Customer Centricity : Future of Marketing Approach

Customer Focused Approach and Customer Centricity seems very similar from outside but they are very distant in principle. Customer focused approach stresses on knowing the profitable and risky customer segments. Then the steps are taken to market accordingly. Generally the importance is given here to the product, price, place (distribution channel) and promotion that appeals to customer. This four Ps approach of Marketing has been very successful when implemented by few of the companies. But this approach has some very serious drawback. This is covered by the centric approach.

The customer centricity is directed towards knowing customers better. Knowing their general behavior and their business value in terms of past purchasing behavior and future potential. It takes the marketeers to make them see from the customers eye. The 4Ps are thus converted to 4Cs here. The product is replaced by Customer wants and needs, upfront price is replaced by Cost to customer, place is replaced by Convenience of purchase and promotion by Communication between the company and the customers. This clearly has certain edge over the customer focused strategy.

The former approach is understanding a demand and pushing the product. The later concept is understanding the demand, knowing the reason behind the immediate demand and predicting chances of the demand movement. Thus shaping the product accordingly. There are some chances of influencing demand shift according to the company's product profile by effective promotion and advertisement.

I would like to mention Human Values here. Evaluate the two statements and try to find which is spiritual and which one is material thought.

1. Everybody exists, therefore I exist.
2. I exists, therefore everybody exists.

First one seems spiritual at the first glance. But in actual, second one is the spiritual thought. Consider Gautam Buddha. We all must accept he is spiritual. But he left his mother in poverty and everyone he knew to enrich himself with the truth. Once he got enriched, he came back and enlighten the world with his knowledge.

The principle of Customer Centricity roots from the Human Values. It is a value based approach. It is knowing the present and future market, and preparing company's product portfolio according to it. This is enriching self. The enriched self (here company), thus emit and brightens the world. While customer focused organization tries to focus more on customers current needs and thus play it right, and weakens self. Thus fail in the long run.

While now all of you must have been convinced about the customer centricity being superior to any earlier marketing approaches. Now I will discuss something about this approach and its implementation.

Steps to Implement

1. Define Market
2. Quantify Market needs
3. Segment the market appropriately
4. Profile the segments to make understandable (by business managers and others, non-technical)
5. Determine value proposition of these segments
6. Predict the possible changes in future
7. Communicate the value proposition to all concerned
8. Deliver the value proposition
9. Monitor the value being actually delivered
10. Evolve with time.

The heart of the customer centricity is customer segmentation and profiling, and evolution with time is the key to keep the strategy functional at any specified time.

Twitter Tricks : Getting 500 Followers in 4 days

Few of my friends called me to Twitter. Had known this company for sometime but didn't have so much energy to start something else again. I was already using Orkut, Facebook, LinkedIn for Networking; StumbleUpon for time pass; knol and blogger for writing articles etc. I was actually over packed.

But Bibek and Lokesh inspired me to join it 5 days ago. I thought of giving it a try. But a greedy Marketer came to existence, when I read that the success out there is to increase the followers! I thought of a strategy for it with all the analytics I have been doing. The result -- 563 followers till now.

Here is my strategy. It has three principles.
1. People usually reciprocate the favor done to them.
2. People who follow other people like you have high chances of being your follower.
3. The chances of conversion are more with more chances given to people.

Implementation Steps:
Step 1 : Go to the Twitter profile of people who are like you.

Step 2 : Follow all his followers

Step 3 : Everyday check who is not following you at FriendorFollow; if someone does not follow you, un-follow him and then again follow him.
Remember people wont know when you un-follow him, but he will keep on getting mail when you follow. Do this till someone follows you. Test his patience or his arrogance what ever you may call :D

My Plan is to get 20k followers in 3 months. Let me see if I can get that. Do you want to do the same? Let me know if you implement this strategy. Would like to know if it works for you or not.

Good Luck!! Happy Twitting.

Want to add me at Twitter. Please do it now.

Everything They Have Told About Marketing is Wrong

I became a big fan of Uncertainty Principle when I first read it. Every Principles said this happens, that happens, this is possible and that is possible; but when I first read Heisenberg Uncertainty Principle, it was so different. It was saying something that is not possible.

Then one day I got to read a book "Brand Failures" by Matt Haig. Matt has listed all live examples of brand failures; not a book which give you suggestions but a collections of true stories. This is something I loved a lot.

Now in this series, Ron Shevlin has come up with a book "Everything They Have Told You About Marketing is Wrong". I just could not stop laughing while reading this book. Very well written and humorous as Ron always is.
The book has criticized all aspects of Marketing and myths of using Marketing Analytics metrics. I could not agree with him more when he replaced 4 P's of Marketing to 3 P's. In his own words :

Pick up any textbook on marketing and you’re sure to read about the four P’s of marketing: product, place, price, and promotion. These have been the foundation upon which marketing education has relied for forty or so years.


Most marketing departments, however, only practice three Ps of marketing (and different ones than the textbooks preach, at that): 1) Predicting what customers will buy; 2) Pushing a bunch of marketing messages out to those customers/prospects; and 3) Praying for a better response and conversion rate than the last campaign.

Predicting, Pushing and Praying!!! Great choice of words. I think, this is what Marketers and the Marketing Analysts currently believes in. This is certainly not the right way. They should be doing something better --

• understanding the product better and give right information to the customers.
  
• be actively engaged in bettering the sales service and after-sales service.
• and more ....
  

Ron calls this Operational Excellence!

"The history of the CIO will repeat itself with the CMO."

His take on the today's problems faced by Chief Marketing Officers role is great. He compares the development of IT as a core business function with that of Marketing. And problems now with CMO were once faced by CIOs.


While WOW is an admirable goal, not only is it NOT the “only marketing that works”, it’s not marketing at all. WOW.

