Rapid Analytics to Rationalize Discounts and Drive EBITDA

A Holiday Guide for Better Discounting Habits in '23 for Manufacturers and Distributors (Part 2)

(Click here to read Part 1 of this article on LinkedIn)

Analytical best practices to rationalize Discounts to drive Net Revenue and Gross Profits

You need to start by analyzing historical transactional pricing data, ideally as part of your company's existing Margin Analytics & Optimization platform. Companies can gain critical insights into how well their pricing strategy is working by collecting pricing data such as historical sales volumes, pricing trends (own and competitive), customer segmentation / RFM analysis, and price promotion performance. This type of transactional data analysis helps B2B companies optimize pricing and discounting models in the future.

Analytical techniques like the Discount Curve Analysis (DCA), Sales & Discounting Stack Ranking, or Customer Discount% vs. Net Sales Matrix help evaluate whether your discounting strategy is being properly executed. They also visually showcase where the most significant improvement opportunities are, whether it's rationalizing Customer discounts for small, low-growth customers or adjusting Discounting guidelines to change sales team behavior.

Discount Curve Analysis (DCA)

Discount Curve Analysis (DCA) is an essential but often underused method that summarizes the % of Units (or Cumulative % of Units) sold at each 1% Price Discount (from 0% to 100%). 

We can build a DCA at various levels, including Company, Region, Product Family, Sales Territory, or Sales Rep. It gives us insight into our company's pricing behaviors, such as:

• What % of my product is sold at List Price (0% discount)?
• What is the Discounting Behavior of my sales organization, and how does this differ by Region, Sales Manager, etc.?
• Is the Sales Org strategic about giving discounts, or is there a tendency to go to the highest discount level that my skip-level Sales Director / VP can approve?
• How much free product are we giving away to Customers?

The elements of Discount Curve Analysis (along with Price Elasticities) can also serve as the foundation for more robust Scenario Analyses that your Revenue Management or Finance teams can run as part of an annual or quarterly planning process:

• What are the incremental Net Sales, Gross Profit, and Operating Profit impacts if we decrease the Sales VP level Discount Authority from 50% to 45%?
• What is the business impact if we limit free product giveaways from $2,000 to $1,000 per annum for non-strategic customers?
• How much Sales and Profit upside can we drive by reducing our Customer or Product-level discount outliers to be in line with the top 25th percentile for the organization?
• What are the results if we close the gap only halfway?

Look at the below DCA for a fictitious manufacturing company. What stands out to you? Let's look at a few things:

1. Only ~ 20% of our units are sold at List Price. Knowing how this compares to Last Year or our plans would be good.
2. Our sales organization tends to give price discounts in 5% increments (notice the unit uptick at 10%, 15%, 20%, etc...Discount levels).
3. We see a considerable jump in units at a 50% Discount (50% is the maximum Discount level a Sales Director can approve). If we give this to only our largest Customers, perhaps it's acceptable. But if there's a wide dispersion of Customer volumes at a 50% Discount, our sales reps frequently ask for the largest discount that can be approved within the Sales org. 
4. We give away 6% of our unit sales for free. Does that align with our plans, or are there excessive product giveaways?

Analyses like DCA are quick and easy but powerful. As a next step, we could collaborate with Sales & Finance leaders to pilot a modified Discount Authority structure for a particular Channel or Region. For example: lowering the Sales Director Discount Authority from 50% to 40% could yield little to no change in unit sales and a substantial lift in Net Revenue and Gross Profits. Much like how we can develop price-testing playbooks for companies (those that don't have dynamic price test optimization capabilities), we can follow a similar approach by testing different discounting strategies. Unlike price testing, my only caution is to not change discounting guidelines frequently - for test and learn purposes make adjustments to your policies semi-annually or a maximum of 3 times per year. 

Customer Discount % vs. Net Sales

The Discount % vs. Net Sales Matrix is another popular analytical method that helps answer fundamental questions for Sales, Finance, and Revenue Management Leaders. It's a dynamic visualization of Customer performance along the axes of Discount % and Annual Net Sales. 

A more advanced version can segment the visualization based on RFM (Recency-Frequency-Monetary) scores or summarize Discount levels by RFM segment. (Ideally, you want to give the highest Discount % to your largest customers or those with the best combination of order size and frequency.)

We can also overlay modeled Price Sensitivities or Gross Profit % for additional decision-making. Segmenting performance based on Customer Price Sensitivities is a compelling way of rationalizing your discounts, often increasing gross profits without too much sales volume sacrifice.

Some of the critical questions that our Discount % vs. Net Sales Matrix can address for Sales Reps and Managers are:

1. What are the Top 10-20 Customer-Product level opportunities negatively impacting my performance?
2. What is the incremental Net Sales $ opportunity for closing the discounting gap on my outliers?
3. What is the Incremental Net Sales $ split by New vs. Existing customers (or based on different RFM scores)
4. Who are my team's best and worst performing sales reps in terms of Customer Discounts and Sales trends?

