How is Analytics Changing the World of Marketing?

Analytics is changing the landscape of just about everything and changing it for the better. Various companies all around the world are investing in Big Data and analytics to identify new opportunities. Essentially, this also means that, if the insights from the analytics are used well, we could have many more engaged customers. Something I learned as we adopted it for Kaya.

India Inc. has joined the bandwagon where organizations across the spectrum are trying to understand and leverage data and come up with new ideas and insights for marketing success. And it’s a trend that is here to stay.

The Rise of Analytics

Have you noticed how, when you are browsing on Google, you see products related to what you had searched for earlier? Well, analytics has a very important role to play in that, which is known as remarketing in marketing parlance. You can use analytics in many other ways to improve customer engagement and enhance their experience as well. In the end, it’s all about retaining your customers and that is only possible if they are engaged.

And that’s where enriching the traditional customer relationship management with analytics becomes very important.

Role of Analytics in CRM

In the realm of customer relationship management (CRM), analytics can do wonders for an improved customer experience. Analytics comes with data mining abilities and helps in isolating patterns, finding correlations, tracking trends among your customers, offering relevant information, and a lot more to help you improve customer engagement—modern day prerequisites for building an engaging brand.

More and more companies are integrating analytics into their CRM programs and tech stacks for better customer understanding and predictive modelling, which of course is a great thing. All these will help in understanding your customers better.

However, when it comes to customer retention, merely engaging in CRM isn’t enough. With the changing landscape, what is required is customer lifecycle management (CLM). It is a much more powerful approach to delivering on Customer-Lifetime-Value.

Kaya Clinic’s CLM journey started in the year 2016, after which it took several quarters for it to stabilize. Consequently, Kaya won the ‘Best Use of Analytics’ at the Customer Loyalty Summit earlier in 2018 among others, and it has only happened as we were able to conceptualize a program that is actually giving great results.

CRM can help companies segment customers according to their behaviour by analysing all touch points (contact centre, social media, app, or email) and this will also allow brands to identify their best customers and offer them special experiences. CLM can enable this and much more.

How are CRM and CLM different?

This is a common question that many people ask me. If you think CRM and CLM are the same, you aren’t completely wrong. CLM, at its heart, is an evolved CRM approach. It involves analysing your customer’s lifecycle with your brand and this involves high levels of analytics.

At Kaya Clinic, we define CLM as an omnichannel, systematic, and enterprise-wide customer engagement solution that helps businesses achieve their goals through the following:

?              Personalized marketing at the customer level

?              Events derived by making use of customer insights

?              Self-learning capability where we take personal customer insights to offer them just what they are looking for

?              Preference-based planning that helps us satisfy customer-specified preferences as much as possible

Unlike in traditional CRM approach, where your outreach depends on brand events based on campaigns and marketing calendars, in CLM, the outreach depends on contact optimization. It includes customer life-stage thinking that involves marrying the identified needs of the customers with business requirements to make the communication more relevant. All the communication to the customers is integrated, through only one umbrella, as opposed to different communication sources which are not in tandem.

Design Components of CLM – Kaya Clinic

The CLM design components at Kaya include:

·      Track and Sub-tracks: Customers enter the Kaya ecosystem via touch points like contact centres, stores, online form-filling etc. If a customer has already interacted with Kaya, through any of our 100 clinics across India, in the past, there’s a predefined track for her/him within the ecosystem. A single customer can be a part of multiple tracks at one point of time. Sub-tracks are subsets or sub-paths within a track through which customers move in and across.

·      Events and Triggers: Customers become a part of one or more track and this solely depends on their behaviour. These tracks have defined customer touch points at defined intervals in the form of communications. When one customer crosses one touch point, a communication from our end is triggered keeping the customer behaviour in mind. This pushes the customer towards the optimal path as identified by CLM. Event-based triggers ensure a more relevant communication and better results.

·      Modules: The lifecycle journey of each customer is divided into modules. The module/s a customer is in depends on the lifecycle stage and the customer’s interaction with Kaya. The entry and exit of a customer into a module depend on a set of rules. The communications made to a customer is tagged to the module she/ he is a part of.

·      Analytical Models for Better Targeting: Logistic regression, user-based collaborative filtering, and other statistical models are used to predict the next-most-likely behaviour of the customers.

·      Personalization: Now that we have a fair idea about a customer’s need, we make the communications personalized thereby making it more relevant and useful. The personalization depends on the data that is available like customer name, skin or hair type, specific needs, last purchase made, products searched for, etc.

At Kaya, we understand that the need and concerns of customers vary and so should their treatments. By taking the help of various statistical techniques, we try and predict the future moves of customers. Relevant communication is made to them and this nudges them towards a path of purchase that has been predicted.

The benefits of such an approach are plenty:

1.             Customers get what they’re looking for based on their concerns

2.             Personalization makes the customer feel special and helps in retention

3.             Better revenues for the company

Kaya Clinic started seeing the results in a few months itself. The response rate increased 8 times while the visit frequency increased by 10%.

Personalization can go a long way in retaining customers if done in the right way. And to do it in the right way, you’ll have to combine the capability of statistical tools and techniques with customer and business understanding. All the best!