Cleaning up
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Tomas Singliar
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18
README.md
18
README.md
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@ -16,13 +16,9 @@ The solution has both visualization components (in Power BI) and interactive sim
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#### Estimated Provisioning time: 20 minutes
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Randomized controlled experiments are the gold standard for estimating causal effects like price elasticity of demand.
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Experimentation, also known as A/B testing, is extremely difficult to set up on existing operations systems.
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Approaches using historical data are mostly “bolt-on”, but tools that guard against the many statistical pitfalls are essential.
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This solution brings such tools to any business with an Azure subscription. It alleviates confounding
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by the "Double-ML" approach, which subtracts out the predictable components of price and demand variation
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before estimating the elasticity. This immunizes the estimates from most forms of seasonal confounding.
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This solution brings price elasticity modeling and pricing recommendations to any business with an Azure subscription.
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It alleviates confounding by the "Double-ML" approach, which subtracts out the predictable components
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of price and demand variation before estimating the elasticity. This immunizes the estimates from most forms of seasonal confounding.
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Additionally, the solution can be customized by an implementation partner to use data reflecting other
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potential external demand drivers.
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@ -58,13 +54,17 @@ for a more detailed discussion.
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# Getting Started
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Start by clicking Deploy on this page. Instructions at the end of the deployment will guide you further.
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[Deploy the solution from the Cortana Intelligence Quick Start Page](https://aka.ms/pricingciqs).
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Instructions at the end of the deployment will guide you further.
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The solution deploys with an example data set of orange juice prices.
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While the solution is deploying, get a head start and
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* peruse the [User Guide](User%20Guide/UserGuide.md) for a full set of usage instruction
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* download the [interactive Excel Worksheet](https://aka.ms/pricingxls)
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For technical problems or questions about deploying this solution, please post in the issues tab of the repository.
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For technical problems or questions about deploying this solution,
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please post in the issues tab of the repository.
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# Solution Dashboard
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@ -1,27 +1,329 @@
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# Pricing Analytics Solution: Technical Deployment Guide
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This document explains how to deploy the default configuration of the Pricing Analytics Solution
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This document explains how to deploy the default configuration of the Pricing Analytics Solution.
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It is intended for technical personnel who deploy the solution and connect it to the business data
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warehouse.
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## Automated Deployment
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## Outline
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Just Click Deploy, or go to this [CIQS solution webpage](https://aka.ms/pricingciqs).
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You will need an Azure subscription.
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1. [Introduction](#introduction)
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2. [Architecture](#architecture)
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3. [Automated Deployment Workflow](#automated-deployment-workflow)
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4. [Connecting Your Data](#connecting-your-data)
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6. [Customization](#customization)
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6. [Building Applications](#building-applications)
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7. [Troubleshooting](#troublehooting)
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8. [Support and Feedback](#support-and-feedback)
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## Introduction
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Please understand how the solution is intended to be used in the [User Guide](UserGuide.md).
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**In what context is the offered pricing solution is suitable?**
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We recommend this solution for retail-like contexts where each customer segment
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faces the same price. It is not appropriate when prices are often negotiated
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individually.
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## Architecture
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The architecture can be summarized in the following diagram:
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The solution architecture consists of the following Azure components:
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* An Azure SQL DB, used to store several different types of data, pre-process the transactional data for modeling,
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and generate pricing suggestions. A premium edition (P1) is recommended as the larger tables take advantage of clustered columnstore indices.
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* An Azure Storage account, used to save the model and intermediate data in Blobs and Tables.
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* A model build web service
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* A collection of several interactive services for querying the model
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* A PowerBI workspace collection
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* Azure Data factory for scheduling regular execution
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Because every business system is different, the pre-configured solution does not include data
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flows from your business system to the SQL database, or the flow of decisions pricing from
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the analyst to the business systems (e.g. ERP).
