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===== Hot or not for solar - Energy customer profiling made easy ===== | ===== Hot or not for solar - Energy customer profiling made easy ===== | ||
- | Want to know who's hot for putting a solar power system of his roof? | + | > **Want to know who's hot** for putting a solar power system of his or her roof? |
- | Why spending unnecessary amounts of dollars for unspecified and misdirected marketing - we know your next solar customer! | + | |
- | Our mission is to | + | > **Why spend unnecessary amounts of dollars** for unspecified and misdirected marketing |
- | For energy service providers and utilities | + | > We know **your next solar customer**! |
- | who want to target specific (solar-)prospects | + | |
- | our product "hot or not for solar" | + | |
- | is a energy | + | |
- | Further potential customers: | ||
- | Energy service providers | ||
- | Utilities | ||
- | Public institutions such as Cantons and Municipalities | ||
- | Planning and engineering companies / product developers | ||
- | Main case: As a " | + | ---- |
- | Service: | + | ===Mission=== |
- | -Generation of individual (energy) persona profiles based on social economic and social demographic data | + | |
- | -Prioritized action list for marketing and presales activities ( | + | |
- | ===== Data ===== | + | |
- | | + | **Our mission is to help increase the penetration of renewable energy production** |
+ | |||
+ | ===Value proposition=== | ||
+ | |||
+ | For **energy service providers and utilities** who want to target specific solar power prospects our product **hot or not for solar** is an **energy customer profiling service** which produces a **prioritized list of customers** with an affinity for solar. Unlike traditional approaches our service takes advantage of hidden information from social demographic and economic values and uses machine learning algorithms for deriving the prospects affinity. In order to improve our algorithms we run surveys (quizz), injected in popular websites such as "20 Minuten" | ||
+ | |||
+ | > **Simplified** - For **utilities** who want to target specific solar prospects, **hot or not for solar** uses social data and machine learning to derive a **short list**. | ||
+ | |||
+ | > **Further simplified** - Hot or not for solar - Energy customer profiling made easy | ||
+ | |||
+ | ===Main User-Story=== | ||
+ | |||
+ | As an energy service provider I want to know which prospects have an affinity for installing a solar system so that I can run personalized and focused marketing/ | ||
+ | |||
+ | ===Further potential customers are=== | ||
+ | |||
+ | * Public institutions such as Cantons and Municipalities how want to tailor information/ | ||
+ | * Planning and engineering companies / product developers how want to develop and sell new products | ||
+ | |||
+ | |||
+ | ===== Background Information about the project ===== | ||
+ | |||
+ | === Data === | ||
+ | |||
+ | BEN Energy (https:// | ||
+ | |||
+ | For detailed information see here: [[project: | ||
+ | |||
+ | === Goal === | ||
+ | |||
+ | The main goal of our project was to test whether social demographic and economic information is a value add in order to determine potential new solar customers. For background information about the business goals see section **Value proposition**. | ||
+ | |||
+ | === Method === | ||
+ | |||
+ | In order to test the hypothesis the team used **Microsoft Azure machine learning** and **R** (both using random forest algorithm). Furthermore the teams tested which attributes have most influence on the results. In parallel team members put together a list of properties they thought would influence the results. After a first iteration, falsifying attributes were removed from the machine learning process. | ||
+ | |||
+ | Finally the team concluded the project by creating a website to showcase the machine learning result as well as the business view. | ||
+ | |||
+ | === Results / Findings === | ||
+ | |||
+ | The team was able to derive a specific list of customers with an affinity for solar. Based on the results it came to the conclusion that social demographic and economic play a secondary role though. However, with a larger and probably more accurate set of information, | ||
===== Team ===== | ===== Team ===== | ||
* [[user: | * [[user: | ||
* [[user: | * [[user: | ||
+ | * @Team - pls add your usernames | ||
+ | |||
+ | === Teamwork / Impressions === | ||
+ | |||
+ | {{: | ||
+ | Teamwork 1 | ||
+ | |||
+ | {{: | ||
+ | Teamwork 2 | ||
+ | |||
+ | {{: | ||
+ | Teamwork 3 | ||
+ | |||
+ | {{: | ||
+ | Business model | ||
+ | |||
+ | {{: | ||
+ | Machine learning sketch | ||
+ | |||
+ | {{: | ||
+ | UX Design 1 | ||
- | * and other team members | + | {{: |
+ | UX Design 2 | ||
===== Links ===== | ===== Links ===== | ||
- | * Relevant documentation ... | + | * [[project: |
- | * Blog or forum posts ... | + | * [[project: |
- | * Tools you used ... | + | * [[project:hotornot:Azure machine learning]] |
- | + | * [[project:hotornot:Teamwork]] | |
- | {{tag> | + | |
- | [[.:R]] | + | {{tag> |