Context. Successful software product management concerns about developing right software products for right markets at the right time. The product manager, who carries responsibilities of planning, requires but does not always have access to high-quality information for making the best possible planning decisions. The following master thesis concentrates on proposing a solution that supports planning of a software product by means of analytics. Objectives. The aim of the master thesis is to understand potentials of analytics in product planning decisions in a SaaS context. This thesis focuses on SaaS based analytics used for portfolio management, product roadmapping, and release planning and specify how the analytics can be utilized for planning of a software product. Then the study devises an analytics-based method to enable software product planning. Methods. The current study was designed with a mixed methodology approach, which includes the literature review and survey researches as well as case study under the framework of the design science. Literature review was conducted to identify product planning decisions and the measurements that support them. A total of 17 interview based surveys were conducted to investigate the impact of analytics on product planning decisions in product roadmapping context. The result of the interviews ended in an analytics-based planning method provided under the framework of design science. The designed analytics-based method was validated by a case study in order to measure the effectiveness of the solution. Results. The identified product planning decisions were summarized and categorized into a taxonomy of decisions divided by portfolio management, roadmapping, and release planning. The identified SaaS-based measurements were categorized into six categories and made a taxonomy of measurements. The result of the survey illustrated that importance functions of the measurement- categories are not much different for planning-decisions. In the interviews 61.8% of interviewees selected “very important” for “Product”, 58.8% for “Feature”, and 64.7% for “Product healthiness” categories. For “Referral sources” category, 61.8% of responses have valuated as “not important”. Categories of “Technologies and Channels” and “Usage Pattern” have been rated majorly “important” by 47.1% and 32.4% of the corresponding responses. Also the results showed that product use, feature use, users of feature use, response time, product errors, and downtime are the first top measurement- attributes that a product manager prefers to use for product planning. Qualitative results identified “product specification, product maturity and goal” as the effected factors on analytics importance for product planning and in parallel specified strengths and weaknesses of analytical planning from product managers’ perspectives. Analytics-based product planning method was developed with eleven main process steps, using the measurements and measurement scores resulted from the interviews, and finally got validated in a case. The method can support all three assets of product planning (portfolio management, roadmapping, and release planning), however it was validated only for roadmapping decisions in the current study. SaaS-based analytics are enablers for the method, but there might be some other analytics that can assist to take planning decisions as well. Conclusion. The results of the interviews on the roadmapping decisions indicated that different planning decisions consider similar importance for measurement-categories to plan a software product. Statistics about feature use, product use, response time, users, error and downtime have been recognized as the most important measurements for planning. Analytics increase knowledge about product usability and functionality, and also can assist to improve problem handling and client-side technologies. But it has limitations regarding to receiving formed-based customer feedback, handling development technologies and also interpreting some measurements in practice. Immature products are not able to use analytics. To create, remove, or enhance a feature, the data trend provides a wide view of feature desirability in the current or even future time and clarifies how these changes can impact decision making. Prioritizing features can be performed for the features in the same context by comparing their measurement impacts. The analytics-based method covers both reactive and proactive planning.