Customer analytics from Anonymous In-Store Customers (In-Store Shopper Analytics)

How to Perform Data Analysis on Anonymous Customers who have Purchased In-Store with Cash

Customers who shop irrespective of whether they have loyalty card leave a trail during payment, Their Credit Card Data stores vital information and can be used for subsequent analysis (Payment Card Industry - Crucial for Real-Time Next Best Action (NBA) - Big Data)


How to Track Anonymous Customers

Tracking Anonymous customers (visitors) on the web is quite easy nowadays. The Cookies, IP
Addresses etc provide a wealth of information and this can be used when the visitors return. Tools like SAS, SPSS, +IBM Smarter Analytics and Manthan Systems's ARC Customer Analytics provide anonymous visitor behaviour analysis .

How about Anonymous Customer in Store and analysing their behaviour. Companies like Catalina uses the information of buying preferences of customers to drive promotions, merchandizing and sales. Catalina claims their data and insights are unique.

Aggregate data from Customers provide rich information about Brand Preferences etc.


Video Analytics is also common in Retail nowadays. RetailNext, uses video footage to study how shoppers navigate and pushes appropriate messages/promotions

RetailNext also uses data to map customers’ paths. What is interesting is the ability to differentiate men from women, and children from adults from anonymous data

How it Works:
Most often the Analytics provider rely on Wifi Pings, the Shopper's phone when it searches for WiFi provided by the retailer, pings the Router and the Router captures the signal strength to identify the location

Euclid Analytics also uses WiFi data to understand the footfalls, the time spent at each aisle etc. Also it measures the signals between a smartphone and a Wi-Fi antenna to count how many people walk by a store and how many enter.

+Cisco's Meraki wireless router has a feature called Presence, which has a good location analytics dashboard that measures key anonymous visitor tracking metrics - Capture Rate , PasserBy, Visitors, Median Visit Length, Repeat Visitors etc.

This information can be used for better in-store promotions, Point-of-Sale displays etc. Cisco Meraki has partnered with Facebook to provide Free WiFi in lieu for providing some information

Amazon is testing its own WiFi network. Currently Amazon devices access the internet through its Whispernet service, which runs on AT&T’s 3G cellular network

Real-Time Customer Feedback

Annik has launched ‘Rapid Insights’ specifically for companies who operate retail stores and want to understand & track customer feedback in real time. The real benefit of this solution lies in taking corrective action in real time to improve overall customer experience. This revolutionary technology is extremely user friendly and can be implemented in retail stores easily.

How it works?
-- Placing of tablet at the retail outlet with platform agnostic proprietary customer feedback module
-- During the retail journey, customer shares experience through tablet
-- The management accesses real-time dashboard to view customer feedback and take corrective action

Create Right Mood at Right Time using Experience Players 

Customers doesn't come for Price/Offers, they come for good customer experience, which is why the retailer need to create Right Mood at Right time. Philips Retail Solutions (PRS) helps retailer create memorable in-store experiences with its Experience Player (EP). EP is a clever little black box that allows one to control individual experiential assets, whether it be animated on-screen content, atmospheric light levels or audio. The player can be scripted or programmed with a sequence of events within a set timeline. This solution is ideal for a Garment boutique or Clothes Retailer

iBeacon and LTE Direct

With more devices on Customer's hands, getting their attention becomes a top priority when they are inside / near the stores

iBeacon
Apple's new mobile notifications system iBeacon is being touted by big brands as the next great opportunity in advertising. Using tiny Bluetooth wireless transmitters affixed to buildings, iBeacon lets companies send iPhone owners specific pop-up notifications based on their location and proximity to stores and products. These iBeacon transmitters need not be a separate device but some of these transmitters are simply iPhones as iPads running iOS 7, which lets these device act as both iBeacon transmitters and receivers

LTE Direct
+Qualcomm's LTE Direct technology is touted as an alternative to iBeacon because of the Privacy it brings. LTE Direct is a device to device platform (synchronous) that helps proximity detection apps in battery efficient manner.
Its discovery is connectionless and is only based on proximity, allowing the devices to discover others without revealing their own identity or exact location. Devices in proximity read the 'expressions' for determining the relevance of one another. This is meant for the autonomous discovery

Fabric is the product of Powered Analytics, Inc., aiming to bring an Amazon-like shopping experience into the physical store. Fabric works using Beacon technology -  it learns the store floormap and tags products and categories with their location in-store. Integrating the application with Web and Mobile applications (Apps) would ensure that the shoppers are tracked across channels, and their location is used to predict the right product at the right time across channels

This way In-Store (Brick & Mortar) customers can be delighted by leveraging the data from their online experience and vice-versa

Read then Follow Now - How to Chose Right Channel - Using Social Media Analytics

Derive Customer Insights from Social Media Information by Big Data Analytics


Not so long before people (or customers) read newspapers, browsed magazines, visited libraries . Then came Web - people browsed, then came the +Amazon Kindle Fire a Book library in your hands. Now it's an era of Social Media where +LinkedIn , +Twitter and +Facebook have made them as Followers!