Word of Mouth has been taken as Marketing work till now. But I have never referred any product or service yet for having great Marketing. But I do for various other reasons. I always refer ICICI Bank for Customer Service and Efficient IT Systems (great Net Banking Experience), and I refer Airtel for reliable network, good Customer Service etc. Have you referred any company or brand for Marketing? I would be interested to know.

Thanks Ron for pointing this in a great way. I simply love it.

I have lots of good things to tell about this book, but then you might read this and settle here. But I want you to go and buy this book so that Ron gets reward and you get the complete information.

Remember, not everything in the book is good. If you are not an Analyst, you might not love it. It has hell lots of criticisms for Marketers and their beliefs; and most of these criticisms have come from a Analytical point of view (Ron is a Analyst; No Wonder He Has Done This.)

The book also has lots of suggestions. You will die if you actually go and implement. These are some of the idealistic views of an Analyst to make things work as a trend. But I know, real world business does not work this way. He has put very little examples of how any company or brand has worked, and what worked and what not. But he is focused on hitting the Marketers head ... might be because Marketers didn't listen to him when he gave his analytical findings. Quite Possible!

Anyway this is a must read book by both the Analysts and Marketers. Analysts to know how they can better themselves and become more business friendly; and for Marketers to understand what Analyst have to say.

Happy Reading!! Do not forget to share your views on Ron and the book here. Will love to know those.

--
Interesting follow up by Deep Sherchan on Engineering Analytics.

Using Market Basket Analysis ... is it sufficient?

By Hari Prasad Kafley

Market Basket Analysis based on affinity algorithms is one among the most popular and commonly used techniques to analyze market basket data mostly applicable to consumer package goods. The association rules tend to identify a basket of products which are likely to be bought together. With increasing complexity and competitiveness in consumer package goods industry due to information revolution, mass merchandising is on decline. Consumers are increasingly recognized into large number of distinct segments. Diverse items are available for sale. Products category contain hundreds of competing products. High degree of differentiation is being achieved leading to strong brand names offering strong competitive advantage. Temporary price reductions, temporary product assortments and placement at stores are not sufficient for inducing customer loyalty. Due to greater degree of information access, consumers are highly mobile and knowledgeable to make purchase decisions based on price and differentiation of products. Eventually, retailers having greater ability to negotiate favorable terms with suppliers and manufacturers reap the advantage.

With maturity of consumer package goods industry, mere opening of new stores to sell to larger number customers hardly ensures profitability. Essentially, it important to retain existing customers with increased product portfolio. Retailers those are able to reduce their operating costs, manufacturers achieving economies of scale increase profitability. Operational efficiency can typically increased with introduction of customer loyalty program leveraging on information technology.

Present day demands great deal of collaboration among retailers, suppliers and manufacturers. Actionable recommendations from analysis should cater to the needs of all partners in the chain. Market Basket Analysis tends to give biased recommendations profitable to retailers. Though statistically the results are credible but in this competitive economic environment and informational open market results may not translate into actionable strategy.

Problem takes origin from affinity algorithms in Market Basket Analysis which takes into account individual and isolated rules. Current need is to generate rules to predict the association among group of products. Complex algorithms being practiced can be used to include product categories, but it leads to increase in rules exponentially. The rules appear to be complex leading to difficulty in interpreting the rules. Furthermore, affinity algorithm in itself does not assume causal assumptions whereas the output of the algorithm suggests causal relationship among the products. Hence there is a need to introduce either a new and causal form of affinity analysis.Extension of algorithm to produce rules to be applicable across larger groups of transactions needs to be explored.

MODELING ON CENSORED DATA

Introduction: The ABC Bank dataset available now has certain issues of censoring. The data contains people, who have been filtered by earlier Model. Also many people might have withdrawn the Loan offer on the basis of:

• Loan Amount given to them
• Interest Rate
• Loan Term

This requires a different approach to modeling. I propose a ‘different’ approach here.

Axiom: The behavior of a customer depends on the personal characteristics and the controllable factors namely, the loan amount, loan term and rate of interest.

The underlying postulates are:

(i) the goodness / badness of a customer is first and foremost determined by his / her personal characteristics;

(ii) the controllable variables can push up or down the likelihood of goodness of a customer which has been already determined by his / her personal characteristics;

(iii) Among the controllable variables, chronologically earliest-decided (by customer) is ‘loan amount’ followed by ‘loan term’; and then follows the ‘rate of interest’ decided by the lender.

Issue: The dataset on the controllable variables available right now is a ‘censored’ one (i.e.) not spanning the entire ‘space’ in which the future data can possibly lie. The usual approach of building a single model, with the personal characteristics and the controllable variables together, would entail ‘EXTRAPOLATION’ of the model in that part of the ‘space’ which the historical dataset has not encountered. Hence, the single model may not suffice.

The proposed approach: This proposed approach involves building multi-stage model(s) which are ‘not affected’ by the ‘censored’ nature of the available dataset on the controllable and hence, can be hopefully applied to the ‘entire’ space of these variables.

The approach essentially tries to build a first stage model that tries to capture the log odds for ‘goodness’ of a customer on the basis of his / her personal characteristics. This first stage model does not account for the effect of the controllable variables on the likelihood of ‘good / bad’ behavior of a customer. The ‘residual’ from this model contains that information.

The models in the subsequent stages, built on the residuals from earlier stages, would help update the log odds for ‘goodness’. The details are given below:

Stage I Model: Build a (logistic) model with only the personal characteristics of the customers as independent variables. Collect the residuals from this model.

How to get the residuals from this model?

