Customer Delight = Customer Expectation plus 1.
This was the simple formula for delighting your customers. For me this is a great formula, but in itself it also raises a number of questions. For example, to deliver a plus one, to exceed something, you must know what that something is in the first place… so do you? Do you know what your customer expectations are at each moment of contact? Most companies believe that they know, but can they show evidence, no! They perhaps show a survey they conducted over 10 years ago and say “I’m sure it’s the same now”!
If you want a clue as to what your customers expectations are then just listen to the words they use. Customers say, “I didn’t expect to be treated in that way”. People use the word expect a great deal……..when they do they are referring to their inner expectations………which they then use to measure your performance against those expectations.
If I ask you to close your eyes and think of landing in an airport in a foreign country you have never visited before. Look around and tell me what it is like and how you feel. You begin to tell
me, feeling anxious, confused, concerned and worried as you have landed in a foreign country and are not certain what to do and where to go, you are defensive. What are you describing here?…..You are describing emotions, you are describing their emotional expectations, you are describing how you EXPECT to feel.
Therefore I think that there are two forms of expectation, physical expectations, i.e. how quickly a product will be delivered, how many rings it will take to answer the phone and emotional expectations, what people EXPECT to feel. If you are to meet your customer expectations you need to understand both! So do you?
Do you know what your customers’ physical and emotional expectations are? When they come into your store? Or when your salesman calls around? Or when you put them through 7 layers of voice menu systems? ……And if you don’t, how in the hell do you expect to meet, let alone exceed, those expectations?
So what are emotional expectations? Let me give you an example. The other day I walked into a store and the woman was stacking bags behind the check out. As I stood in front of her she totally ignored me. I thought, how rude! I was hurt, I felt snubbed. My emotional expectation was that she would have at least acknowledged me; asked me to wait a moment, but no she chose to ignore me.….…another example, last week I brought something from a store and it stopped working. I decided to take it back. I was expecting a row. I emotionally prepared myself for an argument; I had played it out in my mind; what they were going to say and how I would respond. The person behind the counter couldn’t have been nicer and more apologetic. They replaced the item without question. That exceeded my emotional expectations.
My advice for this month is to find out what your customer emotional expectations are. It is only when you understand them that you and your organisation can set about planning how to achieve or exceed them. Without this understanding you are leaving it to chance!
The Data Dilemma
Generating more data, but enjoying it less? According to a recent study released by Forrester Research entitled, "Turning Data Into Dollars," you're not alone. Companies have spent millions on analytical software, only to find that it fails to produce the kind of timely insight they want. After surveying 50 business executives at large enterprises, Forrester said it is time for companies to realize analytics is an area where getting outside expertise is not only warranted, but critical to producing cost-effective decisions.
The Analytical Stone Age
Despite the years and millions spent, Forrester said most firms remain in the analytical Stone Age for several reasons. The biggest problem is speed. Many analytical architectures were built to move megabytes of data weekly or monthly and cannot handle new Web-driven, real-time demands or data volume.
Those architectures are also too rigid and too complex, resulting in answers to questions that are no longer relevant, and complexity that has moved beyond users' ability to comprehend.
Ultimately, the problems go back to companies buying and running their own analytical software, an approach that initially seemed both prudent and cost-effective. But the speed of the Internet and the volumes of data produced quickly rendered those internally built systems obsolete because business needs and wants changed as quickly as the data did.
Making Data Pay Now
According to Forrester, data gathering is not wasted but largely misapplied. Two approaches can be used to turn things around.
In the short term, Forrester said companies should take advantage of packaged models from vendors like MicroStrategy (Nasdaq: MSTR) and Oracle (Nasdaq: ORCL) that leverage previous investments in packaged applications from such vendors as Siebel Systems (Nasdaq: SEBL) and SAP (NYSE: SAP).
Forrester also recommended, in the near term, that businesses concentrate on short-term achievable questions and results and on driving business information to the front lines.
Long Term – Who You Gonna Call?
Perhaps the most telling result in the study is Forrester's determination that few firms have the in-house expertise to run their own analytics.
"Finding and retaining the right staff for the long term will break the bank at most firms," Forrester concluded.
For that reason, Forrester said it thinks smart firms will seek help from two different types of outside service providers -– analytics consultants and analytics outsourcers.
The decision on where to turn should hinge on one central factor: industry expertise.
"Let's face it: Different industries require different analytical expertise. As a result, companies should ask up front about the prospective consultants' experience in their vertical industries -– and quickly show the door to any who can't point to a handful of reference clients," Forrester said.