Yes people follow a lot - +The Boston Globe , and +Bloomberg News is available on Twitter and Facebook. You can subscribe, follow them . You can follow +CIO , +TechCrunch if you are a technology geek. If you are a marketer then +marketingprofs might be in your following list, +Nike and +adidas will be for a Sports geek

So what these tells us .. in the first place it gives some understanding about the customer's behaviour. Why is this so important for any marketer.

Social Media, Marketing and Segmentation

Marketers now use Social media extensively for communicating with their customers. They also use the massive volume of Tweets, Likes to get more insights about the customer.

A simple analysis carried out by a Watch manufacturer shows that those who have bought their premium watches are followers of  GeorgeMichael (+GEORGEMICHAELORG GMORG) Is it time to tap them?

How to Merge Social and Customer Data

Clustering or Segmentation of the data using a combination of Customer, Transactional and Social data is beneficial as it will find homogenous groups.

To arrive at the point - Social media data needs to be aggregated. This is where Big Data comes into picture (Refer How is Apache Hadoop used Big Data Analytics and Inteligence ). Companies like +Hortonworks , +Cloudera Inc provide Hadoop clusters where Social media data can be crunched to get the relevant information out.


Here care must be taken to include/excluded Social data as segmentation variable as it has huge potential to skew the result

Social media information can be used either as :

• Segmentation Variable
• Interpretation variable

The cluster profiling would reveal the impact of the social data. For example, a Retailer found that customers who retweet have constantly responded to a type of promotion. Share your own experiences



See also
How to Segment Social Media Users (Influencers / Detractors / Recommenders)

How to Measure Effectiveness of a Campaign using Marketing analytics (Banking)

Marketing Analytics - Measure Effectiveness of Email Campaign for Bank


In our Case Study: Customer Analytics for Telecom Operator to Cross-Sell and Up-Sell we had a look at the Telecom Analytics .. Now let's have a look at how Analytics has changed the communication between the bank and its customers

Banks have a good understanding of customer as the customers part with various data points (SSN #, Tax #, Address, Employee, Salary etc.) at different points of time (Enrolment, Address Change, Mobile Banking etc..)

This information is used for personalized marketing through email (This is the Channel for analysis - multiple outbound channels can also be fed) .

Processes in Marketing Analytics for Personalized Promotions for Banking

The processes involved are as follows

1. Gather Data
2. Analyze Data , Document Discovery and Create Model
3  Execute Campaign
4. Collect Responses
5. Measure the Results (Efficiency)
6. Fine tune / Optimize the model
7. Refresh Model and Segment

Since the necessary data for understanding the customer is already available / collected, the data is segmented to understand different customer segments that are available and their profiles

Each segmented is treated in unique way based on their behavioural patterns

The segments are divided into control and test groups. The customers in the test groups are subjected to the personalized marketing campaign while the control segment customers are part of regular campaigns

The most important step after sending those mailers is to collect responses. This is a key differentiator between a good and bad campaign management

The responses are subjected to Response Segmentation analysis and the results are measured. What are the parameters that are clearly visible in the responders? What are the feature of the non-responders .. this is an area where business needs to play some role rather than IT / Analysts

The Bank's Personalized Campaign has created a 500% increase in response compared to the generic mailer (Refer Figure)

The model is recreated with the understanding from the new findings - this is called Model Recalibration. The model is refreshed in campaign management system and the process continues



The Next Best Action (here the Mailer) is a continuous learning process

+Microsoft Dynamics ERP   was used as the CRM for the Bank. Third party software was used for Mail blast and custom code was used for model development \


There are also custom tools/solutions for campaign / personalized promotion. For example, Manthan Systems' TargetOne tool uses data from various touch points like point-of-sale (POS) and store vicinity, during ecommerce transactions, email click-throughs and social media engagements to ensure right conversation is initiated with the right customer at the right time through the right medium

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

How to Get Sales Leads from Analyzing Web Data

Web Pattern Analysis for Identifying Prospects and Products

Web is an abundant source of information. The trails left behind by online shoppers / browsers are as part of Web Log, Click Stream data etc.

This data along with Social Media is fed to the Analytical model which analyses the trend and identifies potential customers / products that can be promoted

There are many companies /retailers that are using Special algorithms / tools to crawl the web and derive useful insights.