The actual logit (log odds score) for a customer with DV = 1 is actually ∞ [log( 1 / 0)] and for DV = 0 it is actually – ∞ [log ( 0 / 1)] . But, assuming that the predicted probabilities (for DV = 1) is rounded off to 8 digits, the value ‘1’ can be approximated as 0.99999999 and the value ‘0’ as 0.00000001. Hence, the actual logit for a ‘1’ can be approximated as log(0.99999999 / 0.00000001) = 18.42068073. The actual logit for a ‘0’ can be approximated as – 18.42068073.
From the model get the predicted log odds L^{^} and obtain the residuals as:

18.42068073 – L^{^} if actual DV = 1

– 18.42068073 – L^{^} if actual DV = 0

Stage II Model: Take the Residual (Call Residual 1) from the Stage I model as DV and the ‘loan amount’ as the single IDV and build a ‘linear’ model without an intercept term. This model may perhaps have to include a quadratic term also. Specifically, the model could be

Residual 1 = β₁* Loan Amt + β₂* Loan amt ^2 + Residual 2

Stage III Model: Take Residual 2 as DV and ‘term’ as IDV and build the model

Residual 2 = γ₁* term + γ₂* term^2 + Residual 3

Stage IV Model: Take Residual 3 as DV and ‘rate of interest’ as IDV and build the model

Residual 3 = δ₁* Rate + δ₂* Rate^2 + Residual.

The Ultimate Score:

The ultimate (updated) log odds score for the customer is obtained as

L^{^} + β₁* Loan Amt + β₂* Loan amt ^2 + γ₁* term + γ₂* term^2 + δ₁* Rate + δ₂* Rate^2

Note: The idea above is that the log odds estimated obtained from Stage I Model falls short of the true log odds by a quantity equal to Residual 1. This shortfall is being bridged by building the Stage II Model. Still, there is a shortfall (of a quantity equal to Residual 2) which is captured by the Stage III Model. And so on ……

The ultimate log odds may be converted to probability in the usual manner.

Remark:.This is only an initial proposal which might require modifications. This initial idea might hopefully lead to further fine tuning towards the best possible model.

What do you think of this concept? I welcome suggestions and criticisms on this proposal. Please drop your views by email to me at khanal[dot]Bhupendra[at]gmail[dot]com or through comment to this blog entry.

A 'Where's Waldo' Approach to Problem-solving

Adelino has come up with a manifesto for Problem Solving. I am a big fan of Adelino and have enjoyed this one like his blog. If you are interested in Marketing then I suggest to go through this one. It is really good (I am not discussing about the material here to give you the fun of firsthand reading). Hope you guys will like it.

Adelino de Almeida “We’ve all encountered bad solutions that come from bad problem-solving; heck, we’ve even encountered good solutions that were somehow generated from bad problem-solving…..All you need to become a proficient problem-solver is a basic understanding of the concept behind the Where’s Waldo books: unbeknownst to most, these books encapsulate all the wisdom necessary for sound problem-solving.”

Here is Adelino about the Manifesto ....
Adelino's Blog

And here's the place to download your copy.
View at Change This

Happy Reading!

Retail Analytics : Two Side Complexity

This is a continuation to my earlier post on Retail Analytics (Analytics in Retail, CPG and Distribution). I am trying to break the whole of Retail Operations to two parts and suggesting some analytics solutions.

i. Supply Chain Side:
This is the back-end operation. The typical decisions to be taken here are:
a. Location Decision : Where to locate various like the production units, warehouse, retail stores etc.
b. Production Decision : What to produce and how much to produce.
c. Inventory Decision : How much to store and in what phase, either raw or semi-finished or final product.
d. Transportation Decision : How to optimize the transportation cost while maintaining good logistics management.
e. Efficiency Optimization : This includes increasing overall efficiency of employees as well as processes involved.

Few of the Analyzes and Strategy Sciences Management works that I can remember, which will support these decisions are:
a. Network Design Methodology
b. Category Management
c. Inventory Management
d. Operations Research
e. Merchandise Performance Metrics
f. POP Indicator Analysis

I would prefer to call all these analyzes together as Supply Chain Analytics.

ii. Marketing Management Side
This is more operationally complex. To better this side understanding the customer behavior becomes critical. But this is not enough. There are various decisions to be taken as a Retail Chian store. Those decisions can be:
1. Target Customer
2. Brand Preference
3. Operational Efficiency Optimization
4. Cross-sell Focus Decisions
5. Advertisement Selection
6. Profit Maximization
7. Portfolio Maximization
Most of these topics are understandable from the terminology itself. So I don't get into much detail here.
I would rather move fast to suggest some analyzes and strategy consulting activities leveraging these decisions.
1. Brand Loyalty Metrics
2. Yield Management
3. Markdown Analysis
4. Customer Segmentation
5. Demand and Sales Forecasting
6. Competitor Analysis
7. Sensitivity Analysis (Price, Ads etc)
8. Migration Model (likelihood test to find people going to attrite or change behaviour significantly)
9 Market Basket Analysis
10. Marketing Mix Modeling
11. Advertisement and Campaign Management
12. Portfolio Maximization Analysis
13. POS Indicator Analysis

So many topics just thrown at you!!! Hope you enjoyed it. If not, let me know. I will try to address your concern in coming posts.

Analytics in Retail, CPG and Distribution

I keep on getting queries on use of Analytics in Retail Industry. Now I have tried to answer all those queries over here.

Problems and Issues in the Industry:
Let us first see what the common worries exist in the Industry. I have tried to make a list of issues that is constantly taking the head of Business Managers.

• Where to place the Production facility?
• What is the most appropriate stocking point?
• Which merchants should I buy from?
  
• What is the most suitable sourcing point?
• What is the best way to schedule my production?
• How to balance the work load?
• How to control the quality of the product ?
  
• What is the optimal level of Inventories to be held?
• What deployment strategy should be followed?(push vs pull)
  
• What is the best control policy ?
  
