Conquer the online personalization marathon

Have you ever run a marathon? Imagine the consequences of a beginner brave enough to run 26 miles run in one go with no experience of prior miles clocked. The marathon would have been reduced to a short sprint and ended up in a loud, disappointing thud! If one is serious and wants to be successful, then start with the walk, take the easy run, rest, do some stretching and after some days at it, go for the mile, repeat, perfect and go for the half-marathon, repeat, perfect and then go for the full-marathon. Finally, after at least 15-20 weeks of rigorous practice, take a stab at the full 26+ miles and complete the home run!

Marketers preparing for that perfect online channel personalization would be better off approaching it as preparing for a marathon.

Why personalization?

In a research published by Econsultancy in association with Monetate, 94% of companies confirm that personalization is the key for their current and future success. In addition those who were able to personalize and attribute them have reported 19% uplift in sales. However, it helps to build the customers trust so as to lend credibility to personalization otherwise it will be one of those things that would put off the customer.

Challenges in personalization

Planning for the online personalization is akin to preparing for a marathon and presents challenges to even the smartest of marketers. The numero uno challenge is the availability of single customer view considering there are disparate data sources - Website, marketing, POS, Mobile, Social, other CRM sources, second party data, third party data - the list goes on and on. A whitepaper on Maximizing personalization done by Experian data quality team on 250 US respondents has thrown up the following top 3 challenges:

  1. Gaining insight fast enough (40%)
  2. Having enough data (39%)
  3. Having accurate data (38%)

Is there a way we can solve this by approaching it "Quick and Small" akin to our walk and easy run? Here's a 3-step process to kick things off:

1. Consolidate first party data

Start integrating with a Real-time data aggregation, customer segmentation and analytics platform towards building your single customer view from your online ecommerce and marketing automation platform. By this, the web engagement, purchasing and marketing engagement behavior from the online and marketing channels would be kick started. Chances are that if you are running a Magento Store or even one of the cloud-based SAAS ecommmerce platforms like Shopify, BigCommerce etc. you have ready plug and play data aggregation modules (magento) or apps (SAAS platforms) with analytics capabilities.

2. Build Customer Segments

Create the customer segments in the analytics platform deriving it from the first party data at hand from step 1. Stretch it a bit higher to look at predictive analytics capabilities of the platform to look at cross-sell for specific customer segments that you may deem fit. In some cases, the app itself may intelligently suggest the customer segments as well as to what potentially could be a candidate for a cross-sell to them.

3. Feed it off to Online Store

Feed the segments from the analytics platform to your Ecommerce storefront to create these customer segments and use them at real-time when a customer visits your online store. Many ecommerce platforms as mentioned above come with this feature out of the box. Voila! You've started out on the journey of online personalization. Let's look at a couple of ways on how one could use this first party data intelligence.

How to implement?

Imagine if one of your most loyal customers is landing up after a few months, a live chat window could be opened up to give personal attention. In another case, cross sell could be done based on his previous purchases and engagement he has been showing on some of the products that he has been browsing all with your own data at your disposal. Bravo! First mile is complete, time to go clocking for more miles with handling the disparate data sources - mobile, offline, social before looking at second party and third party data.

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