REAN model to achieve higher conversions through hyper personalisation and recommendations

BangDB implements REAN Model to enable conversion through personalization. It ingests and processes various aspects of users or customers and their other activities to optimize the conversion rate. With the REAN model, e-commerce companies can ingest and analyze the clickstream and all pixel data in real-time to understand the customer and visitors in much more advanced ways. This enables the organizations to personalize the content and recommend the products and solutions in a 1-on-1 manner which leads to much higher conversions and revenue

Introduction

The goal of the document is to lay out the basic building unit indices and KPIs using which we can target customers a lot better. But this is not the end, in fact, it just begins the journey to a “1-on-1 personalization and recommendation” for the organization where the underlying goal is to provide a much-improved customer experience and offer higher values. Once we get what’s there in this document, then we need to use Stream Processing and Even ingestions for ETL, data enrichment, and CEP (complex event processing). Further, we will need to put the Graph structure in place to operate in a highly “context-aware-environment” for personalization and recommendation. Let’s first look into the basic part of the bigger recommendation system here, and then in the next blog we will go into stream processing and Graph

REAN Model is defined as follows.

• • Develop Reach
  • Engage

• Activate and
• Nurture model

REAN Model Does Two Things Very Well

• Firstly, it gives you a very clear indication of the measurement challenges you might have when breaking your strategy down into its component parts.

• Secondly, it can be used to help you define a measurement strategy. You could develop KPIs around each activity (Reach, Engage, Activate and Nurture) and then combine the metrics as matrices of each other.

From a high level, this is how the REAN model is different components are defined.

REACH

• Traffic sources
  • Search Engines
  • Ads, Campaign
  • Email, Newsletter
  • Internal links
  • Partner sites
  • YouTube, Video, Video banners
  • Blog
  • PR
• SEM
• SEO
• Brand Awareness – traffic coming from logo, company names, product names,
• Seeding – from opinion leaders, reviews, articles

ENGAGE

• Shopping carts
• Self-service process – ex; SaaS product sign up etc
• Any creatives
• User segmentation based on behavior
  • Session length and depth      
  • Separate users based on their likes and dislikes [ based on session length and depth]
• Click depth – average click depth and corresponding user’s segment
• Duration – length of time spent on a website
• Offline engagements – relevant for offline stores, etc.

ACTIVATION

• Also, can be interpreted as a conversion
• Purchases
• Downloads of software or documents
• Activation is typically what reach, engagement, and nurture KPIs are measured against.
  • B2B, B2C, Media, Customer service, Branding

NURTURE

• CRM
• Follow-ups – emails, community
• Most importantly
  • Personalization & customization
  • When users, customers are back, how we interact with them
  • Cookie, user management, etc
  • Recommendations
• Recency – Measure of time elapsed between your visitor’s last visit and his/her current one

INDEXES

• Click depth index [# of the session with more than n page views / #total sessions]
• Recency index [ # of the session with more than n page views in last m weeks / #total sessions]
• Duration index [ #num of sessions more than n min / #total sessions]
• Brand index [ # of sessions originated directly (no ref URL) / total sessions]
• Feedback index
• Interaction index [ # of sessions where visitor completed any or tracked activity / #total sessions]
• Loyalty index = ∑(Ci+Ri+Di+Bi+Fi+Ii) per visitors and select top k
• Subscription index [ #num of visitors with content subscribers / #total visitors]
• Content page view index [ count of content page views/total page views]
• Internal banner index [ banner clicks / total clicks]
• Content consumption index [ #page views per content/ #page views]
• System perf idx [ #views from per system / #page views]

KPI

There are the following types of Web Analytics Metrics

• Count
• Ratios
• KPIs – either count or ratio
• Dimension – segments
• Aggregates
• Etc.

Business questions that we need to answer through KPIs

1. What is the best source of traffic in terms of volumes and sales?
2. Where are the visitors coming from? [ top k places]
3. Which channel is most productive? [ top k channels]
4. Which channels are overlapping?
5. Which landing page converts best [ top k landing pages]
6. Do registered users buy more?
7. Most searched pages [ top pages]
8. How many downloads?
9. What’s the value of download for different items [ top downloads by value]?
10. Avg response time for lead response?
11. Internal search stats
12. How engaged are our visitors?
13. What are the Top paths to our website?
14. How are visitors finding the site?
15. What is the cost per conversion (per campaign?)
16. Users by location
17. How many people don’t get through a shopping cart?
18. What are the search keywords?

Page bounce rate – left from a landing page

• Hourly
• 15% deviation – Alert

Page time index – time spent on the page / total time spent on the site

• Hourly
• 15% deviation – Alert

Segmentations

• By paid traffic [ Reach] – campaign, ads, banners, etc.
• Unpaid traffic [ Engage]
• By location [ Engage]
• By search phrase or keyword [ Engage]
• By site pages or behaviors [ Engage]
• By system vars [ device, browser, etc.] [ Engage ]
• By conversion
• By loyalty – repeat visitors, registered, recency, etc.

Attributes for basic segmentation

• Visits
• % Add to cart
• Conversion rate [ #confirmed conversion / # total visits]
• Engagement conversion rate [#confirmed conversion / # total engaged visitors]
• Marketing cost
• Cost per visit (CPV)    
• Visitor volume ratio [ num of visitors from a source / total visitor]
• Video engagement rate [ count of num of times video played / num of visitors to the page]
• Cost per add to cart
• Customers
• Average cart value
• Shopping cart abandonment rate
• Page time index
• Visitor engagement index
• Content page view index [ count of content page views/total page views]
• Internal banner index [ banner clicks / total clicks]
• Content consumption index [ #page views per content/ #page views]
• System perf idx [ #views from per system / #page views]
• Cost per acquisition [ cost of referring source / num of conversions]
• Sales

Attributes for Behavior segmentation KPIs

• Page views per session
• Avg session time

Nurture rate

• Repeat visitor index [ # of repeat visitors / # of visits]
• Email perf index

BangDB is designed to ingest and process high-speed data to extract the intelligence and apply them to ongoing operations for better operational value. BangDB comes with Stream processing, AI, and Graph processing with unstructured data analysis in real-time. Take BangDB free of cost and start building applications

Relevant Readings

• The architecture of modern stream processing platform for real-time data analytics
• Predictive real-time data platform to boost e-commerce sales
• BangDB is the fastest database in the market