Every year, you sell over 100,000 products without direct customer contact.
We show how.

Market situation and challenges

Lack of direct customer contact

In a traditional B2B2C (business-to-business-to-consumer) model such as the bicycle industry, manufacturers sell their products to wholesalers, who then pass them on to retailers. Manufacturers, therefore, mainly receive feedback in retail sales figures but have yet to receive direct feedback from end customers about using their products.

Insufficient user feedback

As manufacturers are remote from actual product usage, they lack valuable insight into how their products are used in everyday life. This leads to a gap in the innovation chain, as potential improvements and adjustments that could result from user feedback are often disregarded.

Dependence on brick-and-mortar retail

Many sectors, such as the bicycle industry, depend heavily on brick-and-mortar retail. Direct customer contact is often limited to the moment of purchase in the shop. Bicycles and their components typically require physical presentation and advice to give customers the necessary information and support their purchase decision.

Low level of digitalisation

Digitalisation in the bicycle industry is not yet very advanced compared to other consumer goods. This applies to both the product itself and the sales processes. Bicycles are physical products made up of numerous components. Although some progress has been made through e-bikes and digital tracking systems, overall digitalisation still needs to improve, especially considering the components.

Lack of data intelligence

Some companies already have a lot of data or have access to it through their partners. Often, this data needs to be structured and used to provide insights into business performance indicators. Can't bad seels be avoided with data support?

A lack of market and customer orientation is the most critical factor in company innovation failure. In the European Union alone, around 350 billion US dollars are invested annually in research and development.

Segmentation strategies during the product development phase

1. Market Analysis

Objective: The market analysis serves to determine the market size, growth potential, market shares and the competitive situation. It is fundamental to understanding and planning the positioning of a product on the market.

Methods: Methods include surveys, secondary research using existing market data and extensive data analyses. These approaches make it possible to obtain a clear picture of the current market situation and the activities of competitors.

2. Trend Study Analysis

Objective: Trend study analysis is designed to identify and interpret long-term behavioral patterns and developments in data sets. It plays a crucial role in predicting the future direction of markets, technologies, or consumer behavior, enabling strategically important adjustments to be made in product planning and development.

Methods: Methodological approaches such as time series analysis, regression analyses and forecasting models are used in trend study analysis. These techniques help companies identify and visualize trends to understand how certain factors might develop over time and influence their products. Advanced statistical software develops future forecasts and scenarios from historical data.

3. Surveys

Objective: Surveys are used to identify consumer preferences, buying habits and satisfaction with existing products. This information is crucial for understanding customer needs and adapting products accordingly.

Methods: Various channels can be used for surveys, including online surveys, telephone surveys and written questionnaires. Each channel has its advantages, depending on the target group and information requirements.

4. A/B Tests

Objective: A/B tests compare the effectiveness of different product variants or functions. They are particularly valuable for making decisions about product features, design, or marketing approaches.

Methods: This method tests two variants in parallel to determine which performs better. Depending on the type of product and the objective of the test, this can be carried out in controlled environments or directly in the market environment.

5. Data Analytics (only for digitally networked products)

Objective: Data analytics aims to gain deep insights into customer behavior, product performance and operational efficiency. This analytics makes it possible to recognise patterns and correlations in large data sets that would otherwise remain hidden.

Methods: Methods include machine learning, statistical modeling and big data technologies. These tools can be used to create predictive models, improve decision-making and create personalized customer experiences. By integrating data analytics into the product development phase, companies can optimize their strategies based on data-driven insights and thus increase their products' chances of success.

Strategies for digitally networked products

1. Data Aggregation

Goal: Data aggregation aims to summarize large amounts of individual data in clear reports to identify general trends and patterns.

Methods: This involves developing dashboards and reports that visualize key performance indicators (KPIs) such as usage times, frequency of use and demographic trends. Tableau or Power BI are often used to visualize complex data.

2. Behavioral Segmentation

Goal: To divide users into groups based on similar behavior and preferences.

Methods: Cluster analysis is a statistical method for segmenting users into groups that exhibit similar behavioral patterns. These segments can then be used for targeted marketing strategies or to personalize product recommendations.

3. Sequence Analysis

Goal: To analyze the sequence of actions that users perform.

Methods: This involves analyzing users' steps before buying a product or using a service. This method helps to understand the user journey (customer journey) and identify critical touchpoints.

4. Time Series Analysis

Goal: To research how user behavior develops over time.

Methods: This analysis examines data points collected regularly to identify patterns, seasonal fluctuations or other anomalies over time. This is particularly useful for recognising and responding to long-term trends.

5. Cohort Analysis

Goal: To compare the behavior of groups of users who started using the product or service at different times.

Methods: Cohorts are formed based on their start date, and their behavior is compared over time. This enables a differentiated view of user loyalty and activity depending on the duration of use.

6. Predictive Analytics

Goal: Prediction of future behavior or preferences based on historical data.

Methods: Machine learning and statistical modeling make it possible to make predictions, e.g., regarding the likelihood of re-use or purchase of a product. These techniques can help take proactive measures to increase customer loyalty or avoid churn.

7. Heatmaps And Clickstream Analysis

Goal: Visualize interaction data to understand where users spend the most time or click most frequently on a website or app.

Methods: Heatmaps show which areas of a website or app receive the most attention, providing valuable insights into user behavior and experience. Clickstream analysis tracks a user's exact sequence of clicks and helps to understand user interactions in detail.

Excerpts from a typical KPI target system for strategic monitoring of the actual user behavior of different customer segments:

1. Daily/Monthly Active Users (DAU/MAU): Measures user activity.

2. Engagement rate: Includes average session duration and interactions per visit.

3. Conversion rate:  Percentage of users who complete a specific target action (e.g. registration or activation).

4. Churn rate: Percentage of users who leave the service.

5. Growth rate: Ratio of returning vs. new visitors.

6. User Flow: Tracks the paths users take through your application or website.

7. Heatmaps: Visualize where users click, scroll and linger to identify interaction patterns.

8. Conversion rates by segment: Analyze conversion rates, separated by different user segments.

9. Feedback rates: Analysis of user feedback through surveys or feedback tools.

10. Net Promoter Score (NPS):  Measure of customer satisfaction and loyalty.

Not all KPIs must always be used, but the relevant ones must at least be instrumentalised in such a way that regular (e.g. monthly) derivations of planned and actual usage can be derived from them in real-time. Measures should be derived immediately.

Björn Bergfeld, Partner at Neue Digitale Partners

Conclusion

Regardless of the degree of digitalisation of their own portfolio, companies should use various quantitative research methods in product development to ensure that they develop products and solutions based on real customer segments.

Companies must increase their digitalisation efforts to promote direct customer contact and collect valuable usage data that contributes to continuous and effective product improvement.

Companies should systematically use data analytics to make data-driven decisions and drive targeted innovations that strengthen their long-term competitiveness and thus massively improve their company's ROI on product innovations.

Companies whose products still need to be digitally networked should expand their own IoT capabilities and data interfaces in the medium and long term or utilize the potential opportunities offered by partners (e.g. component, platform or system partners) wherever this is technically possible/implementable.