Similarly, some critical questions that this analytical technique can answer for Finance and Pricing leaders:

1. Are the Sales teams' price investments generating incremental Net Sales? Are we giving heavy discounts based on Deal Size or Annual Customer Volume?
2. What is the Net Sales $ upside if we reduce Discount outliers to the upper threshold? What if we reduce them to the average discount in a Region? What's the impact breakdown by Product/Customer segment?
3. How do Discounts % by Product or Customer Segment compare vs. prior periods?

The strategic intent here is to reduce excessive discounting variability among customers. Discounting variation is, of course, needed (and encouraged). Still, it has to be a reasonable range and aligned to Customer size or RFM scores (we can define customer size in different ways, the most popular ones being annualized volume and customer lifetime value).

This analytical technique is simple but powerful in identifying quick wins to boost margins by right-sizing discounting:

1. Reduce price discounts for low volume (or low RFM score) customers that receive far greater than average discounts (e.g., in the top 20th percentile).
2. Increase discounts for med-high volume (or med-high RFM score) customers with high price sensitivity who have received far lower than average discounts (e.g., in the bottom 20th percentile).
3. Consider bundled discounts for customers with high order frequency (and recency) but low order size to increase average ticket size and boost overall revenues (and profits).
4. Reduce discounts for medium-high volume, low price sensitivity customers with excessive discounts. Observe how volume, net revenues, and gross profits are impacted pre- vs. post before finalizing a go-forward strategy. It must be done carefully, especially for high-volume customers - the profit upside can be substantial if done successfully (however, modeled price sensitivities may only sometimes reflect reality, especially under fast-changing market conditions).

Sales & Discounting Stack Ranking

Sales & Discounting Stack Ranking is another simple yet effective Revenue Analytics technique to steer Sales behavior in the right direction and drive incremental Net Sales (and Gross Profits).

We can build it at various levels, including the Product, Customer, or Sales Team (Sales Reps and Managers). For ease of consumption, drill-downs, and action, it typically focuses on 4-5 key metrics and dynamically ranks the performance (hence the name), allowing for sorting, filtering, or applying performance thresholds. 

To be most impactful, any Stack Ranking should include a "Size of the Prize" component. In other words, what are the incremental Net Sales, Gross Profit, or Operating Profit impacts if I close the performance gaps for the bottom-performing Reps, Customers, Products, etc.?

See the example below for a simple Sales & Discounting Rep Stack Ranking. If I'm a Sales Director, there are a few things that stand out to me immediately:

1. Over half of my Business Development Managers (BDMs) give Price Discounts to Customers well above our 20% threshold.
2. Some of my team members are growing their Discounts from Last Year while decreasing Net Sales. We need to dig deeper at the Account level to see which Customers and Products are driving these trends and why. (and most importantly, what can we do about it).
3. Some of my BDMs are doing quite well: they are Discounting well below our 20% threshold, have reduced their Price Discounts from Last Year, and are driving positive Net Sales Growth. It is the perfect opportunity to discover how they did it and share those best practices with the rest of my Sales Team. 
4. The BDMs who are Discounting above the 20% threshold have an opportunity to close the gap over the next six months. If my Top 5 Discounting BDMs were to reduce their Discounts down to the threshold, it would be worth an extra $30MM in Net Sales for the team. Translating this in a way that will better resonate with my team: even if we close the gap by 50%, that extra $15MM in Net Sales will be the difference between a 90% or a 105% bonus payout. 

Analyses like the Sales & Discounting Stack Ranking are lightning-fast and easy but powerful. The strategic intent with stack ranking analyses is to encourage Sales Leaders to have ongoing, data-informed conversations with their sales team members and reduce variability in their discounting.

For example, for customers of similar size and type (or similar RFM profiles), there should not be a drastic difference in discount levels from Rep A vs. Rep C within the same region or market. Use your customer, product, and market knowledge on what an acceptable level of variation is, and coach your sales teams to build towards a more synchronized net price execution.

As a next step, we could include the Account and transaction details (with dynamic drill-downs), so when the Sales Director clicks on a Sales Rep's Name, the key Customer and Product level performance drivers show up.

More importantly, this is an excellent tool to recognize Sales Team members and coach them for better performance. Combining effective visual analytics with positive Sales Rep stories and learning opportunities is highly effective in driving organizational change in the right direction.

