Price benchmarking, though often overlooked, can be one of the most impactful strategies in an ecommerce business‘s pricing arsenal. This process goes far beyond surface-level competitor price checks to provide actionable competitive intelligence for dynamic and profitable pricing.
In this comprehensive guide, we‘ll explore what exactly price benchmarking entails, why it should be a core component of your ecommerce pricing strategy, how to implement it effectively, and how new innovations in AI and automation are transforming benchmarking.
What is Price Benchmarking?
Price benchmarking involves thoroughly analyzing competitor pricing data, market conditions, and customer demand signals to determine optimal pricing for your products. It enables businesses to:
- Gauge true market value for products based on real buyer willingness to pay
- Identify gaps where competitor prices fall short of market value
- Uncover opportunities to lead price increases or hold price leadership positions
- Match competitor prices quickly to defend market share when needed
Beyond just reacting to competitors, savvy ecommerce businesses are using benchmarking to set pricing strategies that balance both profitability and competitiveness.
For example, an pet supplies ecommerce company is launching a new line of organic dog treats. Competitors sell similar treats at price points ranging from $15 to $25 per 5oz bag. By using customer surveys and conjoint analysis, they determine buyers are willing to pay up to $18 for their organic attribute.
This indicates opportunity for premium pricing, so they launch at $17.99 per bag. When a competitor drops their pricing to $15.99 in the following month, the company holds at $17.99 to retain perceived value. Their benchmarking and research gives them confidence that buyers will still purchase at this price.
Key Benefits of Price Benchmarking
Beyond powering pricing decisions like the example above, consistent price benchmarking provides many advantages:
1. Optimize Profit Margin Targets
Accurate competitive pricing intelligence enables you to set informed, optimal targets for product margin thresholds across categories and lifecycles. You can identify high-value products with inelastic demand where you can increase margins without volume tradeoffs.
For example, a 2021 poll of 500 US and UK retail executives found that formal price benchmarking initiatives directly increased profit margins by 15-25% on average.
2. Outpace the Market
Leading indicators in your benchmarking can reveal emerging trends and opportunities before competitors catch on. You can preempt changes in market conditions and buyer demand to stay ahead.
Sports apparel ecommerce brand OutdoorLab analyzes purchase data, customer reviews and social mentions to predict upticks in certain product niches before competitors. This has allowed them to consistently price new niche inventory 20-30% higher—and still sell out.
3. Capitalize on Competitor Weak Spots
Granular competitor intelligence allows you to detect areas where other players are falling behind market expectations. You can strategically invest in outperforming them in those areas through better pricing, products, marketing etc.
For example, benchmarking may reveal that a competing supplement brand rarely offers discounts below 15%, signalling price resistance. You could then tactically outperform on flash sales and limited promos to just 10% off knowing they won‘t match you.
4. Informed Negotiations with Suppliers
Understanding true market pricing and competitive positions empowers you to negotiate better contracts with vendors and suppliers. You can better convey where pricing agreements put you at a disadvantage.
5. Agile Response to Market Flux
Rather than reacting hastily to competitor price changes, benchmarking provides the context needed to determine if and how to respond for optimal outcomes.
The above are just a sample of the tanglible business gains you can achieve with price benchmarking across areas like product development, sales, marketing, and channel expansion.
Price Benchmarking vs Price Monitoring
Before we dive deeper, it‘s important to delineate price benchmarking from the related practice of price monitoring:
Price Monitoring: Tracking competitor prices over time. This provides raw pricing data but little context.
Price Benchmarking: Comparing your prices to carefully-selected competitors using key market, product, and demand indicators for insights into pricing strategy.
Think of benchmarking as taking monitoring a step further to answer questions like:
- How are my prices positioned versus competitor sets across segments?
- Based on market forces and buyer behavior, where can I expand margins or likely need to be priced competitively?
- Which product gaps represent opportunities for new offerings and higher pricing?
Monitoring only gives you part of the picture—like knowing your competitor dropped prices. Benchmarking tells you if and how you should react.
Key Challenges of Price Benchmarking
The potential of price benchmarking is clear. But collection and analysis of the data required also poses distinct challenges, including:
Challenge #1: Data Collection Across a Fragmented Landscape
Unlike industries like financial services with centralized data sources, ecommerce data lives across:
- Competitor online stores and marketplaces
- Product review platforms
- Social media channels
- Aggregator and directories
- Reseller sites
Gathering pricing intelligence means scraping this far-reaching, inconsistent landscape.
A 2021 survey found that 78% of polled retail data analysts spend more than 20 hours per week just collecting data for benchmarking and related analysis.
Challenge #2: Apples to Apples Comparison
No two products are exactly the same. Determining meaningful price benchmarks requires carefully comparing products with similar features, positioning and capabilities. This level of normalization is difficult to achieve at scale.
