10 Ways Customer Data Mastering is Transforming High Tech Manufacturing
Guide to Mastering Customer Data for High Tech Manufacturing
Insights from industry leaders such as Analog Devices, Johnson & Johnson and Littelfuse
Despite significant investment in systems (Salesforce, MS Dynamics and SAP) and analytics teams (data science, business intelligence, pricing and planning), most high tech manufacturers still struggle to gain a true-360 degree view of their customers. With many in the industry investing in a cloud computing strategy and the need to execute digital customer strategies, the time to tackle messy customer data is now. We’ll discuss how leaders in the data industry have been addressing the problem of bad customer data through machine learning-driven, cloud-native data mastering to enable customer views across sites, corporate hierarchies and indirect sales channels. We’ll highlight the 10 ways customer data mastering is transforming the industry and share examples of how industry leaders drove results for the bottom line.
What Customer Mastering Means
Customer mastering achieves true customer 360 - the creation of a unified, accurate, enriched view of customer data across systems and sources and a unique identifier so the customer can be consistently tracked. Mastered customer data can feed both operational and analytical systems, from CRMs, ERPs and marketing automation tools to business intelligence tools and dashboards.
10 Ways Mastered Customer Data Drives Results for the High Tech Industry
Automate customer data preparation to increase productivity:
Data preparation, cleaning and categorization are time consuming processes when tackled with rules-based approaches (“if x=y, then”) or sheer manual effort. The work is typically spread across teams - while data scientists and engineers often dedicate the most time to it, data consumers within the business often end up manually adjusting the data given low data quality. This can range from a sales rep wasting precious hours trying to reconcile data in Salesforce to a financial forecasting team manual merging data sets in excel. Through a machine learning approach to mastering customer data, manual effort can be reduced by up to 90. Research consulting firm Forrester estimated that the average company saves $4.9M over 3-years.
Inform rebate and pricing strategy based on trends:
Pricing strategy within the industry is complicated. By unifying and tracking pricing information across corporate accounts, customer data mastering enables a centralized view of pricing. Instead of being merely reactive to quote requests, operations can monitor pricing by market segment, business unit and customer segment to proactively engage sales and pricing teams on commercial terms to ensure profitability is prioritized. Without a data-driven pricing strategy, sales reps can go rogue to ‘sell at any cost’ which may not result in a desirable level of profitability. Marginal improvements in pricing can translate to millions of dollars of revenue growth as the strategy scales across accounts.
Drive upsell by informing product recommendations with purchase history:
Reaching out with the right product recommendation at the right time is essential to driving account expansion. Reliance on sales to ‘know their customer’ and remember purchase patterns becomes unmanageable at scale. By enabling a holistic, clean view of an accounts purchase history - by site, by time period, by corporate account hierarchy - recommendations can be data driven and automated to drive expansion.
Monitor distributors with a complete view of customers across sales channels:
Data from distributors can often be the most difficult to govern. By creating a unified view of customers across distributors and sales channels, partner teams can enhance distributor operations by monitoring performance, informing distributor price authorizations and ensuring distributor margin plans are in line with internal audit and the distributor’s expected ordering profile. At a strategic level, a 360-degree customer view across channels helps to inform distributor selection to assist channel strategy.
Transform customers’ digital experience by providing a complete account view:
Heightened by the progress of B2C counterparts and the growth of remote work, the ability to offer digital self-service for customers has become increasingly important for high tech manufacturers. By providing customers with a holistic view of accounts, companies can better monitor and ensure fulfilment and create a path for renewal and expansion. Sharing information with the customer through digital platforms requires confidence on the company's part that the customer information is accurate and up-to-date.
Gain insights on new sales leads by enriching pipeline data:
Enriching new lead information ensures that Sales is armed with the right information to act quickly. It ensures 360-visibility on existing business with the corporate, and accurate lead contact information for outreach, removing manual effort for the sales team. For one semiconductor company, each new lead activated was worth an average of $8,000. It translated to tens of millions of dollars when data was improved across their sales pipeline.
Optimize marketing budget with a hierarchy view of accounts:
Given the complexity of customer relationships by site, region and corporate hierarchy, it’s critical to focus marketing budget on outreach strategies that target decision-making business units. Unifying customer data based on location and parent hierarchy gives marketing a richer view of customer relationships so that marketing budget isn’t wasted on multiple campaigns to the same companies, bounce rate is reduced and promotions are targeted to the right business level.
Improve operational sales efficiency by avoiding duplicate efforts:
Sales leaders today are focused on reducing all non-customer engagement activities so that account representatives can focus on what matters - the customers. According to research by McKinsey, only 26% of a sales force’s time within the industry is spent on customer-facing tasks such as meeting, calls and account strategy. By creating a mastered data view of customers across site and corporate hierarchies, sellers are armed with accurate account information across CRMs and analytical tools to target customer conversations and avoid duplicate outreach efforts.
