Ola assignment | portfolio

Easy Park : A parking assistant application

Anchor 1

Overview : In urban areas parking is one of the most valuable assets and has to be optimized in order to make most out of it. In this project, solutions are proposed to seamlessly handle parking for a lot of people around major attractions like museums, stadiums etc.

Duration : 24 Hrs (approx.)

BROAD SEARCH

1

Use Case - User searches in parent locality ( big localities which contain multiple sub localities) and see overwhelming no. of listings

Suggestion : Shift to sublocality with in a parent locality. Here a new concept of 'spot light' (purple pins on the map) is introduced. By hovering over spot light pin on the map user can see the comparative price index with respect to parent locality and the no. of listings in sub locality. Quick access to key information helps user to make an informed choice

2

Use Case - Budget Filter is not applied by the user which is very imporatnt to show adequate no. of results.

Suggestion : On the bases of user past search analysis (i.e. user profile) and polygon profile (i.e. locality profile) it is possible to guess what budget range user might choose and therefore could be the by default budget range suggestion for the users. This could reduce their effort of selecting a range. Secondly in this design while selecting a budget range users are aware of the no. of listings they could expect.

Apply this budget to see

listings in Powai

NARROW SEARCH

3

Use Case - User searches in a sub locality or parent locality which has few or no listings

Suggestion : One possible suggestion for the user could be to search in nearby localities. It is important for the users to quickly access 'some key information' about the locality helping them in deciding upon which locality to pick. This piece of information is price point comparision with other locality and no. of listings. Here there are two possible scenarios :-
1. BHK type is selected - then price is shown in definate range. For ex. Rs.10K to Rs. 15K for 1 BHK
2. BHK type is not selected -In thsi case priceis shown as how much percentage a locality is expensive/cheap as compare to parent locality average price.

Existing Scenario

Suggestion

4

Use Case - User searches in a small sub locality which has few or no listings

Suggestion :If the nearby localities also have very few listings then a clear suggestion is made to search in parent locality.

(Marvel Prototype)

5

Use Case - User has applied multiple soft filters (filters other then BHK type, budget and furnishing type ). This reduce the no. of listings matching the criterion. It was found through user research that generally these filters are not deal breakers but the 'good to have' amenities in the house. users are willing to see listings not strictly matching the criterion.

Existing Scenario

Issue : pIt is confusing for the user to whether the filters are removed or user has to remove them. Secondly it is not important to show all the soft filters.

Suggestion 1 : With the help of existing data a 'preference order' of most important soft filters for general users was created. As per this order a user is most likely to remove microwave as a filter and least likely to remove refrigerator or washingmachine. User is persuaded to remove one or the more soft filters

Rule : Applicable when user has applied more then two soft filters. Instead of showing all the filters user would be only show maximum of three filters low in preference order.

Suggestion 2 : Instead of a user is asked to remove a filter to see more results, flats which doesn't match the criterion defined by the filters are also shown along with the listings strictly matching the criterion. As per the user study for ex. if a user has applied microwave as a filters he/she also wants to see listings without microwave as it's not a major deciding facor.

Rule : Applicable when user has applied only one soft filter and there are significant no. of listings available if user removes the applied filter.