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An [integration partner](https://https://appsource.microsoft.com/en-us/product/cortana-intelligence/microsoft-cortana-intelligence.demand-forecasting-for-retail)
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can connect these data paths for you.
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### Known limitations
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The pre-configured solution necessarily makes some simplifying assumptions.
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We will describe how to modify the solution below.
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The known limitations are:
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* We compute short-term elasticities only. In the short term, demand is less price-elastic than in the long term.
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For example, if a grocery store raises prices mildly, customers will pay the higher price, rather than driving to another store. Demand is relatively inelastic.
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In the long run, customers may choose not to come to the more expensive store in the first place and demand will fall more.
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* While the model internally works with arbitrary periods, the solution has a weekly periodicity baked into
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how the data is aggregated in pre-processing the ADF pipeline
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* We don't check any business rules, such as "the pick-up channel must be prices the same or lower as the delivery channel"
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* Segmentation must be provided externally - we don't generate customer segments automatically
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## Automated Deployment Workflow
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To start, you will need an Azure subscription.
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Go to the [CIQS solution webpage](https://aka.ms/pricingciqs) and deploy the solution.
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* The name you give your solution becomes a name for the resource group for tracking your
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resources and spending.
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* Choose a location that is geographically to your business users.
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* Click Create
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The deployment goes through several provisioning and setup steps, using a combination
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of Azure Resource Manager (ARM) templates and Azure Functions.
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ARM templates are JSON files that define the resources you need to deploy for your solution.
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Azure Functions is a serverless compute service that enables you to run code on-demand without
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having to explicitly provision or manage infrastructure. We will describe ARM templates and Azure
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Functions used in this solution in later sections.
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You will need to interact with the installer once, to create a user name and pasword
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for the database administrator account. Remember this password well if you want to
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customize the solution. If you reset it in the [Azure portal](https://portal.azure.com),
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Azure Data Factory may have trouble talking to the SQL server.
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## Step-By-Step Visual Studio Deployment
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### Provisioned Resources
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Once the solution is deployed to the subscription,
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you can see the services deployed by clicking the resource
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group name on the final deployment screen in the CIS.
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### One-time workbook setup
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<div class="todo" style="color:red; font-weight: bold;">
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TODO: rewrite this to reflect the possible configuration of SQL connection
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</div>
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We provide an Excel template for interacting with the solution.
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It has multiple tabs for the different steps in pricing analysis.
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Before the sheet can be used, it must be set up by connecting
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the appropriate web services to the workbook.
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Please connect these services by pasting their request-response URL and into the AzureML plugin.
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The URLs and keys are displayed at the final CIQS page, which you can access
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from the [Deployments section of CIQS](https://start.cortanaintelligence.com/Deployments).
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You can also see all of your services [here](https://services.azureml.net).
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Detailed connection instructions are found in the worksheet, on the "Instructions" tab.
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After adding the services, save the Excel spreadsheet under an appropriate name;
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we use <tt>AnalysisTemplate.xsls</tt>.
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This configured workbook template will be used to load data output from the analytical pipeline.
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### Step-By-Step Visual Studio Deployment
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It is possible to deploy the elements of the solution with a few clicks
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from their Visual Studio solution files, using Cloud Explorer.
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These instructions are currently not available because we are not ready to expose
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the AzureML services externally for IP reasons.
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the AzureML services source code externally for IP reasons.
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Therefore the json for AzureML services is missing.
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This is not fully secure protection and needs to be resolved before going open source.
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## Step-By-Step Manual Instructions
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## Connecting Your Data
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These instructions are currently not available because we are not ready to expose
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the AzureML services externally for IP reasons.
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Therefore the json for AzureML services is missing.
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This is not fully secure protection and needs to be resolved before going open source.
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This section describes the inputs and output datasets of the solution.
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### Input dataset
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The main prerequisite is the sales history.
