Does Hybris commerce have price override capability - sap-commerce-cloud

Does Hybris commerce have price override or appeasements capability at Order level or Item level.

There are mulitple ways how that can be done in hybris, one example is using ASM (assisted service module) that offers special promotions that can be granted by a service agent.
There might also be ways to achieve this using the cs-cockpit (customer service cockpit), I haven't looked into that deeper though.
For ASM documentation look e.g. here: https://help.hybris.com/6.3.0/hcd/8ac06f10866910148d8c850e15b102d2.html
Hope this helps a bit.

It really depends on what exactly are you trying to achieve. If you like some sort of a discount or promotions, you can see the link provided by Sebastion. In the wiki you can also find other promotion and discount engines. Another solution would be to have multiple price rows per product. Hybris supports this. For example for a particular product, you can set a special price for a special customer or customerGroup. Also the same way you can set different prices for different quantities. For example an apple costs 1$ but if you buy 100, the price goes to 0.75$.
More information about the pricerows: here
Keep in mind that Hybris is highly customizable so you can also override some logic from the PriceFactory but i would recommend using first the out of the box price features and then try overriding the PriceFactory.

Related

Azure search solr index definition for supporting multiple markets

I am building a product catalog for an e-comm website. I am having a requirement to build a azure search/solr/elastic search based index. The problem is saving the market specific attributes. The website is supporting 109 markets and there is each market specific data like ratings, price, views, wish-listed, etc. that I need to save in the index eg: Product1 will have 109 ratings(rating is different in each market)/109 prices(price might be different in each market) corresponding to 109 markets. Also I will have to use this attributes to add a boosting function so that when people are searching for this, products with higher view/ratings surfaces up. How do I design the index definition to support this? Can I achieve this by 1 index doc per product or do I have to create 1 index doc per market? Some pointers will be very helpful. I have spent couple of days on this and could not reach to a conclusion that is optimized for this use case. Thank you!
My proposed index definition:
-id
-mktUSA
--mktId
--rating
--views
--price
...
-mktCanada
--mktId
--rating
--views
--price
...
-locales
--En
--Fr
--Zh
...
...other properties
The problem with this approach is configuring a magnitude scoring functions inside scoring profile, to boost products based on the market
Say eg: If user is from Canada, only the Canada based rating/views should be considered and not the other market ratings while Cognitive search is calculating the search relevance score.
Is there any possible work around this? Elastic search has a neat solution of Function score query that can be used to configure the scoring function dynamically
From what I understand, your problem is that you want to have a single index with products that support 109 different markets. Many different properties for your Product model can then be market-specific. Your concern is that the model gets to big, or if it's a scalable design. It is. You can have 1000+ properties without a problem.
I have built a similar search solution for e-commerce for multiple markets.
For price, I specify one price per market. I have about 80 or so markets, so that's 80 prices. There is no way around it. I would probably do the same for ratings and views too. One per market.
In our application we use separate dimensions for market, language and country. A market can be Scandinavia, BeNeLux or Asia-Pacific. You need to clearly define what a market is in your case, and agree with the business which markets you have and how you handle changes. Countries can map directly to markets, but it may also differ. Finally, language is usually shared across markets/countries and you usually only have to support 20-25 languages.
Suggested data model
Id
TitleEnGb
TitleDeDe
TitleFrFr
...
PriceGb
PriceUs
PriceNo
PriceDe
...
RatingsGb
RatingsUs
RatingsNo
RatingsDe
...
DescriptionEnGb
DescriptionDeDe
DescriptionFrFr
...
I try to illustrate that the Title and Description are language-specific. The price and ratings are market-specific.
For the 20-25 language-specific properties, you have to think about what analyzers to use. You want to use language-specific analyzers, and preferably the Microsoft analyzers since they have much better linguistics support with full lemmatization and so on.
When you develop your frontend application you have to keep track of which market, country and language you then refer to the specific properties. This is the easiest way to support boosting and so on.
Per-market index is not recommended
You could create one index per market. I have gone down this route before. I would not recommend this. This means you have to update 109 indexes every time you add, change or delete an item. And Azure Search supports 50 indexes per service at the most anyways.