Some specialized companies like Palo Alto based Infer (https://www.infer.com/) offer an analysis software that can be linked to CRM system like SalesForce (http://www.salesforce.com/)a and rank the customer



Infer's ranking system uses more than 150 signals , which includes many data feeds - Census, Job Boards, Tweets , Facebook Likes and Comments



There are many algorithms that help in analysing the Facebook data

OpenGraph API is one of them


 See Also:

How to Segment Social Media Users (Influencers / Detractors / Recommenders)

How to measure Price Sensitity of Customers

Segmentation Standards / Framework

Customer Analytics - Understand Mood of the Day

How to Analyze Customer Behavioral Changes on Time (Day)






 Netflix - the world's leading Internet television network with more than 33 million members in 40 countries has developed an algorithm (Cinematch recommender tool) that can predict the movies that a customer would watch based on

• Past behavior (view history)
• Product rating
• Recent comparison

Cinematch uses Nearest Neighbor method for predicting a movie

The company decided to have an Open contest to fine tune the recommendations (or the Next Best Action if you wanted it to be called so) - the winner would be the one who beat the recommendation of Cinematch by 10%.

AT&T with the combination of nearest neighbor , singular value decomposition (SVD) methods were able to win the one


Please have a look at http://www.research.att.com/articles/featured_stories/2010_01/2010_02_netflix_article.html?fbid=8rnpOtsYzRv for the full text.

Creating Valuable Data Columns - Aggregate Data

How to create insightful summary data from Operational Data

Analytics is not a magic wand. It produces useful insights when fed with good data, insightful information. The primary source of information is from Operational Data.

The operational data, however, is not quite useful as it is . It needs to be rolled-up to form a good amount of data points.

Some days back we were looking at the importance of Relative Pricing in Customer Analytics. Another real world example would be the Bizocity scores used by AT&T

Bizocity score takes into account the originating call number, the destination, the duration etc to arrive at the score.

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

Customer Analytics - Relative Price as Data Point

Relative Pricing in Customer Behaviour Analytics

We had some discussion on Price Sensitivity earlier. This was used for determining the Right Price of offer for that customer. However, the price sensitivity is more related to Demand Elasticity

Relative Price is the price of a product/service with respect to another product/service. The latter one is usually termed as the base product/service.

How to use Relative Price to understand Customer Behaviour

Relative price provides very much useful insight about the customer's behaviour. The following would be the typical values for Relative price

• High
• Medium
• Low

The relative price is applied to each item based on a base item from that category. For example a segregating a customer based on the
different price preferences (Twinnings Tea might be a High valued one, while Tetley might be Medium priced; depending on the purchase behavior of each individual across category the following Relative Pricing Table is created)

Cust ID	Category	Premium	High	Medium	Low
RF0000123	OVGT120980	0	6	16	0
RF0000227	OVGT120980	11	3	2	0

Relative Pricing Customer Table

)Looking at the above table, one could distinguish between the customers - (for a particular category) - the second customer mostly shops for Premium items, while the former is a bit conservative.

The above methodology can be used and the following can be used as Segmentation variables

• No of High Value Items
• No of Medium Value items
• No of Low Value items

Please try this out and let us know the difference it brought to your segments

How to measure effectiveness of Switcher Campaigns in Retail 

Switcher campaigns are the one that disrupt brand loyalty and tease an otherwise loyal customer to switch over to the competitor. Tetley and Brooke Bond are competitors - what would be the impact of providing an offer on Brooke Bond tea for a Tetley fan.

dunnhumby's analysis on offers finds Switcher campaigns are always the worst performing. According to the reporthouseholds tend to buy more of their favorites, such as Coke products, or a new Coke product, if offered an incentive rather moving to Pepsi for example

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 Find Social Influencers using analytics

How to Identify the best influencers from a given data


Who are these Social Influencers ?

One can associate a Social Influencer with :

One who has the maximum followers
One whose opinion is considered by his social circle (who can influence others easily)
One who creates and shares content regularly
One who makes most calls; accepts most calls etc

Benefits of identifying Social Influencers

Identifying influential persons within a circle will help the marketers expand the business; for example, they might be selected for a Test Drive (as Ford did), evaluate a product/service (Beta Programs from Microsoft etc), conduct surveys and thereby spread the word.

How to Identify the Influencers

Identifying Influencers is a broader statement. There are scores like Klout score etc available to weigh the influence of one in social media

The Klout score is an useful one for Social Media

However, a more deep data mining exercise would be more helpful. This would be more helpful than a specific score as the business goal of the datamining would pave the way for the most appropriate social influencers for a goal.

For example, if a Telecom group wants to acquire (poach in fact) new customers, it can identify customers who are good influencers and whose circle/friends are from different telecom network etc.

Building a robust dataset is very important. One needs to collect all possible information (from the call records - CDRs ) and roll-up the same for each customer,

Once the Data is created, one can use Supervised learning techniques or Link analysis to find the best scoring customers.

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