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The solution expects sales history data in the table <tt>dbo.pricingdata</tt>,
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with the folllowing schema:
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```sql
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CREATE TABLE dbo.pricingdata (
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SalesDate date not null,
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Item varchar(100) not null,
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SiteName varchar(100) not null,
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ChannelName varchar(100) not null,
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ItemHierarchy varchar(500) not null,
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UnitPrice float not null,
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UnitCost float null,
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Quantity float not null,
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CustomerSegment varchar(100) not null
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)
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```
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The solution defines the data as an external ADF dataset, which means the data should "just appear" in this table.
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It is your responsibility to ensure that it does. We recommend that you use Azure Data Factory or SSIS to push
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sales data incrementally from the business data warehouse to this table. While transforming data, please consider
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the following points.
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* Data can be aggregated weekly or left in the original one-row-per-transaction state, in which case the system will be aggregate it to weekly quantities.
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* If you aggregate the transactions, we recommend against defining the price as a weekly average of prices.
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Instead, we recommend you group by distinct discrete prices if possible.
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* ItemHierarchy must be provided as a comma-separated list of categories, highest level first, for example "Cosmetics, Soap, Coconut Soap".
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If your items are not organized in a product hierarchy, use a single string like "Products".
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* SiteName, ChannelName, CustomerSegment also must be provided, if your data does not break down along these
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dimensions, use "All" or a similar constant as well.
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### Outputs
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These four SQL tables are the output tables of the solution.
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You can expect them to be populated weekly.
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They underlie the Solution dashboard and can be used to create other visuals as well.
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* Elasticities
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* Cross-elasticities
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* Forecasts
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* SuggestionRuns
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Current run outputs are appended to these tables: Each has a RunDate column, corresponding to the date on which
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the data was inserted. The schemas are as follows:
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```sql
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CREATE TABLE dbo.Elasticities (
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RunDate date not null,
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RelationValidDate date null,
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Item varchar(100) not null,
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SiteName varchar(100) not null,
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ChannelName varchar(100) not null,
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CustomerSegment varchar(100) not null,
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Elasticity float not null,
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Elasticity90LB float not null,
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Elasticity90UB float not null,
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)
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```
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The Cross-Elasticities table stores all the cross-elasticities that were estimated.
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RunDate doubles as an identifier of the model run.
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```sql
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CREATE TABLE dbo.CrossElasticities (
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RunDate date not null,
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RelationValidDate date null,
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CustomerSegment varchar(100) not null,
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DrivingItemKey varchar(100) not null,
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DrivingSiteName varchar(100) not null,
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DrivingChannelName varchar(100) not null,
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ImpactedItemKey varchar(100) not null,
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ImpactedSiteName varchar(100) not null,
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ImpactedChannelName varchar(100) not null,
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CrossElasticity float not null,
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CrossElasticity90LB float not null,
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CrossElasticity90UB float not null,
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)
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```
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The Forecasts table stores all the forecasts the model has made.
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RunDate doubles as an identifier of the forecast run.
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```sql
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CREATE TABLE dbo.Forecasts (
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RunDate date not null,
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Item varchar(100) not null,
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SiteName varchar(100) not null,
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ChannelName varchar(100) not null,
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LastDayOfData date not null, -- forecast made using data up and including this
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ForecastPeriodStart date not null, -- start of the period whose demand is forecasted
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UnitPrice float not null, -- forecast is conditional on this price
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Demand float not null
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Demand90LB float not null
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Demand90UB float not null
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)
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```
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<div class="todo" style="color:red; font-weight: bold;">
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TODO: describe the SuggestionRuns table
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</div>
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## Configuration
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To set the parameters, update the table <tt>dbo.Parameters</tt> in the Solution's
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SQL database. These parameters are configurable:
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* Lead-time (days before start of pricing period) for producing the pricing suggestions.
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Insert the key-value pair ('sugLeadTime', '[n]') into the table, replacing [n]
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by the number of days in advance. The default value is '1'.
