How Bad Customer Data is
Killing B2B Sales
And What To Do About It
Data-driven selling has more momentum than ever. According to Gartner, 60 percent of companies will transition from experience-and-intuition-based selling to data-driven selling by 2025. There is widespread understanding that good data is the foundation for good decision making about customers. However, achieving customer data quality, and ensuring that sales and marketing teams use the data, remains a major challenge for many.
Despite executive buy-in and large investments in purported technical solutions, results to date have been dismal. In a survey of B2B companies by McKinsey, only one in four respondents said they use data weekly to understand customer needs. An overwhelming 86 percent of respondents believed they could do much better with data. The direct costs of failed investments to fix problems with customer data are hitting the bottom line. An estimated 21 percent of marketing budgets are wasted on bad data quality. For a $1bn revenue company, that’s an estimated $16M per annum wasted, before factoring in the ripple effects on customers or the cost to other departments.
We take a closer look at the factors holding back enterprises in achieving the goal of good customer data and how silos and errors in the data limit sales and marketing’s impact. We give an overview of what true mastering of customer data really means – gaining a unified, accurate, enriched view of customer data across systems and sources that can feed both operational and analytical systems. Finally, we show two enterprise examples from Santander and Littelfuse where mastering customer data transformed sales results and benefitted the bottom line.
Why It Matters
How many times per day is a member of the sales team looking up a customer in Salesforce or MS Dynamics? How many marketing campaign emails are sent? How many customers are bombarded by multiple unconnected sales reps? At the enterprise level, tens of thousands of employees in sales and marketing make decisions day-in, day-out based on the data in mission-critical systems. The scale of the problem is immense. If each touchpoint could be improved - even incrementally - the potential business benefit is immense.
Three Customer Data Issues Killing Sales and Marketing Results
At a time when sales and marketing organizations are told to be more data-driven, they are constantly being let down by the very data they must trust. Instead of encouraging more use of data - the stated goal of most initiatives - this breakdown in trust is reinforcing old behaviors rooted in experience and intuition, instead of quantitative insights and analytics.
While every organization has unique nuances and issues, poor customer data often kills sales and marketing efforts in three ways.
- Poor integration and duplicate customer records across systems, channels and divisions
The complexity of multi-channel sales has increased the need for tight integration of data across systems. While CRMs and ERPs continue to form the foundation for customer data, as the proliferation of data across the enterprise continues, the list of data sources is long and growing - from marketing automation software to online portals to ad hoc CSV files.
For most organizations, it is not their first data integration rodeo. But while most achieved data aggregation, few truly integrated the data across sources or tackled the issue of accuracy.
- Data lakes became data swamps: unnavigable for practical use by the business
- Point solutions remained point solutions: furthered the issue of data silos within departments and often failed to create a true 360 view as a result
- Clean-ups and one-off migrations of ERPs and CRMs were not sustainable: the band-aid only lasts so long and often ignore key DataOps principles of continuity and agility
Not only have most existing efforts failed, but it’s added skepticism among sales and marketing that the problem of customer data can be solved. The data organization and business units alike have been burned out by failed attempts and unmet promises by vendors.
- Inaccurate and out-of-date customer data
Most enterprises continue to struggle with varying degrees of quality and consistency across customer systems. Typical errors include misspelled organization names, wrong contact information, and unintentional duplicate records within databases. In some cases, it can be as extreme as contact names or address information found in fields meant for the company name. 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 links to the need for people and process management as part of a robust dataops strategy for data capture and maintenance. Slip-of-the-finger errors during data entry and a lack of governance are often the biggest contributors to poor data quality. The problem is often worse among certain sales channels with indirect and in-person sales having the least amount of consistency in data capture as sales teams go rogue.
- Difficulty connecting the corporate or regional view of customer accounts
Customers are complicated. While the B2C world typically focuses on individual customers that go by single names with single addresses, the same is not true for B2B sales. Organizations can be classified 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. There is no trivial data-filter solution for the classification of customers at enterprise scale. Such differences need to be thought-through when deciding how a customer for ‘customer 360’ should be defined and what a unique customer ID should link to. Tracking the customer journey across systems needs to facilitate a 360-customer view that reflects how customers are classified, and how decisions are made, by the business.
