Maximizing Value from New Customer Acquisition

One of the easiest ways of acquiring customers is through a campaign on Google, Facebook or any other platform. We all know the importance of ensuring that targeting is right, ad copy appeals to the target audience, landing page must engage with the visitor and enable conversions to registered members. Clearly, all three variables, targeting criteria, ad copy and landing page, are of utmost importance when acquiring customers through Google / Facebook.


However, all acquired customers are not equal. For a local business, a customer from another city has less than "10% value" as compared to the customer from a nearby locality. A customer who is interested in only deals for smaller value products is less "valuable" as compared to a high margin high value product in an ecommerce setting. Since marketing campaigns require tweaking on an ongoing basis, it is important to use the feedback on “value of the customer” as soon as possible.

Campaigns can be optimized for

1. Maximum response (goal is to maximize number of customers acquired),

2. Minimize cost per acquisition provided a minimum number is acquired, OR

3. Minimize cost per unit value of customer acquisition provided a minimum number of customers are acquired.

Today, there are reliable techniques to predict the “value of the customer” as long as rich profile information is getting captured as part of the customer acquisition process. If the value of the customer can be expressed as a function of these variables, customer value can be fed back for campaign optimization. For example, if we know that B.Tech Computer Science Graduates from Bangalore are more in demand as compared to BSc Physics from Bhopal, that data can be used to run Geography focused campaign on Google/Facebook. Keywords can be characterized in terms of demographic profiles of customers they help acquire.

How are you maximizing value from your customer acquisitions? Do share in your thoughts.

Analytics -Making the Transition From “I Feel” to “I Know”

A young, bright, energetic MBA graduate from a top college joined the “Most Innovative Company Pvt Ltd”. He was full of ideas and keen to change the world with his overflowing cup of knowledge. Within one month of joining his company, he presented an interesting idea to his manager. His manager asked him to learn more details about the company, the business context and prepare a proposal. However, by next week, manager was running after quarterly targets and he asked the new joinee to focus on the targets, rather than the new idea he was pondering over.

Three months in the company, manager asked the trainee to focus on another idea, manager’s pet idea, which he has been pushing for a few years and was not able to move forward for lack of time. The trainee was assigned the task of preparing a presentation, putting the stuff together and do the background work. He got to spend a lot of time of his manager and three months down the line, the proposal was ready.

Since Business Head was visiting for annual review, his manager presented the idea to the Business Head. Business Head liked the presentation but did not feel that idea was significant. He asked the manager (and in turn, the trainee) to work on another project which was Business Head’s pet project. Another six months down the line and much effort spent, even that idea was rejected because CEO did not feel that the idea will succeed.

Does the story sound familiar? A lot of ideas get rejected because the idea champions fail to convince the decision maker. If a decision maker FEELS that idea will not work, that is the final outcome. If you look at the whole story, it was all about different people making decisions on what they FELT will work or not. In the end, idea champions failed to present data and customer insight to support their idea. Projects were conceived because managers FELT that they will work, they did not KNOW that they will work.

That’s where analytics is important, in removing the guesswork from decision making. It is important to use customer insights from data and if data is missing, collect it before making the pitch. Make the decision maker believe that you KNOW that the project will work.

Email Marketing – Keeping Customer's Profile Current

Email marketing has multiple objectives like every other business initiative. For example, email campaign may focus on encouraging customers to make transactions that are profitable and also, ensuring that the same set of customers come back again for their next shopping trip. However, today’s profit cannot be the only objective, ensuring customers return in the future is probably equally important.


What is the trade-off between the two? In my last post, I emphasized on how customer segmentation improves RoI of an email marketing campaign or engagement strategy. If a particular email is only focused on current set of deals on offer without ensuring that customer keeps the profile information current (email address, mobile number, location, preferences, like or dislikes), targeting of offers will sooner or later, start missing the direction.

As a marketer, you will miss the current likes and the current interests that have changed since the customer first shared them with you. With limited and probably incorrect information, message will lose relevance. Customer may start marking your emails as spam or simply delete them or unsubscribe.

What percentage of your customers come back through email? Is it less than 2%, or as high as 50%? Do customers lose interest over time and if yes, what is the rate of decay in the level of interest? It depends entirely on your product category.  Travel gets maximum response in and around holiday season or long weekends. Ecommerce is driven by seasons and in category like jobs, interest level dips over time with annual revival during appraisal time.  If a significant percentage of customers come back, can you slowdown the process of customers losing interest? Or by making them update their profiles, bring them back to the active pool.