• What shipment size would be best (consolidated bulk vs lot for lot)?
• What route to be follow for the shipment?
  
• How shall I schedule my shipment ?
  

• Who are my target customers?
• Which brands should I focus on ?
  
• How shall I beat my competitor?
  
• Are the target customers cost sensitive ?
• How brand loyal are my customers?
  
• How effective are the campaigns and advertisement?
• What is the Point of Sale ?
  
• How to maximize my Portfolio?
• How to maximize the yield?
• How effective are the markdowns?
• Whom can I target to cross-sell the profitable items?
  
• How can I increase the efficiency of my sales force?
• What is the expected demand at a certain point of time?

Analytics Offerings:
After saying all this, I will divide the Analytics Offerings in various categories.

1. Supply Chain Analytics

• Vendor Efficiency Model
• Inventory Turns and stock levels
• Fill Rates and stock outs
• Storage Utilization
• Network Utilization and Zone Routing
• Inbound and outbound truck load Analysis
• SKU Velocity Analysis
  

2. Store Analytics

• Store Layout Analysis
• Sales and Margin Rates
• Shrink Analysis
• Return Rates and Fraud Analysis
• Credit Card Fault Detection
  

3. Campaign Analytics

• Campaign Effectiveness Analysis
• Channel Optimization
  
• RFM Analysis
• Mark Down Elasticity
  

4. Financial Analytics

• Financial Fore-casting and Budgetary Analysis
• ROC and Working Capital Management
  
• Shareholder Metrics

5. Merchandising Analytics

• Assortment Optimization
• Product Pricing
• Seasonal Trends
• Category Contribution
• Hot Items Listing
  

6. Customer Analytics

• Customer Life Time Value
  
• Profitability Analysis
• Customer Segmentation
• Market Basket Analysis
• Cross-sell Options
• Brand Switching and Loyalty Metrics
  

7. E-Business and Web Analytics

• Click Stream Analysis
• Web Sales and Traffic Rates
• Subscription Rates
• Store Cannibalization Analysis
• Web Traffic Segmentation and Target Marketing
• ROI and Web Spend Effectiveness Analysis
  

Result of Analytics Usage:
Analytics can help to get good results but only when there is proper implementation and co-ordination among all stake holders. And most importantly, Analytics comes at much later stage of Vertical Solutions.

I would recommend three steps in process improvement.
1. First get the process right. Automate the basic operations and implement best in class IT Solutions. ERP Software Implementation is best.
2. Create Good Reporting System (MIS). Know everything what is going on in your organization.
3. Analyze how things can be improved to become the best in the market.

Analytics helps in getting closer view of three stakeholders of Business.
1. Customers : Know you customers
2. Competitors : know your competitors and their moves
3. Capital : Know yourself, your strengths and capability.

I will be frank with you here. After doing all this, what you can get from Analytics is the last 10% value add. First 90% lies with the first two steps . ... ERP and MIS.

Marketelligent : Analytics with Business Sense

I am more than happy to write this blog entry. We have made an attempt to bridge the gap between Analytics and Marketing. I have joined a group of Marketing and Analytics folks, (Roy and Anunay), to start a Analytics and Consulting startup, Marketelligent.

We see lots of problems arising from not so good understanding between the Analysts and the Business Managers (mainly Marketeers). This is damaging for the company running in-house Analytics and more so for outsourcing or Consulting dependent companies. We plan to develop as a perfect bridge between the two mind-sets and overcome the problem. We will be selling Analytics Consulting products, and their Java implemented Software Equivalents (which clients can directly use in their software system). But more to this we will have exceptional value add to those clients who want to partner with us on off-sourcing or human resource sharing.

* Give us your Business Problems, we will give you our mind. You Give your hardest problem, we give our best mind.

* If you are looking for Analytics outsourcing, then we are ready to support with complete infrastructure. We can be your best possible partner.

* Marketelligent is the company who has all three capabilities. We sell Consulting Services, we implement the Analysis Results and Models to form Business Decisions and Strategies, and we also implement them in case you need.

Management Team
Roy K. Cherian, co-founder and CEO, has over 16 years of rich experience in marketing, advertising and media in organisations like Nestle India, United Breweries, FCB and Feedback Ventures. He is known for his passion for innovation, some of which have been recognized globally. Roy has done extensive and pioneering work on media, ROI modeling and marketing mix optimization.

Anunay Gupta, COO, has over 13 years of industry experience, with a significant portion focused on Analytics in Consumer Finance. He was a core member of the Risk Management team of American Express based in New York City, and held a leadership position with the off-shore Advanced Analytical Solutions group of Citigroup, based out of Bangalore.


Aim and Goal
It is started with a aim of adding one more dimension to the Marketing effort of companies. We plan to enhance business mainly through -
# Partnerships with prospective Business Houses (if you are interested please write to me)
# Client focussed, highly efficient project delivery model (yes, we are committed on this)
# Implementation of Customer Centric Marketing Design for clients helping them to build trust among customers by becoming customer loyal company.

We are open for any type of partnerships that is beneficial to both business houses. It can be in building Analytics Software products, Consulting, Off-shoring, Joint Marketing or Branded Analytics Product sale in Asia etc. Please contact me if you or your firm is looking for partnerships.

Where are we now
Marketelligent currently has office in Software Capital of India, Bangalore. Currently it has around ten people and we are currently hiring people from diverse background. Few Financial Analysts, Statistical Analysts, Analytics and Marketing Consultants and other industry experts have confirmed their joining in near future. And few technical SAS experts will be joining next month. (If you are looking for similar position in Bangalore then we can discuss).

We are having quite a few immediate requirements too. We are looking for Senior Analyst with Retail Industry experience. If you know anyone, please refer to me. I will really appreciate the help.