Enhance sales forecasting with clean data input:
While forecasting is a mixture of art and science, clean data input on customer orders helps to ensure the right input assumptions are feeding models. Tackling the accuracy of customer data in, and across, ERP systems is a common application of master data management. It can enable planning analysts to deep dive into performance trends on customer segments by region and industry to allow more granular insights and enable timely analysis on any deviations against the plan.
Increase ROI from business intelligence tools by feeding clean data:
Investment in customer insight tools to aid data-driven customer decisions within the industry continues to surge. Tools range from business intelligence dashboards like Qlik or Tableau to purpose built software such as contract management and forecasting tools. By adopting master data management, companies can ensure clean, accurate, enriched data is the foundation of all tools. Increasing trust in customer data is critical to aiding adoption of new systems and ultimately, achieving return-on-investment (ROI) from analytics.
Why Customer Master Data is Needed:
Key Challenges Impacting the Quality of Customer Data
Poor customer data often transpires across the industry in the following ways:
Poor integration of data, especially from distributors
The complexity of multi-channel sales within the industry has increased the need for tight integration of data across systems. While CRMs (Salesforce, MS Dynamics) and ERPs (SAP, Oracle) 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 adhoc csv files. On top of the internal data challenge, integrating data from indirect sales channels, such as distributors, can be particularly difficult as integrating external sources into data pipelines can be difficult. While legacy systems and data sources can sometimes be presented as a burden, they are also an incumbent’s greatest asset if leveraged appropriately. Given the expansion of regulation for data sharing, the importance of first-party data comes back into focus. Many of the more established players in the industry have the opportunity to use the wealth of customer data gathered to out-manoeuvre newer players.
Inaccurate, out-of-date and multilingual customer data
Many high tech companies 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. It can be particularly hard with indirect sales where governance of distributor input of data can be sometimes non-existent. The global nature of many within the industry also leads to language standardization and translation challenges. For one of the world’s leading semiconductor companies, customer data was received in 54 languages.
Connecting site-specific sales to the corporate customer relationship
Customers can be classified by site, country, region, contracted entity, legal entity or corporate parent. Often, the most important place to start is site-level customer data to gain a 360-degree view by location that can then be rolled up into regional and corporate views of customers. There is often variation in how customers are classified within and across departments - how Sales classifies 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.
Hon Hai Precision Industry vs. Hon Hai vs. Foxconn Corporation vs. Foxconn Texas
One of the most common, highly visible challenges is that customer names are often spelled differently: Hon Hai, Hon Hai Precision Industry, Hon Hai Precision Industry Co., Ltd, Hon Hai LIMITED, Foxxconn Corp., Foxxconn Corporation - the list can feel endless and accommodating name variations or misspellings via rules (if [nameX] = [nameY] then..) becomes unmanageable. Mastering customer data requires using often imperfect information and judgment to decide if two customer entities are connected which is why a machine learning approach is vital.
The Value of Machine Learning for Solving Issues of Scale
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, struggle to allow for the nuanced relationship levels of our Lenovo example. 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:
Case Studies: Examples of Companies that Transformed Customer Data
Below are two examples where customer data mastering solved longstanding data challenges and made a meaningful difference to the companies’ top-line revenue growth.
Littelfuse unlocks insights from customer data to drive greater upsell and cross-sell opportunities
Littelfuse, a successful $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.
Global semiconductor company drives sales by establishing customer data hierarchies
By more accurately matching leads to existing accounts, a $63bn market cap global semiconductor company super-charged its cross-sell and upsell account strategy, while reducing reliance on costly, slow third-party data-matching services.
With a global presence that extends to over 100,000 customers, this $5bn global semiconductor company has an ambitious cross-sell and upsell strategy that depends on clean, accurate, and actionable customer data. Unfortunately, the manufacturer struggled to match new leads to existing accounts, with only 36% of leads matched to corporates using its current data mastering approach. Critical customer data was locked in dozens of siloed data platforms, including Microsoft Dynamics and Adobe Campaign, and sourced across sales stages from POS to shipment data. It also included external sources such as web scraped data and external providers like D&B. With a wide range of customer languages, and a large proportion of existing data in Chinese and Japanese, language standardization was difficult.
Using modern, cloud-first B2B customer data mastering, the company achieved a 50% increase in the match rate of sales leads to existing accounts within the first two weeks. It reduced the reliance on third-party data matching by improving in-house capabilities and leveraged Google Translate enrichment to tackle language challenges across locations.This improvement in match rate enabled faster response times by sales and customer management, and informed fresh marketing campaigns targeted at new leads; all contributing to closing more deals in less time. With Tamr, the high-tech manufacturer can:
- Improved cross-sell and upsell by using existing relationship information to inform the lead-marketing strategy.
- Reduced lead-response times from weeks to days increasing the close rate of new sales opportunities while saving hundreds of thousands of dollars and reducing the reliance on third-party enrichment services.
- Achieved a global view of its accounts, enabling holistic contract management and more-informed negotiations with key accounts.
With each qualified lead worth thousands of dollars, better data insight on how to convert opportunities can dramatically improve marketing and sales performance.