How to Identify Right Shopper for Promotions / How to Categorize Shoppers based on Loyalty

Identify Profitable Shoppers using Statistical Modeling

Not all shoppers are profitable - the statement sounds bit confusing. But a careful analysis shows that there are some who buy only during Mark Downs, select low margin products in discounts etc. In the previous posts What are the steps in NBO (Next Best Offer) to give Personalized Promotion and Find Right Channel we get an idea of how to select a right product for promotion and what would be the appropriate time for sending the promotion/offer

Well before attaching an offer to the customer, one needs to select if the customer is profitable for the loyalty program

There are many methods to check if the customer is profitable. This is is based on the past transaction history and applying methods / models on them

CLV and NPV are some techniques to use in the current scenario. Segmentation of customers based on the above would provide a good insights

This is a challenge for many US Retailers whose customers enroll in multiple loyalty programs (Average US household enrolls to 23 loyalty programs) and hip hop to other retailer based on Sale/Discount. Price plays an important role in this

According to a study by +McKinsey on Marketing & Sales targeting High Value customers is one of the way to improve the Basket Size (both items and Value). One of every retailer's goal should be to Increase the Basket Size by appropriate promotions

Case Study: Customer Analytics for Telecom Operator to Cross-Sell and Up-Sell

How to find products/services to Cross-Sell / Up-Sell using Customer analytics / NBO

The Telecom operator has a good customer base and wants to drive more sales using existing customers. The operator decided to craft a NBO strategy for increasing sales from existing customers (this is not just retaining the customer - by reducing Churn)

As with any NBO - the first step is to determine the Objective

Step 1: Define The Objective

The objective here is - Increase sales from existing customer through cross-sell or up-sell

A customer with two or more services is four times less likely to Churn than a customer with a singl service - this means that cross-sell not only increases the incremental sales but also increases Loyalty. The strategic objectives like these should be driving the NBO objective

With the goal being defined very clearly, the next step is to collect required data for the NBO

Step 2: Collect Data

The Telecom operator has data on the services/products that are used by each customer, the plan, the usage history, subscription data,  the CDRs , Customer Demographics data and Internet usage data (for the subscribers) . This data holds wealth of information and needs to be mined.

The entry channel (the channel through which the customer was acquired) would be a vital information.

The Location data also plays an important role in understanding the customer, Location history would explain if the customer is a frequent traveller, when and where he/she makes uses the service.

If the internal data collection is done, one can look for Syndicate data or External Data (Payment Card data)

Step 3: Analyze, Model  and Execute

Once the Data is available, it needs to be cleaned and normalized. Then the customers are segmented based on their purchase behavior, subscription plans etc. There are also industry specific segmentation standards like Prizm. This should be a good input for rolling up data

Understanding the customer is a first stage in NBO. The customer's behavioral attributes are
analyzed using statistical analysis (clustering, link analysis, decision trees), predictive modelling etc.

It's just not about understanding the customer, the Telecom company needs to analyze the product/service that can be offered to a particular segment. Understanding the Service/Product Offering is the next stage .

For example,

• Wireless devices
• WiFi Services
• VOIP Call services
• Local call services
• Broadband Direct connection
• loud hosting

For example, a propensity model can be created for knowing the probability of the customer for purchasing/trying a particular product / service. For example, a Broadband plan for a customer who is not using the services from the operator (and using the one from a Competitor) with competitive pricing / value add would entice the customer to switch-over.

The propensity of a customer who is currently using the service is set to Zero. This will avoid irittating the customer with the product/service she already uses.

The propensity of each service is ranked along with the best suited time of  sending the offer. The Offer is then sent to the customer through his prefered Channel and at the appropriate context.

Context is always the key - if someone has a 4G-enabled phone, he has a fair chance of upgrading to a 4G network. Knowing the user's device information is helpful here.



Tools like SAS and SPSS can help in achieving this faster. There are Open Source Software - like R, which is catching up fast

Step 4: Measure and Recalibrate

Every offer that is sent is an Test Offer. An offer can be accepted/ignored/rejected. The best NBO strategy is to measure the response of the offer and take the learning back to Analysis stage.

The offer response data is fed back to the database. If there is any need for further data points, it needs to be collected. The model is then refreshed based on the new analysis and deployed.

Next Best Offer Solution Framework

This is a continous cycle and the role of Statisticans, Business Leaders play an important role

Payment Card Industry - Crucial for Real-Time Next Best Action (NBA) - Big Data

How to Collect Data (Big Data) for Real-Time Next Best Action (NBA)

Data is more vital for sending Promotional Messages, Recommendations or Any Action for any Customer / Segment

The main focus for any retailer/bank/airline/hospital is to build a comprehensive customer signature. The customer signature provides vital information of the customer in the form of :

• Demographics
• Geography
• Behavioral
• Location
• Purchase Behavior
• Social behavior

Collecting this data and rolling up accounts to 70% of Customer Analytics. However, there is always a limitation to it. The customer might shop only specific items with the Retailer / avail certain services from the Bank. If the Retailer / Bank is a specialized one (like Apparel/Jewelery) the information for individual customer will be limited to few tens / hundreds of records /year.