Our Services

Our services can add value to a wide range of businesses, and are broadly covered under three heads Consumer Goods/Retail, Banking/Consumer Finance, and Media.

In addition, we have outsourcing and consulting capabilities for Web Analytics and Web Marketing too. We help our clients to take the benefit Web Analytics throws and optimize their web marketing spend.

A Where’s Waldo Approach to Problem Solving

Thinking about what the topic means!! Well, I am also. I didn't chose the topic, but Adelino did. He is going to write a ebook on this topic that will teach how to solve the business problems.

This is all he says about his upcoming book.

"We’ve all encountered bad solutions that come from bad problem-solving; heck, we’ve even encountered good solutions that were somehow generated from bad problem-solving! In fact, I am sure that there are days when we feel trapped in a Dilbertian world of mangled logic in which decision makers seem to channel the babbling gods of fallacy. But our days in the office don’t have to be like that, problem-solving is less an art or science than a process, you don’t have to endure two semesters of formal logic to approach thorny business problems. All you need to become a proficient problem-solver is a basic understanding of the concept behind the Where’s Waldo books: unbeknownst to most, these books encapsulate all the wisdom necessary for sound problem-solving. So quit channeling the gods of fallacy and channel Elvis instead, this ebook lays out the basic rules of solid problem solving and will turn you into a decision-making pro. "
-- Adelino (on his book)


And that book is going to be free. WOW!! I am just excited to read the post about the launching of this book in Adelino's blog and I immediately thought to inform you all. He has a wonderful blog that teaches Marketing and Analytics inside it. It is worth spending sometime there too.

Lastly a important notice. Adelino wants to collect vote on whether he should write the book or not. If you want the book to come, you need to vote here.

Cost of Retention Vs cost of Acquisition

Ron Shevlin has an interesting post on the same topic. He has attacked the myth these in and around these topics. I am excited about the post and I want to add something more to it.

Cost of Acquisition and Cost of Retention are two abstract concepts. They can neither be calculated nor be compared against each other. One may use or disuse a medium of advertising or a particular service offering in a fair way but varying it for retention or aquisiting prospective can be done only in an unfair way. Let me explain it with an example.

Suppose ICICI bank offers Gold Credit Card with certain benefits. (list taken just as an example)

1. No membership fee.
2. Cash withdrawal upto the overall card usage limit.
3. Balance transfer interest free for upto 50 days.
4. One reward point for each Rs. 50 spent from the card.
5. Interest free shopping for upto 50 days.
6. Various holiday packages and gifts for different reward points.

ICICI uses various mode of advertising like TV, Radio, Internet, Daily New paper, Business Magazine and display boards.

Now there are two catagories of information here. One the card attributes and other the medium which reaches customer. These both are primarily targetted for acquisition and not retention, but in actual is it true?

Secondly, ICICI has one of the best customer care service in India and an excellent online terminal (amonst the best in the world). This is their expenditure on retention, fair enough?

I challenge both myths. These both are complimentary. There can hardly be any expenditure that can be fully retention focussed, even if something is done for that cause it compliments the acquisition. Word of mouth and customer feedback of product and aftersales service are the two major sales drivers. And this comes from the combination of both.

Seperate acquisition and retention cost can be incurred in a scenerio when a customer is about to migrate and bank gives some benefit to him to retain. Or a customer asks for an extra benefit while taking the card. I would call these types of extra efforts unfair and may be they are not practised by bigger companies. Business should run in values and not in favor. And this is paid in the medium to long run for sure.

Now, let me come to the point of cost of acquisition and retention. The acquisition cost can be the marketing cost and the retention cost can be the operational costs. If we compare these two, the difference depends highly on company structure and the portfolio type. Any component can be high.

The surprising thing is people tend to attach loss given default to the acquisition cost and make it high for which I see no sense.

I agree with Ron on all his points and would like to add that we would better call Marketing cost, operations cost, loss given default, product X to Y cross-sell cost etc. and compare these cost across various market segments like existing customer vs new customers, old age group vs young age group, New York vs Washington etc. Here we can say marketing cost in Washington for product A for industry D is twice than that of New York, where A and D are variables. The variables are important and mention of specific cost also is.

One thing can be done. If someone prefer to call acquisition cost and retention cost, he needs to give his definition of these costs, industry vertical he is talking about and also the portfolio within the vertical.

I am done with all these. What do you think? Please share your feedback in comments.

Impossibility of Marketing Analytics Software

One of my friend showed me this picture (above picture) and praised web analytics industry. With it, he had complain about Marketing Analytics geeks. His take was, the web analytics softwares put the results very well and Marketing Analytics people don't have any good software at all. He was actually comparing apple with orange, the two can not be compared at all.

I am a big fan of Google Analytics and it has fascinated me a lot. It has very good graphics and it is damn user friendly. It has rightly shown the necessary web metrices necessary there.

Why don't we have some Marketing Analytics Software in that line? The question went on me for some time.

Probably, the results, as good as this one, will make life easy for Marketers. But this is easy if the results to be shown are just the straight data insights. And this is done by almost all leading Marketing Analytics vendors. The predictions and segmentation results may be quite difficult.

I am finding it very hard to distinguish the proposed Marketing Analytics Software from the CRM solutions already available. Most of the data insights are shown in all good CRM solutions, and Whatever Marketing Anlaytics vendors do now, is the more sophisticated modeling and analytics works, which are more customised for a company or a portfolio. The market segmentations are also done for special marketing campaigns or for portfolio management requirements. This makes it hard to generalise. And data security is the other issue. No one will want his data to be used for building some Marketing Analytics Software, as it will expose him to his competitors. To add more problems, the data giving will be a regular task as the validity of data expires quite frequently.

