Ecommerce Personalisation Roadmap

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This is a presentation I gave at an Infectious Media breakfast briefing on the challenges and opportunities for ecommerce personalisation.

The content is related to roundtable sessions at Econsultancy's Digital Cream which discussed the challenges businesses faces when planning and implementing personalisation projects. The key learning is that many businesses are paralysed by perceived complexity, unable to prioritise work or generate a clear ROI model.

The key learning in this presentation is that by starting small and using existing data you can make quick wins with ecommerce personalisation and then create a roadmap to plan for the long-term and increase the sophistication.

Thanks
James

Published in: Marketing, Technology, Business
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Ecommerce Personalisation Roadmap

  1. 1. Knowing Me, Knowing You Creating a simple roadmap for ecommerce personalisation
  2. 2. What’s holding us back? Lack of ownership Rare to find a centralised personalisation structure with spokes across the teams it touches. Data blindness So much data, so little time. Data is often in silos and disconnected e.g. website vs. stores. No clear ROI model It’s shiny and new and people struggle to know how to model the financial benefits, so investment goes to ‘known’ channels.
  3. 3. Creating a roadmap ✗ ✓
  4. 4. Start simple – web analytics data What do we know? What can we do with it? New vs. return customer. Tailor brand value messages. Geography. Tailor USP bar to be country specific. Gender / Age. Showcase relevant products. Traffic source. Replicate campaign creative / tailor landing pages. Device. Promote relevant mobile content.
  5. 5. Personalisation starts with the basic things
  6. 6. A helping hand…
  7. 7. Progression: data enhancement  Integrate VoC data points - match customer feedback with online behaviour > most common is survey.    Simple – create segments and compare behaviour. Advanced – statistical regression (e.g. cluster analysis) to increase sophistication of segments*. Define buying cycles at category/product level – retargeting tailored to individual buying journey.   Promote helpful content to customers when they’re indicating interest in a product/service e.g. customer views sofas on 2 visits in X days but doesn’t buy – email sofas buying guide. When they come back, use content zone to surface this guide with strong CTA.  eCRM to build the customer profile.  Customer browsing/order data influencing on-site marketing – don’t blanket bomb real-estate messages. * Interesting IBM research paper: How to get more value from your survey data
  8. 8. Power of surveys + analytics The ultimate aim is to identify customer segments for targeting
  9. 9. Encourage customers to share data
  10. 10. Use what you already know: site search I previously bought 3x polo shirts online for delivery to the same store (Oxford Street): • Jack & Jones • Criminal • Duck and Cover What else could be done with my data?
  11. 11. Are retailers missing a trick? Order by 7pm tonight and collect from our Oxford Street store tomorrow after 12pm If the customers uses click & collect, promote store delivery New from your favourite brands: Use purchase history to surface relevant brands/products
  12. 12. The end game: tying up loose ends Wow, you remembered me!
  13. 13. And keep your house tidy  Basket.  Wishlist.  Giftlist.  Recently viewed.  My favourites.  My sizes.  My brands. to name but a few….
  14. 14. Multi-channel: why not this? Push notification within store geo-fence: “Hi James, welcome back. New Ted Baker range now available with £10 off when you spend over £100. Enjoy!” Engagement from CSA based on recent history: “Hi James, I see you recently went to the Paul Smith clinic at Oxford Street. Did you have a good time? Would you like us to alert you of any future events in that store?” Engagement from Personal Shopper” “Hi James. I know you usually buy from brands like Paul Smith and Ted Baker but can I suggest something a bit different this time, we’ve got an amazing new range from Ralph Lauren.”
  15. 15. Case study: member organisation  The challenge:  Online and offline data not connected for the utopian ‘single customer view’.  The brief:  Identify a low cost way of increasing the connectivity to enable cross channel data to be used to improve targeted marketing.  The approach:  Focus on 1 offline activity to connect dots with online behaviour – events.  Tag all browsing activity to member ID captured via analytics.  Use analytics browsing data to identify user journeys relevant to events.  Export data and run queries to identify event prospects based on closely defined criteria:  Purchase activity.  On-site content activity.  In-app content activity.  Match against demographics of core event audience.  Targeted email campaign with personalised message in ‘My Account’.
  16. 16. The results  Increase in open rate of event email campaigns.  Increased response rate from members.  Increased number of attendees. Long term goal:  Increase overall member satisfaction for this segment – measured via biannual satisfaction survey.
  17. 17. Thank you! @jamesgurd

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