Transparent Cost-to-Serve Customer Relationships (TCR) – Part Two: The Journey to TCR

Steps to Achieving Transparent Cost-to-Serve Customer Relationships


With Transparent Cost-to-serve Customer Relationships (TCR), 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. Here we examine a potential path to achieving TCR.


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:

  • S&OP and S&OE — S&OP typically involves monthly planning cycles with a 3- to 24-month planning horizon that can work well for adjusting on those time scales. However, S&OP is not responsive to issues as they arise and prescriptions provided to sales are on a macro level, rather than a deal-by-deal level. The same challenges exist with S&OE, which allow for tactical adjustments in response to demand on a weekly cycle over a 1- to 3-month horizon. Yet, S&OP and S&OE identify imbalances and opportunities that can become key inputs into a CRM-enabled TCR process.
  • Cost-to-serve analytics in isolation — Custom-built cost-to-serve analytics run as standalone reports become out-of-date quickly, especially if using ABC models, usually lack deal-level insights, and are expensive to maintain. What is better suited for TCR are analytics providing deal-level insights, integrated into the CRM system, and maintained by the solution provider.
  • DOM — Distributed Order Management optimization often focuses narrowly on inventory availability and transportation costs and may not incorporate unique/custom cost-to-serve factors. DOM is typically designed for automated fulfillment engines, rather than salesperson-driven negotiations. However, TCR capabilities can and should be added to automated DOM engines.
  • Profitability targets — Profitability targets for salespeople are typically based on gross margins, calculated using standard costs which do not account for near-term COGS fluctuations, nor the cost of value-added services and special requests. It is better to provide salespeople with the granular deal-specific insights and actions needed to achieve those targets.

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
  • Shortage of a key product component causes low or out-of-stock positions, inability to meet 100% of demand.
    • Sales is immediately advised of the shortage, recommended to charge full price (no discounting), limit purchase quantities, and allocate limited supply to key customers with active product demand.
    • A list of alternate substitute products is provided to recommend when appropriate.
  • Oversupply, above a preset threshold, creates excess inventory at risk of obsolescence.
    • Alert sales to introduce sales incentives now for overstocked products, to avoid steeper markdowns later.
  • Custom kitting execution is made more expensive, due to stop-start throughout the week.
    • Recommend a specific day each week for kitting promised to customers.
  • Special pack-sizes require extra warehouse labor by breaking down pre-packed quantities into odd lots, reducing available pick, pack, ship staffing.
    • Communicate preferred standard pack-sizes.
    • Provide recommended surcharge prices for non-standard custom-pack-size services.

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
  • Costs rise because contracts include SLAs without accounting for the cost of meeting those service levels.
    • Costs for a standard set of SLAs is calculated and communicated to sales as a negotiating tool.
    • A process is set up for requesting custom SLAs, with same-day turnaround to calculate and communicate the costs of providing those SLAs.
  • Customers requiring ‘freight collect’ are given discounts by sales on the theory it’s cheaper if they pick up the freight.
    • Cost-to-serve calculator shows total costs are higher for collect due to complexity of handling many different customers’ routing guides vs. prepaid shipments.
    • A specific surcharge is recommended for customers requiring freight collect for targeted accounts.
  • One-off sales-led promotional events, not part of regular distribution and delivery of goods, get scheduled without consideration of supply chain circumstances or costs (e.g., ready to display SKU or higher packaging costs).
    • Promotion plans are shared with the supply chain team from the start. Cost-to-serve is calculated and communicated, including specific cost drivers and amounts.
    • Recommendations of alternate ways to lower the cost-to-serve are provided.
    • Cost transfers from operations to sales are implemented for promotion execution, based on calculated cost-to-serve, helping to ensure promotion execution stays within budget.

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,
for example:

  • 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
  • A competitor’s production problems create a sudden spike in demand for specific products.
    • Pricing for those products is automatically increased within the ecommerce engine.
    • For the specific products in short supply, alternatives are suggested to consumers.
  • Customers that place high frequency orders in small quantities are provided incentives to choose a structured delivery day with guaranteed on-time delivery and credits.
  • Promotion bundles are selected to be merchandised on eCommerce sites based on drivers of traffic, revenue, and available inventory. ‘Ship efficiency’ scores are provided to marketers and merchandising teams to inform them how efficiently each bundle will ship out, alone or with other add-on items in the order. This drives better decisions on the composition of the promotional bundles.

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 ManagementReturn 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.

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