Telling this all, I have made my views clear that I don't see any possibility of specialised Marketing Analytics Software. But developments are happening on building specialised tools for each Marketing Analytics activity like Segmentation and Other Analyses. MA will thus grow, but always remain a external analytic consulting work or the internal analytics team's challenge. It will need man hours all the time.

What is your take in this matter? Please share your views in comment.

Marketing Analytics is growing

Sandeep Mittal has written an article in web edition of business world on Marketing Analytics titled "The Big Thrill". I tend to agree with him in saying use of Marketing Analytics (my earlier on MA)is increasing. But the best part, I took from it, is the need for marketers and analyst coming together.

Sandeep says that Marketers are too market savvy and they undermine the power of data. While the Analysts are too technical and they are lost in statistics. The Analysts lack business knowledge and their findings from data are very poorly mapped for business gain.

This is a very good point. Analysts and Consultants have to collect more general business knowledge (specially about marketing). The Marketers may also need to give some more time for analysts to make them understand the business while they analyse the data and also make some effort in understanding analytical findings.

I don't see this as big problem and difficult to solve, but if this part is not solved then it may be hazardous. May be company's higher management needs to take this seriously and have some internal knowledge transfer sessions or external trainings for this.

What Data Mining Can Do and Can't Do

Jim Novo has written a blog entry cautioning the marketers for the use of analytics and has rightly pointed few issues in data mining and analytics application in business. I am taking forward the discussion here. (The original discussion started here).

I agree with Jim on the issues he has raised and his concerns are genuine. But I would like to add more depth in the use of the data for analysis and modeling purposes, and also its use in actual business scenerio.

As Jim said, segmentation may be highly predictive but may not be useful. To make it useful is a challenge and we need to take up the challenge. Just saying it may not be useful is not the solution. This just undermines the very basic use of analytics in Marketing space.

I have found objective and non-objective segmentation useful in different cases. First one may give good business insight but may not be actionable while the second gives very natural segments and actionable but of very little business gain. I would say, a combination of both is the necessity of the hour. We analyst have to be able to give business related natural segments. The segments then have to be profiled well so that the business manager does not need to dig analytics jargon to implement the data driven strategy. And this is all possible (may be hard).

Regarding models, I have seen a combination of few models is very necessary in any business scene. While talking of banks, I would suggest Revenue Model, Risk Model, Response Model, Bad-debt Model etc to be used together. The importance can be given to the objective that is more important at a particular point of time. The business needs may be to acquire more customers or increase profitability; it can be to reduce attrition or to improve ROI etc. We analyst must be able to give well designed strategy to the marketers that is data driven and the less we expect from marketers the better. Simply building models or segmenting the population is not enough.

So, my say is analysts/consultants need to do more while the data base team need to collect more data (not less). Marketers need to explain the business goals to the analysts and should not shy in seeking their help for successful implementation. Afterall knowing customers is the key and data tells it better than expert opinion, all for lack of psycological bias.

About Life Time Value of Customers

Adelino started the discussion about LTV (Life time value) of a customer a week back and Jim Novo and Ron Shevlin took it forward. I am adding something to the discussion.

While Adelino seems to be averse in using this metric, other two seems in favor of its use. I could also find that Adelino, even if he explicitly say once to drop all unprofitable customers, has some how recommended some engagement plans to act for that segment to bring them to profitable category.

LTV is one metric I am a big fan of. I have seen many corporates grow by the effective prediction of this metrics. I have seen two Indian banks grow by just focusing on getting more bank accounts in the corporate sector as employee salary account when the profit from these accounts were low or negative for freshers. Trend says, they turn profitable when the freshers start saving and using other products from the bank. The already established relationship with one bank through bank account drives huge cross sells.

I don't say to keep all the customers in the unprofitable segment but dropping all of them is not at all a good way. Marketing is first about engagement and then profit (similar to Customer Centric approach which talked in this post). A LTV analysis just help there. Before cleaning the unprofitable customers list, doing a LTV will actually benefit in applying the forced (customer) migration strategies.

Once I suggested a charity organization to follow this metric to see the donors charity trend and overall potential. The findings were too interesting. They could make very good improvement in their business.

So, the weakness is not with this metric LTV but with using it. There are very few smart people in the world who actually can use this metric in good way and also there are few companies who can actually make the business manager of their client understand this fully. And I think, guys are just blaming the metric for not being able to use it properly.

Earlier I gave an example of two Indian banks, these are no other than top two private banks in India, ICICI and HDFC. The short term benefit is good, but if it keeps the long term interest intact. And, if properly planned and executed, the long term strategy gives better fruit than their short term counterparts.

The bottom line :
1. Most profitable customers are not loyal
2. Brand can be of two different meaning, one line Apple other like IBM. Apple (before iPod) was a strong brand with very less business volume but high per customer profitability while IBM was a strong brand with the mix of profitable and non-profitable customers, thus IBM has bigger business and thus enhanced brand equity in extended market space. Apple remained strong in a very small market segment.
3. Profit may be direct or indirect. Indirect profit like word-of-mouth is very hard to determine. I have seen that loyal but unprofitable customers are very good source of advertising. I would say they are investment for advertisement.
4. There is another type of profit that comes with huge volume. In most cases as the volume of business increases, the cost of production comes down. This makes the direct unprofitable customers actually profitable for long run.

Seth Godin is wrong on CSR

Marketing Guru Seth Godin has an interesting call for Marketers. He sees Corporate Social Responsibility as a part of Marketing department. Please follow the link below to get the full article.
Responsibility by Seth Godin

I feel very hard to agree with Seth. Corporate Social Responsibility matters but that does not relate so directly to the Marketers as he said. It is more of the concern to the top most level of the company, board of directors or CEO to think about it. Marketing department is built to increase sales, and they do it. No matters whats the result. He cannot be held responsible for what he markets. But he will certainly be responsible for not being to market the product.

