Elasticity of Demand / Demand Elasticity in Customer Analytics

We briefly touched upon Price Sensitvity in the last post. Let's study a bit on the effect of Price on the customer using Demand Elasticity / Price Elasticity of Demand (PEoD).

PEoD = Q_New-Q_Old/(Q_New+Q_old)
              ------------------------
              P_New-P_Old/(P_New+P_old)

Where
Q_{New and} P_New  
             

stands for new quantity and price respectively.

Product	Price Elasticity	P(old)	P(new)	Qty(Old)	Qty(new)	Elasticity	Column1
Wheat	-0.11	10	15	120	117	-0.06329	-0.06329

The above table shows the Calculation of Price Elasticity for Wheat  the effect of change in Price on Customer's buying behavior

The following table shows the elasticity for some common products

Product	Price Elasticity
Wheat	-0.11
Eggs	-0.14
Milk	-0.21
Onions	-0.67
Cigarattes	-1.1
Beef	-1.3

Negative price elasticity shows that when the price increases, demand decreases. The above table shows that the customer is more sensitive to Beef than Wheat. An Offer on highly sensitive product can induce the customer to buy more.

Identifying price sensitivity of products/category , the subtitute products and its sales over the period would help to get the right price / product for promotions

How to measure Price Sensitity of Customers

How to know if a Customer is Price Sensitive?

Understanding the customer based on his price bracket has lot of benefits. This would help the retailer stock products of appropriate price in the category and help the marketing team with sending suitable discounts / promotions.

What is Price Sensitivity?

Before we dive deep into this, it's better to have it defined to avoid ambuigity later. This is the change in buying behavior of the customer of a particular product / category due to the change in price. This means a customer will have different price sensitivities for different product or categories. This is sometime called as Price change sensitivity too

How to measure the price sensitivity?

There are various ways to measure it for a customer-product. One way is to find the demand elasticity.

We will discuss in detail on demand elasticity with some examples

Can a product's sales increase even when there is increase in Price. Though strange there are chances that it would be - these are called Giffen goods

What are the steps in NBO (Next Best Offer) to give Personalized Promotion

Personalized Promotion / Next Best Action (NBA) in Retail

Increased Personalization is a trend that is common in all industries , particularly, the Retail one
According to a Research, a personalized upsell or cross-sell offer is 30% more effective when delivered within two seconds of initial product selection

The steps, models or algorithms in NBO (Next Best Offer) to give Personalized Promotion are shown below:

The combination of these models would provide a great insight into the customer behaviour and his product and offer preferences, which in turn can be used to provide him context and location senstive appopriate product as the promotions


Big Data and Personalized Promotions

With the advent of Big Data - the Analysts can store huge amount of data and analyze them using Big Data Analytic tools. One such is +SAP HANA , which can be used collect personal customer information and provide optimized offers based on their individual histories and preferences

There are many open source softwares like 'R' and +Hadoop that are spear heading the personalized promotion algorithm development

With the cost of big data hardware on the decline this trend is definitely going to speed up

How Recommendation Engine Works

Recommendation engines are a craze these days. +StumbleUpon  recently fired half of their marketing staff and hired Data Analysts to build a recommendation engine that predicts accurately (or more accurately) the links that it provides to the customers

Recommendation engine work on the same principle of other statistical engines - they are sometimes called personal promotional engines. The key here is to

1) Analyze Data
2) Create Segments / Groups
3) Create Models (Statistical)
4) Send and Measure Offers
5) Re-Calibrate the Model
6) Re-Issue Offers

How to Create Personalized Direct Mail Offers based on Shopping Patterns

Kroger - one of the world's leading grocery chain creates personalized offer on individuals (not segments).
Kroger tracks each customer as an individual. The quarterly mailers it sends contain 12 coupons specific to an individual household and are carefully designed, thanks to dunnhumby's insights. Each part of the coupon is carefully popluated with product choices from the customer's previous
shopping patterns. The last two coupons are for experiments, such as adjacent products — a purchaser of baby food who doesn’t buy diapers might see an offer for diapers.
Apart from suggesting the offers for the customers, dunnhumby also provides insights on placement of these products with the help of data analysis

CVS issues weekly sales circular using a feature called myWeekly Ad. 

This feature uses insights generated based from customer data and transactions to arrive at appropriate offers

Open Source Software for Analytics

R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. Refer http://www.r-project.org/ for more info

What is Next Best Offer and How to Achieve It

What is Next Best Offer

Simply put, it is to know the customers needs before he/she acts. This means that apart from his Product Preference, one should know the Price, the Channel and the Time he is looking for or interested in.

How to create NBO

With the combination of Right Product and Right Pricing the Promotions are made through Right Channel and at Right Time. This increases the probability of redemption of that particular offer.

The above process starts with setting the Objectives - the Retailer has to have clear goal in mind what they want to achieve from NBO - a Retailer who has launched a new store wants more Footfalls, an established retailer wants increase in margins  

Once the goals are set, the retailer has to understand their customers. The Retailer has to collect data from various sources like:

• Point of Sale -Transaction Data
• CRM - Customer Demographic Data etc
• Call Center Data - Customer Service Desk
• Social Media - Twitter, Facebook etc
• Product Offerings - Product Information, Private Label - CPG Brand, Profit Margins, Inventory etc

This is vital step for NBO. The availability of data holds the key

Once the data is collected, cleaned - it has to be analyzed. There are many predictive modelling techniques available to understand customer behaviour and map them with related Offers.

Once the offers are made, the Retailer has to measure the Offer Response constantly and create new offers based on this performance.

This is a continuous learning process... Blog Directory

What is this blog about

Analytics! Analytics! Analytics! Everyone talks about it, lot of buzzwords come up - Customer Analytics, Supply Chain Analytics, Next Best Offer, Next Best Action, Consumer Behavioral Prediction, Predictive Modelling, PMML, Statistical Models, Statistical Modelling, Revolutionary R, SAS, SPSS, Demand Forecasting, Product Affinity Analysis, Offer Response Model, Offer Effectiveness, Promotion Effectiveness .. what is it all about? Let us start divulge each of these in coming days and months .. What is it? Why it needed? How it is done? What is the benefit out of it? I hope you would like to come with me through this journey! Happy Reading!