E-commerce: Analytics' delight

Electronic commerce or e-commerce is an evolved-through-technology specimen of commerce, and is a widely welcomed and embraced way of doing commerce. And as competition is growing, a need to differentiate from competitors is growing too. In e-commerce data is a key strategic business asset. E-commerce website owners cannot see the buyers or visitors, so, their best actions (best CRM moves) have to be driven by the visitors’ foot-prints click-prints. Each click on website gives birth to data. Ability to manage this data as a strategic asset, and use it as a strategic differentiator is a key contributor to the success of an e-commerce operation. At the cost of stating the obvious, let’s compare a customer’s e-commerce shopping with commerce shopping.

E-commerce: Website visitor logs in to the e-commerce website

Commerce: Potential customer walks in to the retail store (representative commerce platform)

E-commerce: Or, Website visitor visits the site, but logs out within seconds, maybe just after seeing the home page

Commerce: Potential customer enters the retail store but comes out within minutes

E-commerce: Within the e-commerce website, he/she visits one of its page (or browse from one page to another)

Commerce: Potential customer visits section(s) of the retail store

E-commerce: Within a page, Website visitor moving to another page as a result of clicking on to the product-icon

Commerce: Potential customer seeing or checking some product(s) in the retail store

E-commerce: Website visitor, after spending some time on web-page explaining the product, doesn’t select it for e-cart

Commerce: Potential customer checks some product, say XYZ, but does not put it in the cart/basket

E-commerce: Website visitor selects the product and adds it into the e-cart

Commerce: Potential customer selects the product and put it into the cart/basket

E-commerce: Website visitor checks out of the e-cart, and continue for making the payment

Commerce: Potential customer selects his bundle of product(s) and move towards the payment counter

E-commerce: Website visitor selecting the payment option and begin filling his/her details for making the payment

Commerce: Potential customer communicates his/her mode of payment at the payment counter, and…..

E-commerce: Website visitor finally completes the details and makes the payment - converts into buyer.

Commerce: Potential customer makes payment – converts into buyer or customer

E-commerce: Or, Website visitor begin filling payment related details, but logs out without finishing the payment

Commerce: Potential customer finally, at the nth minute decide not to buy anything, leaving back the selected cart

In e-commerce, scope of conversion of action/behavior of a customer (Website visitor) into data is a lot more than in commerce. E-commerce’s edge lies in capturing “what happened in between?” and “what happened even when customer did not finally buy anything?” Or, to put it in some detail - in e-commerce, entire journey of a Website visitor from logging in to the website to his/her final purchase gets recorded in form of data – Yes, this aspect of covering the web pages customer visited, the products he/she checked before finalizing his/her cart covers “what happened in between?” part which is not usually captured when a customer is doing similar things physically inside the retail store (example of commerce). Similarly, in e-commerce, entire journey of a Website visitor from opening the website to his/her final decision of not to buy anything gets recorded. Or, we get data on a customer’s doing window-shopping e-window-shopping.

“With great power comes great responsibility” – a famous dialogue from a famous movie Spiderman. A lot of information getting captured in data puts high responsibility on analytics. In e-commerce analytics sits in the heart of decision-making.

Churn management

Cell/Mobile number becomes a part of a person’s identity. When concept of number portability was not there, it caused some degree of loyalty to the telecom operator even if customer was not fully happy with the services or pricing as changing telephone operator meant changing the number which can be a strenuous task, as it comes with an additional responsibility of informing the acquaintances about the new number. After number portability, this element of ‘forced loyalty’ got diluted significantly. If unhappy with the telecom operator, the user can change the telephone operator without changing the number.

As per telecom literature, cost of acquiring a new customer is at least 5 times of cost of retaining an existing customer. With penetration reaching saturation in many markets, telecom operators are eyeing the same pie, making acquisition all the more difficult, thereby increasing the focus on retention programs. Retention programs are the actions to retain the existing customers. However, retention/CRM managers find that they get less than acceptable returns on their retention programs. This is mostly because retention programs are not targeted sharply enough. A large proportion of customers targeted with retention programs are often customers that would not have churned in the first place.

To bring sharpness in targeting of customers, for optimal utilization of marketing budget, predictive analytics is deployed. Analytics at work for churn management, in form of Regression models/Decision trees are build with an aim of knowing in advance “Who will churn?”, for optimal targeting, i.e. allocating resources on the basis of probability of attrition (maybe along with profitability of the customer or other factors). Basic data requirements of churn analysis are: 

• Demographic data from customer information file like age, sex, zip code etc
• Contractual data from service account file such as pricing plan, activation data, contract identification etc.
• Usage & Payment data from billing system such as number of calls, airtime, fixed line time, total amount spent, no. of times calls made to customer care center, change in price plan etc

However, in the process of building single customer view, handling many variables sometimes causes dilution in focus on price elasticity aspect of telecom services. Looking at different price-quantity coordinates and drawing insights from the change in trends/patterns is an important exercise which often gets overlooked. Also, time to expiry of contract is an important variable diligently tracked by the telecom operators. Out of leaving customers, majority leave after their contract expires. Quite a few times because of this reasoning Survival analysis, an analysis which tries to answer both questions “Who will churn?” and “When will he/she churn?”, gets ignored.

Importantly, none of these methods answer the question "Why does a customer leave?". Answer (to a limited extent) to this question calls for a serious inclusion of telecom CRM analytics into telecom CRM strategies, rather than seeing it like any other number crunching exercise.