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* Maximum allowed deviation of suggested price from current price (percentage).
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Please insert the key-value pair ('maxPriceDeviation', '[x.x]') into the table,
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replacing [x.x] by the desired fraction. The default value is '0.2', representing
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a maximum deviation of 20 percent.
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<div class="todo" style="color:red; font-weight: bold;">
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TODO: describe the parameters table and check parameter names
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</div>
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## Building Applications
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While many users find the Excel environment natural, many will also require features
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that are not directly supported by our core models and pipelines.
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Whether you are building a customer-specific user interface in Excel or another application,
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you will want to call the web services.
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We first give an detailing example of calling the Elasticity web service from Excel
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and describe its inputs and outputs. The other services operate analogously.
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### Elasticity service from Excel
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You can inspect the elasticities of every product by navigating to the "Elasticities" tab of the
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Promotion Suggestion report and opening the "Elasticity" service pane of the AzureML plugin.
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 {style = "width: 300px"}
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Because elasticities can change in time, a query date is required in cell A6 of the spreadsheet.
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We recommend using the date of the most recent model. The A5:A6 range of cells is the input.
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The output location should be set to a convenient location in the spreadsheet, normally Elasticities$A20.
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The "datasetName" parameter is of the form "M<date>", where data refers to the day on which the model
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was created, using data available up to that day. You can explore previous models as well
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[Future pointer: we should offer the "list models" feature and if model isn't listed, use latest]
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The output consists of a dataframe containing the estimated elasticity of every Item at every
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Site, as well as the 90% confidence interval for the estimate. The range of elasticities can
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be seen in one glance by clicking on the generated external figure link containing the elasticity histogram.
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To get the service to behave as if it was a first-order Excel function and update
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values in its output cells every time the input changes, check the Auto-predict box.
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### Calling REST APIs from anywhere
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The services are simply AzureML RESTful APIs. You can go to (https://services.azureml.net)
|
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to grab sample code to consume them from R, Python, or C#.
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See the swagger documentation to the services to understand their input and output requirements.
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The same information is reflected in the VIEW SCHEMA pane of the AzureML plugin.
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### Individual Services Descriptions
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There are three types of ML service in this solution, batch model build, interactive retrieval
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and bulk retrieval services.
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The batch model build service is BuildModel and is responsible for all estimation tasks.
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Depending on data size, it can run minutes to hours.
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||||
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The interactive services are:
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||||
* Elasticities
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* CrossElasticities
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* Forecasts
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* PromoSimulation
|
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* Outliers
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* RetrospectiveAnalysis
|
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|
||||
These services are expected to return within a few seconds (the first query after a while may be slow
|
||||
as the containers "warm up").
|
||||
|
||||
The bulk services are used to export the data from the model to the database.
|
||||
* BulkElasticities
|
||||
* BulkCrossElasticities
|
||||
* BulkForecasts
|
||||
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
* If you are experiencing timeouts on the BuildModel service, try increasing the
|
||||
timeout period in the <tt>retrain_AzureML_Model</tt> ADF activity.
|
||||
* If you get "blob does not exist" errors, open the storage account and make sure
|
||||
a model with the given datasetName exists. The name is case-sensitive.
|
||||
|
||||
|
||||
## Support and feedback
|
||||
|
||||
Please contact Tomas.Singliar@microsoft.com with questions, requests or concerns about the solution.
|
||||
|
|
|
|||
|
|
@ -1,23 +1,22 @@
|
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# Pricing Analytics Pre-Configured Solution: Technical Guide
|
||||
|
||||
This document is intended for the end user of the solution. It will guide you through
|
||||
the use of the solution in its default configuration. U
|
||||
the use of the solution in its default configuration.
|
||||
|
||||
## Introduction
|
||||
|
||||
The Pricing Analytics Pre-Configured Solution (PCS) is an Azure Cloud solution consisting of a set of
|
||||
tools to help set prices for wholesale and retail products based on transaction records of past sales.