Impact of Poor Customer Data
What is Customer
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.
At the most basic level, customer mastering must consistently integrate customer data across sources – tracking the journey of a customer from spreadsheets to CRMs to ERPs and marketing automation tools across all sales channels. At an enterprise level, this typically ranges from tens to hundreds of critical data sources. However, with a machine learning approach, customer mastering can move beyond foundational data integration to improve customer data quality and extend the customer view.
The Value of Machine Learning
for Master Customer Data
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 do not scale and, in our example above, struggle to allow for the many versions of JP Morgan Chase. 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 a fraction of the manual effort, while maintaining team input and influence on how the data should look. Poor underlying data quality can mean piecing together partial information like half-completed address fields - a task built for a machine learning approach.
Three Important Technical Features for
True Customer Mastering:
Success Stories: Customer Data Mastering Initiatives That Drove Sales and Marketing Results
Customer data mastering, if correctly done, offers sales and marketing organizations a powerful way to accelerate data-driven selling and enable customer insights for competitive advantage. Below are two examples where enterprise investments in customer data mastering solved longstanding data challenges and made a meaningful difference to the companies’ top-line revenue growth.
Global high-tech manufacturer unlocks insights from customer data to drive greater upsell and cross-sell opportunities
Littelfuse, a $7bn market cap multinational electrical components manufacturer with a vast distributor network, needed to improve insights into its B2B sales process and results—starting with understanding how many customers it actually had. With sales data coming from 200+ transactional data sources, such as SAP and Oracle, understanding the answer to this fundamental question wasn’t easy.
Recognizing the problems caused by their poor customer data, Littelfuse chose to master their customer data with machine learning. The company created a repeatable pipeline for tying customer quotes to the millions of resulting transactions through a persistent ID that tracked the customer journey. By successfully mastering its data at scale, the company was able to fuel new on-demand analytics and insight into the corporate hierarchies of its customers and reinvent its sales process. The results were game-changing.
- Advanced from one-off reports to on-demand analytics to analyze sales pyramids and churn reports
- Enabled prioritization of top accounts & identification of upsell opportunities by consistently tracking performance
- Gained insight into channel performance to better inform distributor exclusivity rights
Not only did Littelfuse streamline and speed up its sales process, critically, the company now trusts its customer data to inform decision making at the highest level of its business.
One of the world’s largest banks cuts lending time in half by solving its customer data problems
Santander UK has 14.4 million customers and £22.3 billion in corporate loans. While the organization had plenty of data, critically it lacked a single, trustable view of its customers. As a result, it's lending decisions were slow.
By investing in customer data mastering capabilities, the bank now has accurate, up-to-date 360-degree information on its customers, allowing for much faster lending. The bank combined data from 45 sources, encompassing tens of millions of records, in under four weeks to drive digital transformation.
- Cut credit decision times in half with the rollout of the new lending system.
- Increased the reliability of reporting data with unified data feeds for financial, credit risk, and regulatory reporting.
- Empowered sales with cross-sell and up-sell opportunities revealed by a holistic view of customers across divisions and products.
The Santander UK team were recognized for their digitization efforts and efficiency gains by winning the 2020 Celent Model Bank Award for Commercial Lending.
A successful customer mastering initiative can be the catalyst for a lift in sales and marketing performance
Many companies know that their customer data has the potential to be an incredible asset and drive business results. But realizing the potential from the data to improve sales and marketing results remains a struggle. Early investments in technical solutions aimed at solving the customer data issue (data lakes, rules-based master data management, and point solutions) have largely failed because they cannot address both the scale and variability of customer data.
A dataops approach to data mastering is a new way to solve the persistent issues of poor customer data. By embracing next-gen technology like machine learning and the cloud, clean and up-to-date customer records become the status quo. Mastered customer data can be fed into operational and analytical systems, putting the data to work and creating a flywheel effect for digital transformation.