Solutions Range optimisation

Product Range Optimisation

An effective product range should always be evolving to ensure that it stays relevant among changing preferences, meets requirements set by channel pressure, and remains at its most competitive.

Through survey-based product range optimisation, you can efficiently:

  • Discover the trade-offs that people make between products,
  • Build an optimised portfolio to maximise reach, revenue, or margin,
  • Find optimal price points for each product,
  • Discover optimal promotion mechanics,
  • Reduce costs through a simplified range,
  • Minimise market share cannibalisation among your own products.

Conjoint.ly offers custom research services for product range optimisation, using two main methods — TURF analysis and preference share simulations — employed through our MaxDiff and conjoint analysis tools, respectively.

Solutions Range optimisation

Product Range Optimisation

An effective product range should always be evolving to ensure that it stays relevant among changing preferences, meets requirements set by channel pressure, and remains at its most competitive.

Through survey-based product range optimisation, you can efficiently:

  • Discover the trade-offs that people make between products,
  • Build an optimised portfolio to maximise reach, revenue, or margin,
  • Find optimal price points for each product,
  • Discover optimal promotion mechanics,
  • Reduce costs through a simplified range,
  • Minimise market share cannibalisation among your own products.

Conjoint.ly offers custom research services for product range optimisation, using two main methods — TURF analysis and preference share simulations — employed through our MaxDiff and conjoint analysis tools, respectively.

What are the benefits of survey-based range optimisation?

Survey-based techniques offer an efficient method of range optimisation while providing more detailed insights compared to traditional sales data-focused methods.

Removing the need for market testing, survey-based techniques provide unique insights into topics like the trade-offs consumers make when choosing between products and the impact of hypothetical scenarios, such as NPD launches or SKU delistings.. Conclusions from such methods are based on current consumer preferences, which produces the most up-to-date results possible.

Unlike survey-based range optimisation, traditional optimisation is reliant on historical results, meaning that all market changes such as NPD launches, competitor adjustments, and changing preferences will impact the validity of results. However, sales data can be used in tandem with survey-based methods to enrich the overall results.

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Product range optimisation importance

Providing value to your customers and retail partners

Product range optimisation ensures that your product range covers your consumers' needs.

  • Find the optimal pricing for your products and see how adjusting the price of a SKU can impact preferences for the entire range.
  • Discover opportunities within the market that your range is not currently covering.
  • Investigate different segments of consumers to understand varying preferences.
  • Ensure that consumers can easily differentiate your products and see the unique value of each.

Aligning your goals with those of your retail partners will set your product range up for ongoing success, while providing each partner with valuable information for retail assortment.

  • Learn which promotion mechanic works best for your range and how it will impact other competitor products.
  • Find out if an NPD launch will grow sales for the entire channel or primarily take share from existing products.
  • Realise if adjustments to your product range will increase traffic for your retail partners.
  • Discover opportunities for cross-selling that you may not yet realise.

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Provide value to customers and retailers

Using simulations to perfect your product range

Simulations help companies analyse the market through a competitive context by creating different possible scenarios. They help to identify the most attractive products offerings and set the foundation for consistent product range expansion.

Simulations also offer detailed insights into the impact of adjusting your product range on metrics such as preference share, revenue, and gross margin. You can easily understand the impact of pricing changes by finding the price elasticity of demand of your different products.

What-if scenarios allow you to quickly identify the impact of adjusting your current products' features, delisting products from your current range, and adding NPDs to see the impact on your overall portfolio.

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Preference share simulation

Optimise your promotional pricing strategy

Well-implemented promotional pricing is a valuable tool in achieving both the goals of your company and your retail partners.

Promotional pricing can help build brand recognition and exposure, drive sales, and improve customer loyalty. From the retailer's perspective, an effective promotion may lead to growth for the entire channel, increase foot traffic, and potentially lead to cross-selling opportunities.

Product range optimisation enables you to simulate the impact of various promotional pricing strategies. These simulations ease your ability to evaluate trade-offs as the reduction in revenue compared to increased brand expouse from running promotional campaigns.

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Finding optimal promotion levels

Optimising your product’s market reach

It is important that your overall product range appeals to the greatest number of consumers. This is not simply choosing the products that perform well individually but instead finding a portfolio of products that complement each other well.

Common challenges for FMCG/CPG companies such as limited shelf space, communication space, and other budget constraints mean you must identify which product combinations will maximise your reach to the most customers.

Total Unduplicated Reach and Frequency (TURF) analysis helps you determine which combination of SKUs will reach the largest number of potential customers. You can also use TURF to determine how many SKUs you should ideally launch in your range and in which order. Conjoint.ly's automated TURF Simulator allows you to perform TURF analysis on up to 50 items.

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Choosing product combinations

Prioritise product development

Range optimisation helps you effectively prioritise your NPDs for launch, ensuring you maximise the incremental benefits from adding products to your range. Prioritisation will help you develop a scenario plan, an indispensable tool when working within a competitive environment.

A scenario plan maps out the expected incremental impact of making adjustments to your existing range, including launching new products and adjusting or delisting current products. You can use this to predict key metrics, such as the expected rate of switching to alternative products and the source of business of your market share gains.

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Optimal product range

Case studies: Range optimisation with Conjoint.ly

Which range of flavours give the highest market penetration?

All Natural is a global snacks manufacturer. All Natural wants to start a new range of fruit drinks to complement their current range of snacks.

Example conjoint analysis report for preferences in ice-cream cones

To find the optimal product range, simulation analysis performed with different price points, flavours and sizes.

Which SKUs can be removed when streamlining a product range?

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Context

Comforty is a global personal care company which has many SKUs for its foot care product. Comforty wants to streamline its current product portfolio while maintaining their current volume share within the market.

Goals

After research and extensive talks with their strategic team, Comforty would like to remove every possible redundant SKU no matter which range it comes from. The SKUs cover different sizes, different functions, different types of shoes for both female and male.

As both shelf spaces are limited at two channels, Comforty would like to know which SKUs can be removed with minimal impact on volume share.

Outputs

  • Through conjoint analysis, Comforty found the preference share for 20 of their SKUs, along with other competitor products.
  • Through preference share simulations, they compared the impact of removing different combinations of SKUs to find the impact on overall Comforty preference share.
  • Comforty proceeded to streamline their range through removing the 14 least competitive SKUs. This reduction had minimal impact on their total preference share, while reducing both costs and complexity.

Looking for expert support with your research?

Do you need support in running a pricing or product study? We can help you with agile consumer research and conjoint analysis.

Book a quick call today to talk with the team who specialise in product and pricing research.


Understand range optimisation in detail

Conjoint Preference Share Simulator

Conjoint.ly allows you to simulate shares of preference for different market offerings. Specifically, you need to describe offerings that are available on the market in the language of the attributes and levels that you chose to include in the study, and the system will estimate the percentages of preferences for these offerings.

Explaining TURF Analysis and When to Use It

TURF analysis (Total Unduplicated Reach and Frequency) is a statistical technique that ranks combinations of products by how many people will like these combinations.

Product Variant Selector

Conjoint.ly Product Variant Selector is a comprehensive methodology for testing up to 300 product ideas that helps you identify the most appealing product variants for your brand or product category. It combines several techniques that our team have developed and refined on full-service projects for FMCG brands.