Marketing should just focus on two things:
1. Short and medium term sales
2. Long term prospects and brand management

It is well outside of the Marketing department to think of the effect of the product on the society. Better board and the government look at those matters and give guidelines to the Marketing geeks. Their duty is to be within the given guidelines and market as much as possible.

I would simply call Seth's remark as a try to put human face to marketing. But it is not that simple. Human Values differ from people to people and no people will go against his values.

I, as a Marketing Hobbyist, will happily market Tobacco or Liquor products if I am given the tasks. I will try to sale to each and every market (all demographics, all age groups) provided that is legal. And if anyone calls me weak in values, I would simply consider him a emotional fool.

I was once member of the pioneer organization for Human Values, Vivekananda Nidhi. I would like to share few things I learned there with you guys. There are four values.
1. Personal Value
2. Professional Value
3. Organizational Value
4. Social Value

While many times in life these values work in the same direction, sometimes they tend to contradict each other. In the case of contradiction, it is wise to prioritize personal the most, then professional, organizational and lastly social. (People may chose any hierarchy of priority. It is absolutely personal. The given priority is just the generalization of the common priority set by most successful people and influential leaders over time.)

Now, I take CSR as a part of Social Value which is of least priority among four. It does not mean I don't give any importance to it. Of-course any value has very high importance in anyone's life. The only point I want to mention here is, it is in conflict with the other two values, Professional and Organizational.

Some may have this case attached to his personal value (I think, Seth does). If someone feel that the publicity of Tobacco (which he is marketing) is not good and he should stop, then I would call his resignation from the Marketing Post for that product. His Personal Value asks that. And he should do that. But in case he remains in the posts and try to be undermine his professional and organizational gain, he will probably end up being the most unhappy person and an unsuccessful marketer.

So I would like to contradict Seth's statement on "Responsibility" and call marketer to be more responsible to themselves, their profession and their company than bothering about the social cause. After all people and organization make society, and not otherwise.

Marketing is not an ART

"Marketing is not the art of finding clever ways to dispose of what you make. It is the art of creating genuine customer value."

Philip Kotler

I am a big fan of Kotler. His marketing insights are really great. With respect to other areas like Finance and Management, there may be few people in Marketing who actually turn the performance of a company. And who turn the future of the company generally does in a big way and he succeeds in all ventures he start. This has made us accept Marketing as an art. But I feel very hard to agree with him in considering Marketing as art. I consider Marketing as Science and it can learned, controlled, improved with time and automated.

Let me start the discussion with the very basic definition of Marketing.

Marketing is a science of increasing the short and medium term revenue of a company without distorting the long term prospects.

Many people take Marketing as long term work while sales as short term. But I consider Marketing as the superset of sales and brand management. For investment in various field including the marketing activities, short term sales have to be stable and growing. And Marketing geeks can not afford to ignore the day to day sales activity for the long term. The long term simply may not come. But, it has to be taken care that in maximizing the sales, the goodwill of the company should not be adversely affected.

Now let us talk about how to increase sales in short and medium term. There are many different ways:
1. Good Advertising
2. Celebrity Endorsement
3. Attractive Packaging of the product
4. Marketing Campaigns (with free product offers, cash discounts etc)
5. Entry into New Market
6. Distribution of free trial product
7. Studying competitors and taking counter moves
8. Price Reduction
9. Cross sell options
10. Better Service
......... etc.
These are the few things I can think of now.

Now let us analyze where the artist apply his art. Ahhh I don't see anywhere. May be in presenting the strategy document to the mass a good presenter can do better. Except that, it is just hit and trial in prediction and/or actual implementation till success. The process may be quite costly for some while lucky few get a good marketing mix to succeed. Later are just more intelligent and lucky folks.

How about machine doing all this!!!! OR quite a few of analysis suggesting the best move to take.

Then the only challenge left to the Marketing Manager is to be precise on what he wants to achieve in certain period of time. He can now spend his time with the top level management in formulating long term goals for the company giving the marketing insights. He can suggest to produce better marketable products and tap the untapped potential. This will fulfill the void of marketing knowledge in the top brass. That will probably lessen the product and/or company failures. The products and the company may then turn quite market friendly giving easy days for marketers too.

Let me give some illustrations for manageable scientific marketing. I would call this Predictive Marketing. I consider two cases here.

First case: While using certain strategy for the first time or launching a product.
At this point of time, there is no historical data with the company. Some gut feeling and bit of art is necessary here, but an intelligent marketer can use a survey or others data to analyze and find the best possible move. Pre-Market Offer Testing is one of the best way to test the product in the market before launch to avoid any sudden hit backs. This will give a good prediction of how the product is going to perform once it enters the market. Some people even use it to design the product so as to make it market friendly.

Second case: Company has the data.
With few months to years of experience in a particular field, people with good business insights and mental capability remember the good/bad moves. They make good decisions going forward, but it may not necessary be optimal one. Data analysis can give the result associated with each earlier moves. And the decision management tools can suggest the best possible move that can be taken at any point of time.

Consider a scenario where the effect of price, promotion, target customer and advertisement channel on the target attributes like Revenue, Income, Sales Volume, number and type of customers are all with you. Then, there is hardly any requirement of art to market the product. And all this is possible through Marketing Analytics.

The analyzes and models associated with some of the marketing works can be:
1. Good Advertising - Sensitivity Analysis, customer segmentation, channel management, Response Modeling
2. Celebrity Endorsement - customer segmentation
3. Attractive Packaging of the product - Sensitivity Analysis, Customer Segmentation
4. Marketing Campaigns (with free product offers, cash discounts etc) - Campaign Management Analysis, Customer Segmentation, Loyalty Metrics Analysis, Attrition Modeling
5. Entry into New Market - Market segmentation
6. Distribution of free trial product - Customer Segmentation
7. Studying competitors and taking counter moves - Trend Analysis
8. Price Reduction - Price sensitivity analysis, Customer Segmentation
9. Cross sell options - Market Basket Analysis, Behavioral Modeling
10. Better Service - Sensitivity Analysis, Efficiency Analysis

With all these reports, Marketing geeks can expect results pretty close to the predicted figures. Thus, it makes Marketing pretty interesting and actionable science. Good bye to the Kotler's ART of Marketing.

