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- D2C Caffeine - Issue No. 16
D2C Caffeine - Issue No. 16
Building Predictability into D2C Marketing
Building Predicability into the D2C Marketing
Better communication, and at the right time
Sales. Something that grows when a brand understands its customers, their behaviour, and most of all - the accuracy in predicting the actions of both customer and potential customers.
It's even better when it's consumer analysis from 300+ brands, helping optimise for hyper-targeting.
The Basics of Prediction Engine
The Who - To understand the end consumer
Are they students?
Are they casual users?
Are they subscribers? Etcetera
Because identifying who they are is the first step in predicting the future probability of similar user conversions
Followed by
Which particular segment/subset of users should you be reaching out to?
What order do you reach out to them in? Based on priority i.e. Most/Least likely to convert?
Finally
Building a 360° view of Consumers by stitching together their actions (product usage), demographics, and other data all in one place. Covering 3 key databases:
Product analytics
Conversions/Billing
CRM
Segmenting users - who are most likely to convert, buy more, recommend etc.
Integrating into workflows for automation by syncing target users and related information into various personalisation
Like - Products & Offer Recommendations, Item Restock Notifs, Abandoned Cart Recovery and more
Basically, brands sync the users who critically qualify into their CRM and communications channel along with relevant information (demographic, behavioural and conversion likelihood) that would help personalise Marketing campaigns, automation, and also sales team pitches.
Personalisation x Prediction
1. Segmented Audience -
Messages will be sent on the preferred mode of communication - based on usage and contributing factors
The style/format of messaging - Formal, semi-formal etc; based on Gen-Z/X/Y
Timing - The right time to send notifications is decided by active user time on specific platforms. To increase CTR (Click Through Rate)
2. Holiday Targeting -
Understand which holidays suits best for your individual products
Gauge the period in which sales is maximumExample - 2 weeks before Christmas; bases on which you can plan campaigns before that period etc
Speaking of Holiday Targeting, Checkout Pragma’s WhatsApp Suite, and how we average a 11X ROAS (Return On Ad Spend) → bepragma.ai/product/whatsapp
Allocating flows based on the stage of the funnel - top, middle, or bottom of the funnel.This is so that you don’t sound redundant by sharing basic information to the bottom of the funnel, and vice-versa.
Understanding Drop-offs - based on the exact instance of the drop-off, brands can share messages to counter behaviour. And this also helps optimise the stage, and the brand messaging for future circumstances.
4. Automation -
Flows that can be automated with the data from predictions to retarget consumers to a use-case specific chatbot to help conversion - (obviously on different platforms - WhatsApp, Facebook, Instagram)
Chatbots for order management and other information
For product queries
For support etc
Other advantages of Building predictability into D2C Marketing?
Sift through as many users as you want (millions), enabling an improved sales bandwidth for your team
Combine the data and run complex analytics to set up a seamless communication system to optimise various segments of the brand
PS: Pragma’s WhatsApp Suite is the ideal channel to couple with our prediction data from 300+ Brands → bepragma.ai/product/whatsapp
That’s the end of our glance into 'D2C Marketing and Predictions'...
See you on the next coffee date!
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