‍Better communication, and at the right time!

Sales. Something that grows when a brand understands their 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.

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The Basics of Prediction Engine

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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

1. Which particular segment/subset of users should you be reaching out to?
2. What order do you reach out to them in? Based on priority i.e. Most/Least likely to convert?

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Finally

1. 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
   
   
2. Segmenting users - who are most likely to convert, buy more, recommend etc.
   
   
3. Integrating into workflows for automation by syncing target users and related information into various personalisation
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Like - Products & Offer Recommendations,
Item Restock Notifs, Abandoned Cart Recovery and more

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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.

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Personalisation x Prediction

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1. Segmented Audience -

1. Messages will be sent on the preferred mode of communication - based on usage and contributing factors
   
2. The style/format of messaging - Formal, semi-formal etc; based on Gen-Z/X/Y
   
3. Timing - The right time to send notifications is decided by active user time on specific platforms. To increase CTR (Click Through Rate)

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2. Holiday Targeting -

1. Understand which holidays suits best for your individual products
   
2. Gauge the period in which sales is maximum
   Example - 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 )

3. Conversion Funnel -

1. 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.
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2. 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.

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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
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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
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PS: Pragma’s WhatsApp Suite is the ideal channel to couple with our prediction data from 300+ Brands  → bepragma.ai