Research shows that even humans struggle to match more than roughly 140 product pairs per hour with 70%+ accuracy. Most retailers track orders of magnitude more SKUs.
Challenge #3: Analysis Overload
The volume of data needed for effective price benchmarking is more than any human can reliably process and analyze. Key indicators must also be monitored over time to spot trends and monitor strategy impact.
Industry research indicates 80%+ of all competitor pricing data changes within a typical 30 day benchmarking cycle. Most organizations lack visibility into how these changes impact goals.
Challenge #4: Responding to Flux
Competitor pricing rarely stands still. Temporary promotions, policy changes, external events and more create pricing fluctuations. It‘s demanding for organizations to continually monitor and determine if and how to respond.
Overcoming Benchmarking Challenges with Automation
Many organizations find manual price benchmarking ultimately breaks under the weight of the above challenges. This leads to sporadic or shallow analysis that fails to achieve potential competitive advantages.
Fortunately, innovations in automation and AI remove benchmarking barriers through:
1. Comprehensive Data Collection
Specialized web scraping technology can now gather, structure and organize benchmarking data from thousands of sources automatically. This includes everything from product details to consumer sentiment signals across the web.
2. Apples-to-Apples Normalization
Automated normalization algorithms analyze product attributes and descriptions to classify items into categories. This enables comparison of pricing for similar items across retailers.
3. Analysis and Monitoring at Scale
Once data is aggregated, dashboarding, reporting and alerts enable users to visualize key pricing metrics. Data science techniques like regression analysis and predictive modeling provide actionable insights from the intelligence.
4. Continuous Response Recommendations
By coupling benchmarking metrics with business rules and constraints, automation can prescribe tailored pricing responses for conditions as they emerge. This delivers agility at scale.
Purpose built competitive pricing automation combines all of the above capabilities into continuously-running systems. This amplifies the impact of benchmarking while lowering resource overhead for organizations.
An Actionable Framework for Price Benchmarking
While solutions can offset the heaviest technology lift, price benchmarking success still requires cross-functional participation. Here is a high-level plan:
Step 1. Define Business Objectives
What key business outcomes do you want to drive with better pricing intelligence? Lift on certain products or categories? Improved market share in emerging segments? Margin enhancement? Building alignment upfront ensures metrics and actions align.
Step 2. Map the Competitive Set
Which other retailers compete for your buyers across which product segments? How do shopper decision journeys differ? Outlining competitive landscapes guides data collection. Monitor 2-3 competitors per segment as a baseline.
Step 3. Determine Key Inputs & Metrics
What data points beyond pricing are most relevant for insights? Consumer demand signals? Market basket metrics? Feeds selection of benchmarking metrics like relative price position, price elasticity, promotional depth and more.
Step 4. Prepare Intelligence Systems
How will you automate data collection, aggregation, monitoring and analysis mechanically? Capabilities like web scraping, categorization, business rules engines and workflow integration all come into play.
Step 5. Operationalize for Agility
With foundations in place, accelerate value realization through seller training, pricing team protocols, executive-level reporting, alerting thresholds and interlock with broader price optimization cycles.
The above roadmap combined with the right technology partners helps retailers realize quick wins from price benchmarking while building capabilities over time.
An effective price benchmarking framework encompasses both intelligence gathering and pricing strategy response (Image credit: Humanloop)
Now let‘s explore some pro tips and advanced tactics to further amplify your price benchmarking outcomes.
Pro Tip 1: Gauge Market Demand Elasticity
While competitor pricing analysis is core to benchmarking, you should also gather broader market metrics to enrich insights. Demand elasticity is key among these KPIs.
What is demand elasticity?
Demand elasticity refers to how sensitive demand for a product is to changes in price. Products with:
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Inelastic demand -see limited change in volume sold when prices increase or decrease. Buyers purchase regardless of incrementally higher prices.
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Elastic demand – see dramatic rises in volume sold from price drops, and vice versa. Buyers are price sensitive and willing to switch products based on small cost differences.
Understanding where your products and their competitive sets fall on elasticity spectrums guides pricing strategy.
Inelastic products present opportunity for pushing maximum prices without demand falloff.
Elastic items require sticking closer to market rates to defend sales. Getting elasticity wrong leaves money on the table through overpricing or unnecessary discounts.
While complex to measure pre-automation, demand modeling integrated into price benchmarking now provides elasticity guidance. This amplifies the market context received to optimize pricing.
Pro Tip 2: Employ Clustering Algorithms
Recall a core benchmarking challenge: accurately grouping similar products across retailers for apples-to-apples comparison. Product matching algorithms are unlocking this.
Clustering algorithms can rapidly group products based on:
- Keyword semantics (descriptions indicating product purpose and attributes)
- Category classification codes like GTINs
- Image recognition cues like shape, color and design
Once items are clustered, price variances can be isolated and analyzed.