Successful Marketing = Historical Information Synthesis + Predictive Modeling + Intelligence in Application

Customer Focussed versus Customer Centric

Today I got a query from Mr. Ravi (from Hyderabad) about how customer centric concept forwarded by Fair Isaac is different from general Customer Focussed Marketing approach. I was writing a mail to him but thought it would be better if I make a blog entry for everybody to learn.

The two concepts seems very similar from outside but they are very distant in principle. Customer focussed approach stresses on knowing the profitable and risky customer segments. Then the steps are taken to market accordingly. Generally the importance is given here to the product, price, place (distribution channel) and promotion that appeals to customer. This four Ps approach has been very successful when implemented by few of the companies. But this approach has some very serious drawback. I will discuss that in a while.

The customer centricity is directed towards knowing customers better. Knowing their general behavior and their business value in terms of past purchasing behavior and future potential. It takes the marketeers to make them see from the customers eye. The 4Ps are thus converted to 4Cs here. The product is replaced by Customer wants and needs, upfront price is replaced by Cost to customer, place is replaced by Convenience of purchase and promotion by Communication between the company and the customers. This clearly has certain edge over the customer focussed strategy.

The former approach is understanding a demand and pushing the product. The later concept is understanding the demand, knowing the reason behind the immediate demand and predicting chances of the demand movement. Thus shaping the product accordingly. There are some chances of influencing demand shift according to the company's product profile by effective promotion and advertisement.

I would like to mention Human Values here. Evaluate the two statements and try to find which is spiritual and which one is material thought.

1. Everybody exists, therefore I exist.
2. I exists, therefore everybody exists.

First one seems spiritual at the first glance. But in actual, second one is the spiritual thought. Consider Gautam Buddha. We all must accept he is spiritual. But he left his mother in poverty and everyone he knew to enrich himself with the truth. Once he got enriched, he came back and enlighten the world with his knowledge.

The principle of Customer Centricity roots from the Human Values. It is a value based approach. It is knowing the present and future market, and preparing company's product portfolio according to it. This is enriching self. The enriched self (here company), thus emit and brightens the world. While customer focussed organization tries to focus more on customers current needs and thus play it right, and weakens self. Thus fail in the long run.

While now all of you must have been convinced about the customer centricity being superior to any earlier marketing approaches. Now I will discuss something about this approach and its implementation.

Customer Centric approach can be summarized as:
1. Define Market
2. Quantify Market needs
3. Segment the market appropriately
4. Profile the segments to make understandable (by business managers and others, non-technical)
5. Determine value proposition of these segments
6. Predict the possible changes in future
7. Communicate the value proposition to all concerned
8. Deliver the value proposition
9. Monitor the value being actually delivered
10. Evolve with time.

The heart of the customer centricity is customer segmentation and profiling, and evolution with time is the key to keep the strategy functional at any specified time.

Something about Marketing Analytics

One of my friend called me today and asked about my job profile at Fair Isaac. I said, I work as a consultant in Marketing Analytics within Fair Isaac Analytic Consulting Group. He was quick to ask me, "which major Retail Chain is your client?" My answer was simple, we have a few retail chains as our clients but currently I am doing an Insurance Company project. He was surprised!!!
I have quite a few of such experiences.

Last month I was having a discussion with one of my friend working for another analytics company. He asked me what we do in Marketing Analytics apart from building response models.

These two incidents make me think seriously. I see two myth surrounding around Marketing Anslytics space :
1. Marketing Analytics applies only to Retail Chains.
2. Marketing Analytics is all about building Response Models.

We actually do a lot more. Marketing Analytics team at Fair Isaac helps clients across different industry segments including Financial Services (Banking, Insurance), Retail, Healthcare, Telecom, Food Processing etc. The services provided are customer segmentation and profiling, Pre-Market Offer Testing, Predictive Scorecards (Response and Risk) etc. along with Strategy Consulting. For these services tools used are Model Builder for Predictive Analysis, Segmentation ART Module, ClusterBots, MarketSmart for Acquisitions, PMOT etc. [Each of these tools are proprietary Software of Fair Isaac Corporation].

The areas where Marketing Analytics help to make better business decisions can be:
1. Target Marketing
2. Cross sell decisions
3. Advertisement Selection
4. Sensitivity Analysis
5. Market Basket Analysis
6. Yield Management
7. Campaign Management
8. Marketing Mix Management
9. Brand Loyalty Metrics
10. Portfolio Management
11. Brand Positioning
...... etc.

Customer Segmentation and Profiling is one of the best bet Fair Isaac has, and is ahead of any other competitors. Most competitors still use objective segmentation and non-objective segmentation to find business relevant segments and natural segments. But Fair Isaac has gone one step ahead finding business relevant natural segments. We often call these as customer segments. A segment is a cluster of naturally similar customers having similar business dealing with the client. Thanks to ClusterBots. The segments are then profiled to make the solution less technical which is easily interpreted by business managers.

Fair Isaac's Marketing Analytics team is very experienced and has worked with many leading companies of the world in many verticals. Most of the Fortune 500 companies seek help of this team to form their marketing strategy. It helps its clients to take better tactical decisions to remain ahead of their competitors.

Want more about Fair Isaac Marketing Analytics Team. Click here.

(The writer is not the official speaker for Fair Isaac Corporation. These are just his personal views.)