Datamining Methodology
1-Business Understanding:
(Determine Business Objectives, Assess Situation, Determine Data Mining Goals, Produce Project Plan)
This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary project plan designed to achieve the objectives.
Data Understanding:
2-Data Preparation:
(Collect initial data, describe data, explore data, verify data quality)
This phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
3-Data Understanding:
(Select Data / RFM, Clean Data, Construct data, integrate data, format data)
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool) from the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection, data cleaning, construction of new attributes, and transformation of data for modeling tools.
4-Modelling:
(Select Modeling Technique, Generate Test Design, Build Model, Asses Model)
Starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
5-Evaluation:
(Evaluate Results, Review Process, Determine Next Steps)
Models should be evaluated from a business perspective based on cost benefit analysis and return on investment. The results of the model may show some interesting patterns but acting on them may not provide the iincremental revenue or cost savings that would justify their use. One of the simplest ways to evaluate a model is to test the results in the real world.
6-Deployment:
(Plan Deployment, Plan Monitoring, Maintanance, Produce Final Report, Review Project)
Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process. In many cases it will be the user, not the data analyst, who will carry out the deployment steps. In any case, it is important to understand up front what actions will need to be carried out in order to actually make use of the created models.
(Determine Business Objectives, Assess Situation, Determine Data Mining Goals, Produce Project Plan)
This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary project plan designed to achieve the objectives.
Data Understanding:
2-Data Preparation:
(Collect initial data, describe data, explore data, verify data quality)
This phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
3-Data Understanding:
(Select Data / RFM, Clean Data, Construct data, integrate data, format data)
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool) from the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection, data cleaning, construction of new attributes, and transformation of data for modeling tools.
4-Modelling:
(Select Modeling Technique, Generate Test Design, Build Model, Asses Model)
Starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
5-Evaluation:
(Evaluate Results, Review Process, Determine Next Steps)
Models should be evaluated from a business perspective based on cost benefit analysis and return on investment. The results of the model may show some interesting patterns but acting on them may not provide the iincremental revenue or cost savings that would justify their use. One of the simplest ways to evaluate a model is to test the results in the real world.
6-Deployment:
(Plan Deployment, Plan Monitoring, Maintanance, Produce Final Report, Review Project)
Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process. In many cases it will be the user, not the data analyst, who will carry out the deployment steps. In any case, it is important to understand up front what actions will need to be carried out in order to actually make use of the created models.
Event based marketing
EBM means that automatic or manual processes with pre-defined rules and actions on particular events that customers involved in.
For example, if a valuable customer A has called your call center more than ten in one month, make a care call.
EBM is a strategic process designed to enhance the dialogue and relationship a company has with each of its customers.
• Shape how the customer interaction takes place, while the customer determines what, when, and where.
• Engage each customer based on his or her actual individual behavior and interests rather than on a product push marketing campaign or sales promotion based on a targeted segment.
• Fulfill the promise of CRM by triggering helpful interaction with each customer at the time that
customer has implicitly signaled a clear and specific need or interest.
The effectiveness of marketing campaign return decreases after the event!!
For example, if a valuable customer A has called your call center more than ten in one month, make a care call.
EBM is a strategic process designed to enhance the dialogue and relationship a company has with each of its customers.
• Shape how the customer interaction takes place, while the customer determines what, when, and where.
• Engage each customer based on his or her actual individual behavior and interests rather than on a product push marketing campaign or sales promotion based on a targeted segment.
• Fulfill the promise of CRM by triggering helpful interaction with each customer at the time that
customer has implicitly signaled a clear and specific need or interest.
The effectiveness of marketing campaign return decreases after the event!!
Word Of Mouth Marketing Techniques WOMM
Check out this SlideShare Presentation:
Typical Purchasers groups
Reactive Traditionalist:
•They make no effort to anticipate buying requirements (search over internet, watching ads),
•Relatively Price Insensitive,
•Buy Locally or by Catalogue,
•Have a formal Buying Process (watch ads, go market ..),
•No price comparison methods (logical choice of product)
•Sometimes Negotiated Prices :)
Service Oriented
•Require Distributors to take an active role in helping them manage their demand,
• Formal Buying (watching ads and then go market ..),,
• Have price comparison methods (supermarkets are cheaper, this firm sells cheapest products, looking for cheap suplementaries),
Multi-Source purchasers
• Have different suppliers(supermarkets, internet, malls) and Have price comparison methods,
• Logical buying (necessity of product and then market),
• Price and quality sensitive
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