This method can also spot revenue leaks
By Caroline Japic
If a unified view of the customer is important in consumer businesses, it’s even more crucial in business-to-business (B2B), where the relationship is everything. It’s harder to achieve, too, in a world where negotiation dominates sales interactions and relationships are embodied in the complex legalese of contracts, amendments and addenda.
In B2B, “knowing your customer” means knowing the terms and conditions they expect, what discounts are active and when their contracts come up for renewal. Providing an exceptional customer experience requires placing critical information at the fingertips of your customer-facing teams so they can shape fluid interactions that build customer loyalty and expand the business.
But most B2B companies struggle with this. And understandably so, given the complexity of customer relationship data that it is scattered across many systems, including contract repositories, CRM platforms and billing tools.
The human AI-enabled breakthrough
There’s exciting news, though. New artificial intelligence (AI)-based commercial relationship intelligence solutions are finally enabling B2B organizations to construct complete views of their commercial relationships, including customer lifetime value.
The breakthrough comes from combining machine learning technologies with a managed services platform that supplies crucial human expertise, i.e. human-assisted AI. The technology stack includes document capture systems and AI algorithms that extract relationship data from unstructured text in complex and highly-negotiated contracts with up to 99% accuracy and combines it with data from other systems. The managed services team of data scientists, commercial experts and lawyers then configure and tune the technology stacks to address any data quality gaps.
The result is a single system of record and source of truth for all of a B2B company’s commercial relationship data. It provides a centralized location for critical details on pricing, commitments and key dates, and associated documentation.
Hunting revenue leaks for fun and profit
Not only does human-assisted AI provide a unified approach to the customer, but it will earn its keep in additional revenue that might otherwise be “leaked” when front line employees don’t have the latest or most accurate data available.
This “revenue leakage” is a major concern for many B2B organizations. It occurs when they do not have good grips on their customer relationship data.
For example, if customers commit to purchasing certain volumes of products in a year, but they are below that level towards the end of the year, sales reps that don’t have either the contract or usage data available won’t know to bill at the accurate rate. Another commonly missed event is the expiration of a customer’s time-limited rebate, resulting in continued billing at the discounted rate. That’s revenue leakage.
Filling gaps like these can have a significant impact on your company’s bottom line, which can be demonstrated even before you invest in any solution.
Here are three best practices and practical steps to help companies like yours eliminate revenue leakage:
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. Identify and understand the leakage zones. To track down revenue leaks, it’s helpful to know the environments in which they thrive. We’ve identified several:
- Entitlement and billing reconciliation. It’s hard to compare actual usage to the contract, leaving you unsure what the customer owns versus what they’re paying for. Result: overcharged or undercharged customers;
- Contracted pricing variables. Sales teams lack programmatic guidance, and make pricing decisions on the fly. This leads to missed contractual increase opportunities. Result: missed revenue growth;
- Sales process productivity. Relationship information is scattered, inaccurate and incomplete, so sales must spend time assembling and correcting it. Result: fewer deals processed;
- Renewal management. Understanding which customers need special encouragement to renew their contracts requires sorting through years of documentation. You miss opportunities to expand the customer relationship. Result: excess churn;
- Service obligations. Finding and figuring out complex service obligations is challenging, so you have no real idea of your performance. Result: unnecessary service penalties;
- Expansion opportunities. You’re not sure what customers own versus what you could sell them; precious time is lost in tracking down the data. Result: Suboptimal expansion offers; and
- Deferred revenue. Unbeknownst to you, the payments terms are too long for a customer, so you defer way too much revenue. Result: delayed revenue.
2. Identify the areas of your customer base where you can have the biggest impact. This is where the marketer’s expertise and deep knowledge of the customer comes in. Choose the customer segments that will likely give you the biggest impact in the shortest time.
It may be your top dozen or so accounts, unless they are already getting a lot of support and attention. If they are well serviced, you may want to consider a wider band of mid-level accounts. Look for customers with contracts coming up for renewal in the near future.
Do you have any relationships that involve product sets or pricing structures that are particularly knotty and hard to manage? Is there a strategic area of your business, where you’re focused on increasing growth or profitability? Those are good places to look and start segmentation; and
3. Look for quick wins. Now you’re ready to assemble the data needed for each of these relationships. Most of the data will reside in documents such as contracts, amendments, order forms and statements of work. You may also need information from order systems and billing tools. Comb through the materials looking for opportunities based on the revenue leakage zones you selected.
Prioritize those that can deliver fast time-to-value. If you’re focusing on pricing variables, for instance, you may be able to identify opportunities to take action immediately without modifying the existing relationship. As examples companies often overlook already-negotiated price increases based on the consumer price index or cost pass-throughs.
If you’re focusing on contract renewals, you might want to look at those coming up in, say, the next quarter. Renewals are a great opportunity to renegotiate unfavorable terms and conditions and broaden the offer to grow the revenue stream.
Once you’ve road-tested a revenue leakage project based on this approach, you’ve not only demonstrated the bottom-line value of better customer relationship intelligence, you’ve done much of the legwork needed to implement a human-assisted AI solution like the ones I described above.
With the right combination of machine learning and human expertise, you can automate the entire process, extend it to new revenue leakage areas and ensure that it’s sustainable for any new relationships you develop in the future, as well as for customers already on the books.
The human component ensures that the solution is good for the long haul by keeping all of the data accurate and up-to-date (something machines alone find hard to do.) Look for cloud-based platforms that can integrate with the systems your customer-facing teams use every day, so that they always have access to the most up-to-date customer information.
Whether you go the human-assisted AI route or not, look for ways to make the elimination of revenue leakage a habit in your business: one that flows naturally from a deeper understanding of the customer.
Caroline Japic is chief marketing office of Pramata. Formerly with Hewlett Packard Enterprise and joining in 2016 she is responsible for determining the strategic direction for Pramata’s marketing initiatives. Caroline has a long history of leading high-impact marketing teams and helping companies establish their brands. Previously, she led marketing at Tidemark and Bunchball and held senior marketing roles at Taleo, Polycom, Hyperion and Tibco. Caroline graduated magna cum laude from San Jose State University with a B.A. in public relations and an MBA in marketing management from Santa Clara University.