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How Adaptive Conjoint Improves Your New Product Development Results

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New Product Failure Rates Are High

New products fail at an alarming rate. Dozens of studies put failure rates at 60% to 80%.

So, why do so many new products fail?

Most companies rely on some form of product research to understand their consumers' choices, preferences and buyer journeys. However, fewer companies dig deeper to understand how these preferences differ across multiple segments and how they change in response to new competitive offerings, prices and disruptions.

In addition, many decision makers don't have good information to determine if investments in new product features will earn a suitable return in market share and margins. Finally, even when you launch an innovative new product at a very aggressive price point, you cannot expect your competitors to sit still. They will counter your move with price reductions and/or new product features of their own. These and other challenges contribute to the high failure rates of new products.

These innovation challenges put a great deal of pressure on brand managers, product managers and other marketing decision makers. To survive and thrive in this environment, decision makers must rely on the right kinds of new product development research throughout the innovation process.

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Some Methods Contribute to Innovation Failures

Simply put, many product testing methods are too simple. When you ask customers what they want, they usually say "all of the best features at the lowest price." If your product research and development relies on simple rating scales about preferences and willingness to pay, you will almost always fail to understand how your customers really make their buying choices during their buying journeys. Why? Because this type of over-simplified research fails to understand the trade-offs that all customers make when choosing among a variety of alternative products with different brands, features, benefits and prices.

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Adaptive Choice-Based Conjoint Predicts Purchase Behaviors

The use of conjoint analysis in the product innovation process goes beyond simple research. It simulates a real-life approach for understanding your customers' attitudes, opinions and buying behaviors in the competitive market. At its basic level, conjoint analysis learns which combinations of features your customers prefer at different price points.

This type of product market research builds your customers' actual decision-making processes and preferences into your product design, marketing and pricing decisions. What's more, the conjoint-based market simulator tells you where you will take market share and how you may need to react when your competitors shift their own product/price strategies. Remember, they will not sit still.

Today's innovation leaders must be able to predict the purchase behaviors of their target customer segments when they are faced with multiple brands, features and prices. This is where the power of conjoint simulators really shines. Managers can accurately predict how their target customers will purchase when they offer certain products and assume certain competitive responses. This flexible and forward-looking approach to innovation can separate the failures from the successes.

Success_rates_are_2X_higher

In fact, when used in 3,000+ products over 20+ years, product concept development research and predictive analytics (as part of a formal innovation process such as Stage-Gate) produce success rates that are 2X higher than average companies and 3X higher than the bottom 20% of companies. 

How Adaptive Choice-Based Conjoint Works

So, how does it work?

Adaptive Choice-Based Conjoint (or ACBC) is one of the most advanced approaches to consumer choice (or preference) modeling. ACBC combines the strongest elements of Choice-Based Conjoint (or CBC) with Adaptive Conjoint Analysis (or ACA). Unlike traditional CBC, the ACBC survey research is done in stages. At first, the customer is asked to build his or her “optimal product configuration”. This is often called the build your own product stage (or BYO). Next, the “must-have” and “unacceptable” features are considered. Finally, the customers’ real-life decision making trade-offs are assessed by offering different product configurations (brands, features, benefits, prices, etc.).

Now, what are the real business benefits?

Benefits to Product Managers, Brand Managers and Marketers

This approach to product and service innovation delivers benefits across a wide variety of industries, including: food, beverages and other consumer packaged goods (or fast moving consumer goods, FMCG), consumer electronics, life insurance, health insurance, financial services, retail, telecom and others. Regardless of your industry or category, here are some key benefits you can expect from this approach:

1) You identify the optimal product configuration, price and marketing strategy based on how customers buy products in your unique category and market

2) You identify the prices your customers will pay and if they will drive sufficient margins (price testing)

3) You accurately forecast profitability and/or market share for your product vs. competitive alternatives

4) You anticipate competitors' reactions and plan your counter strategies to their price changes, features additions, benefit claims, etc.

5) You forecast the effectiveness of different advertising strategies, names, logos, messages, and benefit statements

6) You forecast the impact of different pricing strategies in the event your competitors make changes or your cost structure changes (up or down)

7) You measure the value of your brand vs. competing brands and estimate how consumers make trade-offs between brands, price, features and benefits (this is a practical measure of your brand equity vs. your competitors)

8) You can segment your consumers based on their product "choice drivers" so you can offer specific product configurations (and price points) that will satisfy each segment


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Improve Your Odds of Innovations Success

In summary, the use of advanced conjoint analysis provides real, immediate and long-term benefits to decision makers throughout the innovation process. It accurately measures consumer preferences from among thousands of product (or service) alternatives. Conjoint analysis allows you to simulate and predict how your customers will think and buy in the real world. As a result, you identify the best product features, benefits and price points that will win in the real market. In a world where 60-80% of innovations fail, these and other new product market research methods will give you a far greater chance of success.


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Dino Fire
Dino Fire
Dino Fire

Dino serves as President, Market Research & Data Science. Dino seeks the answers to questions and predictions of consumer behavior. Previously, Dino served as Chief Science Officer at FGI Research and Analytics. He is our version of Curious George; constantly seeking a different perspective on a business opportunity — new product design, needs-based segmentation. If you can write an algorithm for it, Dino will become engaged. Dino spent almost a decade at Arbitron/Nielsen in his formative years. Dino holds a BA from Kent State and an MS from Northwestern. Dino seems to have a passion for all numeric expressions.