Should an email marketing campaign necessarily contain information that urges the customer to update the preferences and the interests that you have. Probably yes…

Or can you do one better? Include this only for customers who have not updated their profile information for some time. For customers who have recently updated the profile information, they continue to get mailers focused on maximizing transactions.

Big Data and Analytics

Data Analytics is as old as the data itself. Extracting information from data is as ancient as one can think of. However, there is a major hype around Big Data, Web Analytics in the recent past. What has changed–

(a)   Most businesses across the globe have implemented some form of ERP and transaction automation solutions. Customer Relationship Management (CRM) solutions have been in existence for more than a decade and a large number of organizations have crossed the bridge in terms of integrating different systems across the organization. It is now practically feasible to understand a customer in terms of her journey across the organization from a lead, to sales pitch, to customer, service/product delivery and fulfillment.

(b)   Transactional data is large in volume, often captures the demographic information and behavioral information in terms of likes (products purchased represent a Like behavior). Large volume of data naturally can be used to identify patterns and patterns can be used to characterize customers. For example, a customer purchasing lot of sugar free items is possibly health conscious or may be diabetic. Customer buying lot of sports goods is obviously interested in sports.

(c)    Analyzing Retail transaction data and interpreting basis the purchase history was subject of intense analysis in Research in the Nineties. Since then Ecommerce has provided additional input from data analysis perspective. Ecommerce companies can track the browsing behavior of customers on their site. Which products they looked at, compared and what did they finally purchase.  Now this is enough to characterize customers as price conscious, style conscious etc. While transactional data can be used to derive similar inferences, click data is more definitive as it captures the customer’s exploration of products prior to making a decision. In the end, objective of all analysis to drive business decisions which can help the customer as well as the business.

(d)   It’s been more than a decade of big data and analytics in the Western world and Indian companies are now catching up. ERP which was big in early part of the last decade is now commonplace. Consultants are now keen on selling Analytical Tools and making a pitch of analytics. In the end, transactional automation solutions were also sold with the promise of data analytics. Customers are also demanding results from the same vendors.

The hype around big data and analytics is probably here to stay, unlikely to go away in a hurry. If a business analyzes several projects it undertakes, it does not take too long to realize than RoI from analytics projects is probably the highest.  As one of my friends once said “If you did not find the patterns in data that you were looking for, you did not look hard enough”. Data always throws patterns, patterns drive understanding of customer behavior, and hence, better decision making.

Customer Segmentation in Email Marketing

Email marketing is as old as the internet. It is probably the lowest cost customer acquisition and engagement tool.  Variables of interest for an email marketing campaign

1.       #emails opened – No tool today provides that with certainty. Most vendors provide a pixel open method of identifying how many emails were opened and how many were not. #Emails opened is equivalent of #Impressions in marketing parlance.

2.       #clicks – This is most obvious and probably the most important data that measures the success of any campaign.

3.       Actions on the landing page – Every email marketing campaign needs to have a landing page which will trigger a further sequence of actions to deliver the business benefits to the marketer. If you are running a Facebook Like campaign, the #likes you get is the goal. For an ecommerce company or a deals site sending out deals, Add to Shopping Cart is the goal of the landing page.

While the above metrics are most commonly used to track performance of a campaign, a smart marketer only begins with these. She can go to the next level …..

1.       Identify the customer segments you sending mails to.

2.       Repurpose the content before the mail is sent to meet the aspirations of the segment audience.  For example, subject lines for new buyers can be customized basis what they have recently bought and offers can include related products. That is, a customer who recently bought a Tablet from your electronics store is more likely to open an email with subject “Buy a matching leather case for your tablet”.

3.       As you repurpose the content of the mail, you can also provide custom landing pages for each customer segment. An existing customer will land on a page which is customized with previous profile information. If we know that she also owns a phone of the same brand, cross selling another product that goes along with that, might be a good idea.

4.       Tracking the response by customer segment, number of actions items in the email and landing page gives immense insight in customer behavior. Learn and improve the email content, landing page further…. It’s a virtuous cycle.

RoI from customer segmentation in email marketing can easily exceed 100% depending on richness of data you have on the customer. Better targeting is not only good for the marketer, but also for the consumer. It is a win-win situation.