The numbers of marketing and customer metrics that are floating around seem to sometimes inundate the marketing professional. In fact with competition at its highest, each consulting company tends to create a new metric (that is sometimes marketed as a black box) to impress potential clients. While there may be a large number of metrics, here are the top 7 metrics that we feel any business should track if they are interested in ensuring a good sustainable client base.

1. Customer Lifetime Revenue
Customer lifetime revenue is the total amount of revenue that a customer is likely to get in for the company. This metric was discussed in detail on our previous post.

2. Average Purchase Amount
The average purchase amount is the revenue that the business gets per purchase order. It is the revenue that the business gains per sale or per order. Knowing the average purchase amount for customers can help you segment them and service them accordingly. It can also be coupled with the conversion rate so that a business can forecast at the expected revenue for the next 3. year.

3. Purchase Frequency
Purchase frequency can be defined as the number of times that a customer makes a purchase in a given period of time. This is a metric that can be calculated for a week, a month, 6 months or a year depending on the specific category that is being studied. When you find that purchase frequency for certain subset of customers is lower, depending on the category that they are buying, can lead to strategic marketing initiatives for upsell/cross-sell.

4. Recency
This term refers to the amount of time that has lapsed since the last purchase. While there is a lot of emphasis on frequency of purchase and the number of times that a customer comes back, recency is the one metric that can actually help retailers to a large extent. When tracked, recency can help create targeted communication that is more effective due to the timeliness of the communication. While there are other aspects such as the content of the communication, offers, discounts and more that also impact customer behavior, the timing of the communication is paramount. Tracking campaigns across various companies has shown that when the first email is sent seconds after the customer signs up for an account, it is likely to have the highest open, click through and conversion rate as well.

5. Retention Rate
The customer retention rate is a metric that indicates the proportion of customers that have stayed with you for a while. The retention rate can be calculated annually, monthly or weekly. The periodicity depends on the purchase cycle and the frequency at which the purchases are generally made.

It is known that acquiring new customers 5 times costlier than retaining existing ones. This means that maintaining a high retention rate can save the company precious dollars every year. Working on the retention rate also ensures that there are less disgruntled customers leaving your company. This means that you will have a lesser amount of bad word of mouth.

6. Customer Engagement
Customer engagement is a step ahead of customer satisfaction. When a customer is engaged, he or she becomes a marketing manager for your product. The factors that need to be considered while computing customer engagement include product involvement, frequency of interaction, extent of referrals, purchase behavior, virility of information shared. There is no standard model for calculating customer engagement and most companies develop their own based on the category in question.

This metric can be used to target specific groups of customers in order to increase their involvement with the company. It can also be used to measure the effectiveness of various campaigns that have been targeted at increasing retention and customer engagement. Web based strategies to increase customer engagement can be created to target individual customers.

7. Share of Wallet
Share of wallet can be defined as a metric that tells you the proportion of dollars that the customer is spending on your brand. The calculation can be done at various levels and you can define the share of wallet on the base of specific variants of a category, the entire category or even monthly expenditure if the category merits it. Calculating share of wallet is especially important for categories where the customer operates from a basket of preferences. The metric allows marketers to understand the average number of competitors that it is dealing with along with the overall position that the brand has.

There you have it, our set of the top 7 customer metrics that every online retailer should track. What other metrics do you consider important?

Even when email marketing that is done using an opt-in list there are some effectiveness concerns that need to be addressed. We have heard a zillion times about the right communication at the right time. And when we use mass email marketing we are basically trying to use the same strategy on all the customers irrespective of their purchase frequency, online behavior, loyalty, satisfaction or engagement. In addition, mass email marketing does not even customize the content of the emails based on the basic segmenting parameters such as gender, age or income.

Creating emails based on customer segmentation is far more effective. This is something that can be done by using smart consumer analytics options. Consumer analytics can help you slice the data in various ways. You can look at gender specific behavior, age related purchase patterns, purchase frequencies of heavy, medium and light users, product purchase mix differences and the manner in which the products are upgraded.

When you use consumer analytics data judiciously you shall be able to ensure that your customers are happy and satisfied; something that is a step towards increasing the customer lifetime revenue. In addition to looking at various segments of people, predictive analytics can also ensure that you are able to take corrective measures to retain customers that may be on their way out. Look at the historical data of defectors, analyze the reasons why they have defected and then study the patterns of behavior that have led to the defection. Once this is analyzed, you shall be able to identify similar patterns in the current customers that you have. Being able to change the course of customers that are likely to defect can be one of the largest savings that you provide the company.

