Comment pouvez-vous identifier les préférences des consommateurs en matière de caractéristiques des produits ?

Approaches such as conjoint, max-diff and other techniques can add value in terms of understanding the specific value customers or prospects place on individual attributes or features. They’re worth looking in to - though you may need an agency to support you with this type of approach.

If you are working within an existing product category, start your development process with a detailed competitive analysis. List the product feature details of your competitor’s brands. Ask a sample of prospective customers to select which version of each feature is most preferred. Also, ask for features that are missing. Use this information to craft a conjoint analysis or other technique that will produce a value score for each variation of each feature. Apply the results of your conjoint analysis survey to identify ideal product configurations and trade-offs. Leverage this choice data to look for differences in preference among existing segments and to identify new segmentation frameworks based on patterns of product feature preference.

Les sondages offrent un moyen flexible et diversifié de recueillir des feedbacks clients, qui permettent une segmentation détaillée et la collecte de données quantitatives et qualitatives. Cependant, leur efficacité est limitée par la profondeur des insights qualitatifs et les biais potentiels. Pour maximiser leur valeur, je conseille de les combiner avec d'autres méthodes de recherche qualitative, d'accorder une attention particulière à la conception des questions, et d'adopter une approche nuancée dans l'analyse des données. Les sondages, bien que utiles, doivent être utilisés en complément d'autres techniques pour une compréhension riche et complète des comportements et attitudes des clients.

Surveys & Feedback: Gather direct input through surveys, reviews, and customer feedback mechanisms. Market Research: Conduct focused studies, including social media monitoring, to understand consumer sentiments. Prototype Testing: Develop prototypes and conduct user testing to evaluate feature preferences. Data Analytics: Analyze sales data and website interactions to identify patterns in consumer behavior and preferences. Competitor Analysis: Study competitors' products and feedback to learn from successful features and innovations.

A trade-off study can offer valuable insight to what is important to the consumer. An experiment can provide richer and more reliable information about consumer choices but may not be feasible depending on the product or service your company offers and the timeframe allowed to obtain results.

Open A/B testing on the web is a good tool, as long as you are not including confidential or strategic features you don't want to divulge. One low-cost and fairly quick way to do experiments for understanding features might be to use your own Company or Institution: you can do mock ups or prototypes and ask your work and schoolmates about them. In this case the insights you'll collect are qualitative, and might help on early stages of development.

Experimentation, ah, the marketer's bread and butter for slicing through assumptions to the heart of what truly resonates with customers. Having dabbled in everything from A/B testing to conjoint analysis over my 6+ years in marketing, I'm all too familiar with the thrill of uncovering actionable insights that directly impact the bottom line. Yet, I often find myself wondering if we're too quick to chase after incremental gains, missing the forest for the trees. Sure, optimizing conversion rates and retention through experiments is crucial, but let's not forget to step back and question whether we're also enhancing the overall customer experience. After all, what's a boosted conversion rate if your customers don't genuinely love you offers?

Running experiments can help you figure out what is best for your customers. The methods are already mentioned for various types of experimentation so I wouldn’t get into that.

There are different techniques to run experiments on product and features like conjoint, multivariate methods. We need to use these experiments contextually to understand the real time impact.

I will add here AI perspective in traditional approval Run, such as A/B testing, help determine consumer preferences by offering different versions of a product to segments of the market and observing the response. AI Integration: AI can enhance this process through more sophisticated experimental designs, like multi-armed bandit algorithms, which adapt in real-time to direct more users to better-performing options. This not only helps identify preferences more quickly but also maximizes engagement during the testing phase.

Customer product feature analyse the need of him/his taking his past purchases and similarly how particular products competitions taking edge to promote hence need to be make cross over it to check what can be interesting analysis come out. Feedback / Survey or test marking - At the end you have to analyse and look which features does like the most. Finally analysis also give idea about type / geography / age / gender / pattern / period and so many thing.

Je tiens à souligner combien les réseaux sociaux sont cruciales pour capter en temps réel les préférences et attentes des clients vis-à-vis des produits. L'immédiateté avec laquelle nous pouvons observer et réagir aux tendances, aux feedbacks et aux désirs exprimés par les consommateurs est inégalée. Personnellement, j'ai constaté à maintes reprises que les réseaux sociaux nous offrent une fenêtre directe sur l'âme de notre marché cible, permettant des ajustements rapides et pertinents de notre stratégie produit. C'est cette réactivité et cette proximité avec le client que je valorise particulièrement, faisant des données sociales non seulement un outil, mais un véritable baromètre de l'adéquation produit-marché.

I’m my experience Data provides decent amount of information required, still bottleneck’s we can go for personal interaction

If you are working in a brand new category, consumer research is important. If you are going compete with someone else, you can find a lot helpful information from Google Reviews, BBB or other review platforms. Those feedbacks can talk to you what target customers care about.

Adding AI to Traditional Approach which is Data analysis, involves examining sales figures, customer feedback, and other data sources to identify trends and preferences. AI Integration: Machine learning models can process vast amounts of data from diverse sources (social media, purchase history, website interactions) to identify patterns and predict future trends. Predictive analytics can forecast consumer reactions to new features before they're even introduced.

