This article is an excerpt from the report: Transparent Cost-to-Serve Customer Relationships.
A copy of the full report can be downloaded here.
In Part One of this series, we looked at an example of profit erosion due to a salesperson’s lack of visibility into supply chain circumstances when they were crafting deals. Here in part two, we explore what it means to fix that by cultivating Transparent Cost-to-serve Customer Relationships (TCR) and how to achieve that in a phased approach.
What Is a Transparent Cost-to-Serve Customer Relationship?
For most companies, cost-to-serve implications are opaque to the salespeople and their prospects/customers. Smart companies are taking steps to achieve Transparent Cost-to-serve Customer Relationships (TCR) where the salesperson has full visibility into the cost-to-serve implications of the customers’ requests, such as requests for short-supply items, special services, custom pack sizes, specific delivery dates, and frequent small quantity orders. This enables the salesperson to manage those costs and offer alternatives much more effectively. With TCR, organizations can:
- Dynamically calculate cost-to-serve, to accurately reflect the current reality and anticipated future;
- Make cost-to-serve transparent to salespeople for smarter decision-making throughout the sales cycle;
- Turn cost intelligence into experiences that influence customer buying decisions, to lower and/or recapture some portion of costs that are not contributing to customer lifetime value;
- Manage inventory in short supply more strategically to maximize short and long-term results. Better deploy excess inventory or capacity as add-on business for incremental profit.
|TCR Should be a Key Element of Strategic Customer Relationships|
On the buy-side of the business, sophisticated procurement organizations have long employed strategic sourcing approaches to work collaboratively with key suppliers to improve mutual performance. Outcome-based sourcing1 is a related advanced practice where the buyer specifies the outcome they seek, rather than issuing an RFQ with overly prescriptive specifications on how to achieve that outcome.
Analogously on the sell-side of the business, smart companies are moving to strategic Transparent Cost-to-serve Customer Relationships (TCR). This is where the seller and the customer/”buyer have a mutual understanding of A) the customer’s objectives and reasons for specific requests they make (such as for short-supply items, special services, custom pack sizes, specific delivery dates, etc.) and B) the cost-to-serve for those requests. This enables the salesperson to transparently explain how those requests will impact the price and present alternative options to meet the customer’s objectives at a lower cost. Ideally, the customer becomes a partner in jointly finding a creative solution that meets their needs at the lowest cost.
How to Build on Today’s Tools and Approaches
The natural place to view and take action on TCR is in the CRM system that the salesperson currently uses. CRM systems already help the salesperson segment customers based on differentiated sales channels, customer journeys, product bundling or pricing
options, and more. Sales and marketing teams often have one piece of the ‘right response’ equation, using CRM tools for customer retention, acquisition, and upsell. Common examples include predictive models that flag what to offer next (‘next best offer’), optimal pricing is given market comparables, and the likelihood to close the deal. CRM systems extend to marketing and customer service teams, making it the ideal junction to influence profit margins of demand. Thus, the CRM system provides a foundation to build on to achieve TCR.
In addition, companies already use a variety of existing tools to address cost-to-serve such as S&OP (sales and operations planning), S&OE (sales and operations execution), cost-to-serve analytics, distributed order management (DOM), and profitability targets for sales. While each of these has limitations, they can still play an important role in supporting TCR (see the following).
|Building on Existing Approaches|
Existing approaches often fall short in achieving cost-to-serve-based customer relationships but can be leveraged and integrated into a TCR initiative:
Achieving Benefits Sooner
Whether during periods of stability, growth, or volatility, companies cannot afford to be caught flat-footed when costs threaten margins or disruptions rattle the supply chain. Capabilities need to be in place for fast response, balancing customer value creation, to optimize long-term results. What is needed is:
- Incorporating Operational Awareness into Salesperson Action — the ability to take the everchanging knowledge and awareness that operational personnel have — about ongoing disrupting events, cost-impact of special requests, capacity constraints, and other cost-to-serve dimensions — and distill all that knowledge, making it instantly accessible, organized, and prioritized to sales — in a format the salesperson can easily digest, within the systems they already use.
- Accurately Modeling, Predicting, and Optimizing Cost-to-Serve — accurately modeling and predicting cost-to-serve on a deal-by-deal action-specific basis and by customer, and prescribing optimal deal-specific actions for salespeople to take. This requires integrating data from a variety of backend operational systems such as ERP, WMS, TMS, GTM, MES, procurement, and so forth. It requires algorithms that properly allocate costs to specific requests and AI/machine learning that can figure out the optimal course of action.
- Automating Cost-to-Serve Optimization for Self-Service Sales — automating the optimization of cost-to-serve within automated selling and fulfillment platforms, such as ecommerce, CPQ2, and DOM3 systems.
The full vision laid out above does not have to be implemented in one big project. In fact, substantial value can be realized quickly by tackling this in discrete steps, as described below. Savings generated by the first phase can be used to fund the later phases. Synapsum provides solutions aligned with these three phases of adoption.
