Large-scale investment in customer insights and analytics teams have propelled the use cases for customer data. From customer segmentation to predictive pricing models and multi-touch channel strategy, the field of advanced customer analytics has more tangible applications than ever. As organizations focus on accelerating digital transformation projects, it is often tempting to skip over the sticky underlying data challenges. Most organizations continue to struggle with customer data quality, resulting in the latest and greatest customer analytic tools and operational systems being fed with bad data.
We look at why customer data issues stubbornly persist, how to address them and the value of doing so.
- Key Customer Data Challenges
- Impact of Bad Data on Sales and Marketing
- What is Customer Data Mastering?
- Business Value Drivers & ROI
Key Customer Data Challenges
1. Poor integration & duplicate records across sales, marketing, and finance
While CRMs and ERPs continue to provide the backbone of customer data, the list of core sources is long and growing for enterprises across channels, departments and external providers. Data aggregation has been achieved through data lakes and traditional MDM approaches but true integration of sources and ensuring data quality for the business remains a struggle. Frustrated sales, marketing and finance teams often take the matter into their own hands. Departments abandon the enterprise goal and turn to point solutions to try to rectify the issues, furthering the issue of silos and partial customer views.
2. Inaccurate and out-of-date customer data
Misspelled organization names, wrong contact information, and unintentional duplicate records continue to plague databases. Inconsistent data schema and metadata can fuel misunderstanding of what data was intended to be captured. While there is not a single root cause of the problem, it often links back to the need for people and process management in data capture and maintenance as part of a robust DataOps strategy.
3. Difficulty connecting the corporate or regional view of customer accounts
Customers are complicated. There are many ways to classify accounts – by site, country, region, contracted entity, legal entity or corporate parent. While there is often clear variation across business units from sales and marketing to finance to risk, it can also vary within departments – how sales classify customers for team coverage might be different to how they classify customers for contract management or marketing campaigns. Tracking the customer journey across systems needs to facilitate a 360-customer view that reflects how customers are seen, and how decisions are made, by the business.
Impact of Bad Data on Sales and Marketing
What is Customer Mastering?
Customer mastering is about achieving true customer 360 – gaining a unified, accurate, enriched view of customer data across systems and sources that can feed both operational and analytical systems. It should directly address the problems discussed – data silos, errors and hierarchy classification – to give the business a view of customers based on how customers are managed.
To achieve high accuracy and carry out the mastering process efficiently, a machine learning approach is needed. The backbone of most traditional approaches to master data management are rules (at the most basic level, ‘if-then’ type statements). Rules require consistent high manual effort from creation to maintenance and become a complicated web to untangle for any data team once scale is reached. By leveraging a machine learning approach, it allows customer data to be mastered with up to 90% less manual effort, while maintaining team input and influence on how the data should look.
Why Matching Customer Records Isn’t Easy
Three Key Technical Features Needed for True Customer 360
1. Persistent customer IDs: A unique ID that connects customer records within and across systems and enables a golden record view of customers.
2. Consistent data enrichment: Built-in pipeline enrichment capabilities to ensure critical customer information such as address, email and URL are kept up to date.
3. Hierarchy classification: Flexibility to classify customers based on how you manage customers, from sites and sales regions to corporate hierarchies.
Business Value Drivers & Return on Investment (ROI) for Sales & Marketing
Just as technical data improvements are quantified, it is critical to measure and track the impact on business outcomes to ensure return-on-investment from mastered customer data. While there are typically significant efficiency gains from reducing manual effort, it is critical to tie improvements in the data to strategic priorities. There are typically many – customer data impacts an enterprise’s most important decisions by driving revenue, decreasing operational costs and reducing risk.
1. Improve customer experience (CX): 360 data enables customers to have a similarly holistic experience – the foundation for good CX. Reports, account reviews, billing, and customer support benefit from an accurate, complete account view. Gone are the days of receiving three emails with the same offer. Removing daily frustrations drives customer satisfaction and ultimately, retention rates.
2. Target marketing campaigns effectively: A mastered customer view removes noise from customer segmentation efforts and predictive marketing models. It ensures that duplicates and disjointed customer views no longer distort analysis and key attributes such as product purchase history or channel behavior can be used to inform buying intent.
3. Drive upsell and cross-sell within accounts: By enabling unique customer IDs and account hierarchy classification, sales representatives are better able to identify who they should be talking to within an account. Once a target is set, sales are armed with 360 account information to identify product gaps and make informed recommendations.
4. Attract and convert leads: Knowing who-is-your-customer is the foundation of knowing who-isn’t. By mastering customer data and matching it against the total addressable market, enterprises can identify new accounts to target. For industry leaders, this is particularly valuable. The more saturated the market becomes, the more difficult it becomes to recognize greenfield opportunities.
Lower costs & risks
1. Enable self-service for customers: As more and more customers expect any-time account access, unified customer views can help to enable self-service platforms. Unique customer IDs are critical for good governance and accurate permission access for accounts.
2. Optimize sales account coverage: Customer data should determine the appropriate account coverage – by channel, by sales representative and by campaign. Sales leaders can ensure that high-value and high potential customers are given high-touch treatment and the ratio of account coverage per sales rep is balanced, removing duplicative efforts.
3. Streamline operational processes: Additional manual checks, onboarding processes and customer support steps that became necessary due to poor data quality can be removed. Reducing the number of errors in the data leads to less frequent and less time-consuming customer support requests.
4. Operational risk: Errors in the data lead to mistakes in execution. Ensuring accurate customer data greatly reduces operational risks, from wrong deliveries to miscalculating customer account exposure. For industries like financial services, risk from bad data extends well beyond operations to regulatory and credit risk. Customer 360 is the foundation for know-your-customer (KYC) compliance.
While sales and marketing are often the first to reap benefits, the benefits of customer mastering are felt across departments, from finance and risk to supply chain and product management. The impact of customer mastering is truly enterprise-wide, which is why so many enterprises choose customers as the first critical business entity to master.