Some of the steps that marketers have been able to take by using such consumer analytics and predictive analytics include:
- Sending smart and timely emails to customers for the next product that they could purchase.
- Emails about discount offers that are relevant and usable.
- Updates about the various innovative products that the company is engaging in
- Invites to special events
- Detecting fraud loan or mortgage customers can help reduce the level of loan defection and therefore losses.

Modeling can be done to ensure that you contact those people who are likely to respond. This can ensure that you do not waste time trying to contact people who are not likely o respond anyways or those that are likely to take action irrespective of your email.

While these are marketing tactics that most people use, the timeliness of the emails and the manner in which they are created for the right audience is what can make a difference in the level of conversion that you get.

Software solutions have evolved in such a manner that they have kept pace with the changing marketing world and requirements. While there were options that helped document storage and management in the 1990’s, software options that helped customers track changes and share them across users were more popular in the early 2000’s. Predictive data analysis software options started to become popular in around 2005 and the evolution has brought us to a time where we can get real time data that can be aggregated in seconds. The future holds much more!

At the beginning of the century, marketing software allowed people to track some data and share it with their colleagues and clients. This is something that became possible with the increasing penetration that the internet was witnessing. However, even at this time large file transfers were not easy to do and this required physical transfer of disk drives and CD’s. At this time some sophisticated analysis tools for market research had surfaced like SPSS and SAS. Most of the other tools available in the market for marketers were in house tools developed for specific purposes. Microsoft Office started to become the most commonly accepted norm for datasheets, presentations and documents.

Towards the middle of the first decade of the 21st century, software options that allowed marketers to create marketing plans, annual budgets, tax returns, product costing and more started to surface. At this time there were also integrated software options that helped marketers in conducting email marketing. This software option allowed users to choose options and program the software in such a way as to use various content options and emails from databases for effective email campaigns.

More recently, there are various kinds of marketing software that can help marketers look at the efficiency of their campaigns. These software options have exploded with a higher number of internet marketers. Today there are special software options that help in tracking affiliate programs. There are also various options that can be used to manage multi level membership programs. Automation is now possible to such an extent that once the software is set up, only supervisory level attention is required. Today there are software tools that can help you crunch data in real time and this is true for surveys that are carried out on the internet and other areas as well. There are tools for arriving at the right keywords for search engine optimization, creating Pay Per Click ads, finding profitable niches, link building tools and more.

With so much of the marketer’s tactical work taken care of, the only aspect that seems to be left is the actual decision of what action should be taken. This is also something that we can expect the next generation marketing software to do. Tomorrow there will be software that will predict future scores, suggest future course of action, liaison with another software tool and shoot mails or make changes in the marketing program automatically.

Marketing has seen a fair amount of change in the last few years. Companies have been forced to adopt a certain tolerance towards change in order to stay afloat and competitive. There is now a need to engage the customer and make him feel valued. Consistency in delivery has achieved paramount importance due to the ease with which you can lose a customer to competition today.

Despite some of these changes, the movement does not seem to slow down. On the contrary, what we are seeing is a rapid change in marketing techniques, consumer behavior and more to such an extent that it is becoming extremely tough to keep pace. The changes are primarily being triggered due to the changing face of technology and the high access to technological products that our customers have.

The future of marketing is:

Bidirectional – Marketing is no longer going to remain a one way street where the marketer tells the consumer about how great his brand or product is. Marketers who do not look for customer opinions are likely to misinterpret customers’ needs and pains. The feedback today is far more genuine and objective than ever before. Platforms like company blogs, social website pages, direct feedback sections on websites are some of the ways in which companies can look at suggestions for improving. However, there is a need to create a structured way to analyze this plethora of information that is now available.