I once heard one of the world’s top researchers say that insight happens when new knowledge meets experience. I have seen amazing results produced by observational research, but magic happens most often when the observer has a deep experiential knowledge of the behavior they are watching. Observational research alone is rarely enough. The researchers may see others’ behaviors through their own filters and biases. The researchers may attempt to assign motivation to observed actions that completely miss the true drivers of that behavior. Ask for explanations. The knowledge gained from the explanation may turn on the light of insight that leads to innovation.

In the world of understanding consumer behavior, it's key to realize how people act when they know they're being watched. This "social desirability bias" can really sway how consumers behave during research studies. To tackle this, I recommend to be sneaky; making sure folks feel comfy, and keep things anonymous. While watching how people really use products is super helpful, it's smart to mix in other methods, like checking out the competition and doing some a/b testing. By doing this all together, businesses can really get a grip on what consumers want, tweak their marketing, and stand out in the market.

It's said that customers are always talking, they're always expressive of their needs, wants and desires if we just listen, hence, the word social listening. Listening to them can help improve existing features or inspire new ones.

This is about understanding the patterns/ behaviours of your customer. You don’t have to reach out to your customers to gather insights, just observe how they”re using your platform. You can do this by various tools available such as Google Analytics, heap, Hotjar, etc.

In addition to all the very useful tech-driven tools, even spending as little as one hour in the marketplace on peak traffic (previous permission form the retailer), watching shoppers browse, interact, and choosing products can open the possibilities of confirming and/or developing very useful purchasing insights.

Je considère les focus groups et les entretiens qualitatifs avec les clients comme essentiels à la compréhension profonde des préférences et des besoins des utilisateurs. Cette méthode directe et interactive offre une richesse d'insights que les données quantitatives ne peuvent simplement pas capturer. Ayant mené plusieurs sessions de ce type, je suis toujours étonné par la nuance et la profondeur des feedbacks recueillis. Les conversations ouvertes révèlent non seulement des avis sur les produits existants mais ouvrent également la porte à l'innovation et à l'amélioration continue. De plus, l'engagement personnel dans ces échanges renforce la relation entre la marque et ses clients, créant un sentiment de valeur et de confiance mutuelle.

We all have been busy & many of us been successful in satisfying customers; but what we should focus on is Delightment of customers. The new era / generation have different demands compared to those couple of decades ago.

Reaching out directly to consumers/brands is one of the best ways to understand customer preferences for a product as it provides us with direct insights of the market. You can also gain insights on essential parameters about a product w.r.t pricing, preferred features, trending variants about a product and also demographic preferences.

Directly asking customers about their preferences seems so obvious, yet many of us in marketing, myself included with over 6 years in the trenches, often overcomplicate things. We hide behind our screens, deploying tools like Zoom and Qualtrics, hoping to decipher the cryptic language of customer desire. But let's be real—how often does this method lead to genuine innovation rather than just echoing our own assumptions back to us? While it's crucial for building trust and loyalty, I challenge the notion that it's the most effective way to uncover true, game-changing insights. Perhaps it's time we question whether we're really listening, or just hearing what we want to hear.

Aqui vale a máxima: quem quer ouvir o consumidor acha caminhos. Não importa se sua base é grande ou pequena, importa você começar. Monte pequenos grupos de conversa, vá na sua loja e converse com quem está comprando ou quem está deixando de comprar, convide potenciais clientes para um bate papo, mostre seus produtos, permita que toquem, falem e expressem. O ponto chave é sair da sala do escritório e gastar a sola do sapato para genuinamente entender o que faz sentido para esse consumidor.

You want to work with partners who have relevant knowledge, skills, and resources to support your research goals. You also want to work with partners who share your vision, values, and standards.

Put youself in the consumer’s shoes! Think like a consumer and think whether the feature would make sense and make consumer’s task easy. Think about the cost vs value proposition. Think about Ease of use and the user experience.

Adding Additional AI-Driven Approaches: Social Media Sentiment Analysis: AI tools can analyze social media data to understand public sentiment towards product features, identifying what consumers are excited about or what they dislike. Predictive Consumer Segmentation: AI can segment consumers not just based on demographic data but also on behavioral patterns, predicting which segments are most likely to favor certain features. Personalization Algorithms: By personalizing experiences, AI can test and learn individual preferences at scale, offering different features to users based on their predicted preferences and observing their reactions.

Going against the grain here: don't prioritise features, prioritise problems. Features are just today's (and frequently yesterday's) solution to a problem. If you spend all your time ranking existing solutions, and requests associated with existing solutions, you will likely fail to adequately solve the customer's problem over the long-term as that problem & the landscape evolves. This is particularly true when emerging technologies, such as AI, can completely replace existing features while solving the exact same problem. By continuously researching customer problems, you'll tune in to what really matters to your customer today & tomorrow. You can prioritise solutions for value, discover opportunities to innovate and serve new customers.

Customer behavior can, until a certain point, be predicted through their past purchases. It is key to group customers into preferences and do the same to products, to build a powerful database in which you link each customer to the products they normally buy or similar products, to foster cross-selling or even to increase the basket with products which are normally purchased together.