Sales & Marketing

6 Data Management Practices Lead to Channel Growth and Customer Lifetime Value

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A manufacturer’s information universe extends far beyond its own data sources to include those of distributors, resellers and other business partners. These constituents contribute insight and intelligence to the vast and constantly changing knowledge base upon which a manufacturer’s success depends. This intelligence is becoming even more important in the era of the Internet of Things as manufacturers seek to add value across connected products. But this information environment is understandably fragmented as supply chain players maintain unique data sources that operate at different intervals and in differing formats. 

Investing in sound channel data management processes – which span the supply chain – allows manufacturers to take advantage of lucrative opportunities that can drive new growth and improve customer lifetime value (CLV). CLV is an important metric that takes into account the total revenue generated over the lifetime of your relationship with a customer after subtracting all sales and service costs. When an organization increases CLV, it means that it continues earning revenue after the customer’s initial purchase transaction and that a higher level of business performance has been achieved. 

While sales, as a practice, is evolving today, CLV still isn’t top of mind for every organization in a manufacturer’s value chain. Most resellers remain focused on expanding their customer base and signing up new logos. 

But the more successful channel partners also recognize the importance of increasing CLV. These organizations have a clear understanding of the significant revenue opportunity in after-sale maintenance contracts and recurring service revenue. They recognize that nurturing the customer relationship after the initial purchase – with appropriate and timely offers for new and related products and services – not only creates new recurring-revenue streams but also builds brand loyalty and stronger retention rates. 

With this in mind, manufacturers should consider the following six channel data-management best practices to maximize CLV as well as channel service sales growth. 

1. It takes a team. Teamwork is best when it comes to driving CLV and uncovering recurring service revenue opportunities. Manufacturers should work closely with their distributors and channel partners to collect the customer data required to properly track, manage and predict next steps with existing customers. 

To do that, put in place proven data-management strategies such as an optimized Extract-Transform-Load (ETL) process, to fuel a more effective way to identify end customers’ expiring service contracts, as well as their tech-refresh and upsell and cross-sell needs. Data can be extracted from CRM, quoting, ordering and other transactional systems across the supply chain. 

2. Standardize. Systemic data issues are a direct result of the lack of standardization among data environments as well as the inability to share data throughout the supply chain. A sound channel data-management process resolves the degradation of data by merging data from each of the associated parties, filling in the gaps and creating a comprehensive data record for each asset. Automated data feeds should be scheduled at a regular frequency across the supply chain, and policies surrounding data input and refreshes should be clearly defined, shared and revisited. 

The most important data points to keep in mind as you build and grow your data core might include product and service serial numbers, service expiration dates and contract duration, product and service end-of-life dates, the install site for the product or service, customer-specific discounts, supply chain partner contact details, and more. 

3. Dig deep. Find and flag anomalies so that the data-capture process can be properly evaluated. Consider factors such as the number of data sources in each system, how they are connected to each other and how often they speak to each other (e.g., nightly, daily or not at all). One of the biggest data-management challenges for manufacturers is dealing with data dispersed across different locations, existing in different formats, running at different intervals and operating in silos. 

4. Keep data definitions abstract. Customer data comes in different shapes and sizes, which vary by provider. Build processes and utilize technologies capable of looking at data in the abstract (e.g., by end customer, product lines or geographic location, to name just a few). 

As business models evolve and new ones emerge, this practice becomes increasingly important and allows for easier maintenance of the data-management process across complex supply chains well into the future. 

5. Apply business intelligence. Capitalize on the power of aggregated data to support business intelligence and predictive analytics strategies. Don’t be afraid to ask questions of your data set, and be prepared to act upon the answers you derive. Data management with all of these issues in mind can be particularly useful to predict or prescript selling motions into unique customer segments, as well as across customer behaviors and trends. There are many different ways to target your data, and BI tools can be of great value in those applications. 

6. Make an ongoing commitment. Data constantly changes; and if systems and channel-management processes are not designed to scale and evolve, then data-management practices will become outdated and returns will start to diminish. A commitment to data is an ongoing journey – it never stops. Define and revisit data-management strategies, as they are iterative processes and their repeatability is most critical to long-term success.  

Moving your supply chain partners to a CLV-driven focus obviously won’t happen overnight, and it may require a transformational shift in the way they do business. How can you help them make it a bigger priority? Start by doing all the heavy lifting for them. Take the burden of data aggregation and management off of their busy schedules. Then, let your data tell them a story about what has been sold, and more importantly, what has been overlooked across your channel partners’ services businesses. Identify data insights that will uncover ways for them to grow their customer relationships after the initial sale has been made. 

The reality is that most channel organizations suffer from outdated or incorrect customer data and, as a result, miss out on significant recurring revenue in the form of renewal, upsell and cross-sell opportunities with existing clients. By taking the initiative to cleanse and enrich that data, you can create an essential foundation for a comprehensive CLV initiative in which everyone – from the end customer to your supply chain partners and your organization – wins. 

Kelly Crothers is vice president of global marketing for MaintenanceNet, Inc., a leading provider of service revenue-generation solutions. MaintenanceNet helps the world’s leading manufacturers and their channel partners expand the scope and success of their service sales initiatives. Her strategic marketing vision has guided the company since 2004.

 

 

 

 

 

 

 

 

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