Personalized – The days of the ‘Black Ford’ have gone, welcome the days of extreme personalization in marketing messages. With mass media, we communicated to a large set of audience hoping that the target consumer will find the message relevant and move towards conversion. A large number of eyeballs and resources were wasted. Over time, the marketing message became more targeted and focused. With the internet boom along with refined technology, ads delivered to a person can be customized to what the individual’s personality is. So a teen is served relevant ads for funky clothing, married men generally get to see ads for a family vacation and more. A typical day in the future of marketing may allow customers to select the kind of ‘mode’ or ‘mood’ they are in so as to customize their internet experience in terms of what they want to read, see or shop for. A marketer may one day send email messages that are personalized to the customer’s taste, purchase behavior, and web surfing history.

People based – Word of mouth marketing will become far more pervasive due to the ease with which people can give their opinion and recommend products. Customers do not need to go on to review sites to see testimonials of a product, most of which are paid reviews anyways. References are and will continue to be sought on social networking sites to ensure genuine feedback. Positive word of mouth will become one of the most potent and effective means of spreading good word about a brand. The role of the marketer therefore will be to ensure that a customer is so happy with the experience that he passes on the delight to others to create a viral effect.

Data driven – The data captured by point of sales, CRM, and web software can help marketers understand their customers and their respective purchasing patterns. The data will then be used to recommend specific products and content to consumers. This is something that has already begun with recommendation engines that personalize the search results based on the country that you are in (for example). There are also movie download stores that keep track of the kind of movies that you have downloaded or bought so as to suggest the ones that you are more likely to enjoy.

Multi-Channeled – The one thing that marketers cannot afford to ignore is that integrated marketing is continuing to add various other touch points into its fold. So while mass media like television, newspapers and radio were considered to be the mainstay at one time, there were others like the internet and retail that were added on. Now there is likely to be further addition as mobile and tablet internet access increase over time.

It is clear that the changes that are taking place have the customer at the center since the technology and automation allows customers a higher level of power than ever before. Customers have a far higher access to information and a far wider choice for marketers to get complacent.

Most of the customer satisfaction surveys and products that you are likely to come across in the market are likely to have complicated algorithms for calculating the customer satisfaction index. But in all this ‘big’ talk, you are likely to get none of the company professionals to explain the specific analysis that is used to arrive at the customer satisfaction index. This is mainly because these customer satisfaction products are created by third parties. The whole thing is covered up by saying that it is a ‘black box’ and that they are not allowed to give out the details of the calculation.

While some of the customer satisfaction models actually use good partial correlations and regressions to arrive at the factors that are important in driving customer satisfaction indices, the fact is that the overall satisfaction is merely always a weighted average of the top four or five questions about the product quality, experience, differentiation, recommendation or loyalty.

It is therefore best to be able to calculate the satisfaction index with the knowledge of the elements that go into it. In addition to that, a programs or algorithms that allow you to set the weights based on the needs of your specific industry are better.

The Mineful Customer Satisfaction Index
The Mineful Satisfaction Index is calculated in a simple manner. It takes in a total of at most 5 questions. As a user you can enter the weight that you want to attach to the specific question. For example, you may choose to have the following questions at an overall level for the customer satisfaction index and the corresponding weights.

Based on the customer responses that are received, each response is allotted a value of the rating scale minus one times the question weight.
The sum of this for all the question is then scaled to produce a 100-point index. See formula below and an example for one response.

The overall customer index is then calculated by adding the weighted average responses and then scaling it
to 100-point scale. The weights are automatically standardized to add up to 1. See final calculation below:
(100/7)((7-1)*.25 + (5-1)*.35 + (1-1)*.15 + (6-1)*.15) = 52.14

The Mineful software allows you to create this customer satisfaction index at the click of a button. It can allow you to trend the indices over time and plot them so that you can see how your company is doing over time. In addition to that you can also calculate the customer satisfaction index for different segments like males, younger customers and the like to understand the customers better.

If you know the specific factors that have gone into calculating the customer satisfaction index, you will know what you need to do to take corrective action. With the knowledge about the specific aspect that you need to work on, you can create retention strategies to ensure that you do not lose customers. For example, if the customer satisfaction index is low because the customers are seeing your product or service as a ‘me too’ then you need to ensure that you create some sort of differentiation (real or perceived) in the minds of the customers. Alternately, if there is low satisfaction with the product, you may have to go back on the bench and try and come up with some innovative variants with more resources allocated in research and development.

The customer acquisition cost should be calculated by adding up all your costs for sales, marketing and dividing it by the number of new customers that you have received in the specific period. The total cost should include heads like salaries, headcount related allocations and more. If you want to assess the specific customer acquisition cost of various kinds of marketing activities, you will need to look at the amount of money that you have spent on the activity and the man-time that has been spent on managing the activity to arrive at the cost of the acquisition.