Promotion Effectiveness Analyses

Many mid-market manufacturers struggle to bridge the gap between their promotional spending and gross profit growth without access to robust Promotional Effectiveness & Optimization solutions. As a result, they cannot quantify the returns they get from their price investments. Furthermore, it's common for distributors and retailers to take advantage of manufacturer promotions by heavily stocking up during promotion periods but not necessarily passing the entirety of the price investment to end customers. This behavior typically leads to sharp post-promotional sales declines, making promotional investments an overall negative ROI investment.

Fortunately, businesses have the means to build sophisticated and actionable Promotion Effectiveness & Optimization solutions with data assets and the technology stack they already have.

At a foundational level, companies must calculate the below key performance indicators (KPIs) for themselves and their channel partners. Simple performance segmentation of your promotional events and the subsequent corrective actions can significantly benefit your bottom line.

For example, if you're a $500MM mid-market manufacturer that spends 15-20% of its gross revenues on channel incentives and customer promotions, each 1% improvement in your Promotional ROI can translate to nearly $1MM in Incremental Gross Profits - an incredible return on investment! Making small, iterative improvements over time can add up significantly and be massively beneficial for your Operating Profit.

At this point, it's clear that investing in Promotional Effectiveness Analyses is essential for any business looking to maximize its returns from promotional activities. By leveraging transactional data analysis techniques such as building Promo ROI scatterplots or experimenting with different promotional strategies, manufacturers and distributors could soon see impressive results - increased sales volumes and improved gross profits (and EBITDA).

1.   Promotional ROI: How much extra Gross Profit $ am I generating for each $1 of Promoted Price Investment?

• It is calculated as the ratio between Incremental Gross Profit $ and Total Promotion Event Spending. Incremental Gross Profit $ is the product between Incremental Consumer Units (from syndicated, IRI, or Nielsen data) and the company's internal GP$ per Unit (for a particular Customer-Product combination).

2.   Promotional Lift %: What is the % Net Sales or Unit Lift due to Promotions?

• Calculated as the ratio of Incremental Sales $ (or Units) and Baseline Sales $ (or Units). Baseline Units are a statistical estimate of how many Units the Company would sell under regular or baseline prices. If you are a CPG, you can take baseline unit metrics directly from syndicated data or model them with some basic statistical or ML models.

3.   Cost per Incremental Unit: How much $ am I spending to sell one incremental unit to a Consumer?

• Measured as Total Promotional Event Spending / Incremental Units

4.   Promotion Pass Through %: How much of my Promotional Investment is passed on to the End Customer by the Distributor or the Retailer?

• ((Base Price – Promo Price in market) x Total Units) / Total Promotional Event Spending 

Understanding these KPIs at the detailed, promotional event level and a total brand or customer segment level, you can effectively rationalize which promotions deserve more frequency (or depth) vs. which ones need to be cut from the next quarter. Knowing the above KPIs also enables you to have significantly more meaningful conversations with a distributor or retail partner on getting the right level of support. The key is demonstrating how specific promotions (and additional partner funding) generated outsized returns for your channel partners.

While most price promotions are profit dilutive in the short-term (long term, they help brand building and encourage trial and repeat purchase), you can use analyses like the below to rationalize your promotions:

1. Low ROI - Low Incremental Sales (or Gross Profit $) promotions: minimize or eliminate these.
2. High ROI - Low Incremental Sales promotions: try to get more competitive on pricing and get better quality (display, advertising) support.
3. High ROI - High Incremental Sales promos: increase the frequency on these.
4. Low ROI - High Incremental Sales promos: try renegotiating funding levels with channel partners by demonstrating "what's in it for them". For example, instead of funding 70% of your promotions and channel partners funding 30%, go for a 50/50 split, with additional quality support that generates incremental units and GP$ for both you and your channel partner(s).

 (Read our case study for a more detailed overview of how to build an organic Promotional Effectiveness and Optimization capability.)

Summary

Determining the optimal discount levels and rationalizing your existing discounting practices or promotions can be complex. Still, following some simple analytical best practices outlined in this article, you can boost your profits while attracting new customers or increasing repeat purchases.

These simple yet effective analytical techniques should not exist in isolation but as part of specific modules within a complete Margin Analytics & Optimization platform. 

This platform should also have pricing scenario analysis capabilities with built-in flexibility. By setting up pricing and discounting models with adjustable parameters, B2B companies can quickly and easily adjust everyday prices and discounting levels based on various factors such as seasonality, customer segment, product group, RFM scores, or competitive prices. It will help ensure that your pricing models, discount guidelines, and promotions are continually optimized for profit margins while providing enough discounts to be optimally positioned in the marketplace.

At Revology Analytics, our specialty is linking your pricing and promotional strategies to your business's Gross Profits and EBITDA. Our Margin Analytics & Optimization and Promotion Effectiveness capabilities provide detailed insights into your pricing and promotional models so that you can optimize your profits without sacrificing volume or customer lifetime value. 

(you can read the full article here)