|
||||
It is targeted at mid-size companies with small pricing teams who lack extensive data science support for
|
||||
sophiticated
|
||||
pricing models.
|
||||
sophiticated pricing models.
|
||||
This document is aimed at the pricing or purchasing manager. It explains how to install the solution in your
|
||||
Azure subscription, load your transaction data in it, and run
|
||||
the tools from your desktop using Excel, to generate pricings, e.g. for a monthly promotion, and predict
|
||||
the effect of these pricings on sales and revenue.
|
||||
|
||||
This document explains the pricing theory behind the tools, documents the solution architecture, and shows
|
||||
how to use and customize the solution. The [Implementor's Guide](../ImplementorsGuide.md) goes into more detail
|
||||
about how to integrate the solution with your cloud or on-premise data.
|
||||
how to use and customize the solution. The [Technical Deployment Guide](Technical%20Deployment%20Guide.md)
|
||||
goes into more detail about how to integrate the solution with your cloud or on-premise data.
|
||||
|
||||
|
||||
## Automated Installation
|
||||
|
|
@ -98,50 +97,6 @@ Then you should increase prices at lower elasticities (e > -5), and decrease the
|
|||
|
||||
This PCS includes a model that computes an elasticity for each combination of (Item, Location, Channel).
|
||||
|
||||
## Solution Architecture
|
||||
|
||||
The architecture can be summarized in the following diagram:
|
||||
|
||||
|
||||
|
||||
The solution architecture consists of the following Azure components:
|
||||
|
||||
* An Azure SQL DB, used to store several different types of data, pre-process the transactional data for modeling,
|
||||
and generate pricing suggestions.
|
||||
A premium edition (P1) is recommended as the larger tables take advantage of clustered columnstore indices.
|
||||
* An Azure Storage account, used to save the model and intermediate data in Blobs and Tables.
|
||||
* A model build web service
|
||||
* A collection of several interactive services for querying the model
|
||||
* A service for uploading the pricing decisions
|
||||
* A PowerBI workspace collection
|
||||
* Azure Data factory for scheduling regular execution
|
||||
|
||||
The solution does not include data flows from your business system to the SQL database,
|
||||
or the flow of decisions pricing from the analyst to the business systems (e.g. ERP).
|
||||
An [integration partner](https://https://appsource.microsoft.com/en-us/product/cortana-intelligence/microsoft-cortana-intelligence.demand-forecasting-for-retail)
|
||||
can connect these data paths for you.
|
||||
|
||||
### Known limitations
|
||||
|
||||
The pre-configured solution necessarily makes some simplifying assumptions.
|
||||
We will describe how to modify the solution in the PCS guide for implementors.
|
||||
|
||||
The known limitations are:
|
||||
|
||||
* We compute short-term elasticities only. In the short term, demand is less price-elastic than in the long term.
|
||||
For example, if a grocery store raises prices mildly, customers will pay the higher price, rather than driving to another store. Demand is relatively inelastic.
|
||||
In the long run, customers may choose not to come to the more expensive store in the first place and demand will fall more.
|
||||
* While the model internally works with arbitrary periods, the solution has a weekly periodicity baked into
|
||||
how the data is aggregated in pre-processing the ADF pipeline
|
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* We don't check any business rules, such as "the pick-up channel must be prices the same or lower as the delivery channel"
|
||||
|
||||
Future improvements include:
|
||||
* Future pointer: we should just be able to query any grain against any grain, e.g.
|
||||
Peanut butter sold at City1 in person vs Jelly delivered from online store
|
||||
* We should just generate .xls files, rather than require the user to pull in data.
|
||||
* Create automatic customer segmentation algorithms
|
||||
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|
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## Using the Pricing Engine (for the pricing analyst)
|
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|
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Once you've installed the Solution on Azure you work with it from a PowerBI Dashboard and
|
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