers that you understand their needs but are also a sure focal point for business success.
Final Product of Data Quality
With the right type of data and the right degree of accuracy, you can discover insights and assess customer behaviors to enable customer experience which not only increases customer retention but also loyalty. Having data quality processes in place, not only enhances the value of your data, but also enables operational excellence. Therefore, having clean and accurate data can make positive impacts to your business and help you better identify methods for increasing revenue and generating greater return on investment.
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