Over the years, from the onset of loyalty programs, marketers have relied on the RFM (Recency, Frequency, Monetary) model. With customer retention the outcome of the programs, this model possibly sufficed. Enabling marketers to analyse and understand their customer behaviour over a period of time. This model also helped slot customers to cluster or segments or groups – defined based on their buying behaviour. 

The engagement was done via physical mail and with the advent of e-mail and SMS – brands shifted to these unidirectional channels to reach out to customers. The content of the communication would have been limited to a favourite brand, offers, coupon, product catalogue, event etc…

Personalisation meant, knowing which category bought into, the brand purchased, product, colour and when bought. And the analysis also helped brands understand the CLV, predictability and empowered the front line with a list of the store’s top customers. 

Customer analytics in times of customer centricity

The time also saw the rise of search, internet and social media usage. With people flocking to these mediums, meant brands had no choice but to be present there. Customers now had access to more options and convenience of shopping online. These channels also provided customers with the possibility of communication with the brand, in real-time. This data, a combination of demographic, psychographic and social behaviour, was now available for marketers. Combining this with shopping data could create a treasure chest of information about a customer. 

With the shift from product to customer-centricity, marketers need to look beyond the traditional RFM models. There is a need to create one consolidated repository of this enriched customer data. The customer analytics process using state of the art AI and ML tools will help brands gather sharper insights into the customer and enabling the creation of personas. That would help a marketer develop better targeted and personalised campaigns, thereby treating a customer as an individual.

Multichannel marketing in times of personalisation

To deliver personalisation the engagement now needs to move beyond just e-mail and SMS. With rich insights, marketers now have the power to connect with the customer on a medium of his or her choice and at a time the customer desires. Giving him information that is relevant, timely and where the customer is present. Thereby pushing marketers to consider a multi-channel engagement strategy. 

The advantage of a multi-engagement strategy are plenty: 

  • Be where your customers are, as your customers can be anywhere 
  • Channels could be a combination of indirect and direct communication channels – websites, retail stores, direct mail, email, SMS, mobile notifications, Facebook, Instagram,   etc
  • Customers will receive one unified communication from a brand at a day and time they prefer
  • Customers targeted through multi-channel communication spend 3 times than a regular customer

For eg., if the task at hand is to increase customer’s BPO and the offer is about increasing ticket size then the customer will see the same message across SMS, Facebook / Instagram, e-mail. Campaign efficacy can also be improved by studying which medium evokes the best response. It is therefore critical for a  multichannel engagement strategy to drive personalization.  

What will help to do a multichannel engagement strategy right?

  • Create and maintain a single view of the customer across all channels
  • Campaign management – build capabilities for segmentation, workflow creation, execution
  • Establish a multichannel marketing platform
  • Advanced analytics that also includes predictive analytics, campaign optimization
  • Response attribution and the collaboration of digital technologies
  • Create consistent customer experiences across all channels

The XENO way to multi-channel engagement and personalisation

Xeno, the marketing automation tool is helping more than 200 brands run multichannel personalized marketing campaigns. With the power of Artificial Intelligence and Machine Learning, Xeno helps achieve an increase in campaign conversion by over 150% as compared to the current way.

This has helped increase the productivity of the marketing teams by over 200%, so if a team of 2-3 people was earlier sending 4-5 campaigns a month Xeno will enable the same team in the same time to create 4-5 million campaigns and that too personalized to each customer. 

For more on how XENO can help you improve its loyalty program metrics, request a demo and our experts would love to take things forward.

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