This is an exercise that will help you decide the specific marketing activities that you want to undertake for customer acquisition as well. It is also important that you look at the Lifetime Value of the Customer (LTC) to understand whether you are spending more money to get the customer than what you can expect back from them in terms of profit.

When William Sealy Gosset published the t-test in 1908, he was thinking business (specifically Guiness’), but not necessarily marketing. More than 100 years later, this test has found its way into many fields including marketing. In this post you will find two examples of applications of the t-test in marketing. For more information about the t-test visit this wiki and for details on how to run a t-test using Mineful, go to our t-test analysis page.

Example 1: Pre-Post Marketing Campaign
You have started a new marketing campaign and are seeing the sales numbers come in. The numbers look slightly higher year-to-year and you want to know if they are indeed different or are they slightly higher by pure chance. Since you are taking measurements on the same “process”, but in two different occasions, you want to run a “Paired t-test”. The paired t-test eliminate the effects other confounding factors by matching observations of similar traits. For example, sales in October 2009 vs sales in October 2010. Or in observational studies, the blood pressure of a person before the pill paired with the blood pressure of that same person after the pill. Join that with many other people or months of sales data, and you have a paired sample.

Basically, the t-test will determine whether the average sales numbers are significantly different before and after the new marketing campaign. Go ahead, upload or connect your before and after campaign data and determine if something has changed significantly.

Example 2: Average Satisfaction Between Two Groups
You have survey data and used the Average by Category analysis to see the average overall satisfaction by gender, market, customer segment, or other group. The results show that in Chicago the average satisfaction is 5.6 and in Madison 6.1. But is the .5 difference significantly different? Should I take action and question my service people in Chicago because of this difference? A two-sample t-test allows you to test if this difference occurred by pure chance or if there is something else that’s driving customer satisfaction lower in Chicago.

There are many other uses of the t-test in marketing. Want to share one with the internet? Comment below and tell us how you used the t-test in your marketing research.

What is TURF Analysis?
TURF analysis, also known as Total Unduplicated Reach and Frequency is an analysis technique that is used to calculate the unduplicated reach of a product line or a range of products. It is a very common technique that is used in market research to assess the combination of products that will ensure maximum reach, frequency of purchase and therefore revenues, when launched.

TURF analysis was first used by media planners to estimate the unduplicated number that will be reached in a specific campaign when the campaign is run across various media vehicles like television, print and radio. This very technique was adapted for market research and is now used to estimate the flavor combinations, color options and variant baskets for products.

In most cases, TURF analysis is used to optimize product portfolios. It is known that launching multiple SKU’s (Stock keeping Units) is not the right strategy to use to expand reach. A large portfolio puts significant pressure on resources and makes the task of managing logistics, retailer stocking and production planning far more difficult.

In addition to that, the return on investment that you get with additional launches is never an additive. In most cases, new launches cannibalize some of the existing variants as loyal customers move to try out new variants.

Benefits of TURF Analysis
A TURF analysis helps in identifying the proportion of customers who are likely to try out the new variant. It helps in identifying maximum reach for the entire product line in a realistic manner since the technique exposes the audience to all the variants that are likely to be in the market. This is therefore a technique that can also minimize the number of SKU’s while ensuring that the revenue is not compromised.

By performing ‘what-if’ analysis based on various assumptions and scenarios, the incremental value that each new variant can add can also be calculated. The technique itself is a sequential one that assumes a certain product line and then adds on to the existing variants to ascertain the best option or combination to be launched.

Question Types
TURF analysis can be performed with various kinds of preference questions for specific variants. Some prefer to use a purchase intention scale (ranging from ‘would definitely buy’ to ‘would definitely not buy’). Others can also choose to ask product preference on a desirability scale and yet others opt for a multi-select choice question. All respondents are exposed to all the products.

An Example
For example if product A gets has a preference of 80 percent; product B 60 percent and product C 40 percent, it cannot be assumed that product A and B will form the best combination. This is mainly because it is possible that there is a large overlap between those who prefer product A and B; in which case a combination of product A and C would get the company better returns.