Incorporating Operational Awareness into Salesperson Action
The first phase is enabled by Synapsum ProfitStream Manager,© which brings planning into action through the CRM. The application installs into Salesforce, where it processes operational insights and applies actions to targeted customer accounts to prompt sales and other front office functions directly in the CRM they use every day. Front office teams will be notified sooner when there are supply chain risks or efficiencies and will know what steps they can take to improve financial and customer outcomes.
Flexibility can be built into action-task response to allow for sales manager discretion where required, as the goal is to improve aggregate efficiency vs. dogmatically pursuing 100% compliance. Because a business can get started without backend integrations, ProfitStream Manager can be implemented within a few weeks to help operations planners coordinate faster, more targeted sales-side responses to supply chain efficiency opportunities and cost shocks. This capability can then be naturally extended to respond to integrated operational system data triggers (e.g., severe SKU line shortage or excess, contract renewals to guide negotiation based on TCR, etc.). Check out the section Getting Started to learn how and where to begin.
|Example Use Cases, Risks, Opportunities|
Accurately Modeling, Predicting, and Optimizing Cost-to-Serve
In the second phase, which can also be a starting point for some organizations, Synapsum Cost-to-Serve Optimizer© pulls in historical data from operational systems and forward-looking cost inputs to model and predict cost-to-serve.
If a company has a logical starting point to reduce or recoup operational costs driven by demand, Synapsum’s Micro Cost-to-Serve point solutions4 help businesses quickly identify and act. Pre-built models with data mapping compress time to realize benefits. More comprehensive cost-to-serve models can be developed in tandem that provides a broader view of product and customer profitability, accounting for specific labor, transportation, and overhead costs. These models consider constraints on inventory and capacity to specify ways to optimally allocate limited resources. It is possible to accurately predict the cost-to-serve for variations of each deal — such as predicting the cost of different delivery dates, different product mixes, different lot and pack sizes, and so forth. Over time, AI/machine learning recommends optimal actions for each prospective deal.
|Example Use Cases, Risks, Opportunities|
Implementing the second phase requires connecting to targeted data sets in operational systems such as ERP (Order Management and Financials), WMS, TMS, GTM, MES, or procurement systems.5
It also requires calibrating Synapsum’s cost-to-serve model, based on supply chain activities and costs, to accurately reflect the customer’s specific business. Initial implementation of this phase can be shortened by starting with a subset of integrations and proxied costs, then incorporating more data granularity and systems over time where there is value in doing so.
Automating Cost-to-Serve Optimization for Self-Service Sales
Once an organization is dynamically modeling costs and prescribing optimal actions, those capabilities can be embedded into the company’s automated, self-service selling platforms. This enables cost-to-serve optimization when there is no salesperson involved. Some advanced companies have already implemented automated demand-shaping optimization in their self-serve platforms,
- Amazon entices customers for specific items to ‘place order by’ to ‘deliver by’ dates to manage lead times. The eCommerce giant also encourages customers to select longer ship times on specific orders in exchange for digital media credits or fewer boxes, driving down the cost of fulfillment and transportation.
- Airlines offer a mid-tier loyalty customer (e.g., Gold Status) a choice of extended legroom seats, no-fee change options, and complimentary upgrades during low-demand travel periods, which may be restricted or offered at premiums during peak travel dates. This helps bolster demand during off-peak periods.
- A UK-based retailer offers narrower (2-hour) delivery windows for a fee. The fee varies based on whether the time slots occur when the truck will already be near the delivery address. Those optimal slots are promoted as ‘green’ options because they reduce the carbon footprint of the delivery.
|Example Use Cases, Risks, Opportunities|
Synapsum Embed Engine© is being developed to provide this capability, influencing buyer behavior directly for higher margins. Self-service customers can be guided to, presented with, and incentivized to select certain options based on the company’s business strategy. This puts the customer in control but positions the business for higher profitability. Different options can be priced to accurately reflect the true cost-to-serve.
In the Third and final installment of this series, we look at the types and magnitude of improvements to expect from a TCR initiative.
1 For more details see Outcome Sourcing: Buying Result and vested outsourcing. — Return to article text above
2 CPQ = Configure, Price, and Quote software — Return to article text above
3 DOM = Distributed Order Management — Return to article text above
4 Micro Cost-to-Serve point solutions address specific, targeted influences that customer orders have on supply chain costs. Rather than trying to create an all-inclusive cost-to-serve model, each point solution targets a specific problem with the data connectors to pull required information, and a model with prebuilt data mapping taxonomy to model the cost impact by order, customer, and customer segment. Examples include SKU movement (on-hand-to-safety stock vs. SKU velocity), manufacturing capacity vs. demand, peak vs. off-peak period costs to pick/pack/load and transport, customer order size and frequency, and many more. — Return to article text above
5 WMS = Warehouse Management System, TMS = Transportation Management System, GTM = Global Trade Management, MES = Manufacturing Execution System — Return to article text above
To view other articles from this issue of the brief, click here.