Payment GatewayLocation Based Offer / Message using NBA

 

To overcome this a Bank / Retailer can have alliance with Loyalty firms / use syndicate data etc. The Credit Card / Payment gateway company holds the key.

Take a case of the customer shopping in a high-end Apparel retailer, a Jewelery shop and have paid using Credit card in both places.

The Credit Card company doesn't have the Transaction level information but knows that Customer X has shopped for the following in the same locality

a) Apparel
b) Jewelery

If this information can be used prudently by the speciality footwear store in the same location. Probably the customer might be interested to drop -in provided he / she knows about the store. If the Footwear retailer forms an alliance with Credit card company and push appropriate messages, the sales would definitely go north!

+MasterCard for example collects and stores the transactional data from the customer for data analysis. Customers have the option to opt out.

Credit Card Industry

 Companies like BeyondAnalysis use the Payment Card  (Visa, +MasterCard )'s Transaction data and provide the Retailers like  +Waitrose information about the customers who are purchasing from other supermarkets as well. This information would be limited to aggregate level and not at an individual level there by protecting the privacy of the individual and at the same time generating necessary customer insights

 

 

 

 

 

 

 

How to Deliver Next Best Action (NBAs) in Real-Time

How to create Location-based Real-Time Next Best Offers (NBOs)


NBAs are supposed to understand the customer's need and fulfil them even before he/she wants to act on it.

The main purpose of the NBA/NBO is to happen real-time. With the advent of smartphones delivering the Action in appropriate location and time is now easy.

Any Long distance passenger have to wait for few hours for the connecting flight; The Airline knows the customer’s demographic info, location, time (both are very much accurate) - a good amount of info for Analytics

The data of all passengers are fed to Analytical system and their past travel history, lounge purchases, social habits and syndicate data is crunched to figure out the need for each passenger. The needs are then matched with the Offerings of the Airline's partners (Hotels, Boutiques, Spas) in that particular airport and appropriate recommendations are created

Real-Time Next Best Offer (NBO) Flow

Timely message / offer is sent to passenger’s mobile phone (e.g., offer on antique jewelry in Airport boutique ) based on the time/location and her Inflight purchase/interaction behaviour. The last part - knowing his/her immediate purchase/interaction is more important. This is fed into the Analytical system to validate against the recommendaion that are waiting to be delivered - the recommendations are refreshed and delivered. This is what makes the 'Offer' - Real-time (Right Product/Service)

The above example uses Mobile Phone as the Delivery Channel. Please refer What are the key channels to be considered for Promotions for other Channels.


The omni-channel world requires product information to be managed consistently. The main focus areas are
• Provide consistent data across all channels and update the information real-time
• Offer product information at the right time and place;
• Compile a complete data set and deliver rich, unique data;
• Optimum utilization of social media information

What are the key channels to be considered for Promotions

How to Find Right Channel for NBO

One of the key feature of Next Best Offer is to provide the right promotion through Right Channel. It is worth to listdown the channels that should be considered normally.

Channels for NBO Delivery

• call centers
• direct mail
• email
• in-store display
• in-store staff
• kiosks
• mobile devices
• POS receipts
• web recommendations

The Channels are not limited to the above.

Tools and Metrics to Understand Mobile Data

Courtesy: www.telegraph.co.uk 

Mobile users contribute to more than half the percentage of traffic for many Retailers. The Mobile shopping, however hasn't grown with equal pace

Mobile Data Analytics involves collecting, understanding and reporting the insights based on Mobile data

Mobile Data Collection: 

Collecting mobile data has its own challenges. The variety of devices and the Applications prevent a standards for mobile data collection technique

Tags are key for Mobile Data Mining. Tags are small piece of code embedded inside the apps which relay information back to the provider

Mobile Websites (mobile version of websites) are similar to the normal Website but light weight and simple design for mobile.

Real-Time Next Best Action Based on Mood - Voice Analytics

Real Time Delivery of Offer is always a challenge, whatever the Channel might be. It's even challenging in Call Centrer where the CCE (Call Center Executive) is talking to the customer over phone. Now, Computational Voice analysis is helping the executives identify the mood of the customer on the other end. This is also called as emotion detection / mood detection

Identifying the mood of the customer can help drive more insights into the customer herself. Yuval Mor, the CEO of Beyond Verbal the company which has created mood detection algorithms for call centers says the program can also pinpoint and influence how consumers make decisions. “If this person is an innovator, you want to offer the latest and greatest product,” Mr. Mor says. “If this person is a more conservative person, you don’t want to offer the latest and greatest, but something tried and true.”

The company is also providing APIs for developers to develop Emotional rich apps

+Flurry, Inc., which had been an App analytics platform developer has extended it to Mobile Web as well

Email Marketing - An effective tool till date

Email marketing has been and remains a primary marketing lever for businesses," said Geoff Alexander, +iContact . It's always been a tool and with Dynamic Messaging (Hope your remember Microsoft's Bing campaign)

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