For example, a home goods etailer sells a set of rustic wood napkin holders. Competitors offer 100s of similar but non-identical napkin holders. An automated benchmarking system would first cluster these into a single segment for price analysis based on language, imagery, etc.
The retailer could then determine they are priced 7-15% below the average market rate for this rustic wood style of napkin holder. This indicates opportunity to increase pricing to better align with demand.
Continual automated clustering surrounds products with the most relevant peer benchmarks as catalogs evolve across the web.
Pro Tip 3: Construct Price Waterfall Charts
Price waterfall charts provide an insightful way to diagnose your price positioning versus competitors. They visually break down how product rates build from costs to target revenue goals.
Below is a representative electronics price waterfall chart. The retailer sees competitors pricing an平均 camera drone model at $380. By breaking down their own cost basis, operating expenses and margin target at various drone price points, they can intelligently position themselves.
Waterfall signals whether overlapping competitor price points adequately cover margins and operating expenses. If $380 drone pricing would lead to losses, for example, the retailer knows they must differentiate their product offer further to expand willingness-to-pay and support higher pricing.
Pro Tip 4: Continuously AB Test
Price testing is a natural extension to benchmarking. But rather than intensive catalog-wide repricing, focus on selectively A/B testing pricing on best-bet items.
Volume drivers with the highest sales have the lowest risk for finding new optimum price points through testing. Monitor volume change at higher price targets to gauge elasticity.
New products without historical performance are also ideal for iterative testing. Find the ceiling willingness-to-pay without sacrificing introduction traction.
Use change triggers like competitor promotions and cost input shifts as cues to re-run controlled experiments.
Continuous controlled testing both maximizes commercial opportunity and tightens feedback loops to improve benchmark model accuracy.
Pitfall to Avoid: Limited Competitor Sets
One of the most common price benchmarking pitfalls is concentrating data collection on an overly narrow competitor group. Often, convenient targets get prioritized over representative views.
Instead, categorize the competitor universe into primary, secondary and tertiary tiers based on decision journey overlap. This provides breadth without overextending resources.
Here is an example schema:
Primary Competitors
The 3-4 brands commonly considered and compared for similar products during evaluation. Requires the deepest continuous tracking.
Secondary Competitors
Retailers shoppers identify with the product category but may not directly cross shop against. Monitor bi-monthly.
Tertiary Competitors
More tangentially related sellers or those only competing for a segment of products. Screen quarterly for surprises/validation.
Customize tiers and cadence based on your product breadth, shopper journeys and markets. But maintain vision into the complete set.
Pitfall to Avoid: Data in a Vacuum
Another risk to price benchmarking success is relying exclusively on analytics without broader market context. This leads to pricing decisions in a vacuum.
Research shows that higher performer retailers instead utilize an insights->strategy->data-driven decisions cycle.
Customer surveys, win/loss interviews, focus groups and more qualitative data provide powerful emotional and circumstantial color to humanize benchmarking stats.
Does the average market price say buyers will pay 15% more for eco-friendly attributes? Talking directly to them may reveal a vastly higher 50% willingness to pay premiums for sustainability. This type of insight fills analytical blindspots.
The most effective benchmarking initiatives interweave quantitative measurements and qualitative perspectives into their pricing strategy foundations.
Elevating Capabilities Over Time
The above tips demonstrate how price benchmarking stretches well beyond basic competitor price monitoring to incorporate advanced analytics, market perspectives and agile testing.
As benchmarks become ingrained, pricing teams can level-up both data science and overall pricing maturity:
Benchmarking Data Science
- Apply multivariate regression analysis to reveal optimum price influencers
- Build price sensitivity and elasticity models to maximize revenue
- Feed benchmarks into AI pricing engines driving constant optimization
Pricing Team Maturity
- Expand metrics beyond margin to lifetime value, CLV and beyond
- Gradually decentralize benchmark-empowered pricing decisions across geography and business units
- Interlink insights across cohorts like willingness-to-pay and promo efficacy
Ongoing development of competitive intelligence capabilities shifts organizations from reactive to strategically leading markets.
Key Takeaways and Next Steps
The insights we‘ve shared demonstrate how price benchmarking, powered by workflow automation, evolves into an accelerator of sustainable competitive advantage. Key themes to drive strategy and next actions:
Price benchmarking maximizes willingness-to-pay realization by positioning pricing against precise market demand signals and competitor actions
Effective benchmarking requires cross-functional collaboration to balance insights, policy and agile technology
Choose automation tools capable of collecting, categorizing and acting upon comprehensive pricing data feeds
For many ecommerce operators and omnichannel retailers, price benchmarking remains an aspirational capability area given perceived data and technology barriers.
But as solution providers rapidly deliver self-service access to these specialized engines, benchmarking moves into the operational must-have column.
The organizations moving most aggressively today will reap the greatest competitive advantages over the coming years. Will you lead markets or follow?