TURF Analysis with Mineful
The above example has been simplified and uses three product lines. But when you need to do this analysis for a variety of variants, it does become a complicated and messy task if it has to be done manually. Mineful now offers TURF analysis that you can perform with a few clicks. The entire exercise can be completed in a matter of minutes. Learn how to run TURF analysis with Mineful!

Many basic concepts of mathematics are used in everyday life. But you will also find that many Elementary and High School math concepts come in handy if you have chosen a career in marketing and market research. To give you a sneak peek into some of the concepts that are learned in school and applied in marketing later, consider some that we have put together. These are beyond the basic concepts like addition, subtraction, multiplication and division, the application of which is fairly obvious.

1. Linear equations – While algebra may not entice you a lot with all the ‘x’ and ‘y’ variables, it is used to a large extent in everyday marketing. Whether it is plotting the sales of specific products and brands over a period of time or calculating the ROI of a campaign, if you have learned to solve linear equations in school and internalized them, you will not have to grapple with these calculations. While you may not use quadratic equations to a large extent, you will find that some complex situations may lead you into the use of simultaneous equations as well.
2. Percentage change – The most basic of all concepts and yet you will find that there are people who end up calculating the percentage change based on the wrong figure. Those who understand how to use the delta over the previous number and to convert it into a percentage do not have to struggle with calculating the increase in sales, revenues for a ‘year on year’ or a ‘percentage increase over last month analysis’. Calculating the CAGR (Compounded Average Growth Rate) also requires an understanding of percentage change in addition to interest calculations that are taught in high school.
3. Scales – For those in market research, a revision of the scale concept comes in handy when they have to devise and create scales. Only when you know about the nominal, ordinal, interval and ratio scales can you decide the right kind of analysis that can be performed on the data. For example, perceptual mapping requires that the data be captured in a nominal ‘yes/no’ or association data format. On the other hand, if the market researcher wants to use factor or cluster analysis, the data should have been gathered using an interval scale.
4. Statistics – One of the most used areas of mathematics in marketing and market research, concepts like statistical testing, hypothesis, probability and correlation and regression are used very often in analyzing data. T-tests and probability is used in assessing whether the response of one group is actually different from another group. Regression is one concept that is often used to understand the cause and effect relationship among various independent and dependent variables, like assessing the effect of salary, office environment, a good appraisal system, a friendly boss and other such attributes on overall commitment of employees.
5. Indices – Used less often but often in the development of models, indices and exponentials are often used to understand the growth patterns that are being observed in various parameters in marketing. This could apply to revenue, advertising spend or other data collected through primary research as well.

So before you start thinking about the futility of what you studied in school or have tots who question the relevance of the syllabus, remember these concepts and appreciate that you are using what you have learned.

To keep of the changes in marketing analytics, one needs to spare some time to self developmental activities so that the latest trends can be used for clients. Here is a list of 10 marketing analytics and market research blogs that you may want to subscribe to or at least visit regularly to keep up with the latest news in the industry. Each of these has something unique to offer and therefore the list should not be considered as a ranking.

1. Vault Analytics is a company that is dedicated to one of the most difficult aspects of marketing analytics, namely predictive analytics. The team of professionals offers a range of services that include creating an algorithm a specific marketing problem, contracting analysts and training employees to be able to use predictive analysis. Given that the company is involved in training people in the marketing analytics arena, their blog has extremely valuable tips and latest news about marketing analytics.

2. The Forrester Blog for Market Research Professionals covers a large variety of topics. Published by a company that is known for its expertise in market research and richness in secondary research, this blog can provide you valuable data on various niches, tickle your mind with new and emerging thoughts on models and helps you with various market research methodologies and industry trends too.

3. Research Live not only provides you with the latest trends in data gathering and analytics, it also keeps you abreast with the movements of research professionals. The blog is nicely divided into sections like the latest polls, young ones, media, research technology, social media and various cases of specific surveys that have been conducted on respondents with regards to market research itself.

4. For a complete rundown on almost anything that you may want to know about analytics starting from statistics and software to methodology and analysis, the Market Research World helps you get all the details that you may have otherwise missed out in your busy schedule.

5. The Google Analytics Blog is a great place to understand web analytics and the manner in which you can use the valuable data that you gather to understand your customer, segment the funnel and understand bounce rates, keywords and other interesting analytics information for the ecommerce guy or the Internet marketer.