Blockchain Application Architecture: UML & Use Cases

For my internship, I need to implement a blockchain based solution to manage a drug supply chain. The management of this supply chain implies to track-and-trace (geolocate) a drug on the chain, but also to monitor the storage temperature to see if the cold chain is respected. For that I created a mock-up of the POC my Dapps (https://balsamiq.cloud/sum5oq5/p8lsped)and also I wanted to prepare myself by doing a UML and a use cases. However, I didn't find a lot of information about blockchain's UML and use cases besides two literatures which were quite different, so I don't know if what I did was correct or not...
The users of my Dapps will be the following ones:
The stakeholders (Manufacturers, Distributors and Retailers) which will use the Dapps to place orders and also monitor them. They also can search in the historic a specific order. Finally, trough IOT sensors they update the conditions of the order (temperature & location).
The administrator which roles is to update the Dapps and its rules. But also to add or delete user while also defining the rights that they have on the blockchain (I intend to use a permisionned blockchain). Finally, they are also here to help in case of technical problem.
The Dapps that I'm thinking about works in the following:
A user, the customer, can place an order (a list of products) to a
certain seller and choose the final destination of the order.
The order is then put together before being shipped or stocked in the
depots of one of the stakeholders (distributor or retailer) with a
description of the stocking and/or shipping condition of the product
(for example the product must be stocked or transported in a room
with a temperature of less than 5°C). During the shipping and
storing, an IOT device will feed the drops with the temperature and
geolocation of the product by updating the data each 5-10mn.
Obviously they will be a function that allows all the users to see
the history of the order passed and search inside a specific order.
In case where the temperature doesn't respect the temperature
recommended, then the smart-contract send an alert. The same if the
collocation of the product is "weird" like being in some European
countries and not in an Asian country, an alert will be sent again by
the smart-contractual. Finally, in the case where the product is sent
to the asked location by the customer, then the money for the order
will be paid to the seller.
So based on what I explained, I came here in hope that someone tell me if the use cases and UML that I did were correct or not.
I thank in advance anybody who'll take the time to help me.

FourSquare vs. Google Places vs. Yelp API

I am trying to create an app that will help users find restaurants/movie theaters/malls/etc. to hang out based on ratings and distance. Other than just the place itself, I would also like to know more detailed information about the place. For example, if I were to look for parks, I would also like to know if theres a basketball or tennis court there. Ratings and popularity would also be an important aspect to prioritize suggestions.
After looking through all three of the APIs, I could not really find any substantial differences other than their search limits. Could anyone really differentiate each API for me? Maybe even recommend one based on my specific need?
Thanks!
The Foursquare API would fit this use case perfectly because you can supply very specific filters through the API. Also, they have extensive coverage around the world, unlike Google or Yelp.
I would check out the venues/explore endpoint and use a categoryId of Parks. You can use a query parameter of "basketball" or "tennis" to find parks that have courts for these.

Azure ML Recommendations

I want to use Azure ML to find related products using information from receipts from a store.
I got a file of reciepts:
44366,136778
79619,88975
78861,78864
53395,78129,78786,79295,79353,79406,79408,79417,85829,136712
32340,33973
31897,32905
32476,32697,33202,33344,33879,34237,34422,48175,55486,55490,55498
17800
32476,32697,33202,33344,33879,34237,34422,48175,55490,55497,55498,55503
47098
136974
85832
Each row represent one receipt and each number is a product id.
Given a product id I want to get a list of similar products, i.e. products that was bought together by other customers.
Can anyone point me in the right direction of how do to this?
This seems a good fit for their frequently bought together service (https://datamarket.azure.com/dataset/amla/mba). You may have to preprocess the dataset to get it in the required format. This service has a web UI as well: https://marketbasket.cloudapp.net/
This is a typical problem for Recommender, you can use a model called Machbox recommender to cover such a problem.
Recommender typically use Scoring about items to propose and the use some tricky calculation to predict scores for items users had not scored yet ( a score would be typically 1 user bought the item, 0 he did not)
If you need more details let me know ..(you have access to a free version of Azure ML where you can try all this)
Regards

Magento geographical search and product recommendation

I'm evaluating Magento for a travel company who will need to do product searches and recommendations based on geographical distance. The company is creating custom holiday packages based on various components (eg: accommodation, tours, restaurant vouchers, etc). These components potentially have overlapping locations (ie: a particular tour might be close enough to several hotels to be considered related to each of them).
As a user builds up their custom package by adding stays at various hotels, I'd like related product recommendations to appear based on geographical location. And, if they search for tours, I'd like closer tours to be weighted toward the top of the catalogue search results.
Nice to have: the ability for the user to select how close / far they consider "close enough" to be (eg: 10km, 50km, 200km, etc).
My research indicates there isn't out of the box support for any sort of spatial queries in Magento. The best solution I could come up with was custom product attributes which list "location" where each product is manually assigned to various locations. But I think that's going to get pretty hard to manage for more than ~50 locations. Is my research correct? Is there an add-on / extension which will fulfil this scenario? Do you think overlapping 50 locations will be manageable in the backend?
Coming from a Microsoft background, my natural inclination would be to enable SQL Server 2008+ spacial functionality and do the queries in the database. Obviously, this option isn't available in the LAMP stack. Am I wrong? Does MySQL support spatial queries like WHERE productA.Location.GetDistanceFrom(productB.Location) < 50km?
Mysql supports spatial queries as well http://dev.mysql.com/doc/refman/4.1/en/spatial-extensions.html but nothing will help you or free you from entering the relations between products and it's location and you have to implement it yourself as well as extend the search based on location

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